Volpe Raffaele
email: raffaele.volpe@u-bourgogne.fr

Da Silva Arthur

FERRAND Valérie

L E Moyne

Experimental and numerical validation of a wind gust facility

Keywords: Fluid mechanics, vehicle aerodynamics, crosswind, unsteady aerodynamics, yaw angle, TR-PIV, 3D CFD

The study of a vehicle moving through a lateral wind gust has always been a difficult task, due to the difficulties in granting the right similitude. The facility proposed by Ryan and Dominy has been one of the best options to carry it out. In this approach, a double wind tunnel is used to send a lateral moving gust on a stationary model. Starting from this idea, the ISAE has built a dedicated test bench for lateral wind studies on transient conditions. An experimental work has been carried out by means of Time-Resolved PIV, aiming at studying the unsteady interpenetration of the two flows coming from each wind tunnel. Meanwhile, a 3D CFD model based on URANS was set up, faithfully reproducing the double wind tunnel. Both experimental and numerical results are compared, and the evolution of the reproduced wind gust is discussed. Conclusions are finally taken about the validity of this kind of test bench for ground vehicles applications.

Introduction

Experimental simulation of lateral wind gusts on ground vehicles

One of the most studied research topics in vehicle design has been the reduction of the aerodynamic resistance. Current trends on CO 2 emissions and the need for energy efficiency, along with growing concern for safety in transports under any situation, ask for deepened research on aerodynamic optimization. This led to the optimization of automotive wind tunnels and measurement techniques for steady state flow tests, leaving the transient effects understudied. However, in the last years, more and more importance is being given to the knowledge of a vehicle's aerodynamic behavior faced to a side unsteady flow, such as a wind gust.

For example, Hémon and Noger [START_REF] Hémon | Transient Growth of Energy and Aeroelastic Stability of Ground Vehicles[END_REF] set up a linearized quasi-steady model of vehicle dynamics in order to study the transient growth of energy phenomenon. They stated that energy amplification occurs when a vehicle is subjected to a steep change of wind direction, so that estimation of lateral stability should not simply rely on static data. As a matter of fact, the response of aerodynamic side force and yaw moment to a sudden change in lateral wind is not linear but can present transient effects that can lead to peaks of force, and so to a potential source of hazard for the driver's behavior [2; 3]. For many years it was thought that steady yaw wind tunnel tests could give enough information in the evaluation of the dynamic stability, even if already in 1967 Beauvais [START_REF] Beauvais | Transient Nature of Wind Gust Effects on an Automobile[END_REF] showed that, when the yaw angle becomes greater than 15°, the yaw moment unsteady peak can be up to 20% greater than the same effort measured in steady state yawed condition.

Recently, several techniques have been conceived to study the evolution of aerodynamic coefficients to a time-dependent side wind. The first dynamical approach was displacing the model on a rail crossing the wind tunnel section [START_REF] Beauvais | Transient Nature of Wind Gust Effects on an Automobile[END_REF][START_REF] Cairns | Lateral Aerodynamic Characteristics of Motor Vehicles in Transient Crosswinds[END_REF][START_REF] Baker | Assessment of the Adequacy of Various Wind Tunnel Techniques to Obtain Aerodynamic Data for Ground Vehicles in Cross Winds[END_REF][START_REF] Chadwick | Crosswind Aerodynamics of Sports Utility Vehicles[END_REF]. Though nearly all the authors agree in estimating the unsteady force peak being 20 to 50% greater than the yawed vehicle steady force, little concordant results have been presented on the evolution of force as a function of time, especially when discussing the sudden wind direction change. Beauvais [START_REF] Beauvais | Transient Nature of Wind Gust Effects on an Automobile[END_REF] states that aerodynamic forces reach their steady state condition after the vehicle has covered 4 times its length in the gust, whereas for Cairns [START_REF] Cairns | Lateral Aerodynamic Characteristics of Motor Vehicles in Transient Crosswinds[END_REF] and Chadwick [START_REF] Chadwick | Crosswind Aerodynamics of Sports Utility Vehicles[END_REF] 5 vehicle lengths do not seem to be enough.

Another approach, extending the classical tests, consists of giving a periodic yaw angle to the model in a steady wind [START_REF] Garry | Comparison of Quasi-Static and Dynamic Wind Tunnel Measurements on Simplified Tractor-Trailer Models[END_REF]. Even if low yaw angles and oscillation frequencies were tested, this kind of test bench carried out an interesting result: a phase angle difference is visible in drag force behavior, which means that there is a delay in the formation of the same drag between steady yaw angle tests and the corresponding dynamic yaw position. This was explained by Chometon et al. [START_REF] Chometon | Experimental Study of Unsteady Wakes Behind an Oscillating Car Model[END_REF] using PIV measurements. It was found that vortex structures developing from the rear side do not adapt instantly to the new yawed position, but persist in the flow, as if they had a kind of "vortex inertia". Both of the presented techniques, involving the vehicle motion, suffer from the presence of signal noise because of the vibrations induced by the moving facility, lowering the reliability of results. This noise heavily affects the measured signal, especially when using on-board balances, so that many identical tests are required and care must be taken when processing these data.

Due to these problems, experimental techniques using static vehicle models have become of interest.

The most commonly used method is the estimation of the transient effects from the steady yaw coefficients and the wind spectral density by means of the aerodynamic admittance function [START_REF] Cooper | Atmospheric Turbulence with Respect to Moving Ground Vehicles[END_REF], which can be measured once from high turbulence tests [11; 12] or with a device creating an oscillating flow upstream the vehicle [13; 14]. Despite being a reliable and well documented technique, it gives little information about the unsteady phenomena of the interaction between the gust and the vehicle.

An approach possibly resolving this problem comes from Dominy [START_REF] Dominy | A Technique for the Investigation of Transient Aerodynamic Forces on Road Vehicles in Cross Winds[END_REF]. His idea was to place a static vehicle model in a longitudinal flow. Then, a lateral moving gust was simulated by means of an auxiliary wind tunnel whose communication with the main flow is controlled via a sliding door. This experimental device has been reproduced differently like in Ryan's [START_REF] Ryan | The Simulation of Transient Cross-Wind Gusts and Their Aerodynamic Influence on Passenger Cars[END_REF] work, whose approach will be reviewed in detail in this paper, in paragraph 2.1.

Along with expensive experiment tests, computational approaches have been developed, as presented in the next section.

Computational Fluid Dynamics approaches

Most of the simulations presented in literature regarding crosswind were validated simply on the basis of steady state yawed model experiments (mostly because of the lack of unsteady experimental data for the chosen vehicle), so particular care should be taken when generalizing these results. CFD simulations of vehicle motion, especially in presence of crosswind, still suffer from a lack of information in literature. This probably comes from the unavoidable use of sliding or deforming meshes, which can cause numerical convergence difficulties and unreliable results. As an example, Tsubokura et al. [START_REF] Tsubokura | Computational Visualization of Unsteady Flow around Vehicles Using High Performance Computing[END_REF] tried to reproduce the yaw oscillating vehicle proposed by Garry and Cooper using a rotating grid. Even if some results could be processed, they presented important numerical irregularities caused by errors in calculation of the mass flow when the grid was changing.

In the commonly used approach, static grids are used. Firstly, the steady vehicle running condition is simulated, then unsteady boundary conditions reproduce the gust passage. This kind of modeling is actually the same as the experimental test bench proposed by Dominy.

Due to the high Reynolds numbers (varying from 10 5 to 10 6 ), DNS simulations are computationally expensive, so that turbulence modeling is needed. The classical models are DES and LES, combining both accuracy in calculation of turbulence high scales and a smaller computational effort. Tsokubura et al. [START_REF] Tsubokura | Large Eddy Simulation on the Unsteady Aerodynamic Response of a Road Vehicle in Transient Crosswinds[END_REF] used LES on a real car shape with this method and showed the evolution of aerodynamic force tensor for a stepwise gust presenting a 30° yaw angle change. The most important dynamic effects were the overshoot in the yaw angle and an undershoot in the drag force; it was also visible that the relaxation time for the three moments is longer than the imposed gust ramp and corresponds to that of the lift force. The authors related this mostly to the evolution of pressure in the under-body, especially near the windward wheels. Favre [START_REF] Favre | Aerodynamic Simulations of Ground Vehicles in Unsteady Crosswind[END_REF] reproduced the effects of a moving trapezoidal shape gust on a simplified vehicle shape for different angles by DES. Tsubokura's results were confirmed and they showed how the pressure field evolves during the gust, suggesting that a square back geometry may be the better compromise between better fuel consumption and crosswind stability. Hemida and Krajnovic [START_REF] Hemida | Transient Simulation of the Aerodynamic Response of a Double-Deck Bus in Gusty Winds[END_REF] also used DES for simulating a double deck bus exit from a tunnel to a windy bridge and found that potentially hazardous unsteady effects on the side force can be seen before the actual passage in the gust.

The objective of this work

In recent years, the ISAE in Toulouse (France) started working in the development of a test bench for the study of a wind gust by means of laser techniques. Relying on the literature presented in previous sections, it was decided to produce a double wind tunnel, on the idea by Dominy. In particular, a version of this device inspired by Ryan [START_REF] Ryan | The Simulation of Transient Cross-Wind Gusts and Their Aerodynamic Influence on Passenger Cars[END_REF], in which the sliding door is replaced by a series of shutters, was adapted to an already existing wind tunnel. In this paper is presented the work concerning characterization of the gust produced by the new device in the measurement region. The main difference with Ryan's work is the use of an non-intrusive measurement technique such as the Time-Resolved Particle Image Velocimetry (TR-PIV), whereas he had to sample his test section using a hotwire probe. In parallel, a numerical approach is presented with a 3D CFD model of the same test bench. This CFD simulation approach was also proposed by Ryan, but using a 2D modeling. Furthermore, Ryan's geometry did not include a secondary wind tunnel, but a lateral inlet section of the computational domain with transient Dirichlet boundary conditions.

In the following, both experimental and numerical methods are described. In each case a double wind tunnel is set up and the wind gust penetration into the main steady state flow is reproduced by the opening and closing sequence of a series of shutters placed in the auxiliary wind tunnel, before its intersection with the main tunnel.

Experimental set-up

Description of the "Rafale latérale" test bench at ISAE

On the basis of the issues seen in the moving model experiments, the ISAE developed its own wind gust test bench as a stationary model facility. The inspiring idea was the one from Dominy and Docton [START_REF] Dominy | Passenger Vehicles in Unsteady Cross Winds[END_REF] and then further developed by Ryan [START_REF] Ryan | The Simulation of Transient Cross-Wind Gusts and Their Aerodynamic Influence on Passenger Cars[END_REF], in which the gust is simulated by introducing a side moving jet in the steady flow of a standard wind tunnel. This is achieved by means of an auxiliary wind tunnel whose communication is granted by a system of electrically driven shutters (Fig. 12). The relative angle is 30°: as seen by Macklin et al. [START_REF] Macklin | Comparing Static and Dynamic Testing Techniques for the Crosswind Sensitivity of Road Vehicles[END_REF], at this yaw angle occur the maximum values of yaw moment and side force in steady state experiments.

The interior of the auxiliary wind tunnel is divided in 20 channels, with a shutter in each of them controlling the air flow; every shutter can be opened and closed by means of an electromagnetspring system, which can be driven remotely by a LabView interface. To ensure mass flow conservation through every channel whatever the number of open doors is, the auxiliary wind tunnel has a second exit whose opening is controlled by a twin row of shutters, Fig. 3. So, when the opening of a door is commanded, at the same time the twin door is closed. In order to make the side jet move along the main wind tunnel, the shutters are not all opened at once, but one by one, in sequence. The time between the opening of one door and the following is set up for having the "front" of the jet moving at the same speed of the main wind tunnel; the opening time of a single shutter corresponds instead to the desired wind gust duration. If this value is high enough to make the equivalent gust length greater than the width of the auxiliary wind tunnel (which is the case of the presented results), all the doors stay opened for the required duration, then close sequentially with the same law (see Fig. 4).

TR-PIV measurements

Once the test bench was produced, the priority was to check if the generated gust could be considered realistic, that is if the transverse velocity seen at the future car model position had the expected stepwise evolution whereas the longitudinal component remained constant (Fig. 5). The main wind tunnel velocity was set to mainWT u  = 9 m/s, since this is the upper limit for the shutter system to operate the doors. The tests were held at atmospheric pressure and air temperature was 20°C.

The corresponding Reynolds number is An experimental campaign based on time resolved PIV was made to finely characterize the evolution of the side jet in the main steady flow. The results have to match the desired velocity profile (Fig. 5).

We used a PIV system provided by Dantec: the laser is a Nd-YLF, with a lengthwave of 527 nm, energy 20 mJ per pulse and a maximal frequency of 10 kHz. A iNanoSense MkIII camera, with CCD sensor resolution of 1280 1024 pixels and maximal sampling frequency of 1 kHz, was also used.

The flow was seeded upstream of each wind tunnel fan. Smoke generators were set up in order to guarantee an homogeneous smoke concentration in the measurement region. Moreover, the Stokes number (defined as the ratio of the response time of the seeded particle and the characteristic time of the studied phenomenon, , where ρ s and µ s are the density and the molecular viscosity of the seeder and d p the particle diameter) was calculated from laser diffraction measurements. We had St = 0.002, indicating that the chosen seeding particles will behave as good gas tracers.

PIV acquisitions were synchronized to first shutter opening, by means of a trigger signal generated by the driving interface of the test bench, so that phase averaging was possible. It was observed that the repeatability of the results is quite high, therefore all the presented results are averages of 15 runs.

The sampling frequency was 500 Hz.

In Fig. 6 the measured field is represented: it is a horizontal plane located at half-height of the section of the main wind tunnel, corresponding to the 3/4 of height of the car model in its future position; this field was decomposed in 9 windows, each one sizing (134 105) mm 2 .

In Fig. 6 is also included the reference system used in this paper. The velocity fields were calculated by means of the Dantec software "Flowmanager", more precisely they were deducted from adaptive cross-correlation of images (see [START_REF] Dynamics | FlowManager software and Introduction to PIV Instrumentation[END_REF]): a 64 pixels final size of the interrogation area and a 50% overlap granted a spatial resolution of 3.5 mm.

The vectors were validated by means of two filters, based on signal to noise ratio and spatial velocity fluctuation.

Numerical approach

The used software is ICEM v11 for grid generation and Fluent v6.3 for solving fluid equations.

Continuous Navier-Stokes equations

The governing equations of the simulation are the well-known Unsteady Reynolds Averaged Navier-



in the (x,y,z) reference defined in Fig. 6. Let us recall the URANS equations, in their dimensionless form with the Einstein notation:

0 u = x + j + j and + j + j + i t + i + + j i + j + + i x x Re + Re + x p = x u + t u 1 1 u u 2 (1)
where

t ref ref t ν L = Re u
. The Boussinesq turbulent kinematic viscosity ν t is unknown in the URANS approaches. The two chosen turbulence URANS models used in this work are Spalart-Allmaras [START_REF] Spalart | A One-Equation Turbulence Model for Aerodynamic Flows[END_REF] and SST k-ω [START_REF] Menter | Two-equation Eddy-Viscosity turbulence Models for Engineering Applications[END_REF].

In Spalart-Allmaras turbulence model the kinematic numerical viscosity is directly resolved by means of a single differential equation. A new parameter is introduced,

1 ν + t + f ν ν ~, representing a non- dimensional damped turbulent viscosity. The term 1 ν
f is a viscous damping function, whose formulation is detailed in [START_REF] Spalart | A One-Equation Turbulence Model for Aerodynamic Flows[END_REF].

Even if the readers can easily find the equation of Spalart [START_REF] Spalart | A One-Equation Turbulence Model for Aerodynamic Flows[END_REF], let us rewrite its equation in its dimensionless form and briefly show the meaning of each term:

                                                                 term trip õf diffusion õf n dissipatio 2 õf production 2 õf transport u u Re 1 Re 1 Re 1 Re 1 2 1 ũ ~1 2 2 2 1 1 2 3 2 1 j j t j j b j j t b w w ij ij t b j j f x x C x x d f C f C d f f f C x t (2)
where the unknown is + ν ~, ij is the rotation tensor and

ref L d d
is the non-dimensional distance to the nearest wall. For further explanation of the other terms and constants, see [24; 26].

In the SST k-ω (Shear Stress Transport k-ω) model of Menter [START_REF] Menter | Two-equation Eddy-Viscosity turbulence Models for Engineering Applications[END_REF], the unknown quantities are the non-dimensional turbulent kinetic energy

                                   k j t k j k k j i ij k j j x k x k k x x k t k of diffusion of n dissipatio * of production * of transport Re 1 10 , u min u ( 3
)                                       term diffusion cross 1 of diffusion of n dissipatio 2 of production of transport 1 1 2 Re 1 2 u 2 1 j j j t j ij ij j j x x k F x x x t ( 4
)
More information on the introduced terms can be found in [25; 26].

The partial differential equations (1-4) are discretized into a set of algebraic equations using the finite volume approach, which are then solved in an iterative fashion.

Computational grid and boundary conditions

The geometry of the numerical domain aims at reproducing the very same test bench presented in paragraph 2.1 and is represented in Fig. 7. The geometry of the auxiliary wind tunnel is faithfully reproduced from the honeycomb straightener to its outlet in the main wind tunnel; the length and height of the main wind tunnel are the same as the real model. Its width is twice the wind tunnel section, to avoid numerical problems when placing a pressure outlet boundary condition too near to the studied region. The size of the domain is 7.05 5.7 1.34 non-dimensional lengths. A structured grid was set up, counting 1.4 million cells; the quality of 90% of elements, measured with the determinant method, is above 0.55 and the worst element quality is 0.32: the minimum recommended quality to avoid calculation errors is usually 0.2.

In Fig. 7, the mathematical formulations of inlets/outlets boundary conditions are also shown. At the inlets the velocity components are set to corresponding values in the real test bench (see paragraph 2.2).

Turbulence boundary conditions are given by the hydraulic diameter

H L H L H L L L L D 2
(L L and L H being the length and the height of the considered section, respectively) and turbulence intensity ratio

i i i i I u u u u ' ' (where i i u u
is the mean velocity magnitude). The latter quantity was estimated by hot-wire probe measurements and is I = 2% for both the main and auxiliary inlets.

Once these values are known, it is possible to set the turbulence values at each boundary. For Spalart -Allmaras simulation, the following formula was used [START_REF]FLUENT User's Guide[END_REF]:

H i i + t D I = ν 0.07 u u 2 3 ( 5
)
For SST k-ω simulation, the values of + k and + ω at the inlets were calculated with [26]: The 20-channel shutter system was also reproduced (Fig. 8): the channel walls and the shutters have been simplified with simple planes. In particular, each shutter was designed both in its closed and opened position. The shutter actuation is simulated by setting a wall boundary condition (no velocity and zero normal pressure gradient) to the right plane, as explained in Fig. 8a. It is easy to reproduce with this approach the opened and closed shutter configurations, as shown in Fig. 8c, where the last five interior walls have been hidden to make the image clear.

H ref + + ref i i + D L k = ω ; I = k 0.038 u u u 2
Our approach is different than Ryan's 2D simulations [START_REF] Ryan | The Simulation of Transient Cross-Wind Gusts and Their Aerodynamic Influence on Passenger Cars[END_REF]. As a matter of fact, his test bench is represented by a rectangle with no auxiliary wind tunnel. To replace it, transient Dirichlet boundary conditions on a side of the rectangle are imposed. The way the shutters are modeled is quite similar to ours. Indeed, the side representing the auxiliary wind tunnel presents 20 subsegments on which the inlet velocities are set respecting the opening shutter sequence represented in Fig. 4.

Returning to the discussion of our simulations, the technique described in this paper implies some simplification hypothesis: in first place, the shutters are supposed to open/close instantly (less than on time step), when the actuation time is around 12 ms for opening and 30 ms for closing for the experimental test bench.

The shutter geometry was also simplified by ignoring its axis and by placing it orthogonally to the channel (Fig. 8b): this was made for having better element quality in the auxiliary wind tunnel. This simplification also implies that the closed shutters are perfectly airtight whereas some air leak is present on the real test bench. After the simulation, the value of the non dimensional CFD wall distance A second order central difference scheme was used for space discretization, whereas time was discretized by means of an implicit first order scheme.

The solution was initialized with the results of a preliminary steady state simulation of the test bench with all the shutters in closed configuration. The time step size was constant and set to 2 ms.

Numerical and experimental results

The origin of the reference system, represented in Fig. 6, is placed at the beginning of the main wind tunnel in the center of the lower half section.

The u + components of the steady state imposed velocity in both wind tunnels respect the condition

auxWT + mainWT = u u +
as shown in Fig. 5.

The dimensionless time,

ref ref + L t = t
u represents the number of reference lengths covered by the main flow.

We recall that, because of the setup of the PIV system, t + = 0 corresponds to first shutter opening.

When t + = 1, the main flow has covered one model length.

Even if the geometry of the system is three-dimensional, it has been observed that w + << u + in the measurement zone, so that it will only be discussed on u + and v + components of velocity.

Figure 9 shows the evolution of u + and v + for five chosen points, both in terms of PIV measurements and CFD results. The coordinates of the chosen five points, as well as the non-dimensional distance y D to the end of the auxiliary wind tunnel, are reported in Tab. 1. It is easy to recognize in all the presented graphs the passage of the gust at the chosen points. In the following, we will identify with + i t the instant of the arrival of the gust front and with + f t the end of its passage (see Fig. 9a). In the time delay + Δt between + i t and + f t , the considered point is subject to the fully developed flow coming from the auxiliary wind tunnel. We will call this period the "established phase".

Firstly, we can note that + Δt < 10 for all points, whereas it should be the same as the duration of the imposed gust, 10 = t + gust , as shown in Fig. 4. The difference between + Δt and + gust t is partially due to the presence of a delay to reach the established phase. Let us note this delay δt + , as represented in Fig. 9a.

It will be discussed on the δt + value further. Moreover, there is also an additional delay after + f t when returning to the starting flow conditions.

The evolution of u + is different from the expected constant (see Fig. 6b), especially because of the overshoots at + i t and + f t . Figure 10 provides a scheme explaining this phenomenon, already described in [START_REF] Volpe | Validation d'un banc d'essais reproduisant les rafales de vent sur véhicule terrestre. Caractérisation de l'écoulement par TR-PIV[END_REF]. In these drawings, the flows coming from each of the two wind tunnels are marked with a different color. At the start of the gust (Fig. 10b), the flow coming from the main wind tunnel is forced to bypass the jet front from the auxiliary wind tunnel. The main flow accelerates, giving the u + velocity overshoot visible in Fig. 9. For

+ f + + i t < t < t
, u + gets its correct value (Fig. 10c) because the transverse flux has fully developed. When

+ f + t t
, the shutters are closing, so that the auxiliary air mass is progressively no more alimented by its wind tunnel (Fig. 10d). The main flow has to evacuate the residual air mass, the latter becoming a pressure drop for the former. The longitudinal velocity component of the main flow is then reduced, creating the u + undershoot visible at + f t . These imperfections are due to this kind of testbench: as a matter of fact, such profile was also seen in the hotwire measurements by Ryan.

Concerning the component v + , its profile is similar to the desired evolution of a stepwise function (see Fig. 5b), except for the delay δt + . The value of δt + increases with the distance to the shutter system, from 0.8 in position E to 1.5 in position A, corresponding to 24.9 and 46.7 ms, respectively.

Nevertheless, these delays can be considered short enough to be approximated as instantaneous, when measuring unsteady forces.

During the delay δt + , at some points it is possible to recognize an undershoot/overshoot sequence in the velocity component v + . In particular, the nearest the point is to the end section of the auxiliary wind tunnel, the more these peaks are intense. Such a behavior was also seen by Ryan. The undershoot is caused by the main flow bypassing the auxiliary one (see again Fig. 10b). Concerning the overshoot, Ryan thought it could be due to the honeycomb straightener equipped on his test bench between the shutters and the end section of the auxiliary wind tunnel. In our test bench, this element is not present, but the imperfection still subsists. Another hypothesis could be the different pressure drop between the "opened" and "closed" shutter configuration (Fig. 3), generating undesired over-speeds in every channel of the shutter device when changing to "opened" configuration (for example in the scheme of Fig. 10b). This issue will be born in mind when setting the auxiliary wind tunnel velocity during the next campaign in presence of the car body.

If we focus on CFD results, it is possible to see that the dynamics is quite well reproduced, especially for u + velocity. There is no particular difference of behavior for the two chosen turbulence models. The model fits well the experimental results, even if it tends to anticipate the arrival of the gust and reduce its velocity. It is also visible that the peak for v + velocity fades too slowly and the established phase is not properly attained. The difference might come from the fact that the numerical actuation of the shutters is modeled by an instantaneous boundary condition switch, whereas the experimental sequence takes time, 12 ms for opening and 30 ms for closing, as illustrated in Fig. 8 from section 3.2. Because of the instantaneous opening of the numerical shutters, the differences on flow development as mentioned before (Fig. 10) between experiment and calculations are more visible during starting and finishing phases. Logically, crosswind penetration is also stronger in numerical approach than in experimental tests. The situation is reversed when the shutters are passing to the closed configuration (Fig. 10d), so that there is a kind of imbalance that causes slight oscillations in numerical values of v + .

When planning this model, a way to avoid this phenomenon could have been the use of moving grid for problems such as those mentioned by Tsubokura et al. [START_REF] Tsubokura | Computational Visualization of Unsteady Flow around Vehicles Using High Performance Computing[END_REF]. As explained in the introduction, even if some results could be processed by these authors, they presented important numerical irregularities caused by errors in calculation of the mass flow when the grid was changing. Comparing the different results among the five positions, it appears that the best agreement between numerical and experimental results is achieved at point E, especially for u + velocity. Nevertheless, the unsteady effects for v + are very strong at this position, as for the experimental data. This is consistent with the fact that it is the nearest point to the shutters. At point E, the delay δt + for the gust establishment is indeed the lowest, the velocity v + is the highest during the established phase, and the velocity u + keeps quite constant. This effect will be carefully taken into account when selecting the future position of a car body. Indeed, no confusion will have to be made between these overshoots due to the test bench facility and real dynamical unsteady efforts.

The numerical model was used to check if the presence of the shutter system affects the flow in the measurement region. A simulation of steady crosswind without the shutter system was then set up. We observed that the flow turbulence is only incremented in the auxiliary wind tunnel and in a small part of the main wind tunnel. For non dimensional distances y D greater than 0.4 there is little difference among the two cases. Even, when y D > 0.7, the turbulence of the case presented in Fig. 9 is lower than in the simulation without shutters. As an example, at point C, the turbulence intensity is 2.1% during the established phase (t + = 7.97), and 2.5% in the simulation without the shutters. As a matter of fact, The evolution of β, for the same 5 positions of Fig. 9, is represented in Fig. 12. If yaw angles are compared to the respective velocity components evolutions (Fig. 9), it is possible to see that the effect of v + dominates over u + , so that the imperfections of the latter will not be taken into account.

Concerning experimental results, the yaw angle obtained during the established phase tends to reach the imposed angle of 30° between the two wind tunnels, varying from 23° at position A to 27° at position E. Nonetheless, because of the greater influence of v + , the very same sequence of undershoot / overshoots appears for the points nearer to the end of the auxiliary wind tunnel. In particular, the overshoot at position E visible at t + = 4.7 is 30% greater than the β value during the established phase.

This fact has to be carefully considered when placing the model in the wind tunnel, since this percent is also the variation measured for aerodynamic actions in moving model tests ( [START_REF] Beauvais | Transient Nature of Wind Gust Effects on an Automobile[END_REF][START_REF] Cairns | Lateral Aerodynamic Characteristics of Motor Vehicles in Transient Crosswinds[END_REF][START_REF] Baker | Assessment of the Adequacy of Various Wind Tunnel Techniques to Obtain Aerodynamic Data for Ground Vehicles in Cross Winds[END_REF][START_REF] Chadwick | Crosswind Aerodynamics of Sports Utility Vehicles[END_REF]).

In CFD results, yaw angle does not succeed to stabilize during the established phase, because of strong crosswind penetration, due to the instantaneous switches of boundary conditions, as previously explained. As the phenomenon described in Fig. 10 is accentuated, the overshoots and undershoots at + i t and + f t , are more intense, so that a kind of imbalance makes the yaw angle slightly oscillate.

However, it qualitatively reproduces the experimental trends and can also predict the undershoot/overshoot sequence at + i t when approaching to the shutter system.

Conclusions

A test bench reproducing the passing of a lateral unsteady gust on a vehicle, based on the double wind tunnel proposed by Ryan and Dominy [START_REF] Ryan | The Simulation of Transient Cross-Wind Gusts and Their Aerodynamic Influence on Passenger Cars[END_REF], was built and validated by means of TR-PIV. This test bench, designed with a series of shutters at the end of an auxiliary wind tunnel, reproduces yaw angles decreasing with the distance to the shutters and spacing from 23 to 27°. These are typical values of high speed ground vehicles, who suffer relevantly from unsteady aerodynamic effects. The yaw angle is similar to the desired evolution of a stepwise function except for the presence of a delay to attempt the established phase. Even if it increases with the distance to the shutter system, it has been observed that it remained very short everywhere, compared to the gust duration.

During that nearly instantaneous time, an undershoot/overshoot sequence is visible just before the established phase of the gust, for positions near to the end of the auxiliary wind tunnel. The peak value of yaw angle reached during this sequence can be up to 30% greater than the one seen in the established phase. It has to be paid attention to this phenomenon because such kind of overshoots can be seen for aerodynamic efforts in moving model facilities. When writing this paper, a new experimental campaign is near to start. This time, a car model will be put in the test section and aerodynamic efforts will be measured with an unsteady balance. Thanks to the results presented here, it has been possible to state that it is safer to put the model far from the shutters in order to avoid the parasitical overshoots, even if the yaw angles are lower than the project value. If some effort peaks will be seen, they will be representative of the true unsteady response of the vehicle, rather than being possible consequences of the yaw overshoots introduced by the test bench.

Meanwhile, a 3D CFD model based on URANS equations was developed and compared to these experimental results. It was possible to see that the flow dynamics is quite well reproduced, especially for the longitudinal velocity. The model fits well the experimental results, even if it tends to anticipate the arrival of the gust. As far as the yaw angle values are concerned, slight oscillations might come from the fact that the numerical actuation of the shutters is modeled by an instantaneous boundary condition switch, whereas the experimental sequence takes time, 12 ms for opening and 30 ms for closing. These sudden switches cause a kind of imbalance that does not allow the stabilization of yaw angle values during the expected established phase. When planning this model, a way to avoid this phenomenon could have been the use of moving grid for shutters, but it was discarded in order to keep the model as simple as possible and to avoid other problems such as those reported by Tsubokura et al. [START_REF] Tsubokura | Computational Visualization of Unsteady Flow around Vehicles Using High Performance Computing[END_REF]. Better results can be achieved by discretizing the auxiliary wind tunnel outlet on a greater number of shutters whose width is smaller, but at this stage of research, we wanted to reproduce faithfully the same geometry of the ISAE test bench, and compare to the preliminary results. When writing this paper, a new numerical campaign was started, this time replacing completely the instantaneous boundary condition switch by the introduction of a concentrated pressure drop in the shutter region.

The shutter actuation can now be simulated by a smooth change of the boundary conditions. The preliminary results are encouraging, since no oscillations of the transverse velocity component were seen.

Figure 1 :

 1 Figure 1: Wind gust generator by use of an auxiliary wind tunnel.

Figure 2 :

 2 Figure 2: CAD drawing of the ISAE testbench

Figure 3 :

 3 Figure 3: Projected side view scheme of a channel of the shutter system: (a), closed shutter configuration, (b), open shutter configuration

Figure 4 :

 4 Figure 4: Opening/closing door sequence scheme

Figure 5 :

 5 Figure 5: Velocity vectors imposed by the two wind tunnels: (a) vector composition, (b) expected time evolution of the longitudinal and transverse component of velocity, at a generic point of the measurement zone

  the reference car model length L ref = 0.28 m. The auxiliary wind tunnel velocity was set to auxWT u  = 9/cos(30°) = 10.39 m/s, in order to satisfy the vector relation shown in Fig. 5a with the imposed relative angle between the wind tunnels. This gives a transverse velocity component of v  = 5.20 m/s. The case of a wind gust duration of 10 vehicle lengths, corresponding to

Figure 6 :

 6 Figure 6: Part of geometry from Fig. 2 (enlarged side view) with position of the measurement plane

  Stokes equations (URANS) of momentum and the equation of continuity restrained to incompressible flows. All the terms presenting a "+" symbol in the following averaged equations indicate their nondimensional form. The physical quantities used as reference are the car body length L ref , the longitudinal velocity of the main wind tunnelmainWT ref = u u and the air molecular viscosity . For a generic point of coordinates (x + , y + , z + ) at a generic time instant t + , the corresponding velocity vector will be expressed as )

  are related to the dimensionless turbulent kinematic viscosity in equation (1) by the relation, damping function which has the same goal as 1 ν f in Spalart-Allmaras model. Each quantity is resolved by a new dimensionless transport equation:

  of the non dimensional Boussinesq turbulent kinematic viscosity + t ν can be calculated by means of the kω f function, previously presented. The outlets of the fluid are simulated by setting to zero the pressure gauge between the static pressure on the face and the atmospheric pressure.

Figure 7 :

 7 Figure 7: Three dimensional CFD: geometry and boundary conditions

  * is the friction velocity of the fluid and h c the distance of the barycenter of the first computational cell from the wall boundary) was checked. It was seen that it varied from 51 to 89. According to FLUENT's guide recommendations [25], both Spalart -Allmaras and SST k-ω model perform well if 30 > y + CFD .

Figure 8 :

 8 Figure 8: Shutter system CFD simplification: (a) shutter boundary conditions, (b) comparison between real and simplified

Figure 9 :Table 1 : 20 Figure 10 :

 912010 Figure 9: Unsteady gust, profiles of non-dimensional velocity components in 5 points. Comparison of TR-PIV data with CFD models results. (a) to (e), profile homonymous point, (f) chosen points and measuring field positions.

Figure 11 :

 11 Figure 11: Unsteady gust, TR-PIV measurements vs Spalart -Allmaras CFD simulations of yaw angle field. (a) : t + = 3.79, (b) : t + = 7.97, (c) : t + = 13.5

Figure 12 :

 12 Figure 12: Unsteady gust, profiles of yaw angle β in 5 points. Comparison of TR-PIV data with CFD models results. (a) to

the shutter system walls homogenize the flow exiting the auxiliary wind tunnel, as an honeycomb would do.

One important parameter for evaluating the quality of the reproduced gust is the yaw angle β. For a given point of the measurement region, at a time t + , it is defined as the angle between the velocity u and the x direction:

Moreover, it is known (Baker [START_REF] Baker | Ground Vehicles in High Cross Winds Part I: Steady Aerodynamic Forces[END_REF]) that in steady yaw wind tunnel tests the most important efforts, such as side force and roll moment, proportionally increase with β. The yaw angle is expected to approach 30°, the angle between the two wind tunnels.

In Fig. 11, some snapshots of yaw angle field are represented: at the gust arrival (t + = 3.79, Fig. 11a), during the established phase (t + = 7.97, Fig. 11b) and at the passage of the gust (t + = 13.82, Fig. 11c).

Both turbulence models gave quite the same results, as seen in Fig. 9, so only Spalart Allmaras model is used for this comparison. A masked region is visible in the PIV results because of lack of seeding when measuring in these positions.

Even if some discrepancies are visible between TR-PIV measurements and numerical results, there is a good agreement, in Fig. 11b, when the gust is fully developed across all the test section, at t + = 7.97.

Then, at t + = 13.82, for x + > 3.2, the gust has nearly crossed the measurement region, as shown in Fig. 11c: this is well predicted by the CFD model. The main differences appear when simulating the arrival of the gust front: the CFD model is in little advance, comparing to experimental data.


Lara S G Piccolo
email: lara.piccolo@open.ac.uk

Kenny Meesters
email: k.j.m.g.meesters@tudelft.nl

Roberts

Building a Socio-technical Perspective of Community Resilience with a Semiotic Approach

Keywords: Community Resilience, Human-Computer Interaction, Crisis, Organisation Semiotics, Socially-aware Computing

Situated in the diversity and adversity of real-life contexts facing crisis situations, this research aims at boosting the resilience process within communities supported by digital and social technology. In this paper, eight community leaders in different parts of the world are invited to express their issues and wishes regarding the support of technology to face social challenges. Methods and artefacts based on the Organisational Semiotics (OS) and the Socially-Aware computing have been applied to analyse and consolidate this data. By providing both a systemic view of the problem and also leading to the identification of requirements, the analysis evidences some benefits of the OS-based approach to consolidate perspectives from different real-life scenarios towards building a socio-technical solution.

Introduction

Natural catastrophes, man-made emergencies, accidents, or social issues threatening human rights are constant challenges to humans' ability to live in peace and harmony, both individually and as a society.

Coping with crisis situations and recovering from them are complex processes that may involve resources, several stakeholders, logistics and, above all, collaboration. Thus, communication and information awareness are increasingly being required in disaster management or peace-building processes [START_REF] Hyman | Peace technologies' enable eyewitness reporting when disasters strike[END_REF]. The availability of digital information has been fostered by the broad adoption of technology and the willingness to share information online. Though, to be effective in supporting individuals and stakeholders' actions, a digital platform for crisis needs to reflect real-world practices of affected populations and the responders both in social and technical terms [START_REF] Comes | D2.1 Requirements for boosting community resilience in crisis situation[END_REF].

Ushahidi (www.ushahidi.com) is a platform that provides situational awareness in crises by enabling anyone to share geo-located information in real time. Since the violent process of the Kenyan presidential election in 2007, scenario that originated Ushahidi, this platform has been deployed in more than 159 countries and translated into more than 35 languages to support communities to recover from hurricanes, flooding, to fight against corruption, among other issues [START_REF] Hyman | Peace technologies' enable eyewitness reporting when disasters strike[END_REF]. As an example, the earthquake that devastated Nepal in 2015 generated 2031 geo-located reports on the platform, 1289 of them demanding an action [START_REF]Quakemap[END_REF]. Fig. 1 (obtained at [START_REF]Quakemap[END_REF]) illustrates a) a report requesting help with water, food and shelter, and b) aggregated reports represented on the map.

This research aims at adding new features to Ushahidi for boosting the capacity of the platform to support community resilience. As a globally adopted tool, a socio-technical solution for Ushahidi should be capable of coping with the diversity of contexts without neglecting particularities of each scenario.

As acknowledged in the literature [START_REF] Terp | Evolution of the Humanitarian Data Ecosystem[END_REF][3], involving communities in the platform design improves their purposefulness and usability, also increases the community's awareness and adoption of such tools in times of real crises. The design is then based on the participation of communities to discuss beyond technical features or current usability issues, but to reflect towards establishing a common meaning of community resilience supported by a digital and social tool.

In this paper, the initial steps of this research are reported. Eight community leaders in different contexts have been interviewed expressing their needs and wishes related to technology to support the resilience process. The analysis of this material has been supported by methods and artefacts of the Organisational Semiotics (OS) [START_REF] Stamper | Organisational semiotics: Informatics without the computer? Information, organ. and technology: Studies in organisational semiotics[END_REF][9] revealing i) the complexity of stakeholders involved; ii) informational needs in technical and social aspects; iii) key elements in a shared understanding of community resilience supported by technology.

By providing both a systemic view of the problem and the stakeholders involved, as well as leading to eliciting socio-technical requirements, this analysis evidences the benefits gained through the application of methods and techniques based on OS approaches to consolidate the diversity of perspectives.

In the next section, works related to community resilience and social platform for crisis are introduced. Then, the research method is described. The OS-based analysis is split into two sections: understanding the problem and socio-technical requirements. In the sequence, the results are discussed by building a situated perspective of community resilience, followed by the conclusion.

Related Works

As stated by [START_REF] Patel | What Do We Mean by 'Community Resilience'? A Systematic Literature Review of How It is Defined in the Literature[END_REF], community resilience is an amorphous concept. In the disaster-management literature it has been defined and applied in many different ways, but commonly referring to the ability of a community to cope with emergency situations [START_REF] Wickes | Community Resilience Research: Current approaches, challenges and opportunities[END_REF].

Finding systematic ways to boost resilience within communities has been a concern for governments and policymakers worldwide. Situated frameworks maps stakeholders and their roles, stages in the resilience process, tools, and practices. Examples as the United States' one [START_REF]A whole community approach to emergency management: Principles, themes, and pathways for action[END_REF], and the United Kingdom's in [START_REF]Community resilience framework for practitioners[END_REF], which centres resilience in planning, response and recovery. Despite the growing number of crisis-related research, the focus is usually given to responses by authorities instead to empowering citizens [START_REF] Wetzstein | Crises and social media. A meta-study on pertinent research and practice[END_REF]. Less attention has been given to bottom-up approaches, especially in the recovering phase [START_REF] Vos | Guest Editors' Introduction: Community Resilience in Crises : Technology and Social Media Enablers[END_REF], justifying the need of local communities not only as information providers but as actors therein [START_REF] Linnell | Citizen Response in Crisis: Individual and Collective Efforts to Enhance Community Resilience[END_REF]. Linnell [START_REF] Linnell | Citizen Response in Crisis: Individual and Collective Efforts to Enhance Community Resilience[END_REF] lists some conditions to promote citizen participation in community resilience, such as managing voluntarism and matching needs/skills/knowledge, reinforcing the culture of collaboration, etc. In line with that, the framework in [START_REF] Edwards | Resilient Nation. Demos[END_REF] suggests an approach focused on Engagement, Education, Empowerment and Encouragement (the 4 "Es"). By informing (educating), enhancing social capital (empowerment and encouragement), and connections (engagement), the author recognises social media as a potential tool to boost community resilience collectively.

Such potential has been confirmed in recent crises, so that big players have launched specific services to cope with emergencies. Twitter Alert broadcasts and highlights critical information to public when authoritative accounts mark Tweets as alerts. Facebook provides the Safety Check, for people in a disaster area to check if they/their friends are safe. And Google, through the Crisis Map displays many types of geographic information, such as storm paths, shelters, and power outages from a variety of sources, including official and user-generated content. Beyond these services, Houston et al [START_REF] Houston | Social media and disasters: a functional framework for social media use in disaster planning, response, and research[END_REF] analysed 15 applications for disaster situations and came up with a set of features for all the phases of an emergency, which includes mental-health support, detecting related events, discussing implications of and responsibilities for events, among others.

Initiatives like that boost resilience by offering psychosocial support, locating missing people, helping users to provide and share information, etc. However, they are not fully driven by community resilience frameworks and guidelines, which can direct the design of features to maximise support for resilience.

Towards this direction, Turoff et al [START_REF] Turoff | The design of a dynamic emergency response management information system[END_REF] state that the design of resilience information systems must consider the processes that emerge in the field and are influenced by cultural traits. The authors suggest 9 design principles including treating exception as norms, sharing information at the community level, connecting people with authorities and resources, and adaptability in assigning users' roles and profiles.

Method

Building on the disaster-management literature, this research understands community resilience driven by technology as 'a process of continuously enabling a broad range of actors to acquire a relevant, consistent and coherent understanding of a stressing situation, empower decision makers and trigger community engagement on response and recovery efforts, including long-term mitigation and preparation.' [START_REF] Comes | D2.1 Requirements for boosting community resilience in crisis situation[END_REF]. This particular study aims at finding what sociotechnical requirements should be considered to design a social platform to boost communities' resilience. The solution is built on real-life experiences in different scenarios. To this end, 8 community leaders were interviewed to understand: i) the meaning of community resilience in their own contexts; ii) how they operate in a disaster situation supported by technology; iii) how a new technology could improve it.

The interviewees were Ushahidi users, potentially collaborating with the co-design of innovative features as the research advances. They were in Nepal, 2 in Nigeria, 2 in Indonesia and 1 in India. For privacy reasons, the identity of the communities and the interviewees have been preserved in this paper.

The social issues they fight against include securing shelter and subsistence after an earthquake, elections monitoring against corruption and violence, sexual harassment and abuse, pursuing human rights, youth empowerment, and environmental issues in urban contexts. Such diversity of actions was pursued to build a comprehensive picture considering multiple possible roles of technology.

The interviews happened by phone lasting approximately 1 hour each. They were semi-structured and recorded for further analysis with the consent of the interviewee. The questions included:

 How is your organisation structured (formal/informal)?  What is the organisation goal? Who are the users and the beneficiaries?  Who are the main stakeholders involved to achieve this goal?  How do you establish connections with key stakeholders?  When would you consider the project a success?  Can you exemplify an (big) achievement? Why was it successful?  What are the major issues you encountered / shortcomings?  What factors play a role in effective community engagement?  How do you ensure that the community has adopted your project/tools?  What is the relationship between information and a resilient community?

The analysis has been grounded on the concepts of the Socially-aware Computing [START_REF] Baranauskas | Social awareness in HCI[END_REF] and the Organisational Semiotics [START_REF] Stamper | Organisational semiotics: Informatics without the computer? Information, organ. and technology: Studies in organisational semiotics[END_REF][12] [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF]. The Socially-Aware Computing is an approach to design technology informed by sociocultural aspects. It relies on the involvement of stakeholders with a diversity of experiences for understanding the problem from different perspectives, also considering how the new technology is expected to impact the community [START_REF] Baranauskas | Social awareness in HCI[END_REF]. The design is seen as a three-layer process considering first the informal aspects of a society (e.g. people's values, beliefs), then the formal aspects (regulations, rules, procedures), towards the construction of a technical system. The technical layer, on the other hand, impacts back on the external layers towards influencing the society. This understanding suggests that innovation risks to fail if only the technical level is considered and is not compatible with people's values, beliefs, or current regulations [START_REF] Baranauskas | Social awareness in HCI[END_REF].

For translating social aspects into design elements and technical features, the Socially-aware computing is grounded on the Organisational Semiotics, an approach for understating information in a social context [START_REF] Stamper | Organisational semiotics: Informatics without the computer? Information, organ. and technology: Studies in organisational semiotics[END_REF][12] [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF], based on semiotics principles by Peirce [START_REF] Peirce | Collected Papers[END_REF]. The OS artefacts applied in this analysis are part of a set called Problem Articulation Methods -PAM, usually helpful in the initial stage of projects when the problem definition is still vague and complex [START_REF] Baranauskas | Guiding the process of requirements elicitation with a semiotic approach[END_REF]. The artefacts are: i) Stakeholders Identification Diagram [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF], which enables a systemic view of the stakeholders' according to their levels of involvement, interest and expectations. ii) Evaluation Frame [START_REF] Baranauskas | Guiding the process of requirements elicitation with a semiotic approach[END_REF] for revealing issues that worthy attention from the stakeholders' perspective. iii) Semiotic Framework [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF] that helps to understand the problem as an information system.

This OS-based approach has been chosen due to its capacity to deal with a diversity of meanings and perspectives, and the adequate support to transform social issues into sociotechnical requirements. The artefacts and results are described in the next sections.

4

Semiotic-based Analysis

Stakeholders and Their Concerns

The stakeholder analysis evidences the complexity and diversity of actors involved with the socio-technical system, enabling a systemic view of the forces (expectations, concerns) from the interested parties [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF]. All the interested parts mentioned by the community leaders during the interviews are represented in the layers of the Stakeholders Identification Diagram (SID) [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF] (Figure 2).

Three main groups of stakeholders were identified: those related to the platform supply, local community members assuming distinct roles, and the responders, organisations that will act upon the information obtained through the platform. They are represented with different colours in the diagram: members of the local community are in orange, platform suppliers in purple, responders in green. Those in black are members of the society exercising different roles.

Fig. 2. -Stakeholders Identification Diagram

Some stakeholders are in more than one layer. The interested parts acting upon the society are in the Community layer, they are: volunteers, affected people and local people not directly affected by the crisis. Partners or competitors are placed in the Market one, such as the United Nations, other civil protection authorities, NGOs, international aid agents. Those providing any type of information, like technical support, platform instructor and reporters are in the Source, followed by those contributing more directly with the system in the Contribution layer, interface designer, community leader, analysts, and again the reporters. The stakeholders related to the technical and operational aspects are in the core, in the Operation layer. They are activists, deployers, and the technical leader, which is also in the contribution layer.

Derived from the SID, the Evaluation Frame [START_REF] Baranauskas | Guiding the process of requirements elicitation with a semiotic approach[END_REF] reveals stakeholders' main concerns towards the information systems, as described in Table 1 below. Offer any sort of assistance Not affected local people Have the necessary conditions to act as reporters In the next step of the analysis, the focus on the stakeholders is shifted to the information, as further described.

Towards socio-technical requirements

The Semiotic Framework [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF] has been applied for understanding the way communities deal with information, and also to translate their current practices and wishes into sociotechnical requirements. The framework considers how information operates in the six levels of a "semiotic ladder" representing the perspective of the Physical, Empirics, Syntactics, Semantics, Pragmatics, and the Social world. Issues at the three lower layers will answer questions as to how information is structured, used, transmitted, what its properties are, etc. The upper layers are concerned with the use of signs, meaning in the communication, intentions, etc. [START_REF] Liu | Semiotics in Inf. Systems Engineering[END_REF], evidencing the information that is related to the social environment from those that are part of the digital system. At this stage of the analysis, all the issues pointed out in the interviews were captured, with no filtering; therefore, controversial issues pointed out by different communities are possible. In the same way, some issues may refer to Ushahidi's existing features, beyond needs and wishes.

For being in line with this research aims, Turoff's design principles [START_REF] Turoff | The design of a dynamic emergency response management information system[END_REF] for a community resilience platform have also been considered in the framework. Issues that were similarly mentioned by different interviewees were grouped and prioritised for further consideration. In total, this analysis revealed 43 issues related to the social environment and 40 to the technical system. In Table 2, the most popular issues are presented as an example of the results. The more than 80 issues collected have been analysed, generating then the sociotechnical requirements. In Table 3, some examples of socio-technical requirements are described, followed by the list of stakeholders most benefitted by the features. Possible solutions to the identified concerns in Table 1 also evolved to requirements.

Situating community resilience

Building a situated notion of community resilience supported by technology, key elements that emerged in the analysis were mapped in the three layers informal, formal, and technical, following the metaphor of the 'Semiotic Onion' [START_REF] Stamper | Organisational semiotics: Informatics without the computer? Information, organ. and technology: Studies in organisational semiotics[END_REF] (Fig. 3). People's beliefs, values and motivations are in the informal level, the elements that regulate the way people act are in the formal one, and the technical aspects are in the core. The three levels constantly influence each other from the moment the technology is conceived until its appropriation [1][11].

Fig. 3. Community resilience key elements

The main challenges revealed in the informal level are related to engaging local people and policymakers to adopt the technical platform. The lack of familiarity with technology or specifically with the platform were frequently cited as barriers for engagement. Still in the informal level, the platform should be perceived as trustworthy and reliable by all users and stakeholders.

The informal and formal aspects are related to the organisation of the communities, referring to the way they are structured and interact with others, and partnerships between communities, agencies, responders, NGOs, etc. These aspects differ from one scenario to another, and the platform may improve such connections and communication. Also, the platform has to be in line with local government policies, not only to be accepted by them, but also adopted for influencing decision-making.

Technically speaking, the most evidenced problems are network infrastructure, which may be precarious in some disaster situations, the desired integration with other media and communication platforms, such as WhatsApp, voice platforms and Facebook, and, finally, the challenge of dealing with different languages, which may even co-exist in the same scenario.

The analysis evidenced that engaging a community encompasses not only raising awareness of their problems and possible solutions, but also how to use the platform in technical terms.

Discussion and Conclusion

Translating real-life constraints of adverse environments into requirements is a crucial step of designing a platform for crisis situations. In the context of this research, is also imperative that any technical solution be adjustable to different realities in social, technical, economics, and cultural terms.

Traditional methods of software engineer may fail in supporting a system designer to build an understanding of the problem dealing with a variety of perspectives, and equality considering requirements from both social and technical angles.

The principles of meaning articulation, participation, and the reciprocal impact between society and the technical solution by the Socially-Aware approach supported building a systemic view of the problem, situating the notion of community resilience. Likewise, methods and artefacts of the Organisational Semiotics provided the necessary resources for transforming constraints, wishes and needs into socio-technical requirements, evidencing the suitability of this approach to complex contexts as emergencyrelated platforms.

The analysis suggested that to achieve a real social impact, the introduction of a digital platform to promote community resilience should also consider placing engagement strategies like: i) developing digital literacy; ii) raising the community voice (including on social media) to influence local government decisions; and iii) involve policymakers and responders with the platform.

In terms of socio-technical features, results pointed out, for instance, ways of sharing and presenting information, integration of other communication channels and social media, and the evident need to check the validity of the information, building a trustworthy environment for affected people and responders.

This paper represents an ongoing research that aims at identifying, developing and evaluating new features for Ushahidi for boosting the potential of the platform to support a community resilience process. Next steps of this research include participatory design activities to start translating the requirements into design elements.

Fig. 1 .

 1 Fig. 1. -Screenshots of the Ushahidi platform a) report requesting help b) aggregated reports plotted on a map [16]

Table 1 .

 1 -Evaluation Frame

	Stakeholders	Concerns
		Operation level
	Activists or deployers	Create an instance of the platform efficiently
	Technical leader	Provide conditions for the platform operation
	Contribution level (actors, responsible)
	Technical leader	Provide platform training for the community staff
		Engage volunteers and staff (meetings, adverts, etc.)
	Community leaders	Solve local conflicts to enable actions Assess current situation and vulnerabilities
		Coordinate actions connecting volunteers with locals
	Analysts	Process the information provided by reporters Ensure the reliability of the information provided
	Interface designer	Design an attractive and easy to use platform
	Source level (customers, providers)
	Reporters	Provide impressions and reports from the field
	Platform instructor	Training the technical leaders on the platform
	Technical support	Ensure the platform availability
	Market level (partners, competitors)
	Responders (UN, civil	Identify the most relevant issues
	protection, NGOs)	Find the necessary information to act upon the issue
		Identify the most relevant issues and find the neces-
	Local government	sary information to act upon them Establish guidelines on how to act on the emergency
		Get prepared for next disaster and reconstruction
	Community level (viewer, lawmaker)
	Affected people	Get information on the current situation
	Volunteers (i.e. donors)

Table 2 .

 2 Issues mapped according to the semiotic ladder

	Step / Main topics	Examples of issues
	Social (12 issues in total)	Engaging local government and policymakers
	Engagement Trust Social Impact Familiarity with technology	with the platform is difficult People should not be afraid of making a report due to conflicts with other stakeholders People are familiar with SMS, social media, WhatsApp, but not necessarily with Ushahidi
	Pragmatics (15 issues)	Approaching the local government is more effec-
	Community impact General communication Policymakers and govern-ment involvement	tive with organised tasks and groups Complement the platform with physical commu-nication (printed maps, posters, leaflets) Connect producers/consumers, donors/receivers
	Semantics (16 issues)	Complement the platform with physical meetings within the community on how to solve issues
	Community perception	Convincing the community that the platform is
	Reliability of information	meant to help, not to manipulate
	Policymakers perception	Check status reports with the community

Table 3 .

 3 Examples of derived socio-technical requirements

	Requirements	Most benefited stakeholders
	Automatically estimate and inform the degree of reliability of the reports	Analysts and responders
	Graphical evidence of the most reported issues Responders
	Offline access to reports	Community members, affected people, responders
	Integration with social media (i.e. Facebook)	Reporters
	Self-explanatory user interface (no training)	Reporters, community leader
	Print maps	Community leader, responders


Xiaoxue Zhang

Aimin Luo
email: amluo@nudt.edu.cn18674864900@163.com

Gang Liu

Junxian Liu

Method of Operational Activities and Processes Optimization Design in Architecture

Keywords: Architecture, Operational activities and processes, Optimization, Evaluation

As the foundation and basis for developing architecture products and data, designing operational activities and processes is the key in architecture design. Current research lacks the optimization design method about them. To solve this problem. Firstly, this paper analyzed the data meta-models related to operational activities, proposed the process of optimal design of the operational activities and processes. Secondly, the objective function of the optimization of operational activities and processes are studied considering the time, success rate and cost. Thirdly, the process of simulation evaluation of operational activities and processes is designed, then we propose the method of how to convert activityrelated architecture data into object Petri-net simulation model. Finally, three means of improving the operational activities and processes are given to arrive the optimal design objectives.

Introduction

During the specific architecture design, the design of operational activities and processes includes many aspects, such as the decomposition of top-level tasks, the generation of operational activities and the optimization of the operational activities and processes. All of these mainly be conducted by operational commanders. The design of the operational activities and processes is the basis for the development of system view, service view and so on, Only after the operational activities and process has been established can systems and services supporting the operational activities and processes be followed. So, the design results are directly related to the completion of the entire operational objectives. In this way, the design of the operational activities and processes is the most basic and crucial issue in the design of the Information systems architecture.

Currently, in the field of Operational activities Process design, the research focuses on two aspects: operational task decomposition and the generation of Course of Action (COA). Literature [START_REF] Pack | Perez.Hierarchical Task and Motion Planning in the Now[END_REF][2] studied a method of applying Hierarchy Task Net (HTN) planning to combat task decomposition and combat operations. Literature [START_REF] Yajie | Study on Method of Generation Requirement for Weaponry and Equipment Capability for Meta -activity Decomposition[END_REF] put forward the concept of meta-activity, take meta-activity as the activity of atomic transactions in combat operations. The current methods of generation of course of actions can be divided into calculation-based methods and simulation-based methods. The COA generation based on the calculation method is divided into two kinds. One kind is based on mathematical programming, the other kind is based on the use of Artificial Intelligence (AI) [START_REF] Belfares | Multi objective genetic algorithms for Courses of Action Planning[C].Proc[END_REF] [START_REF] Freuder | et al.Constraints and AI planning[END_REF]. The simulation-based COA generation method takes the generation of operational action plan as a simulation process, considers the factors that influence the plan when generation in the simulation process, and uses the simulation results to represent the COA plan [6][7]. In current research, there is still a lack of method to optimize the design of operational activities and processes in specific architecture design. This paper supposed that the operational activities and processes has been preliminarily generated, explored the evaluation method of the Operational activities Process using simulation model based on object petri-net, then studied the method of improving the Operational activities Process to achieve the ultimate optimal design goal. In section 2, we analyzes the data meta-models related to operational activities and puts forward that the optimal design of operational activities and processes includes three parts: the decomposition of operational tasks, the generation of operational activities and processes and the optimization of operational activities and processes. In Section 3, we put forward the goal of optimizing the design of operational activities and processes. Then, we study the simulation design of operational activities in Section 4. Finally, in Section 5, three means are given to optimize the process of operational activities.

Analysis Based on Activity Related Data Meta-Models

Architecture data meta-model standardizes the relationship between architecture logical data [START_REF]DoD Architecture Framework Working Group. DoD Architecture Framework Version 2.0 Volume II: Architectural Data and Models[END_REF]. Among them, Activity is the core element, the design of other architecture data is closely related to Activity. Architecture operational activities Process design is mainly aimed at the optimization design of the data related to combat activities. Figure 1 constructs the activity-related data mate-model of the architecture.

In Figure 1, the arrow with a solid arrow indicates the relationship; the arrow with a hollow arrow indicates the inheritance relationship. The data related to operational activities mainly include several types of information:

1. Performers of activity execution: including Activity, Performer, activityPerfomedbyPerformer, activityMapstoOperationalNode, etc. These data described that activities are performed by which combat command unit. Here, Performer can be the organizational unit, battle node, system or service that is responsible for the activity. 2. Conditions when activity executing: including Activity, Condition, activityExcutedUnderCondition. These data describes the information of state and condition when executing activities, including the preconditions described in the rules and the state of the activities will change after execution. 3. Rules of the activities: including Acitivity, Rule, ruleConstraintActivity. These data mainly describes the rules that activities need to fulfill when executing. 4. Goal of activity execution: including Activity, Goal, goalDirectActivity. These data mainly describe which mission objectives are fulfilled by the activity execution.

Consumption and output of Resource when activity executing: including Activity,

Resource, activityResourceOverlap. These data mainly describes the relations between activities implementation and resource consumption or use. Resources can be divided into three types:

 Consumable resources: the execution of activities will consume such resources, such as missiles.  Releasable resources: the execution of activities requires the use of such resources, but once the activity is completed, such resources will be released, such as missile vehicles.

 Sharable resources: Such as information resources, the implementation of activities has no effect on the resources themselves.

The process of operational activities and processes design includes three problems, as shown in Figure 2, First is how to decompose the operational objectives and the mission into executable atomic activities. Second is how to generate the Operational activities Process, that is, considering the resource constraints, the conditions and the execution rules of each activity, how to generate the sequence of executable combat activities, called as preliminarily Operational activities Process. The third is to optimize the Operational activities Process that have been generated, we need to evaluate the existing operational activities and processes, analyze the existing problems in the design, and then optimize the operational activities and processes and modify the activities-related design.

The Optimization Goals of Operational Activities and Processes

After the operational preliminary process generation method is implemented, feasible operational activities and processes is obtained. How to improve the operational activities and processes is a problem that need to be solved in the optimization of operational activities and processes. In general, the indicators that optimize the design of the Operational activities Process include three factors: time, success rate and cost. For time and success rate, we can use the simulation method in Section 4. For the factors of cost. We can convert different types of resources into unified value. The objective function value Z (BP) for a Operational activities Process includes a weighted composite of success rates, time, and costs, as shown in Equation 1. If the target value satisfies the set threshold () Z BP   , the process solution is considered as feasible solution. If the optimization method can increase the value of Z (BP), then accept the optimization, otherwise, do not accept. The formula for calculating the objective function is as shown in Equation 2to Equation 4, where represents the weight of the indicator of number i. Before setting the weight of three factors, we need unified units and normalize each value. Relative to time and success rate of the indicator weights, the cost or resource consumption in the military information system has a lower weight value. The cost C in Equation 3 is obtained by weighting the normalized resources. The success rate V in Equation 2 and the time T in Equation 4 can be simulated by the executable model. Section 3 describes the design of the executable model and the design of the simulation process.

() ii i w z BP Z BP    (1) 1 ( ) ( ) z BP V BP  (2) 2 ( ) 1/ ( ) z BP C BP  (3) 3 ( ) 1/ ( ) z BP T BP  (4)

Simulation Process Design of Operational Activities and Processes

In the architecture design, the design result of the Operational activities Process use architecture model to represent, and stored by the architecture data. This section explores how to translate architecture data into an object petri-net simulation model. After the simulation model is established, through the simulation experiment, the result of the time and success rate of the operational activities and processes design can be obtained.

In constructing the simulation model using object petri-nets, we need firstly define the mapping relationship between the architecture data and the object petri-nets simulation model. The hierarchy of activities decomposition can be represented by object classes, and the sub-activities formed by decomposition are the core components of the object class. During model construction of an object class, we can use transitions to represent activities, and resources are represented by the attributes of the tokens in the location, while the conditions and rules for the occurrence of the activity are represented by the transfer function. Table 1 shows the correspondence among the Operational activities Process elements, the architectural data elements and the object petri-net simulation model elements.

Based on relationships between elements of logic data meta-model and elements of the object petri-net model, the process of constructing executable object petri-net is shown in Figure 3. According to the assessment acquirement, we can design simulation experiments, configure models for simulation, deal with the collected simulation data, and correlates these data with the evaluation index, then we can carry out the evaluation of the Operational activities Process according to the pre-determined evaluation model. For complex functions that are not easy to be implemented by scripts, dynamic link library algorithms can be compiled with VC and called in the OPN model. According to plan generated by operational activities and processes, we can configure the action function of the simulation model according to the data of the architecture design, then run the simulation and get the simulation results. The simulation results include the execution time, success rate, and resource consumption over time of the solution.

Optimization Process of Operational Activities and Processes Plan

In the simulation experiment based on object petri-net, we got the execution time, success rate, and resource consumption over time of the solution. The optimization method is mainly aimed at the execution time, execution success rate and resource allocation in activities, so as to achieve the optimal objective function value of the entire operational activities and processes plan in section 3. Drawing on the method of business process re-engineering, considering the characteristics of the operational activities and processes, the optimization methods of operational activities and processes mainly include the following three kinds:

 First method: shorten the execution time of key activities to shorten the completion time of the whole process.

After generating the initial operational activities and processes, key activities that affect the execution time of the entire Operational activities Process can be found.

Compressing the execution time of critical activity can significantly reduce the overall execution time of the entire Operational activities Process.

 Second method: increase the parallel activities to improve the success rate of the completion of operational activities and processes.

Considering the uncertainty probability of a single activity, the total execution time of the processes can be obtained through simulation, and the impact of the success rates of different tasks on the program can be analyzed and compared. First, find activities that increase parallelism, as the number of resources allows. Then, based on the execution of the activities, execute the simulation, the simulation results show the sensitivity of each activity to the success rate. Find the most influential activity in the operational activities and processes, and try to improve the success rate of the influential activity.

 Third method: maximize the efficiency of resource use, to achieve the optimal allocation of resources.

In the case of spare resources are available, reallocate resources to various activities so as to maximize the use of resources in order to optimize the allocation of resources for operational activities. Under normal circumstances, there are two kinds of resource allocation situation: ─ The resources required for activity are only one kind After all operational activities have been met, the remaining resources can be optimal allocated. For example, if all the activities in a combat operations are satisfied, there are remaining reconnaissance resources A and firepower B, then we can search for activities that can be met by resources A and activities that can be met by resources B, then get the remaining resources A and B. Then we can obtain the optimal configuration of several feasible programs, choose the best feasible options to maximize the objective function, in this way, we can get the resource allocation program.

─ When the activities can be completed using different types of resources

In this case, the resource allocation should be based on the success rate, time and cost of resource, then select the best matching solution. Under normal circumstances, the success rate of activities is the first factor to consider, followed by the completion time, and finally the cost of resource consumption.

The overall steps in the optimization of operational activities and processes are:

Conclusions

The design of operational activities and processes is the key issue in architecture design.

In this paper, we take insight in the problems about the optimization design of operational activities and processes, and analyze the three problems involved in the process optimization of operational activities based on the data meta-model. We also study the objective function of the optimization of the operational activities and processes, the simulation evaluation of the operational activities based on object petrinet, and the methods of improving the operational activities. Finally, the objectives of the operational activities are optimized. The next step will be studying the specific case applications and other optimization method for services and organizations.

Fig. 1 .

 1 Fig. 1. Activity-related data meta-model in information systems architecture.

Fig. 2 .

 2 Fig. 2.The process of optimal design of the operational activities and processes.

Fig. 3 .

 3 Fig. 3. Object petri-net simulation model constructing process based on data meta-model in DoDAF2.0.



  Step1: Calculate the objective function value Z (BP) of the plan. If the Z (BP)> θ is satisfied, then accept the scheme. Otherwise, go to the next step.  Step2: Calculate the objective function value Z1(BP) ,if Z1(BP)>Z(BP),then accept the optimization; otherwise, reject;  Step3: Calculate the objective function value Z2(BP) ,if Z2(BP)>Z(BP),then accept the optimization; otherwise, reject;  Step4: Calculate the objective function value Z3(BP) ,if Z3(BP)>Z(BP),then accept the optimization; otherwise, reject;  Step5: Generate new Operational activities Process plan and calculate the objective function value Znew(BP). If Znew(BP)> θ, accept the new process; otherwise, redesign the Operational activities Process and go to Step1 until the objective function value satisfies the threshold θ requirement.

Table 1 .

 1 the corresponding relationship among the Operational activities Process elements, architecture design elements and simulation model elements. Build system model framework and OPN class library. Analyze Activity data items and the corresponding WholePart-Type, converted the activities that only have sub-activities without parent activities into the entire model framework; according to WholePartType and SuperSubType, abstract OPN model between Relationship, construct OPN class library framework. ─ Step2: Determine the input and output ports. According to BeforeAfterType, determine the interaction relationships between different classes. Resource, combined with the description of Activity and transfer rules by BeforeAfterType and Rule, add the script function in the OPN framework, including Action function, delay function and event handling function to realize the related functions of each model. ─ Step5: OPN model evaluation process. According to the established evaluation index system, add the relevant index data collection script to the model to complete the OPN model; ─ Step6: Generate simulation cases and simulate. Add initialization function, instantiated function, instantiation of the top node model to generate the overall simulation case.

	Operational activities Process			Simulation model ele-
	elements		Architecture design elements		ments in object Petri-net
	The hierarchy of activities decom-
	position		Activity,WholePartType		object class
	Sub-activities		Activity			transitions
	Synchronization mechanism	Rule, ruleConstraintActivity		transitions, transfer function
			Resource, activityResourceOverlap,
	Resource		Information			attributes of the tokens
			Condition、activityPerformedUnder-
	Condition		Condition			transfer function
			Condition, activityPerformedUnder-
	Effect		Condition			transfer function
	─ Step1: According to
	activityProducesResource and activityConsumesResource, explicit interaction
	information. According to information and resource data items, clarify the
	characteristics of the interaction information, then determine the OPN model input
	and output ports.
	─ Step3: Establish the OPN framework model. According to BeforeAfterType
	description of activities, establish each OPN framework model. If one activity have
	sub-activities (judge by WholePartType), convert it to an object class, otherwise,
	convert it into switch. Then, connect the input and output ports in models with the
	corresponding transfers.
	─ Step4: Perfect the OPN model. Learn from the description of system performance
	parameters	and	information	interaction	by	ruleConstraintActivity,
	masureOfTypeResource, activityPerformedUnderCondition, activityProduces-
	Resource and activityConsumes-