E Ruffin

R Schiestel

F Anselmet

L Fulachier

SECOND-ORDER MODELLING OF VARIABLE DENSITY TURBULENT JETS: EVALUATION IN THE NEAR-FIELD REGION

This paper is concemed with a complete second-order model of variable density turbulent Jets. Emphasis is given here on the near-fleld region of the flow where it is found that the influence of the density variations is quite important resulting in a complex behaviour of both the mean and turbulent velocity fields. Particular attention has been paid to the rnesh grid and the inirial conditions so that quantitative comparison with the experimental data obtained in the study developed in parallel to that one at I.M.S.T. can be made. Results relative to the velocity field only will be reported herein since quite few studies have been focusing so far on the near-field region where the model shows shoncomings which may not be visible when looking at results obtained in the far-field where pseudo-similarity is attained.

INTRODUCT10N.

The main purpose of this paper is to check the ability of second-order modelling in predicting variable density turbulent flows using the I.M.S.T. experimental data (see the companion paper presented at this Conference by Djeridaneet al.). Emphasis is given on the near-field region where the density variations are strongest. This feature significantly enhances the difficulty of refined flow modelling and may require new approaches since the mechanisms involved in turbulent energy transfers in such flows may be quite different from those encountered in constant density flows. Nevertheless, most of the modeis developed so far have taken advantage of the similar formalism presented by Reynolds averaged equations for constant density flows and Favre averaged ones for variable density flows to infer from usual modelling closure assumprions which are applied to that case (e.g. Gouldin et al., 1986). But recent studies (e.g. [START_REF] Chassaing | 2nd French Workshop on Var[END_REF] show that this is probably not physically justified and there is a clear need for precise comparisons of Reynolds and Favre averaged approaches of variable density flow modelling.

In addirion, since this Situation is often encountered in applied or industrial problems where other problems such äs the flow geometry or the number of 5eme Symposium International, Paris, 7-10 septembre 1993, p. 529-536.

reacting species to consider result in the use of less refined turbulence modeis, it is important to study in detaü the influence of density variations in a basic Situation such äs an axisymmetric jet flow to obtain infonnation about the evolution of important quantities such äs characteristic, dme or length scales, Schmidt or Prandtl turbulent numbers or the rario R of the scalar and dynamical time scales [START_REF] Fulachier | [END_REF]. Indeed, though the modelling problems involved in the kinetic ehergy and scalar dissipadon equadons are not yet resolved for constant density flows, it is crucial to study them for variable density flows. and the proposals made so far are quite different (e.g. Jones, 1979;Shih et al.. 1987).

Another difficulty is related to the comparison of experimental data with the model predictions since this Situation has so far received little attention even for an axisymmetric jet Most of the available results mainly concem the mean velocity or scalar field in the far-field region where approximate similarity is attained (e.g. Pitts, 1991) and the inidal flow and boundary conditions are often only roughiy known or quite difficult to take into account numerically. To our knowledge, the most detailed invesdgadon is that of Panchapakesan and Lumley (1993) who measured third order moments of velocity and tested their results with a new model, but this work is limited to stadons located at distances larger than 50 times the nozzle diameter.

After a short descripdon of the numerical procedure, with emphasis given on the choiee of the initial condidons and the grid mesh in order to be äs dose äs possible to the I.M.S.T. experimental configuradon, results conceming the mean velocity and some of the Reynolds Stresses in the near-field region (K/D. < 30, where X is the longitudinal distance from the nozzle) obtained with quite usual modelling assumpdons will be reported. Possible refinements of the model will be discussed.

^7UMER1CAL PROCEDURE.

The code is based on a fmite volume scheme for ellipdc flows. It solves the second-order equadons for plane or axisymmetric stadonary flows in which a scalar (temperature or species concentradon) may generate density gradients. The model so far includes Favre averaged versions of the condnuity equadon and the transport equadons for momenmm, the Reynolds so-esses, the kinetic energy dissipadon, and the scalar mean value, variance, fluxes and dissipadon, äs well äs the equadons for the local physical properdes of the fluid-AI the moment, these equadons are modelled using quite usual assumpdons and constants are-those proposed by Gibson and Launder (1978). However, the addidonal terms related to the pressure gradients are considered. These extra terms related to the density fluctuations in the tranport equadons for the turbulent Stresses R;, read :

Yj^P.j-^P, (l)
They are modelled on the basis of the following hypothesis :

^41^ • w p dC
where C is the concentration of jet gas in ambient fluid. Buoyancy forces are so far ignored since attenrion is mainly paid to the near-field region.

The caiculaüon domains are shown on figure l where the main different numerical regions are coloured using different grey levels : it is important to note that, for quandtaäve comparisons with experimental data obtained in the I.M.S.T. jet facility, the flow is slightly confined and the nozzle thickness is taken into account. The flow condirions (e.g. longimdinal velocity U,=32m/s for helium, 12m/s for air and 10m/s for CC>2, nozzle diameter Dj=26mrn, Ue=1.2m/s for air in the Dg=285mm enclosure,...) are identical to those in the experiments. Particular attention has been devoted to the grid mesh (Fig. 2) and to the initial condirions (Fig. 3). The primary (fully turbulent pipe) and secondary (fully turbulent annulus) flows are first computed on meshes radially corresponding exactiy to those in the jet part of the domain, using the same secondorder code, since it appeared that this is essenrial for achieving dissipaäon rates compatible with the jet flow computation. For instance, figure 3 shows that when these quantities are evaluated through the usual relarions from kinetic energy measurements, i.e. e = k^/C (3), it takes more than 5Dj for the axis longitudinal velocity Standard deviarion to relax to an equilibrium state. Additional nodes for the nozzle thickness were also found to be essential. The spacings along the radial direction do not vary with the downstrearo posirion, whereas those along the symmetry axis do äs longitudinal gradients become smoother. Caiculations reported herein are obtained. with 80 grid points along the symmetry axis (with X the distance from nozzle) and 72 ones along the radial direction (distance R and velocity V).

Fig. 3 : EfTects of initial conditions (LC.) on airjet caleuIsKion

Though the code computes Favre averaged quandties, comparison is made with experimental data obtained by laser-Doppler anemomeüy which is providing stadstical values closer to Reynolds averaged than Favre averaged ones. However, the estimation of the mean Reynolds velocides from the computed ones shows that this problem can be, at least äs a first Step, neglected.

RESULTS AND DISCUSSION.

The streamwise evolutions of the axis mean streamwise velocides (Fig. 4), spreading of the jet (Fig. 5), streamwise u' (Fig. 6) äs well äs radial v' (Fig. 7) velocity Standard deviarions are reponed for the three gases studied. It is worth noticing that, in order to have a better comparison of the various quantities computed with those measured, the raw data will be presented all along this paper. Indeed, normalization with ihe inidal flow mean velocity Uj seems better if one really wants to study the various problems involved in the model and to avoid error compensating effects that could result from the computadon of, for instance, local turbulence intensities. For mean velocides (Fig. 4), the well known shortcomings of second-order modeis are clearly visible for the air flow, resuldng in an overestimation of the rate of velocity decay. For CO^ (density 1.5 times larger than that of air), this feature is even more important, whereas, for helium (density 7 times smaller than that of air) the relative behaviour is opposite. Gouldin et al. (1986) 's computations of a propane jet (density twice smaller than that of air) with a slightly different second-order model are in good agreement with ours. The caiculated spreading rates (Fig. 5) of the CO^ and helium jets are sligthly different from the experimental Undings but the air jet one is in relatively good agreement. The caiculation gives values of spreading rates that are not distinctiy different for the chree jets. However experimental data indicate a larger spreading rate for the helium jet and a smaller one for the CO^ jet. At this time, this experimental trend still needs to be better analyzed since data from various other studies (e.g. Panchapakesan and Lumley, 1993 ;Sahr and Gokalp, 1991) suggest that other Parameters such äs the Reynolds number and the jet exit condidons may afiect the evolution of the velocky half-widths almost äs much äs the density difference does.

However, the very-near field region is well represented for the three gases. The relatively poor rcsults for this parameter reflect those obtained for the axis mean streamwise velociues. Results relative to the turbulent field (Figs. 6 and7) show deviations from the experimental data globally similar to those previously discussed for the mean field. However, the radial velocity Standard deviadons are in better agreement with experiments than the longitudinal ones. It is obvious that, for the helium jet, the code cannot at the moment take into account the sudden development of the flow in the very first stations (K/D, < 6). It is also interesring to note that, in this region, quite large skewness and flatness factors for u and v are found experimentally, especially for helium, with the depanure from gaussianiiy being significantly larger for u than for v. One can think that this latter behaviour is also characterisic of a turbulent field quite different from usual ones that the code cannot so far take into account. Radial distributions of the Reynolds shear stress <uv> at three stations X/Dj are presented on figures 8, 9 and 10 for the CC>2, air and helium jets respecdvely. For all of these distributions, the positions of the peaks are fairly well predicted but their amplitudes are, here again, overestimated for CO^ and air and underestimated for helium. In addidon, the radial expansions of these quantides are also significantly underestimated for helium and overesrimated for CO^ and air. These findings are also quite similar to those reponed by Gouldin et al. (1986) for propane, who present turbulent quanrities for the Station X/Dj=30. Results reported herein confirm that the usual second-order model is not very efficient for predicdng the development of pure air Jets and this is particularly obvious in the near-field where detailed results such äs those reported here are usually not presented since attention is mainly päid to the self-preserving region. As a consequence, though the relative effects of density variations have the right trend for all the quandties reported here. the code cannot predict with good accuracy the near-field development of CO^, ah-and helium Jets. However, before focusing on the shortcomings of the modelling with respect to the effects of density, it seems essenrial to improve those for the airjet. Thus, new modeis such äs those proposed by Launder (1989) for the redistribution tei-ms of the Reynolds so-ess equations and the corresponding ones for the scalar fluxes, or the development towards the improvement of the modelling of the third order moments involved in the turbulent diffusion tenns (e.g. Panchapakesan and Lumley, 1993), are worth being tested. Nevertheless. the present model seems already efficient enough for studying, for instance, the influence of density variations on turbulent characteristic rime and length scales which are important for lower order modeis such äs those used in the modelling of turbulent combustion since, to our knowledge, such information is not available at the moment.

5-

Fig

  Fig. l : Caiculation domains Fig. 2 : Grid disposition

Fig. 4 :

 4 Fig. 4 : Evolutions ofthe mean longitudinal veiocity on the jet centcriine.

Fig. 5 :

 5 Fig. 5 : Evolutions of the velocity half-width on the jct centerline.

Fig. 6 ;

 6 Fig. 6 ; Evolutions ofthe longitüdinal velocity Standard deviation on thejet centerline.

Fig. 8 :Fig. 10 :

 810 Fig. 8 : Radial distributions ofthe Reynolds strcss for CO^-Airjct

ACKNOWLEDGEMENTS.

Financial support from Electricite de France, Gaz de France, Societ6 Nationale de Construction de Moteurs d'Avions, Institut National de l'Environnement Industriel et des Risques and Conseil Regional Provence-Alpes-Cote d'Azur (access to the Computer center of Institut Mediterraneen de Technologie) for both the numerical and experimental aspects of this work is gratefully acknowledged.


Introduction

The Commission of the European Communities, m a cooperative program with the US Department of Energy/ is developping an accounting framework for identifying and quantifying the extemal costs associated with fuel cycles. A general presentation of this program will be set out later in the Conference by Mike Holland.

The initial phase consisted of the development of a general methodology by american and british teams 1 . This methodology is based on:

-an analysis of the different stages of a fuel cycle and an estimation of pollutants emissions, -a modelisation of transport and chemical transformation of pollutants from sources to receptors (impact pathways)/ -a modelisation of physical effects supported by receptors depending on their exposure (dose-response functions), -a monetary valuation of physical effects.

In the present phase/ INERIS is proceeding to the implementation oF this methodology in France for the fossil fuel cycles (coal/ oil, natural gas) in the context of the production of electricity.

As part of this programme/ we try to evaluate some of the effects on public health of SOz/ NOx and particulate matter emissions by generation plants.

As in the reference methodology/ we particularly consider the effects on human health of particulate matter in air. But our implementation differs from the first EC/US studies on two points:

-we don't only study short ränge but also long ränge effects/ -we don't only take account of particulates directiy emitted by generation plants but also of particulates resulting of the transformation in sulphates and nitrates of S02 and NOx emissions.

As a consequence/ the estimated effects are appreciably more important than in the reference studies.

Background

The existence of risks for the human health resulting from the pollution of the ambient air has been well known for a long time. We could call to mind latin authors or recall the prohibition of the use of coal in London by a royal edict in 1273. In the industrial age/ the hazards of particulate and SOy pollution of urban atmospheres became evident in the conditions of heavy pollution experienced, one more time and particularly/ m London in the 1950s/ causing the death of thousands of persons.

Since these events a lot of efforts have been made to reduce the emissions of air pollutants and a lot of studies have been carried out to found the environmental policies on scientific facts.

International Organizations have had a leading part in the synthesis. We can particularly mention the work realized by Worid Health Organization 2 / United Nations Economic Commission for Europe [START_REF]Commission Economique pour l'Europe[END_REF] , United Nations Environment Program [START_REF]The environmental impacts of production and use of energy[END_REF] / O.E.C.D. [START_REF]Acidic deposition -materials and health effects[END_REF] / and Commission of the European Communities [START_REF]Acid Rain and Photochemical Oxidants, Control Policies in the European Community: A Decision Analysis Framework[END_REF] .

Attempts for integrating the health effects of air pollution in a global evaluation of damage have been made in a number of countries, like, for instance/ The United States and Germany [START_REF] Kleisbauer | Repercussions clmiques et spirometriques d'une exposition chronique aux polluants atmospheriques; Etüde epidemiologique d'un groupe de femmes au foyer de la region du Bassin de Gardanne[END_REF] .

In France/ äs in other countries/ a continuous effort of research has been made in the fields of toxicology (cf. bibliography in paper 8 ) and epidemiology 9 .

For example/ äs early äs the seventies/ Loewenstein 10 showed that relation of pollution levels (S02 -particulates) with total daily mortality in Paris was very significant for old population.

At the same period/ a large study by the cooperative group PAARC on 28 sites distributed in France showed a strong and regulär correlation between air pollution in eitles (S02 concentration)/ respiratory pathology and decreasing pulmonary function (FEV) [START_REF] Groupe Cooperatif | Pollution atmospherique et affections respiratoires chroniques ou ä repetition[END_REF] .

The Authorities in charge of Environment and Public Health in France devote serious attention to the risks of air pollution for human health [START_REF]Ministere des Affaires Sociales de la Sante et de la Ville[END_REF] . A number of articies for the general public show that the opinion is also aware of the problems 13 .

Since his establisment in 1990, INERIS is involved both in toxicological and epidemiological research [START_REF] Blanquart | Etüde m vitro de la toxicite du dioxyde de soufre sur l'epithelium tracheal de mammifere[END_REF] and in damage evaluation [START_REF] Landrieu | L'industrie et l'Environnement, Economie de la Prevention[END_REF] . This later field is a relatively new one in France, äs few studies had been made up to now for evaluating the economic cost of health effects of air pollution [START_REF] Gerpa | Le coüt des dommages dus ä la pollution, essai d'evaluation/ Ministere de l'Environnement et du Cadre de Vie[END_REF] .

General approach

The basic approach of the EC/US project is the methodology of Risk Assessment 17 complemented by economic evaluation.

This approach can be schematized by fig. 2. In this way/ any quantitative evaluation of risks and damage is at first based on a qualitative analysis of the plausibility that a given chemical-compound may have a specific effect on man.

At each stage, uncertainty is an underlying feature of any risk assessment:

-toxicological uncertainty : effects on man are inferred from experimentation on animals ; effects at low doses are inferred from experimentation at high doses;

-epidemiological uncertainty : causal relationship are inferred from statistical correlations ; synergy effects are difficult to discern;

-modelisation uncertainty : it always exist a risk of neglecting some potential pathway setting in contact a chemical compound and a population.

In this matter of damage evaluation/ it often appears that a determinist approach is not really founded and that only a "what if" hypothesis make sens.

Hazard identification of concentrations

SOz and N0\ on public health at low

The hazards for human health resulting of SOz and NOx emissions proceed at first from the direct effects of these compounds but also, indirectiy, from the products of their transformation in the atmosphere (sulphates and nitrate partides/ ozone).

It has been said precedently that these hazards are well known for a long time.

It must be precised that this assertion is certainly justified for the effects resulting of high pollutants concentrations. But it has been more difficult to demonstrate the effects of more routine situations with lower pollution levels.

However the risks of air pollutants present in the environment even at the low present concentrations appears not necessarily insignificant a priori : one needs only to recall after J. Bignon that man inhales each day 12 000 litres or 15 kg of air (... to compare to 1.5 kg of food and 2 kg of water) 18 and to consider the phenomenons of penetration, deposition, retention of, for instance, partides inhalated in the respiratory tract 19 .

A large number of epidemiological research projects which has been achieved in the worid during the last decade concludes to the existence of effects on man of SOi / NOx / ozone and especially particulates even at low concentrations.

Some studies made in France lead to converging results. We would like to mention:

-a study realized äs early äs 1974 -1982 in the North region giving a clear indication of a real pathological influence of pollutants (SOz / particulates and specifically sulphates...)/ even on moderate concentration levels/ lower than european Standards 20 ;

-another study on the site of Toulouse in 1985-1986 showing a strong relationship between the concentration in air of particulate ammonia derivatives (sulphates and nitrates) and acute respiratory diseases of children 21 .

-a study made in 1989 in Paris showing that the frequency of asthma attacks is significantly higher in the days where particulate sulphate concentrations are the most important 22 .

A synthesis of the results of the research on the evaluation of health effects has been made recently by a large advisory board äs part of the program ERPURS 23 . The board has recorded that many research works realized in routine situations representative of the present environment conclude to a statistical relationship between air pollutants levels and human health. More accurately, the evidence of a relationship between mortality by respiratory causes or, secondly/ cardiac causes and particulate pollution is admitted [START_REF] Momas | Pollution atmospherique urbaine et mortalite : une synthese des etudes epidemiologiques publiees entre 1980 et 1991[END_REF] .

It is necessary to make clear that the risks in question are short term risks. The evaluation of risks in the long term/ resulting from exposure to pollutants for many years, is a problem very difficult to treat scientifically, in view of the statistic requirements of epidemiological studies and the constraints of observation of living persons. Some attempts have been made to evaluate risks in the long term but the results may be condidered äs more speculative [START_REF] Heinz | Krankheitkosten durch Luftverschmutzung/ Forschungsschwerpunkt-Program "Kosten der Umweltverschmutzung[END_REF] .

In consideration of the results of all the cited studies, the EC/US project and the french implementation focus at this stage the health damage evaluation on the risks for health of particulates and/ secondly, of S02 • The risks of ozone could be taken in count later.

5

Quantification of effects of particulate matter: dose-response functions

The precise quantification of dose-response functions at low pollutant concentration requires epidemiological studies realized on a very large scale.

That is necessary for disceming weak effect Signals on high background noises and distinguishing with which pollutant these effects are more particularly in relationship, even when the variations of concentrations of different pollutants are highiy correlated.

A number of such epidemiological studies has been done over the last years especially in The United States on behalf of the Environmental Protection Agency 26 . These studies are diachronic, that is they try to investigate correlations between daily variations of pollution at a particular site and changes in morbidity and mortality. Successive generations of research, applied to increasingly large populations -for instance/ analysis of the daily death rate of 10 million people over 10 years -with increasingly stringent methodologies/ appear to confirm the conclusions of the early analyses concerning the effects of particulate pollution and quantify short-term statistical relationship between particulate concentration and the state of health of a population : mortality/ lung function deficits, desease Symptoms/ increased hospitalization.

For instance/ the relationships between pollution and mortality appearing äs a result of those analysis are approximately linear. These relationships seem identical for different climates (warm-cool/ dry-humid...). It has not been possible/ in the ränge of current pollution levels being investigated/ to show any threshold below which a change in the level of pollution would no longer have any effect.

A meta-analysis made by J. Schwartz 27 come to the conclusion that the doseresponse function is of the following form :

Relative Variation ofthe daily mortality rate observed in a population exposed to pollution = 0,6 .10 -3 (confidence interval : 0,5.10 -3 -0,7.10 -3 )

x Variation of daily mean air concentration of particulates (jig TSP/m 3 )

In a predictive form, the dose-response relationship can be expressed äs follows:

Individual Health Risk (Number ofevents attributable to pollution per head per year) = Response Factor

x Variation in annual mean concentration ofpollutant in the local atmosphere

On the basis of the relation of Schwartz and/ if one supposes that the basic mortality of the population is 1% a year and that the effective agent in particulate matter is PM 10 (about 50% of TSP in the american context), the value of the response factor in the preceding case could be:

RF death PMiO = 1 • 2X 1 0-5 death per head, per year, -per {ig PM-io /m 3
On the basis of other epidemiological analysis/ it appears also possible to relate deaths to S02 concentration ( 28):

RF death S02 = 0.2 x 10~5

Expression of the health risk

We shall consider a source of pollutants emitting partides, SO^ and NO,/, which generates a pollution field around itself. The particulate pollution in particular results from the diffusion of partides emitted directiy by the source, but also from the secondary formation of sulphates and nitrates from the SOo and N0^.

The health risk to exposed population resulting from those emissions may be put into the mathematical form : We therefore attempted to construct a model with a very simple mathematical form to represent the diffusion and chemical modification in the atmosphere of gaseous pollutants. A short description of the model may be found in Annex l.

In order to illustrate the caiculation of collective exposure we shall examine an entirely notional but transparent case rather than a realistic and hence complicated example.

We shall consider a source of atmospheric pollution located at the centre of a circular area; the population within this area is distributed uniformly. We may assume for example that, äs France, the area covers 550,000 km 2 -that is a circle of 420 km radius -and the population density is 100 inhabitants per km [START_REF]Regional Office for Europe; Air quality guidelines for Europe[END_REF] .

With the simple model described in Annex l/ we have caiculated the average pollutant concentrations in the area and the collective exposures resulting from the einissions flow of one tonne of fine particulate per year/ one tonne of 50p er year, one tonne of N0^ per year. As an Illustration/ the results are :

Average pollutant concentrations in the area resulting from the source :

^ 0.7 x 10~3 attributed deaths per tonne of NOx emitted

Knowing the risks data determined by epidemiological studies/ it would be possible to evaluate in the same way the number of cases of coughs/ bronchitis and asthma or even the number of working days lost through sickness, per tonne of particulate, SO^ or NOx emitted.

Monetary value

In the reference studies of the EC/US project/ the hypothesis proposed for the value of one "statistical risk of death" is 2/6 .10 6 ECU, that is near 20 million FF.

French economists [START_REF] Desaigues | Evaluation economique des dommages environnementaux; in Implementation of the Accounting Framework in France[END_REF] working on the project consider that this figure is high/ particularly if one consider the price of human life taken in consideration in matter of road safety Investments, that is 3.3 million FF.

Other economists insist on the fact that the death risk may touch more probably persons very old or seriously ill before/ that is people having a lower expectation of life than the average man 32, However/ on the above-mentionned basis, and in the notional case considered/ the translation of the physical evaluation of risk in a monetary form give/ for example: The annual death rate attributed to the plant in the illustrative case is:

Risk (deaths) plant year = ( 480 ' 2.5 X 10-3 ) + (1925 * 0.7 X 10-3 ) + (1925 -0.7 X 10-3) attributed deaths Risk (deaths) plant year" 4 attributed deaths

Extemal health cost

The extemal health cost is caiculated in the same way:

Extemal Health Cost p^ yg= (480 x 100/000) + (1925 x 23/000) + (1925 x 30,000) FF Extemal Health Cost ^ year =s 150 MF Expressing this extemal cost per kWh generated/ we obtain for our typical case:

Extemal Health Cost " 6 cF / kWh 11 Conclusion of the exercise

The above evaluation is primarily an Illustration of the approach used in the Extemal Costs of Fuel Cycles project.

However it highiights the importance of taking into account the secondary chemical forms of sulphur and nitrogen pollution, which was not considered in the caiculations of the EC/US reference studies, unlike the öther studies on the impacts of energy Systems.

In fact, sulphates and nitrates constitute a major part of the production of particulates from human origin (cf. [START_REF] Renoux | Ce qu'il faut savoir sur l'aerosol atmospherique[END_REF] ). Those secondary partides had been taken into account in a number of international health impact studies mentionned in our introduction, äs also in some french studies [START_REF] Mounier | Evaluation technico-economique des systemes de lütte contre la pollution atmospherique provenant des centrales thermiques au charbon[END_REF] .

With the renewed attention paid in recent years to the impact of particulate pollution on health, awareness of the health risk related to secondary particies has become predominant amongst scientists working in the field [START_REF]PM10 Standards and non-traditional particulate sources controls" summarised in Air & Waste[END_REF] .

We could not conclude without recalling that the caiculations made in our exercise concern only some measurable short term effects of a pollution. It is not impossible that those measurable effects are/ äs it has been said 36 / only the emerging part of the iceberg.

Introduction

The EC/US external environmental costs project does not require great precision in the geographical representation of the pollution induced by a source/ for this would be futile in the light of the considerable uncertainties affecting the other stages of the evaluation process. Nor does it require the pollution to be monitored over time/ needing only the mean values at one particular place.

We therefore attempted to construct a model with a simple mathematical form to represent the diffusion and chemical modification in the atmosphere of gaseous pollutants. The works on similar problems by M. A. Doury [START_REF] Doury | Essais de modelisation simplifiee des retombees de l'accident de Tchernobyl[END_REF] of the Safety Analysis Department of the French Atomic Energy Commission (CEA) and F. B. Smith [START_REF] Smith | Emission reductions to meet deposition criteria[END_REF] of the British Meteorological Office suggested to us that such an approach was credible.

The basic concepts of the proposed representation are conventional. The model for the diffusion of a non-reactive chemical compound that we shall describe in the first section is for example that used by the IPSN for an evaluation of external effects at regional level [START_REF]Grand Delta Programme: Comparative evaluation of risks at regional level, summary report[END_REF] .

In the second part of this note we shall propose extending the basic model to the problem of the transport of a pollutant whose chemical form changes with time. Typical such pollutants are sulphur/ emitted in the form of sulphur dioxide SO^ and converted with time to the sulphate/ and nitrogen/ emitted äs nitrogen oxides N0^ and becoming the nitrate.

l. The basic dispersion model

Let us assume that a point source discharges a pollutant into the atmosphere at a constant rate E.

It is assumed that the pollutant is dispersed in space isotropically around the source:

-the pollutant is diffused within a layer of the atmosphere characterized by a constant depth h;

-the pollutant concentration C at a receiver is uniform throughout the thickness of this "mixing layer";

-the pollutant is dispersed centrifugally at a constant velocity u/ representing the average wind speed in the mixing layer.

Pollutant remaining suspended indefinitely in the atmosphere

There are steady conditions and mass is conserved. The pollutant flux 0 which passes through a vertical cylinder of radius D centred on the source is therefore constant and equal to the emission rate E: 0=27cDhuC=E Thus the pollutant concentration C is inversely proportional to the distance D;

by putting a = -----, the expression becomes:

2 K h u C -°^F c -D E

Numerical value of the coefficient a

The height of the mixing layer and the wind velocity at altitude'are changing meteorological variables and the estimate of the mean value to be used differs according to the modeis. However the order of magnitude of the parameter a is fairly well known. The following are the values used in the Harwell model [START_REF] Derwent | An intercomparison of long-term atmospheric transport modeis; the budgets of acidifying species for the Netherlands[END_REF] and by Doury.

Height of mixing layer h m.

Mean wind velocity in mixing layer u m s" 1

a= ^^ sm " 2
Choice of System of units Harwell Doury 800 1000

7.5 5
2.7x10 -5 3.2 xl0 -5
The numerical value for the coefficient a given above implicitly assumes the use of a coherent System of units:

-einission rate g/s -concentrations in g/m [START_REF]Commission Economique pour l'Europe[END_REF] -distances in m

In practice it may appear more convenient to choose another system/ for example:

-emission rate in tonnes/year -concentrations in (ig/m 3 -distances in km In such a system, the new value of the coefficient a (a') is obtained by multiplying the previous figure by 10 [START_REF]Ministere des Affaires Sociales de la Sante et de la Ville[END_REF] (p.g/t) and dividing by 31.5 x 10 6 (s/year) and by 10 3 (m/km). The caiculation gives:

a' » 10-3

Stahle pollutant falling to the ground with a constant deposition velocity

Many pollutants do not stay suspended indefinitely in the atmosphere but are deposited on the ground by Sedimentation (particies) or absorption (gases).

In this hypothesis/ the pollutant flux 0 (D) passing through a cylinder of radius D centred on the source is no longer constant äs it was in the previous case, but decreases äs the radius D increases.

The velocity of pollutant deposition is defined äs the observed ratio between the mass of pollutant falling at a particular place per unit surface area and per unit time/ and the concentration of the pollutant in the atmosphere above that place.

If this Sedimentation velocity vi is constant, the flux decreases with distance from the source to a rate Vl <? l = i~ per unit time.

Besides this "dry" mode of deposition/ the pollutant can also be entrained or dissolved by rain and fall to the ground wet. An equivalent velocity vz and a rate <72 are assigned to this mode of wet deposition. In total, the mean rate of reduction of flux per unit time is

a = GI + ü2
The mean rate of reduction in the flux per unit distance äs one moves away from the source is The expression for the concentration äs a function of distance becomes:

C (D) = ^ e-r D g (2)
For a chemically stable pollutant therefore, the schematic model given above can be used to caiculate the concentration of a pollutant in the atmosphere äs a function of emission rate and distance from the source using two parameters: the parameter a characterising the atmosphere in which diffusion takes place, and a parameter r characterising the deposition velocity of the pollutant.

Significance of the coefficient r

The form of equation ( 2) gives -a second physical meaning: T^g^= -is the mean time during which a molecule of pollutant remains in Suspension in the atmosphere before falling to the ground; ^mean = ~ ls ^le mean distance travelled by a molecule of pollutant in the atmosphere before falling to the ground.

Typical application: transport of particies emitted by a thermal power Station

It is known that fine dust can remain in Suspension in the atmosphere for long periods and be transported over long distances; an example occurs when Sahara sand falls on Northern Europe; the attention of public opinion was drawn to the phenomenon during the wanderings of the Chernobyl cloud.

Particie deposition velocity äs a function of radius

The dry deposition velocity of a partide in the atmosphere depends first on its diameter. Different laws for deposition velocity äs a function of diameter apply to different ranges of partide size.

In the ränge of dimensions that concerns us here, we may refer for example to the equation produced by R. Jaenicke [START_REF] Jaenicke | Proceedings of the llth int. Conference on atmospheric aerosols, condensation and ice nuclei[END_REF] , quoted by A. Renoux [START_REF] Renoux | Aerosol physics and pollution transport laboratory/ University of Paris XII, What you need to know about the atmospheric aerosol[END_REF] . The mean dwell time T in the low troposphere of a partide of radius p is given by:

l l fP-l 2 , l fP-1-2 .. -\ - m T -C [R) + C [R) + Twet u /
where, with time expressed in seconds and radii in microns/ C = 1.28x10 »s R = 0.3 p, Twet = 6.91 x 10 5 s

With the notation used previously the equation (J) can be written:

-.(o^m

02 = _-(a constant)
Applying the height of the mixing layer used by Harwell, we can also write: The caiculated dry deposition velocity appears very low (6 m a day) and it seems that the main cause of fall-out is atmospheric wash-out by rain.

v^ 6.25x10-« (^) 2 ^)-2 ) V2 = 1.16xl0-3 ms-l RJ + [R) } ms ' 1 V2 = 1.16x10-3 ms-l 1.3.
The value of the parameter r in the formula ( 2 The polluting element/ emitted äs a component of a primary chemical compound can/ äs a result of actions taking place in the atmosphere/ gradually turn into a component of a secondary chemical compound.

For example an increasing proportion of the sulphur emitted in the form of sulphur dioxide is transformed with time into sulphate. Similarly, although its chemistry is more complex/ we shall assume/ in a very simplified manner, that nitrogen emitted in the form of nitrogen oxide gradually turns into a component of nitrates. The total concentration of the polluting element in the atmosphere is the sum of two terms/ Cp the concentration of the element in the form of the primary chemical compound and Cs the concentration in the form of the secondary chemical compound: C = Cp + Cs

Concentration in primary form

Let us assume that all the pollutant is emitted in the form of the primary compound and that this compound is consumed by the oxidation reaction at a constant rate T per unit time.

The flux <I> p and the concentration Cp of the primary pollutant are expressed in ways similar to equations (l) and ( 2). They differ only in the fact that the rate of reduction per unit time is now the sum of two terms:

-the deposition rate op = api + op2 (dry and wet deposition) -the oxidation rate T By putting T-, -^0-+5 Os -B E e-YÖ^O factor of 1.93. The concentration of sulphate äs a function of an emission expressed äs SO^ will therefore be obtained by replacing ß in formula ( 6) by: ß' sulphate / S02 = -2.9

Similarly/ if the secondary product/ äs regards nitrogen/ consists of NOoNl-L, the mass of compound formed is the mass of N0^ entering the reaction multip lied by a factor of 1.74. The concentration of nitrate äs a function of an emission expressed äs N0^ will be obtained by replacing ß in formula ( 6) by: ß" nitrate / N02 = 1-7

A test of the credibility of the model using EMEP evaluations

The Norwegian Meteorological Institute (MSC-W) was kind enough to provide us with special data giving the geographical distribution in Europe of fall-out of sulphur and nitrogen (oxides) emissions from the region of Nantes caiculated using the EMEP model.

The areas of constant fall-out density form rings/ more or less regulär in shape, around the source. These data were smoothed to eliminate the Variation of fallout äs a function of direction. Thus the assumption was made that an area of constant density could be characterised by a single distance from the source; this distance is caiculated äs being equal to the radius of the circle whose area equals that of the zone bounded by the outer border of th'e area of equal density.

In this way it is possible to caiculate the cumulative amounts of pollutant falling on successive areas of decreasing density/ i.e., at increasing distances from the source. It is also possible to caiculate the proportion of the emitted pollutant which remains suspended in the atmosphere at a given distance from the source. The following graph shows EMEP data specific to the Nantes emission zone, treated in this way (the points represent a series of zones of equal density).

This graph shows (solid lines) the estimate of the ratio 0 (D) O p + Os ~E~ or --E-from the simplified model we presented and using the numerical values for the parameters proposed above äs an initial approach.

The closeness of the two estimates can be regarded äs encouraging for the mathematical model proposed. The model correctiy represents the distribution of areas on the ground äs a function of the fall-out levels to which they are exposed.

It now remains to be shown that the model accurately reflects the relative concentrations of the primary and secondary forms of pollution. We hope to have data in the future for testing the model from this point of view and/ if necessary, calibrating it by optimising the values of the parameters used.

At the same time it will be possible to improve the model by incorporating directional factors in the distribution of pollution (introducing the pattern of winds on a regional scale).

Caiculation of mean pollutant concentrations in a simple case

We shall consider a source of atmospheric pollution located at the centre of a circular area. m such a Situation, with a circle of radius R around a source with constant emission E/ the expression for the mean pollutant concentrations is obtained directiy by integrating the expressions ( 2), ( 4) and ( 6):

Fig. 2 :

 2 Fig. 2 : The approach of risk assessment and economic evaluation of damage of a pollutant

2

 2 Deposition parameterIt will be assumed that a mean diameter of 2 [im is representative of the partides emitted by a present-day thermal power Station. Using Jaenicke's formula for a partide of average diameter 2 (im, i.e. radius l [-im/ gives the following deposition velocities: vi = O.OVxIO-Sms-l V2 = l.lCxlQ-^ms-1 vi + V2 = 1.23 x 10 -3 m s -1

  chemical form and deposition velocity of the pollutant during transport: a simplified representation of the phenomenon

2

 2 Concentration in the secondary formThe change in the flux 0 s ^d the concentration Cg of the secondary compound is the resultant of two processes: the production of the secondary compound by oxidation of the primary compound at the rate T, and the elimination of the secondary compound by deposition on the ground at a rate <?s = üsi + ös2-For an observer moving with the flow, the Variation d 0 s in the secondary compound flux during an infinitely short time interval dt is: d4»s = T <& p dt -GS <& s

  fl-e-^R l-e-S R"| Csmean(R) = -^-----^--------g---E

partides =s ^ x l®" 3 attributed deaths per tonne of part. emitted

  Similarly a study by the Brookhaven National Laboratory[START_REF] Hamilton | Health and environmental risks of energy Systems[END_REF] , estimates that an annual emission of one tonne of SOn in the United States results in an average collective exposure to sulphates of 31 person.jig/m 3 : this is precisely the figure obtained in our example... Health risks caiculationThe expression of risk above-mentionned is :Risk to Exposed Population = Response Factor x Collective ExposureFor example, it is possible to cakulate value of death risk in the notional case considered on the basis of the dose-response relationship of Schwartz and Hatzakis. 10J5 death P^ head ' PG 1 ' y ear / P 61 ^g part/m[START_REF]Commission Economique pour l'Europe[END_REF]

	fine particulate	:	3.8 x 10~6	p,g /m 3 per tonne part. emitted /year
	S02	:	2.6 x 10" 6	ug /m 3 per tonne SOz emitted /year
	Sulphate The death risk generated by the emission of particulates is : : 0.6x 10" 6 p-g /m 3 per tonne SC>2 emitted /year
	NOi	:	3 x 10" 6	p-g /m 3 per tonne NOz emitted /year
	Nitrate Rlsk (death) partides = 1.2 x 10~5 death per head, per year, per (lg PMio /m 3 : l x 10~6 Hg /m 3 per tonne NOz emitted /year
	Collective exposures in the area resulting from the source : x 200 person. |ig /m 3 per tonne part. emitted /year
	Or:
	fine particulate		200 person. (ig /m 3 per tonne part. emitted /year
	S02 Sulphate N02 Risk (death) Similarly we can caiculate: 140 person. p,g /m 3 per tonne SOz emitted /year 30 person. p,g /m 3 per tonne SOz emitted /year 160 person. (lg /m 3 per tonne NOz emitted /year
	Nitrate Risk (death) S02 =		55 ^-2 x 1 ^"5 death per head, per year, per (ig SOz /m 3 person. (ig /m 3 per tonne NOz emitted /year
	Note	x 140 person. | H,g /m 3 per tonne SOf emitted /year
	It will be seen that although the case proposed is notional it is not unconnected to existing situations. One of the EC/US project's reference studies caiculates/ using the Harwell model, that the Lauffen power plant generates the following mean pollution concentrations in Germany for each tonne of pollutant emitted: Mean concentrations (10' 6 (ig/m 3 ) for each tonne emitted Rlsk (death) S02 = ^ x 30 person. (ig /m 3 per tonne SC>2 emitted /year
	German case Risk/^^ sulphate = 0.4 x 10~3 attributed deaths per tonne SOz emitted Our example
	partides		3.3	3.8
	S02		3.2	2.6
	N02 By adding the primary and secondary risks of SO^ we have: 4.2 3
	Rlsk (death) S02 + Sulphate
	==	0.7 x

x ^~3 ä^ributed deaths per tonne of SO-z emitted Since an emission of sulphur dioxide generates sulphates, i.e., partides, we also have:

Risk (deaths) sulphate = 1 -2 x

10' 3 attributed deaths per tonne of SOz emitted Risk (death) nitrate

Bignon, J.; Cibles cellulaires et reponses pathologiques aux aerosols inhales; Pollution Atmospherique/ avril-juin 1986. 19 Brochard, Patrick; Aerosol et Sante : deposition et epuration des particules inhalees, Pollution Atmospherique, avril-juin 1990. 20 Gervois, M.; Difficultes et enseignements des enquetes epidemiologiques actuelles sur l'influence de la pollution atmospherique urbaine en pathologie respiratoire aigue; Pollution atmospherique, oct.-dec. 1988. 21 Boussin, G., et al., Pollution atmospherique et pathologie respiratoire aigue de 1000 enfants ä Toulouse; mteret des derives particulaires ammoniacaux; Pollution Atmospherique, oct.-dec. 1989. 22 Marzin, Cecile/ et al.; Asthme urbain, habitat et pollution de l'air; Pollution atmospherique, avril-juin 1991. Marzin, Cecile, et al.; Asthme, pollution atmospherique urbaine et meteorologie; Rev. Mal. Resp./1993/10. 23 ERPURS (Evaluation des Risques de la Pollution URbaine sur la Sante): Effets ä court et moyen terme de la pollution atmospherique sur la sante; analyse des etudes epidemiologiques publiees entre 1980 et 1991; ed. Observatoire Regional de Sante d'Ile-de-France.

The model correctiy represents the distribution of areas on the ground äs a function of the fall-out levels to which they are exposed.

Estimation of parameters

When the pollutant "changes vehide" during transport, the caiculation of its concentration in the atmosphere äs a function of emission rate and distance from the source requires two new parameters to be considered: besides the parameter a characterising the atmospheric medium in which diffusion takes place and the deposition velocity in the first form/ it is necessary to take into account the kinetics of the change to the second form and the deposition velocity in this second form.

For a first evaluation of these basic quantities, in the case of sulphur and nitrogen, we shall rely on the estimates of the same parameters used for a more complex model, the TREND model (cf. Derwent, op. dt). 9.17 x 10-6

Pollutant lifetime parameters

Pollutant

1.22 x 10 -6

Secondary compound Sulphates Nitrates

Dry deposition velocity vsi m s-1 2.8 x 10 -3 8.1 x 10 -3

VS1

Dry deposition rate 051 -^ s-1 3.5 xl0- [START_REF]Acid Rain and Photochemical Oxidants, Control Policies in the European Community: A Decision Analysis Framework[END_REF] 10.1 x 10-6

Wet deposition rate 032 s-1 10 x 10 -6 ' 10 x 10 -6

Parameters ofthe model --------m" 1 2 2 x 10 " 6 l 5 x 10 " 6

-y

Value of the parameter ß according to the tvpe of compounds taken into accounf Standard practice is to sum the emissions and primary concentrations of sulphur and nitrogen in terms not of the masses of the elements but of SOn and NOn respectively.

In the hypothesis where the secondary product from sulphur consists of a mixture of equal proportions of S04(N]-Li)^ and SO^HNl-L/ the mass of compound formed is the mass of SO^ entering the reaction multiplied by a


A new method using a catalytic sensor for the identification and concentration measurement of combustible gases Abstract: the present state-of-the-art in gas detection does not allow the precise titration of a combustible gas in air with a single catalytic sensor, because the signal depends also on other parameters than the oxidation of the gas. It is why the catalytic transducers always consist of both a detector and a compensating element. We present here a process which permits the avoidance of the compensating element, and which, moreover, allows the identification of the combustible gas and the recognition of high concentrations for some gases.

Measurement and calculation

The resistance signal on a catalytic sensor heated by the Joule effect is a combination of several processes:

S = S T + S L + S G
where Sj is the signal due to the temperature of the sensor, SL represents thermal losses in the surroundings of the sensor, SG is due to oxidation of the combustible gas (in air, SG = O).

On a same sensor, the catalytic oxidation of a combustible gas depends on the temperature of tide sensor (see figure 1, on a platinum filament). The losses also depend -for a certain part-on this temperature. In a classical catalytic transducer, the compensating element is heated at the same temperature as the detector; the only difference between the two signals comes from the oxidation of the gas on the detector. Here, the single sensor is heated in a discontinuous way, at several increasing temperature steps in a same sequence (see figure 2). This operation is cycled every few seconds (typically, 5 seconds or less). The n signals for different temperatures in clean air are memorised with the use of a micro-controller:

S'n = S'-rn + S' L n (S'cn = O)
When a combustible gas is present in the air, the n signals become :

Sn = Sxn + SLn + Sen
For each of the n temperatures, the measurement is calculated by the micro-controller as:

Sn -S'n = (S-rn -S' T n) + (S L n -S' L n) + S G n
The temperature of the filament is the same in air and (air + gas) mixture, but the thermal losses, due to a different conductivity of the combustible gas beside of air, are slightly different, so:

ASn = S n -S' n = ASyi + Son
The thermal losses depend also on the humidity and the ambient temperature, and coefficients Kn have been previously determined for the different temperatures, so that:

K1.ASL1=ASL2
The measurements for the different temperatures are then compared for each couple of steps:

dl=AS2-Kl.ASl Then dl = SQ2 - d2 = SG3 -K2SG2 d(n-l) = Son -K(n -1) Sc(n-l)
The identification of the gas is given by the differences dl, d2 .... The temperature steps are chosen so as, for each combustible gas to be identified, one step will be below -or just at the beginning-of the oxidation, the next step will be at the top of the response curve, for the higher oxidation of the gas. The temperature couples below the oxidation will give a null or very small signal, the couple in which the oxidation occurs will give the maximum d because SGUI is high and So(m-l) is small, the following d will be smaller because Som and SG(HI+1) have close values. The larger d value corresponds to a certain gas (or a family of gases for which the oxidation temperatures on the sensor are close, as for hexane and butane in our case). When the gas is identified, a coefficient is then applied to compensate the variable sensitivity of the sensor to different combustible gases. These coefficients have also been previously determined for the different gases to be measured.

Application:

We have developed an apparatus which uses this technique ; the sensor is a platinum wire of 80 Jim diameter, on which the curves of figure 1 have been established. Figure 3 gives the scheme of the apparatus. We have selected 5 temperature steps, at 100°, 200°, 500°, 800°, and 1 000°C.

The temperatures are those of the warmest point of the platinum coil, e.g. the center; the higher temperatures were measured using a micro-pyrometer, the lower temperatures (below 800°C) are calculated from the curve T = f(R), R being the resistance of the filament. With these temperature steps, we can identify :

-hydrogen, -ethylic alcohol, -butane, -propane, -methane.

The discrimination of butane from propane is deduced from the difference of their slopes between 500°C and 1000°C. For each gas to be identified, its response curve in temperature -for a fixed concentration -must first be established on the sensor.

For methane, this apparatus gives an indication when the concentration is high (over the stoechiometry): see figure 4 the response curves for 5 selected temperatures, of methane in air, up to 100 % volume. At high concentrations (over 25 % vol/vol) the couple SiOOO -S800 becomes negative, due to a greater conductivity of methane. In this case, the software gives a specific alarm.

Conclusion:

With a short pulse (less than 1 -second) which consists of several temperature steps, we can, with only one catalytic sensor:

-compensate the ambient parameters, -identify and give a good concentration of a combustible gas, -detect for some gases, high concentrations.

The advantages are:

-a lower electrical consumption, -a quicker response, -a better reliability of the sensor, because the calculation compensates, to some extent, a loss of sentivity.

Some more work will be necessary to apply this technique to the identification and titration of a mixing of two, or more, combustible gases. This whole work is patent pending.

Figure 4 :

 4 Figure 2: heating process of the filament Figure 3 : scheme of the apparatus using one catalytic sensor

International Meeting on Chemical Sensors, Rome, 11-14 juillet 1994 y ^ £ (( > • ^


Hedi Nefati

Jean-Michel Cense

Jean-Jacques Legendre

Christian Michot

PREDICTION OF THE IMPACT SENSrnVITY BY NEURAL NETWORKS

A methodfor optimizing the prediction ofimpact sensitivity ofexplosive molecules hv neural networks is presented.

The database we used consisted 0/272 molecules containing C,H,N,0 ofknown sensitivity and belonging to several chemicalfamilies.

Pertinent molecular desriptors were selected by a preliminary multilinear treatment. The effects of the network's topology, the extent of the training, the choice of descriptors were examined and optimized.

The predictions are satisfactory with a correlation coefficient of 0.94 obtained through cross-validation. Moreover 95% of compounds are correctiy classißed in a 3sensitivity scale and the remaining 5% are classißed äs ambiguous which is very encouragingfor a real worid implementation.

The neural networks approach proves more accurate and more general than previous methods.

1-INTRODUCTION:

The purpose of this work was the building of an efficient tool for the impact sensitivity prediction in order to minimize the risks during the handling of explosive compounds.

In previous works the field concemed separate families of compounds and revealed the influence of various molecular parameters such äs the oxygen balance (Ref. l) , the electronegativity (Ref. 2), the bond lengths (Ref. 3), the Charge dissymmetry (Ref. [START_REF] Cheret | Detonation in Condensed explosives[END_REF]), the presence of specific groups (Ref. 5),... Moreover, for most of these studies, the influence of these parameters was considered to be linear and taken into account separately which certainly induces a loss ofprecision for the predictions.

In order to overcome these difficulties, we decided to build an experimental data base äs large äs possible and we used data processing methods able to take into account simultaneously several non-linear correlations: the neural networks.

n-THE DATA BASE:

The initial data base was built from the literature. It consisted of 204 molecules CxHyNzOt the sensitivity of which was measured by the same method developed by the Explosive Research Laboratory (USA) (Ref. 6). These molecules belong to nine families: nitroaromatics, nitroaliphatics, nitramines, nitric esters, nitrotriazoles, nitropyridines, nitroimidazoles, nitrofürazanes, others. The others 68 compounds were disgarded because the sensitivity was measured with a different protocol or because they were clearly non explosive (musks).

The geometry of each of these molecules has been optimized using the semiempirical program MOPAC (Ref. 7). Some molecular parameters have been taken out from these quantum mechanics caiculations and added to the list of the parameters which are the most frequently used in the literature. The determination of the pertinent parameters was carried out by a classical multivariate linear regression. After the elimination of the non-significs 10 ' parameters, the predictions were tested by cross validation. In that procedure, t" 6 linear regression is perfönned on all the molecules except one, then the prediction o the sensitivity is made for the disgarded molecule. This Operation is repeated for a " the data base.

In order to obtain a predicting method accessible to users who do not P 05 âny quantum chemical Software, two different processings were perfönned. l first one concerns only the parameters l to 27: i.e. no quantum mechai"ĉ aiculations are involved; it is a purely topological coding. The second one tajinto account all of the 39 parameters; it is a quanto-topological coding.

A-Topological coding , Âfter the multilinear regression analysis, 16 parameters were disgarde having partial F<2.

The following table presents the remaining parameters :

B-Quanto-topological coding

The same procedure has been carried out with all the molecular parameters including those caiculated from MOPAC.

In this case only 13 parameters remain.

A-Theformal neuron:

A formal neuron is an automaton which is characterized by a state of activity rated between 0 and l. This state is defined by the excitations received from other neurons through the synaptic connections which are the bonds between the neurons of a network.

INPUT Figure 3: Scheme of a formal neuron

The excitations reaching a neuron are weighted by coefficients depending on the synaptic connections (the synaptic weights) then they are added.

The activity of the neuron is then fixed to a value between 0 and l by using a sigmoi'dal fünction of the sum of the weighted excitations and is transmitted to the other neurons through synaptic connections.

B-Layered Networks:

Various types of neural networks may be considered depending on the interaction schemes between the neurons: layered networks, fully or partially connected networks, recurrent networks....(Ref. [START_REF] Rumelhart | Parallel distributed processing[END_REF] Each of these types have advantages and disadvantages and its own domain of applicaüon.

Our purpose (prediction of a property) led us to choose the layered networks since their structure is very well fitted to this problem.

These networks are made of neurons arranged in layers. The neurons of a layer are not connected together. They receive excitation only from the neurons of the former layer and they transmit their activity only to the neurons of the next layer (Figure 4).

Usually a bias neuron whose activity state is always l is added in the input and hidden layers.

Two layers play a special role: the layer which do not receive any excitation which is called the input layer and the layer which do not transmit any excitation and which is called the Output layer.

It is important to notice that the state of activity of the neurons of the output layer only depends on the state of activity of the neurons located in the input layer and ofthe synaptic weights ofthe network.

Therefore such a network may be considered äs a "black box" which answers with the excitaüons of its output neurons to the excitations of its input neurons. The choice of the synaptic weights govems these answers. synaptic connection with a Cij synaptic weight

C-Training and Generaliwtion:

Thie prediction of a property of an object is the association of a dala 5 describing the property to another data set describing the object. The datal describing the object are called the descriptors. ^18 For example the value of the impact sensitivity may be associated wjtll oxygen balance, the presence of some groups, etc.... ^lfey,»g' The prediction of course has to be supported by experimental data:i0? prototypes for which the association description-prediction is already knol^SitT hereföre, in our case, we had to possess a set ofmolecules för which the sensifiyŵ as known.

• '• ^IH Beföre any prediction, the synaptic weights have to be modified in süCh^Sl that, för each prototype, the difference between the prediction and the knownl® is äs small äs possible. This step is the "training" of the network. It is perfbrmIlB an iterating procedure called "Gradient Backpropagation" (Ref. 8). After this Ä the synaptic weights contain the data extracted from the prototypes.

• 'iSB l It is then possible to make predictions. In that case, the descriptolsl^;,, molecule which does not belong to the training set are the inputs ofthe networic.'ESi införmation is propagated through the network towards the Output layer whe%l|(|p rediction is collected. '^laSiS*

D-Precautions for using networks:

The generalization aptitude, in other words, the level of prediction accuracy that can be obtained, is of course the essential quality of a network. This aptitafe depends on many factors: the quality of the experimental data (number, repartitioa), the adequacy of the network structure to the complexity of the problem, tte pertinence of the descriptors... ,^stt A rigorous optimization of these factors has not yet been prposed. Nevertheless, a certain number of constraints on the structure of the netw|Bĥ as to be taken into account. ^% The number of neurons in the input layer is determined by the number | | Parameters used to describe the prototype (here a molecule) and the numbe|V* neurons in the Output layer depends on the descriptors associated with the pseässSF urthermore, the number of neurons in the hidden layer must be chose| such a way that the number of synaptic weights to be determined during the leäca phase does not exceed the number of the prototypes (no more unknownsfi| equations).

---Generally speaking, for a given problem, the optimal network architecture will be that which mininüzes the number of connections, while allowtng the use of descriptors that are rieh enough to enable the discrimination ofthe prototypes.

Indeed, we can verify that, for a given problem, the generalization aptitude of this type of network first increases with the number of its connections until the network is flexible enough to fülly take into account the complexity of the problem and enables proper leaming conditions.

When this aptitude is reached, any increase in the number of connections will induce an excess in the network flexibility and thus reduce the generalization performance.

These remarks are comparable to those made for a polynomial interpolation. Generally speaking, a statistical preprocessing of the prototypes submitted to a network is very useful. By lowering the number of descriptors, it allows the minimizing of the number of neurons in the input layer, and therefore the number of connections in the network.

E-Results:

After the optimizaüon of the structure of the network with 204 molecules, the following results were obtained: a-Topological coding: The descriptors were those previously chosen by a statistical treatment. The optimal network was a 11-4-1 (11 neurons in the input layer, 4 neurons in the hidden layer and l neuron in the output layer. A cross validation gave a correlation coefficient of 0.94 which is the best value we have obtained.

The figure 5 presents these results. One can notice that the correlation coefficient is clearly better (0.94) man in the multivariate analysis (0.89) and that he classified in class 2 and class 3 with the same probability. On the other hand, a sensitivity H=123cm which is the center of class 2 has a maximum probability of being classified in class 2.

During the prediction, the analysis of the activities of the 3 neurons of the output layer give informaüons on the quality of the prediction.

For example, in the case of the prediction of the sensitivity of a class 3 compound, the perfect prediction should be C1=C2=0 and C3=l (Cl: activity of neuron l, C2: activity ofneuron 2, C3: activity ofneuron 3 with C1+C2+C3=1). In reality diese values are never reached.

Several configuraüons may be obtained: 1-If Cl=C2==e and C3=l-2e, with e<0.15 then the network considers that the sensitivity belongs to class 3 and that the prediction is of good quality.

2-If Cl=0 and C2=C3 the prediction is considered to be relativiely ambiguous.

3-If C1=C2=C3 the prediction is considered to be completely ambiguous. 4-Cl>l-2e and C2 =C3=e or C2>l-2e and Cl=C3=e The first case is the most favorable: the network provides a good classification without ambiguity.

Cases 2 and 3 are also acceptable since the network teils the user to be carefül with the prediction.

Case 4 is unacceptable: the network provides a wrong prediction without ambiguity.

This method has been tested for both coding schemes. The structures of the networks were 11-2-3 and 13-3-3.

The results were satisfactory: 95% of the molecules were correctiy classified and the remaining 5% were ambiguous answers. Case 4 was never observed which is encouraging for a real use of this method.

V-CONCLUSION:

The methodology we describe here seems to be promising: it concems a large variety of molecules and the most significant parameters are simultaneously taken into account. Moreover, non linear correlations are directiy considered which is certainly favorable for a complicated phenomenon such äs the impact sensitivity.

However, the quality of the prediction should certainly be much higher if the experimental data base were improved by using a more accurate way for measuring the impact sensitivity, especially for the low sensitivity compounds.

Nevertheless, this new methodology could easily be used with this improved data base which make us think that neural networks will certainly have a brilliant future in the field of explosives and pyrotechnics.

Figure l presents

  Figure l presents the quality of the prediction tested by cross validation. A' perfect value corresponds to a point located on the diagonal.

Figure l :

 l Figure l: MLR Cross validation for the topological coding. (R=0.89, s=0.18, n=204) An average correlation factor ofO.89 is obtained by cross validation.

Figure 2 :

 2 Figure 2: MLR Cross validation for the quanto-topological coding (R=0.89, s=0.18, n=204)

Figure 4 :

 4 Figure 4: Scheme of a layered neural network

Figure 5 :

 5 Figure 5: NN Cross validation for the topological coding(R=0.94, s=0.13, n=204).

Table 2 :

 2 Multilinear regression results for the quanto-topological coding

	Con-elationfactor=0,918 n=204
	Standard deviation =0,18
	Equation	Stand, dev.	Partial-F
	y=	2,411 -0,133x1	0,015	75,469
		-0,129x4	0,040	10,300
		-0,181x5	0,041	18,845
		-0,267x6	0,054	24,442
		-0,200 x7	0,071	7,849
		-0,230x12	0.052	19,267
		-0,390 x20	0,052	55,402
		+ 0,035 x23	0,010	12,353
		+ 0,075 x25	0,017	18,240
		+ 0,075 x26	0,019	15,740
		-0,219x33	0,082	7,132
		+ 0,001 x37	0,001	2,206
		-0,086 x39	0,020	17,401
	The cross validation procedure gives a correlation coefficient ofO.89 whic|ä
	equivalent to what was obtained by the purely topological coding.	äfti

20th International Pyrotechnics Seminar, Colorado Springs, 25-29 juillet 1994, p. 759-767

there are no more badly predicted compounds which is very pronüsing för a realR of this method. A b-Quanto-topological coding: A similar treatment had been perfoimed on these data. The best network has the 13-3-1 structure. A correlation factor ofO.92 has been obtained which is slightly inferior to the purely topological coding.

Figure 6: NN Cross validation for the quanto-topological coding (R=0.92, s=0.17, n=204).

Therefore the results obtained by a neural treatment are clearly better f those obtained by a classical multilinear treatment. They reveal that this new processing method is very promising by allowing non linear corrclations. This method has also been tested on a separate prediction set composed of^ng molecules that were either non explosive (musiks) or whose sensitivity was measuielt? with other protocols. The results are quite satisfactory and the quanto-topological treatment seems to give better results in these cases which promote the use ofsucha coding even if it needs the use of a semi-empirical quantum chemistry program. S

F-Improvement ofthe results:

The main purpose of this work was the buüding of a prediction tool dedicated to a real use. Therefore it was of primary importance to estimate the robustness of the prediction.

Therefore, rather than caiculating a value of the sensitivity, we chose (Bis classify a molecule in 3 classes: class l: High sensitivity (5cm<H<87cm) Jg class 2: Medium sensitivity (88cm<H<160cm) ĉlass 3: Low sensitivity (160cm<H<320cm) ^S The output layer of the network contains 3 neurons; one for each class. During, the training step, the activities of these neurons are set to values that are t||| probabilities of belonging to a class. They depend on the value of the sensitivi^J compared to the extreme values of a class. För example, a sensitivity H==160cm ca^|


G Senfaute

INERIS J Ejosien

France E Mathieu

Real-time microseismic monitoring: Automatic wave processing and multilayered velocity model for accurate event location

At the Provence colliery, France, the remote microseismie monitoring used for the prevention of dynamic phenomena is an integral part of a strategy for exploring zones where there are risks of violent instability or rockbursts. Precise location, in real time, of the hypocentres associated with daily microseismic activity is one of the main objectives in the interpretation of data to optimise countenneasures. A study has been carried out with the purpose of calibrating an inclined wave propagation multilayered model. This artide describes the operaüonal microseismic monitoring network, the method used to calibrate the velocity model, the results obtained and the undeniable contribution which it makes to the quality of data interpretation.

l INTRODUCTION

Mine-induced seismicity is a well-known phenomena wherever mining acüvities take place at great depths. At Provence colliery, France, longwall mining at 1100 meters deep induces numerous, daily tremors, triggered by the caving proeess above the tabular onebody. Many factors play an important part in this seismic activity, characterizing the response of the rock mass to the voids created Underground. These factors are usually identified äs the geology, the tectonics, the natural state ofstress, and the mine layout, all playing a significant contribution to the rate of seismic energy released. In order to improve rockburst prevention and get insight into the rock mass behaviour in all areas undergoing development or human activities, a microseismic monitoring network was installed in 1992. This network has been regularly improved since, in order to integrale this seismic activity in a daily report submitted to the mine management. This infonnation is part of the füll quality control of the data collected Underground for decisions to be taken in matter of safety countenneasures.

Conceming 24 hour seismic data collection, the network permits qualification and quantification ofthe seismic activity following the different districts ofthe mine in activity. It provides the caiculated hypocenter and local magnitude of the tremors recorded by the seismic monitoring System installed äs well äs füll ground motions parameters. Since more than 100 events might be recorded during a day, specific, powerfui Software has been installed in order to provide for high quality, real time microseismic monitoring integrating on-line and off-line füll waveform processing and analysis.

DESCRIPTION OF THE MICROSEISMIC MONITORING SYSTEM

Data acquisition System

The data acquisition network consists ofeight stations ofthe vertical uniaxial geophone type and atridirectional Station (natural frequency = l Hz). Four vertical stations and the tridirectional Station are distributed at the surface. The data transmission from this surface network to the central site is by radio, while four Underground stations are connected to the site by cables (Fig. l) The central automatic data acquisition and processing System was designed for maximum reliability of the processing of microseismic events in quasi-real time. This System is described in detail in [START_REF] Bertil | Etüde de la sismicite du bassin de Gardanne, Intemal report, Laboratoire de detection et de Geophysique[END_REF]. Two PC/386 compatible Computers, arranged in parallel, perform the data acquisition, in a Master/ Salve relationship, at a sampling frequency of 155 Hz. Fortriggering to take plaee, acombinationofcriteriais validated. The PC Master then executes acquisition of the signal during the parameterised time and switches to slave mode, the slave PC itself switching to master mode (Fig. 2). This enables automatic preprocessing to be carried out for the production of a report and permits Eurock'94, Delft, 29-31 aoüt 1994, p. 631-638 Finally, in the event of a mains failure, the complete System is automatically rcstarted once thepowersupply is re-established. The capacity of the hard disks of the 2 PCs is tested before anything is written to them during communicadon difficulües. Backup onto magnetic tape is then initiated. Handling ofinterrupt, breakdown and communication problem messages is integrated in the System, and can be consulted at any time. These precauüons areessenüal in order to ensure remote seismic monitoring ofthe site with a minimum risk of System failure and merefore oflost data. Finally it should be noted that this System has been designed to pemnit the parallel connecüon of several acquisition staüons controlling several seismic sub-networks which are monitoring distant zones of interest and of very different dimensions.

network transmission to a multitask machine for complete processing ofthe immediate signal. The üme needed for these latter operations (a few seconds) is distinctiy smaller than the acquisition time itself which means that the System is never blind. This protocol offers the advantage of providing the necessary operadonal rcliability. In fact, this System automatically detects the disablement of one of the two Computers (change ofconßguration, intervention, breakdown...). In this case the other PC automatically enters the standalone operating mode. Furthennore, in the Situation where transmission of the event file encounters the problem of a congested Ethemet network, the file is stored in the acquisition PC for later transmission.

Data processing

The automatic processing operated by the network multitasking machine enables a report to be issued automatically for each event. Interactive processing, from any machine connected to the network, can thereforc be linked up a few moments after the acquisition for verification of the caiculated parameters (Fig. 3). The permanent updating ofa files System of the database type, on a monthly basis in this case, pennits the statistical study, a posteriori, of the fundamental parameters characterising the microseismic activity induced by different mine workings.

Figure 2: Dual PC based, no Mind time, automatic monitoring and processing System. Steps can be described äs l) trigemng nucroseismic event detected 2) Data acquisition on PC Master shifting to PC Slave and pre-processmg of wavefonns 3) Data transmision on multitasking machine 4) füll wavefonn analysis 5) Dala storage and Data-hase oriented files up-date 6) Inieractive processing of seismic events for checking, automatic ediüon of report, and mapping. Rgure 3: Real-tüne, füll automatic wavefönn analysis: all physical, caiculated results are stored or up-dated in both seianic event file and data-base oriented file.

RESÜLTS

Interactive processing, m a high-level graphics environment, enables the operator to verify and/or corrcct the caiculated parameters, in particular the location ofthe hypocentre of the tremors. The locadon caiculadon, based on automadc picking of first P wave arrival dmes, is in fact very sensidve to the quality of the signal-to-noise rado of the seismograms. It is necessary therefore to be able to validate quickly any tremor located in a suspect zone. The hypocentre caiculadon method is based on total inversion [START_REF] Tarantola | Inverse Problems: Quest for information[END_REF], with automatic establishment of probability in space.

A ccuracy of automatic location

The location of the seismic tremors is an essential Parameter for the interpretation of the microseismic activity ofeach ofthe workings. The absolute accuracy ofthe caiculation ofthe hypocentre depends on different criteria such äs the geometric configuration of the network, knowledge of the velocities and paths of the wave propagations, the sampling frequency of the acquisition System and the reliability of the picking.

Experience clearly shows that the second point, i.e. detennination ofan adequate velocity model, is crucial to obtaining good accuracy of the coordinates (X, Y, Z, T) ofeach event.

IMPLEMENTATION OF AN INCLINED MULTILAYERED MODEL

The velocity model initially used for the Provence basin was a homogeneous velocity model. During 1992 it rapidly became obvious that this model was introducing systematic biases ofmore than 150 metres relative to the epicentres in the South-East direction, and this applied to each area of activity. Similarly, the depth of the hypocentres almost always placed the events below the level ofthe mined layer which seemed to contradict the expected mechanisms of roof caveins, largely observed or expenenced in the workings.

A parameter study of the mean propagation velocity had not made it possible to improve the inaccuracies found.

In 1992, the Laboratoire de Detection et Geophysique (LDG) deployed a mobile network of four extra surface stations, this network being coupled to the permanent network (16 sensors in total). One of the main objectives of this campaign of measurements was to estimate the errors made by the homogeneous model (Nicolas et al, 1993) thanks to a better azimuth coverage of the monitored zone. It was possible in Üiis way to record approximately 260 microseismic events over the 10 days ofthe campaign. The local magnitude of these tremors is between 1.5 and 2.2. The low mining activity over the period in question meant that it was not possible to record events of a higher magnitude.

Given the fact that calibration blasts are not permitted in coal mines, the development of a new velocity model was based on sources ofinfonnadon from:

-a study conducted by the LDG in the Provence basin in 1989; -geological data available from the engineering department of the mine; -recordings of quarry blasts carried out outside the permanent network; -seismic corc drilling carried out in 1982.

Meticulous and cross analysis of this data has made it possible to construct an inclined multilayered velocity model (Fig. 4) for which corrections of stations, caiculated by the PavIis-Bookermethod. have been put forward. The introduction of a geological model comprising three levels and a half-space clearly corrects the epicentres, while the Station corrections appreciably improve the caiculated depths especially at the centre of the network (Nicolas et al, 1933). Nevertheless, a method of calibrating the model has been adopted using the recordings of dynamic phenomena associated with observed damage äs «natural mines blasts».

Hg. 4: Inclined multilayered velocity model (model B) comprising three levels and a half-space.

CALIBRATION OF THE MODEL

At Houilleres de Provence a rockburst is a sudden and powerfül nipture of the coal seam accompanied by violent concussion which can be perceived relatively far away in the mine and can be feit at the surface. When associated with microseisnüc events ofa magnitude greater than or equal to 2.3 they can be used äs calibration references in äs rnuch äs it is assumed that the precise locaüon based on the arrival times of P waves corresponds either to the observed damage or a structure considered to be active (fault, quarry face...).

Between 1991 and 1993, 18 rockbursts or dynamic phenomena of a lesser degree have been recorded at the Provence colliery. The precise location of these events compared with that of observed focuses makes it possible therefore to lest the usefulness ofan inclined sloping multilayered model for the determination of seismic focuses. Three propagadon modeis have been tested on the basis ofthis series of 18 events. These modeis are: the homogeneous velocity model (designated hereinafter äs model A), the inclined multilayered velocity model recommended by the LDG (model B) and finally the latter model complete with Station corrections (model C) (Table l).

Figures 5a and5b show all the relocations for 2 events from the 18. The results have revealed three distinct trends:

1-Model A moves the tremors in the south east away from any possible focus (observed damage, face, faults); 2-Model B moves the location to the west; 3-Model C locates the tremors in the south-east.

None of these three modeis makes it possible to match the theoretical focuses to the apparent focuses for the 18 events studied. A parameter study of the inclined multilayered model has been carried out to improve these results further. The parameters -a priori 9 in number -are: the 3 levels, the 4 velocities, the azimuth and the slope of the levels.

Design of a new velocity model

In order to refme the modelling of the structure of the Gardanne basin, all the data on the structure of the rocks and seismic velocities have been reconsidered and supplemented.

On the subject of geological data, the work by [START_REF] Ch | Le gisement de charbon du bassin de l'Arc (Houilleres de Provence)[END_REF] on the interpretation of 18 exploratory drillings carried out in the region have permitted a detailed analysis of the local lithostratigraphy. For the propagation velocities, the work by [START_REF] Bideaud | Une etude sismique dans le bassin de l'Arc[END_REF] and Lebreton (1980) has provided values for different geological levels. The main results for eachof the fourlayer levels in question can be summarised äs follows: Level I: TheupperFuvelianandBegudiancalcareous series are very well represented at this level (Fig- 6) (exploratory drillings GS1 and S 11) in direct relationship to the overthrust fault of La Diote to the south ofthe deposit. In the north we find argillaceous marl sequences traversed by calcareous packets. Lebreton (1980) obtained powerfui velocities between 4000 m/s and 5000 m/s for the calcareous levels. A quarry blast indicates a velocity of 3717 m/s, in apparent contradiction to the results of another quarry blast indicating a velocity of 3200 m/s. Every consideradon having been taken into account, the velocity of3717 m/ s has nevertheless been retained. The thickness ofthis level has not been chaUenged.

Level n: (depth at point GS1: 380 m). This consists exclusively ofupperBegudian calcareous series (Fig. 6).

A quarry blast indicates a propagation velocity of4480 m/s, a value in agreement with that supplied by a seismic core drilling. It was decided therefore to keep the parameters of this level.

Level III:

The comparison of the geological and seismic data does not offer a clear choice for the wave velocity and the thickness of level in. Nevertheless, the strong reptesentation ofupperBegudian, consisting essentially of marl, makes it possible lo envisage small velocity contrasts. Because the boundary between the lower Begudian (marls) and the Puvelian (limestone) is not easily identifiable, we establish therefore a lower limit for the level in below the roined layer (Fig. 6).

Based solely on the geometrical interpretations we propose a velocity of 3200 m/s, a value smaller than that already used of 3750 m/s.

Half-space: This last level consists of a strongly calcareous assembly traversed by a thin clay level. This data justifies a strong velocity for this layer, i.e. that of 5100 m/s put forward in the old model which seems to be the most suitable.

Rg.7 : Analysis of the average R.M.S. äs a function of the propagation velocity ftom a populaüon of 188 microseismic evente.

Numerical test

For each theoretical focus caleulated by Inversion, the root-mean-square (R.M.S.) of the residual times (difference between the theoretical and picked propagation times) gives an indicaüon of the conformity of the data with the model. We analysed the Variation of the R.M.S. äs a function of several parameters of the new velocity model (model D). For level l we advanced the velocity progressively from 3000 m/s to 4100 m/s in increments of 100 m/s. For each value, 188 located events supplied an average R.M.S. This is the minimum value for the velocity of 3700 m/s (Fig. 7): this result is in agreement with the recommended value of 3171 m/s. Por level ni we varied the velocity from 2700 m/s to 4700 m/s. The minimum overall R.M.S. appears in a ränge from 3000 m/s to 3200 m/s (Fig. 7). This result is also in agreement with the previous results which suggest a value of 3200 m/s. Por the halfspace we varied the velocity progressively from 4200 m/s to 5500 m/s; the minimum R.M.S. is in the ränge from 5100 m/s to 5300 m/s, and this result verifies the high velocity of 5100 m/s obtained by the geological interpretations.

Although currenüy being used, this type ofnumerical test is difficult to interpret since only one space coordinate of the solutions is taken into consideration at any one time. Nevertheless, it is essential for confirming the values obtained from associated analyses.

LOCATIONSOFROCKBURSTSCOMPARED (MODEL D)

For the 18 rockbursts listed, we made a systemauc comparison of the caleulated focus and the observed focus with an accuracy from 0 to 50 metres with respect to the epicentres (Figs. 8a and8b). At depth, the 9). At depth, in all cases, the cloud is located at the level of, and above, the coal seam; only the homogenous model disperses the hypocentres below the coal seam (between 1000 and 1600 metres depth).

Syrck and Kijko (1988) obtained similar results for coal mines in Poland, with seismic tremors located rather at the level of, and in front of the face. These results are in agreement with the results obtained by the new multilayered velocity model.

CONCLUSIONS

In many respects, location accuracy and space-distribution ofthe mine-induced tiemors are two important, sensitive parameters in the interpretation of the microseismic activity provided to the mine industry. The development and testing ofan inclined multilayered velocity model for wave propagation assessment is a very important part of the quality of this interpretation. It provides for two essentials aims: accuracy of the hypocentre locations, stability in time of the model with the Underground activity moving westwards, and deeper. This model has been tested against damages associated with local, Underground dynamic events recorded by the seismic array. Results of locations have been considerably improved by this model taking into account the main features of the geology of the basin. Installation on the site for daily routine ofthis clearly makes possible a better scientific analysis ofthe rock mass response, äs well äs a more comprehensive assessment on rockburst hazard to the benefit of the mine.

  geometry ofthe seisniic anay wilh mined layout

Fig. 6 :

 6 Fig.6: Correlation ofthe tocal lithosiratigraphy from exploratory drillings: GS1, Sll, BB2, GW2, GWl.

Fig. 8 :

 8 Fig. 8: Comparison of rockbursts located with velocity modeis A, B, C and the new velocity model D.

Table l :

 l Main parameters defining each of the velocity modeis A, B and C.

	model	A	B	C
	number of layers	l	4	sameasB
	dip/dip direction	half-space	165° -9.7°3	B
	velocity	4000m/s	275-4480-3750-5100 m/s	B
	depth of interface	Om	80-250-750 m. -	B
	corrections	no	no	yes
	Rg.5 : Compared location of rockburst with velocity modeis
	A, B.C.

Acknowledgements

We would like to thank the Engineering Services pf the Provence Operational Unit ofthe HBCM for the active support they provided throughout the duration of this study. We would also like to tharik Messrs. Bennani and Lizeur, Computer Systems engineers (INERIS) for the numerical developments carried out and for their invaluable help.