ALE -Arbitrary Lagrangian Eulerian RANS -Reynolds Averaged Navier-Stokes FSD -Flame Surface Density ECFM3Z -Extended Coherent Flame Model 3 Zones TKI -Tabulated Kinetics of Ignition AI -Auto-Ignition EGR -Exhaust Gas Recirculation RPM -Revolution Per Minute SOI -Start Of Injection

Keywords:

Among the new combustion concepts envisaged to meet future regulations, the Dual Fuel (DF) concept is considered to be an attractive strategy due to its potential to reduce CO2 emissions and engine-out pollutant emissions levels. A small quantity of high-cetane fuel (Diesel) is injected in the combustion chamber in order to ignite a homogeneous mixture of air and a highly volatile fuel (gasoline in our study). The DF concept has been shown to achieve improved engine thermal efficiency and low engine-out NOx and soot emissions. However, the physical mechanisms controlling DF combustion and in particular, determination of the predominant combustion regime(s) are not yet well understood. In this study, numerical simulations (CFD) and optical engine measurements are used to investigate Dual Fuel combustion. The ECFM3Z combustion model (implemented in the IFP-C3D code) is presented in this paper in addition to preliminary results which have been performed for DF internal combustion (IC) engine simulations. The approach employed in this study allowed determination of the relative contributions of auto-ignition (AI), flame propagation (ECFM) and Burned Gas (BG) to the total heat release and combustion development in terms of the spatio-temporal evolution throughout the engine cycle. The objective is first of all to evaluate the capacity/potential of existing models to cope with the various combustion regimes that might exist in DF combustion strategies and in particular transitions between different combustion modes. Results of preliminary experiments on a single cylinder optical engine are also reported in this paper. The objective was to apply advanced optical diagnostic techniques to characterize in detail the DF combustion process and provide an improved understanding of this novel combustion strategy and ultimately aid CFD model further developments.

INTRODUCTION

In recent years, increasingly drastic measures have been introduced in order to achieve reductions of IC engine CO 2 and pollutant emissions (i.e. HC, CO, NOx and particulates). In order to comply with the stringent international regulations, automotive engine research will have to focus on the development of advanced combustion strategies. Among the emerging combustion concepts, combining fuels with different ignition and evaporation characteristics has shown interesting potential [START_REF] Jiang | Visualization and Performance Analysis of Gasoline Homogeneous Charge Induced Ignition by Diesel[END_REF][START_REF] Inagaki | Dual-Fuel PCI Combustion Controlled by In-Cylinder Stratification of Ignitability[END_REF][START_REF] Kokjohn | Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending[END_REF].

The use of an optimized blend of gasoline/diesel fuel can potentially allow improvements of exhaust gas pollutant emissions. The DF concept is based on the ignition of a homogeneous mixture of air (and possibly EGR) and a highly volatile fuel (i.e. gasoline, ethanol or natural gas) by injecting a Diesel spray.

Over the past decade, there has been a sustained interest for the DF combustion mode. The majority of DF studies reported in the literature tended to focus on achieving The significantly different properties of gasoline and Diesel fuels can provide a means of controlling ignition timing and heat release rate. For example, Bessonette et al. [START_REF] Bessonette | Effects of Fuel Property Changes on Heavy-Duty HCCI Combustion[END_REF] reported that the most suitable fuel for HCCI operation could be a fuel which combines the properties of Diesel and gasoline. Several other investigations have also highlighted the advantages of DF operation on engine performance and identified key parameters such as EGR [START_REF] Han | Ignition Control of Gasoline-Diesel Dual Fuel Combustion[END_REF][START_REF] Leermakers | Gasoline-Diesel Dual Fuel: Effect of Injection Timing and Fuel Balance[END_REF], injection strategies [START_REF] Tamagna | Multidimensional Simulation of the Influence of Fuel Mixture Composition and Injection Timing in Gasoline-Diesel Dual-Fuel Applications[END_REF][START_REF] Hanson | An Experimental Investigation of Fuel Reactivity Controlled PCCI Combustion in a Heavy-Duty Engine[END_REF], intake temperature, engine load and fuel reactivity, all of which have an impact on combustion phasing, heat release and pollutant emissions in DF mode.

Jiang et al. [START_REF] Jiang | Visualization and Performance Analysis of Gasoline Homogeneous Charge Induced Ignition by Diesel[END_REF], Cha et al. [START_REF] Cha | An experimental and modeling study of the combustion and emission characteristics for gasoline-diesel dual-fuel engines[END_REF] and Sauer et al. [START_REF] Sauer | HCCI in off-Highway-Engines as an Alternative to meet Future Emission Legislation[END_REF] investigated the effects of the premixed fuel ratio on pollutant emissions and observed that at low engine loads, HC and CO emissions were generally high. However, for gasoline ratios (i.e. gasoline/(gasoline+Diesel fuel)) above 80%, HC and CO emissions decreased to acceptable levels. A decrease in both soot and NOx emissions was also observed with an increase of the gasoline ratio. In fact, the oxygen concentration is decreased and maximal local temperatures were lower before engine top dead center (TDC). Consequently, the autoignition delay, which depends on the amount of fuel, the reaction temperature and the activation energy, was longer and combustion phasing was delayed [START_REF] Inagaki | Dual-Fuel PCI Combustion Controlled by In-Cylinder Stratification of Ignitability[END_REF][START_REF] Tamagna | Multidimensional Simulation of the Influence of Fuel Mixture Composition and Injection Timing in Gasoline-Diesel Dual-Fuel Applications[END_REF]. Using optical engine experiments and numerical simulations, Hanson, Kokjohn et al. [START_REF] Hanson | An Experimental Investigation of Fuel Reactivity Controlled PCCI Combustion in a Heavy-Duty Engine[END_REF][START_REF] Kokjohn | Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending[END_REF][START_REF] Hanson | Fuel Effects on Reactivity Controlled Compression Ignition (RCCI) Combustion at Low Load[END_REF][START_REF] Splitter | Reactivity Controlled Compression Ignition (RCCI) Heavy-Duty Engine Operation at Mid-and High-Loads with Conventional and Alternative Fuels[END_REF] developed the concept of the reactivity controlled compression ignition (RCCI) concept and showed that DF combustion was sequential and initiated in the more reactive regions within the combustion chamber and moved to the less reactive zones.

A DF concept developed at IFPEN and referred to as In-Cylinder Fuel Blending (ICFB) is based on using port-fuelinjection of gasoline and optimized direct injection of Diesel fuel to control combustion phasing and duration [START_REF] Duffour | IFP Energies Nouvelles Approach for Dual Fuel Diesel-Gasoline Engines[END_REF]. The engine is operated in Diesel combustion mode at low loads while a DF strategy is employed to operate at high loads. Furthermore, the objective of this strategy is to operate at stoichiometric air fuel ratios using DF combustion which will allow NOx, HC and CO emissions after-treatment using a three-way catalyst, thereby alleviating the need for more complex and costly exhaust after-treatment systems. The objective was to control exhaust pollutant emissions over the entire engine operating range while maintaining high fuel efficiency.

Despite an increasing interest in the DF concept and the numerous investigations that have been performed in recent years, there is a clear lack of detailed knowledge about the DF combustion process. In particular, determination of the predominant combustion regime(s) and the transition mechanisms between potentially different regimes is crucial for the further optimization of such a concept. In order to achieve a more detailed understanding, different approaches need to be combined which ideally include engine experiments, CFD simulations and optical diagnostic based engine studies. CFD simulations remain a challenge in the case of DF combustion due to potential contributions from various combustion regimes including auto-ignition, diffusion combustion and possibly premixed flame propagation that might occur.

Current CFD combustion models can help to provide a partial response in terms of identifying different combustion regimes. These approaches are often based on known kinetics of a surrogate fuel whose overall properties in terms of octane and cetane number are similar to those of the real fuel. Part of this study will focus on evaluating the potential of currently available numerical approaches in the CFD code IFP-C3D as a means of characterizing the different combustion regimes and investigation of turbulence effects on such a combustion strategy.

Furthermore, initial results from experiments performed on a single cylinder optical engine are presented. The objective is apply various diagnostic techniques in order to gain an improved understanding of the DF combustion process and provide valuable data to allow validation of new computational models in the long term.

COMBUSTION MODELS DESCRIPTION

Simulations were performed with the MPI parallel solver IFP-C3D [START_REF] Bohbot | IFP-C3D : an Unstructured Parallel Solver for Reactive Compressible Gas Flow with Spray[END_REF][START_REF] Velghe | A high efficiency parallel unstructured solver dedicated to internal combustion engine simulation[END_REF]. This 3D CFD code, devoted to spray and compressible reactive flows modeling, solves the RANS equations using an ALE finite volume method on unstructured hexahedral meshes and a physical time splitting method. Turbulence modeling is achieved with the RNG k-ε model [START_REF] Han | Turbulence modeling of internal combustion engines using RNG k-ε models[END_REF].

Because of the wide variety of engine configurations, many physical models are used in IFP-C3D to describe the three main combustion modes: Homogeneous auto-ignition (AI), premixed propagation flame and diffusion flame. A unified model, called ECFM3Z (Extended Coherent Flame Model 3 Zones) [START_REF] Colin | The 3-Zones Extended Coherent Flame Model (ECFM3Z) for Computing Premixed/ Diffusion Combustion[END_REF], is implemented in IFP-C3D and coupled with the auto-ignition model TKI [START_REF] Colin | Detailed Chemistry-based Auto-ignition Model Including Low Temperature Phenomena Applied to 3D Engine Calculations[END_REF] to accurately predict these combustion modes as well as abnormal combustion phenomena such as knock.

The ECFM3Z Model

In order to represent the local stratification in the computational cell, the ECFM3Z model takes into account three mixture fraction zones: a pure fuel zone (F), a pure air (plus EGR) zone (A) and a mixed zone (M) (see Figure 1).

Figure 1. Scheme of an ECFM3Z computational cell

Initially only the (A) zone exists. In the case of a direct injection type combustion chamber, the (F) zone is formed after the evaporation of the spray droplets. Air and fuel mix turbulently with a characteristic mixing time to form the (M) zone. When the auto-ignition delay is reached, auto-ignition occurs in the mixed area and burned gases fill in the (M)b zone. The combustion evolution is described with a progress variable based on the fuel consumption:

(6)
where is the evolving fuel mass fraction and the fuel tracer, a fictive species transported with the same properties as the real fuel species but not reactant. It accounts for the total fuel mass fraction available after evaporation. [START_REF] Tamagna | Multidimensional Simulation of the Influence of Fuel Mixture Composition and Injection Timing in Gasoline-Diesel Dual-Fuel Applications[END_REF] where is the gaseous fuel produced.

The ECFM3Z model is an improvement of the ECFM model [START_REF] Colin | 3D Modeling of Mixing, Ignition and Combustion Phenomena in Highly Stratified Gasoline Engines[END_REF] which describes the propagation flame evolution based on the resolution of a transport equation of the flame surface density (FSD) :

The TKI Model

Modeling auto-ignition in internal combustion engines needs accurate prediction of ignition delays and reaction rates over a large range of thermodynamic conditions. Detailed chemistry schemes are the most relevant way to do so. However when it comes to represent conventional fuels, they imply hundreds of species to transport and thousands of related reactions to solve. Solving detailed chemistry in the CFD code generates heavy CPU time costs. The tabulated chemistry technique is an interesting alternative since it relies on a priori detailed chemistry calculations. The model used in this study is the TKI model developed initially by Colin et al. [START_REF] Colin | Detailed Chemistry-based Auto-ignition Model Including Low Temperature Phenomena Applied to 3D Engine Calculations[END_REF] and revisited by Knop et al [START_REF] Knop | On the Use of a Tabulation Approach to Model Auto-Ignition During Flame Propagation in SI Engines[END_REF] at IFPEN and based on the tabulation of:

• the cool flame (if present) heat release C 1 . The cool flame is modeled as the first step to enter the look-up table but if doesn't exist, C1 is equal to 0 and the main auto-ignition process is directly computed using progress variable related to auto-ignition (and defined later),

• the AI progress variable reaction rate that describes the rate of fuel consumed by auto-ignition. This variable is stored for discrete values in the look-up table.

Auto-ignition information is retrieved from the look-up table through linear interpolation along with the following local thermodynamic conditions:

Table 1. TKI look-up table

The discrete values of for the AI progress variable reaction rate to store in the look-up tables are computed as follows [START_REF] Cha | An experimental and modeling study of the combustion and emission characteristics for gasoline-diesel dual-fuel engines[END_REF] where c n and c n+1 are the lower and upper bounds of the AI progress variable interval that contains the instantaneous value of c AI , respectively. t n and t n+1 are the instants at which c n and c n+1 are reached during the detailed chemistry calculations, respectively.

The progress variable related to auto-ignition is computed as: [START_REF] Sauer | HCCI in off-Highway-Engines as an Alternative to meet Future Emission Legislation[END_REF] where is the fuel mass fraction consumed by AI and the maximum value that can be reached by , i.e. the fuel rate likely to be consumed by auto-ignition (since ).

Coupling of the Combustion Models

Auto-ignition and flame propagation are two different combustion modes which may interact during knock in spark ignition engines and possibly in the case of Dual Fuel operation. The related models described previously have been coupled in IFP-C3D.

When the ECFM3Z and the TKI models are coupled, the fuel balance equation during combustion writes: [START_REF] Kokjohn | Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending[END_REF] where and are the source terms due to flame propagation and to auto-ignition, respectively. They are computed as follows (12) [START_REF] Splitter | Reactivity Controlled Compression Ignition (RCCI) Heavy-Duty Engine Operation at Mid-and High-Loads with Conventional and Alternative Fuels[END_REF] When the AI delay is reached, auto-ignition occurs and contributes to the propagation flame by creating a flame surface density due to the spatial heterogeneities of the progress variable. This transition is consequently ensured by: [START_REF] Duffour | IFP Energies Nouvelles Approach for Dual Fuel Diesel-Gasoline Engines[END_REF] This initialization of states that the first flame kernels are due only to burnt gases expansion (the stretch effects are not taken into account).

The coupling between the ECFM3Z model and the TKI model has shown to be relevant in the case of knocking in a spark-ignition engine [START_REF] Colin | 3D Modeling of Mixing, Ignition and Combustion Phenomena in Highly Stratified Gasoline Engines[END_REF] but not been validated in the particular case of Dual Fuel operation.

ENGINE CHARACTERISTICS Metal Engine Characteristics

In order to evaluate the coupling between the models mentioned above in representative DF conditions, calculations were performed on a single-cylinder Diesel engine configuration and compared to metal engine experiments performed at IFPEN. Table 2 summarizes the engine's main specifications.

Table 2. Engine specifications

The computational mesh is shown in Figure 2 and only a 60°s ector is considered (6-holes injector). One operating point is presented and discussed in the next section. Its main characteristics are presented in Table 3.

Figure 2. Computational mesh showing the piston-bowl geometry of the PSA DW10 engine Table 3. Operating point characteristics

For simulations, n-heptane is used to represent auto-ignition kinetics of the Diesel fuel and isooctane is the surrogate retained to represent gasoline.

Optical Engine Characteristics and Operating Conditions

Initial experiments on an optically-accessible single cylinder direct-injection Diesel engine have been performed during this study (see Figure 3). The optical engine has the same geometry as the all-metal engine (Table 2). The compression ratio was 15:1 and a simplified piston bowl geometry (i.e. no bowl pip) was employed. The engine was equipped with a 6hole injector positioned vertically at the center of the cylinder head. The injector characteristics are given in Table 4.

Table 4. Injector characteristics for optical engine measurements

The initial diagnostics performed on the optical engine included high-speed (Photron FASTCAM SA1.1), crankangle resolved visualization of naturally-occurring combustion luminosity at the frame rate of 36000 images/ second.

Figure 3. Schematic of the optical engine showing the camera setup for the high-speed combustion luminosity imaging

One important factor taken into account for both the modeling and the optical diagnostic constraints concerns the fuel selection. Since commercial, multi-component fuels are composed of hundreds of compounds, the related fundamental characteristics such as reaction paths, flame velocity, thermodynamic data etc. are not always known. Furthermore the use of commercial fuels for optical engine studies is not always feasible, depending upon the type of diagnostic measurement being performed. This is due to the fact that fluorescent species naturally present within commercial fuels create unwanted background signals and make it difficult to interpret experimental data.

The use of commercial fuels is also challenging for the modeling since developing detailed kinetic schemes is difficult while coupling CFD codes with detailed chemistry models for multi-component fuels is not currently feasible in industrial applications because of the excessive CPU costs. Thus, simplified surrogate fuels must be used. These simplified fuels should ideally be physically and chemically representative of standard, commercial fuels.

For this study, iso-octane was selected to represent gasoline fuel while a blend of iso-octane (36 vol%) and n-decane (64 vol%) was chosen to represent Diesel fuel. The proportions were determined in order to obtain a cetane number of 53 which is comparable to the cetane number of European Diesel. Table 5 shows the properties of the neat compounds and the surrogate Diesel properties.

A comparison of surrogate Diesel with standard commercial Diesel fuel was performed and results are presented subsequently in the paper.

Table 5. N-decane, iso-octane, and surrogate Diesel properties

The engine operating conditions are shown in Table 6 for the DF conditions studied on the optical engine.

Table 6. Engine operation under optical engine experiments

The current paper presents preliminary results of optical engine experiments including an overall equivalence ratio sweep which was performed by varying the premix fuel rate in order to characterize different combustion regimes (The iso-octane ratio varied from 87% to 90% without charge dilution and from 87 to 92% when using N 2 dilution). The equivalence ratio sweep reported in this paper was performed with and without charge dilution. Intake charge dilution was performed by using pure N 2 as synthetic EGR which was mixed with the intake air. Results are presented for cases without charge dilution and with 13% N 2 . Table 7 shows the fuel mixture properties and the IMEP of the selected experimental points.

Table 7. Equivalence ratios and IMEP for the operating conditions without and with charge dilution

RESULTS AND DISCUSSION

Evaluation of Combustion Models on a Dual Fuel Application

Figure 4 shows the experimental and predicted cylinder pressure:

Figure 4. Comparison of experimental (dashed lines) and predicted (solid lines) mean cylinder pressure -SOI = 21° BTDC -Computations were performed with the ECFM3Z/TKI coupling

The predicted mean cylinder pressure is not in a good agreement with the experimental one. Indeed, combustion phasing and intensity are not well reproduced.

The global reaction rate of combustion as well as those related to auto-ignition (AI), burned gas post-oxidation (BG) and flame propagation (ECFM -Extended Coherent Flame Model), are computed respectively as follows and plotted in Figure 5:

(15) (16) (17) (18)
where ρ F is the mean density in the combustion chamber, Δt the time step, n and n+1 the indices of the present and the next instants respectively.

This representation aims to evaluate the contribution of each combustion model to the global reaction rate which writes:

(19)

Figure 5. Contribution of each combustion model to the global reaction rate -SOI= 20.1° BTDC -Computations were performed with the ECFM3Z/TKI coupling

Auto-ignition starts at 5° BTDC. In this first-stage combustion, the local stratification of the progress created by AI initializes the flame surface density. The propagation model is activated and its contribution to the global reaction rate is significant. During the second-stage combustion, the auto-ignition delay is reached and the main heat release is due to AI. This first modeling of the transition between the two combustion regimes given by Eq. ( 13) leads to an underestimation of the overall reaction rate.

To better characterize the initialization of the premixed flame by auto-ignition process, the equilibrium flame stretch ki eq is introduced in the initial flame surface density calculation. This approach relies on the basic ideas proposed by Colin et al. [START_REF] Duclos | Arc and Kernel Tracking Ignition Model for 3D Spark-Ignition Engine Calculation[END_REF][START_REF] Truffin | A Spark Ignition Model for Large Eddy Simulation Based on an FSD Transport Equation (ISSIM-LES)[END_REF] to take into account the turbulence effects on the initial flame surface density deposition for spark-ignition engines. An expression for ki eq is given by:

(20)
where αK t is the flame surface stretch due to turbulence, τ t the turbulence characteristic time, k the turbulent kinetic energy and ε the dissipation rate.

The new flame surface density initial deposit takes into account the turbulence effects on the flame surface:

(21)
where ci eq is a parameter that varies between 0 and 1. It is used to control the transition between the models and the contribution of the turbulent stretch to the initial flame kernel.

Figure 6 shows a comparison between the experimental and the computed (ECFM3Z/TKI with ci eq *ki eq (red) and ECFM3Z/TKI with ki eq (green)) mean cylinder pressures.

Figure 6. Comparison of experimental (dashed lines) and predicted (solid lines) mean cylinder pressures -Computations were performed with the ECFM3Z/TKI coupling and took into account the equilibrium turbulence stretch

It appears that the Dual Fuel modeling is very sensitive to the transition between combustion regimes. When the stretch effects are fully taken into account in the initial kernel growth, combustion starts before TDC and maximum pressure is overestimated. However, the use of the control parameter shows a better description of the combustion process. Indeed, combustion phasing is well reproduced and a better estimation of the combustion intensity and duration is performed. Taking into account the turbulent stretch effect is a relevant choice to describe the initial flame growth after auto-ignition occurs. But in order to realize an optimal coupling between combustion models, it's crucial to determine a more physical criterion.

Figure 7 represents the reaction rates logarithmic scale computed with the ki eq contribution. The auto-ignition model initializes the flame propagation and the latter pilots the combustion until the AI delay is reached. The main heat release is realized by the auto-ignition process as we can see it in Figure 8.

To better understand the transition between combustion regimes and the contribution of each combustion model, incylinder images from the modeling work are presented. Figures 9,[START_REF] Sauer | HCCI in off-Highway-Engines as an Alternative to meet Future Emission Legislation[END_REF][START_REF] Kokjohn | Experiments and Modeling of Dual-Fuel HCCI and PCCI Combustion Using In-Cylinder Fuel Blending[END_REF][START_REF] Hanson | Fuel Effects on Reactivity Controlled Compression Ignition (RCCI) Combustion at Low Load[END_REF][START_REF] Splitter | Reactivity Controlled Compression Ignition (RCCI) Heavy-Duty Engine Operation at Mid-and High-Loads with Conventional and Alternative Fuels[END_REF] show cut planes coincident with the spray axis and colored by AI and CFM reaction rates and by temperature, and flame surface density due to auto-ignition (to identify spatially the beginning of combustion) in Figure 9.

The Diesel fuel is injected in the combustion chamber at 20.1° BTDC. It evaporates and auto-ignition starts locally (Figure 9). The progress variable gradient generated then initializes the first propagation flame kernel.

At 4° BTDC, combustion is located at the periphery of the combustion chamber and is mainly controlled by the propagation model (see Figure 10). Auto-ignition still occurs at the downstream region of the evaporated spray.

Figure 11 shows "pockets" of auto-ignition. These localized AI sites suggest that the AI process ignites the premixed air/ isooctane. The created flame propagates towards the center of the chamber and sweeps the auto-ignition front. The same observation can be made at top dead center (Figure 12) and at 2° ATDC (Figure 13) where the main heat release is reached.

Figure 7. Contribution of every combustion model to the global reaction rate in logarithmic scale -SOI= 20.1°B TDC -Computations were performed with the ECFM3Z/TKI coupling and took into account the equilibrium turbulence stretch

Optical Engine Experiments

The first experiments performed in DF mode on the optical engine were to validate the Diesel surrogate fuel blend (the Diesel surrogate fuel is hereafter referred to as GMA) by performing a back-to-back comparison with standard commercial Diesel. Figures 14 and15 show a comparison between conventional Diesel fuel and the GMA blend in terms of cylinder pressure and heat release rate for both fuels tested on the optical engine.

At a low load condition, i.e. 3.2 bar IMEP (Figure 14), although the AI delay of GMA is comparable with Diesel, a higher pressure-rise rate is observed for GMA. This may be explained by the higher volatility of GMA compared to Diesel fuel which would tend to increase the proportion of premixed fuel consumed during the combustion process.

At a mid-load condition (1200 rpm, 7 bar IMEP), the GMA and Diesel fuel reveal overall satisfactory agreement (see Figure 15) in terms of AI delay and combustion phasing although the pressure-rise rate appears to be slightly higher for GMA leading to marginally higher peak in-cylinder pressures.

Despite slight differences observed between the GMA blend and standard Diesel, the results confirmed that the model fuel (GMA) are overall representative of Diesel fuel in terms of ignition timing and combustion phasing.

The dual fuel experiments were performed with a global equivalence ratio sweep from 0.49 to 0.69 without charge dilution and from 0.56 to 0.96 with 13% N 2 . Experiments were not performed at higher equivalence ratios in order to avoid excessive pressure-rise rates. Figure 16 and 17 display respectively mean in-cylinder pressures and the apparent rate of heat release, for three equivalence ratios without charge dilution. The quantity of Diesel fuel injection was maintained constant for the equivalence ratio sweep while the premixed fuel quantity (i.e. quantity of iso-octane) was increased progressively. As equivalence ratio increases, the heat release characteristics are significantly modified. In Figure 17, a first small peak is observed near 6° BTDC most likely due to the cool flame produced by the initial oxidation of decane present in the surrogate Diesel (since isooctane is a single-stage fuel). Combustion is clearly highly sensitive to fuel reactivity. For the condition where the global equivalence ratio is below 0.6, the rate of heat release is characterized by 2 major peaks, the first occurring at TDC, which is the result of Diesel fuel auto-ignition in the downstream regions of the evaporated sprays, located in the periphery of the bowl. The second peak occurs a few degrees after TDC. An increase in overall equivalence ratio to above 0.6 results in a shorter ignition delay and the rate of heat release is characterized by a single peak and a shorter combustion duration.

Figure 18 and 19 display respectively mean in-cylinder pressures and the apparent rate of heat release, for four equivalence ratios with 13% N 2 dilution. Intake charge dilution significantly modifies overall combustion behavior with an expected increase of the auto ignition delay. Dilution of the intake charge reduces overall oxygen concentration and thereby modifies mixture reactivity.

Figure 18. Mean cylinder pressure for the equivalence ratio sweep -13% N 2 Figure 19. Rate of heat release for the equivalence ratio sweep -13% N 2

For the equivalence ratio condition of 0.56 (same premixed fuel ratio as the first point with no EGR dilution), the heat release rate is characterized by a single peak at 5° ATDC followed by a relatively slow heat release phase until approximately 35° ATDC. This latter phase appears analogous to diffusion-limited Diesel combustion and suggests that the main heat release is principally a result of auto-ignition of the surrogate Diesel injection which entrains a premixed charge of iso-octane/air into the fuel jet.

Figure 20 shows recorded images of combustion natural luminosity at φ=0.56 at different crank angle positions. The imaging data reveals that combustion starts after TDC predominantly in the periphery of the combustion chamber, in the zones where the spray is expected to impinge on the piston bowl wall. The combustion images do not reveal the presence of signal in the central region of the bowl, indicating that the premixed fuel within this zone will likely not be consumed at such low equivalence ratios.

Figure 20. High speed camera images of Dual Fuel combustion at φ=0.56 -13% EGR

At higher equivalence ratios, combustion behavior differs significantly. Figures 18 and19 reveal higher pressure rise rates and more notably, a bell-like single-peak heat release for equivalence ratios of 0.73 and above. As can be seen in Figure 21, 2° ATDC, auto-ignition pockets are located at the periphery of the combustion chamber and gradually move to the center (at 6 and 8° ATDC). High speed movies revealed a rapid motion of burned gases in the center of the chamber which corresponds temporally with the main heat release peak. Although it remains to be quantified, the high speed images suggest that this second-stage combustion could be a result of mass auto-ignition of the remaining premixed fuel.

The mechanisms responsible for the reaction zone to extend from the periphery to the central part of the bowl have not yet been determined although it is believed to be due to sequential auto-ignition of the premixed charge as a result of the increasingly higher in-cylinder temperatures or possibly the result of premixed flame propagation or a combination of both these combustion modes. The combustion luminosity images shown in Figure 21 reveal that the combustion zone extends from the bowl periphery to the central region of the bowl, leading to increased consumption of the premixed isooctane in the central part of the chamber.

CONCLUSIONS

A first evaluation of the coupling between a turbulent combustion model for flame propagation in stratified mixtures (ECFM3Z) and a tabulated kinetics model for autoignition (TKI) was conducted in order to achieve a better understanding of the Dual Fuel combustion strategy. A criterion for the transition between the different combustion regimes was adopted based on the initial flame surface density deposition. An evaluation in real engine conditions has enabled identification of the predominant regime during the engine cycle and to represent the heat release distribution inside the chamber.

The turbulence stretch was then added to the model taking into account turbulent effects on the initial flame surface growth. It showed a faster transition between auto-ignition and flame propagation. The predicted combustion velocity is overestimated compared to experimental reference and presents a one-stage process. It was shown that the results are sensitive to the transition between the combustion regimes.

Representative fuels were selected and characterized experimentally in view of comparisons between experiments and engine CFD simulations. Initial optical engine experiments were performed and high speed imaging of the naturally-occurring combustion luminosity revealed notable variations in combustion behavior as a function of overall equivalence ratio. Although more detailed diagnostics will be performed by the authors in the future, including tracer LIF and OH LIF measurements, these first results reveal a notable effect on heat release rate. Although it remains to be confirmed, imaging data appear to reveal initial auto-ignition of the premixed charge occurring as a result of the pilot Diesel injection followed by a sequential auto-ignition of the remaining, predominantly premixed charge or by flame propagation or a combination of these combustion regimes.

Further improvements are needed to ensure a better description of the transition between the different combustion models. In the near future the objective will be to adopt a different mixing model, based on a presumed PDF and a new transition criterion will be also taken into account. Experimental investigations on the optical engine will continue with the application of more detailed diagnostic techniques which will support a more extensive validation of the numerical simulations.

  Université d'Orléans and IFPEN Stéphane Jay, Julian Kashdan and Cyprien Ternel IFP Energies Nouvelles Christine Mounaïm-Rousselle Université d'Orléans practical PCCI (Partially Premixed Compression Ignition) or HCCI (Homogeneous Premixed Compression Ignition) operation.

( 8 )

 8 where K t accounts for the ITNFS (Intermittent Turbulent Net Flame Stretch). It is computed as follows: α and β are model constants, S L represents the laminar flame speed (computed with the Metghalchi and Keck correlation [21]), δ L the laminar flame thickness, the Favre-averaged turbulent kinetic energy, the Favre-averaged dissipation rate of the turbulent kinetic energy and Γ k an efficiency function that represents the global influence of all turbulence scales on the flame front.

Figure 8 .Figure 9 .Figure 10 .Figure 11 .Figure 12 .Figure 13 .

 8910111213 Figure 8. Contribution of every combustion model to the global reaction rate -SOI= 20.1° BTDC -Computations were performed with the ECFM3Z/TKI coupling and took into account the equilibrium turbulence stretch (KIEQ)

Figure 14 .Figure 15 .Figure 16 . 2 Figure 17 .

 141516217 Figure 14. Mean cylinder pressure (upper graph) and heat release rate (lower graph) at low load -Engine speed = 1200 rpm -Equivalence ratio=0.6 -0% EGR -SOI = 10 BTDC -DOI = 400μs -P inj = 400 bar -P int = 0.7 bar -T int = 40°C

Figure 21 .

 21 Figure 21. High speed camera images of Dual Fuel combustion at φ=0.73

ACKNOWLEDGMENTS

This work was supported by IFPEN. The authors are grateful to Nicolas Dronniou for his suggestions and advice during the experiments.


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F De Smet

O Lemaire

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G De Nunzio

A Sciarretta

Connected strategy for energy-efficient driving of electric vehicle

Keywords:

This paper presents the potential benefits of connected strategies for energy-efficient driving for electric vehicles achieved during the H2020 EU project CEVOLVER. Two strategies are presented. First solution optimizes the route and the recharge planning such as minimizing the battery energy consumption within a suitable trip time (eco-charging). Second solution, coupled with the first, provide the optimal speed profile using predictive information from the ecocharging and topology and infrastructure data from third party webservices (eco-driving). The benefits provided by the developed connected strategies are evaluated in simulation. Gains obtained by the eco-driving are validated experimentally with a mixed urban and extra-urban route, showing a significant energy consumption reduction.

Introduction

In the past decade, electric vehicles (EVs) made significant progress in term of range, while charging stations network has been expended all over Europe. EV sales have recently increased showing customers' interest in emission or fuel dependency reduction. However, EVs have still not attained the level of user acceptance needed to support a broader mainstream market uptake. Long charging times and uncertainties in range prediction are still the main obstacles to overcome for a further widespread use.

Connectivity of EV represents a promising way to overcome the range anxiety. Connection to cloud webservices exploiting geographic information system (GIS) and traffic data offers the opportunity to predict the driving range accurately and to minimize the energy consumption. To ensure long trip, crowdsourced data of existing DC fast-charging stations are useful as well for optimal charge planning. As a result, with the optimization of energy range and fast charging, battery can be right-sized, making EVs affordable.

Within this context, the H2020 European project CEVOLVER [1] aims to develop battery electric vehicles (BEV) that improve user experience over long trips. Project's demonstrator vehicles are designed to take advantage of future improvements of the fast-charging infrastructure, whilst the installed battery is dimensioned for affordability. They also integrate improvements to reduce energy consumption, leveraging connectivity for further optimization of components and system design (rightsizing), as well as vehicle controls (battery preconditioning for fast charging, cabin thermal management, powertrain energy management), and driving strategies (eco-charging and eco-driving). The final objective of CEVOLVER is to demonstrate on open road that long-trips are achievable by EVs with limited extra time with respect to conventional vehicles.

The eco-charging algorithm solves the problem of finding a time-and energy-optimal route for an EV requiring multiple battery charging events to complete a long-distance trip. The proposed routing strategy also provides the optimal sequence of charging events, as well as the optimal average speed for each road segment to reduce charging needs and thus save trip time. The route and charging sequence optimizations are based on a predictive model of the Ford Transit. In [START_REF] Brandes | Smart driving and charging can help reconciliate limited battery capacity and long-distance trips for electric vehicles without compromising on trip time[END_REF], study shows eco-charging can support trips achieved with small battery capacity and long-distance trips. The eco-driving algorithm provides the driver with instantaneous speed recommendations to minimize energy consumption. The developed real-time strategy bases its calculation on: (1) route topology and infrastructure data from GIS, (2) the predictions made by the eco-charging strategy, in particular the average speed, and (3) the real-time ADAS measurements, if available, to anticipate the surrounding traffic disturbances. For more details about the eco-charging and eco-driving strategies developed during the project, developments have been published in [START_REF] De Nunzio | A Time-and Energy-Optimal Routing Strategy for Electric Vehicles with Charging Constraints[END_REF] and [START_REF] Sciarretta | Energy-Efficient Driving of Road Vehicles[END_REF][START_REF] Ngo | Real-time ecodriving for connected electric vehicles[END_REF] respectively.

Few experimental validations on connected strategy for energy-efficient driving of EVs exist. In [START_REF] Zhang | Predictive Eco-Driving Application Considering Real-World Traffic Flow[END_REF], predictive eco-driving strategy considering real-world traffic flow has been validated under real-world traffic environment. In [START_REF] Fleming | Real-time predictive eco-driving assistance considering road geometry and long-range radar measurements[END_REF], the developed eco-driving algorithm has been validated using a driving simulator.

This paper focuses on the potential energy gains provided by the connected eco-charging and ecodriving strategies on a Ford Transit van with a battery electric propulsion system, obtained in simulation and on open road.

The paper is organized as follows. In Section 2, the energy reduction performance provided by the ecocharging and the eco-driving is evaluated in simulation. Simulation of long trip use case with different average speed will confirm the large impact of the average speed on the trip time for the Ford demonstrator vehicle, enforcing the results obtained with the presented connected functions. In Section 3, experimental test results of eco-driving in real traffic conditions on a representative use case are presented and analysed.

Potential energy gain from connected strategies

This section presents the simulation results of ecocharging and eco-driving. First, simulations of a long trip with the Ford demonstrator, with different average speed values shows the impact of the speed on energy consumption. This result is then confirmed by simulations of the complete eco-charging strategy, where both the route choice and average speed are optimized. The saved battery energy reduces the charging needs and thus the trip duration. Then, simulations of the eco-driving strategy on repeated trips in Cologne show how optimizing the instantaneous speed profile can further reduce the energy consumption.

Average speed variation impact on the Ford demonstrator

For the Ford demonstrator, the main benefit of optimal average speed advice is expected for highway traffic where the travel speed can be adapted largely without constraints. In terms of urban applications, it is expected that on the one hand the observance of the speed advice is more disturbed by interactions with the surrounding traffic and on the other hand, dynamic changes of the recommended target speed are more difficult to be considered by the driver. Thus, the focus of the benefit analyses for the demonstrator was placed on the benefits during long distance trips.

For that purpose, the energy demand for a 350 km trip was considered for different vehicle speeds between 70 and 130 km/h assuming for simplicity that the whole trip is driven at constant speed. In addition, the total trip time was evaluated based on the time demand for driving and the time demand for battery charging with 20 to 150 kW just to the state of charge that is required to reach the target destination. The resulting graphs that show the interdependency between available charging power, energy demand and trip time are shown in Figure 1.

It is obvious that the energy demand is increasing strongly with increasing vehicle speed which is due to the huge frontal area of the BEV demonstrator and the resulting air resistance. Comparing the vehicle speed with the total trip time, the graphs indicate that depending on the available charging power an increase of vehicle speed is not always beneficial in terms of trip time. For an average charging power of 20 kW, the trip time demand at 80 km/h is by far less than for 130 km/h. The reason for that is the difference in driving energy demand and the related amount of charging energy that results in heavily increased time demand for charging. This effect is mitigated with the availability of higher charging power but even for 100 kW average the time savings for travelling at 130 km/h instead of 100 km/h are negligible whereas the increase in energy demand is significant.

Eco-charging

The performance of the eco-charging strategy is evaluated on a trip between Aachen (Germany) to Deauville (France) of 545 km. Figure 2 shows the trip planned by the eco-charging algorithm with the stop locations. The table 1 shows a comparison of the solutions given by the eco-charging algorithm with the average speed added as a decision variable in the optimization, where "no speed" indicates that average speed is not a decision variable. The indication "with speed" refers to the optimization with average speed 𝑣 as decision variable with speed varying within a range of [𝑣 -30, 𝑣] in km/h.

Using travelling speed as decision variable benefits both on the energy consumption and the recharged energy, with a reduction of 3.9 and 11.1% respectively. Eco-charging changes the charging planning according to the adjusted speed profile, avoiding an additional charging stop leading to reduce the overall trip time. 3.

The eco-speed is computed using information about the average traffic speed and predictive data from eco-charging about the route segment's end speed.

The speed optimization only takes into account the route topology, the legal speed limits constraints and the traffic prediction, although the developed approach is able to consider the surrounding traffic in real-time as described in [START_REF] Sciarretta | Energy-Efficient Driving of Road Vehicles[END_REF].

Figure 3: Speed limits split within the trip

The driven and the computed eco speed profiles are represented in Figure 4. The real speed profiles show that the test drives are relatively repeatable but surrounding traffic influences strongly the speed profile, in particular in urban area. The eco-driving algorithm smooths and adapts the speed to avoid unnecessary acceleration and deceleration between route segments. Deceleration anticipation as segment end approach is possible thanks to the eco-charging predictions. One can notice that predictive information from the ecocharging about the segment ends speed is quite accurate as closed to the real speed transition.

Figure 4: Driven (gray) and eco (blue) speed profile

In Figure 5, the normalized battery energy consumptions against the total trip time for each drives and the computed eco-speed are shown. The impact of the speed profile optimization on the travel time is limited as the strategy takes as a constraint the travel time accordingly to the traffic data obtained from the GIS providers (HERE webservice is used). About the energy gains, eco-driving could reduce the energy consumption of 3.5% in average and up to 13% on the whole trip (Figure 6). Focusing on the two main parts of the trip, namely the highway and the extra-urban parts, the energy consumption reduction distribution in Figure 7 shows that reduction will be possible especially in extraurban parts.

Figure 7: Battery energy reduction distribution according to route type

Experimental result of eco-driving on open road

The eco-driving was evaluated on open road in Aachen (Germany) with the Ford BEV demonstrator.

Demonstrator vehicle

The Ford BEV demonstrator for the CEVOLVER project is a commercial vehicle from the 2T Transit range. The variant used in this project has a short wheelbase and a medium roof. The vehicle's powertrain specification is based on the e-Transit that recently went into production [8]. The battery has a usable capacity of 67 kWh. The vehicle contains an 11.3 kW onboard AC charger. It also supports DCfast-charging up to 115 kW. The motor-inverter is mounted at the back, behind the battery, where it transmits its forces to the rear wheels. The vehicle's electric motor has a peak output power of 269 hp and delivers 430 Nm of torque. The vehicle is shown in Figure 8.

On-board implementation of the connected strategies

The implementation of the eco-charging and the ecodriving strategies rely on the connected architecture which is composed of a Brand Independent (BI) cloud and dedicated components hosted on a connected device.

The Figure 9 presents the implementation and dependencies between the components.

Figure 9: Implementation architecture The BI cloud serves as a gateway to third party webservices for the connected functions developed during the project (namely eco-charging, powertrain and thermal cabin conditioning optimization, smart fast charging) and hosts the eco-charging webservice. The communication between the connected device and the BI cloud services uses the http/REST protocol. The connected device hosts the eco-driving strategy and serve as HMI for the eco-charging and ecodriving. The device is connected to the BI cloud by 4G connection. on the laptop. A local server, denoted "WSocketServer", handles the WebSocket messages between all the implemented components (HMI / Ecodriving / BI Cloud / Ford RCP SW tool) and triggers the eco-driving and eco-charging services.

Experimental results

The evaluation of the eco-driving strategy on the Ford demo vehicle was performed around the Ford Research Centre in Aachen (Germany). Two routes were selected which represent typical rural and urban routes. The share of the speed limits for the four trips is shown in Figure 10. The route starts for less than 100m in a 50 km/h street followed by a ring road where 70 km/h is allowed. At the end, the trajectory turns into a village with a 50 km/h speed limit, to finally find its endpoint in a 30 km/h shopping park zone. On the way back, the ring road contains a share with a speed limit of 100 km/h. After the exit of the ring road, the last part is a 50 km/h street. This part is about 400m longer than in the other direction. The fact that the exit from the ring road is 400m further away from the start point than the entrance explains most of the difference in length for both directions. Further, a second route with a more urban speed profile was added to the evaluation tests. This route was driven in both directions as well which will be called trip 3 and trip 4. The length for both trajectories is 2,97 km. This route has a 50 km/h speed limit, apart from the last part where it ends also in the shopping park zone. All the trips have been driven repeatedly, whereby the target was three times for each case. The number of drives per case is shown in Figure 11. The repetition of the routes is needed for plausibility of the results, also because the traffic lights influence strongly the speed profile. In general, the average speed advised is lower than the average speed that would be driven without using the advisory function. One other difference that has been noticed is that the ECO-driving function defines a more economic acceleration profile. A zoom on the speed profile from the mixed rural-urban drive as shown in Figure 13 illustrates this. The acceleration between 70 and 500s, when entering the ring road, is much slower with the ECO-driving speed advice.

Figure 13: Zoom on an acceleration with and without ECO-driving

The energy savings have been quantified based on the total battery energy consumption over the trip. The Figure 14 compares the averages for the different cases. The Figure 15 reports the average time duration. For all the trips, the average time loss ranges from 65 to 79s. On the other hand, the battery energy consumption is always lower with ECO-driving actively used. The gain is lower for an urban profile (9-10%) than for a more rural type of trip (13-15%). This is also expected, as the urban profile is typically a more dynamic driving profile. The pre-knowledge of the route allows the advisory function to explore opportunities to anticipate stops or, for example, inclinations in the road.

Conclusion

This paper has presented a connected strategy for an efficient-energy management of EVs, in particular for the Ford Transit BEV. A comprehensive strategy which optimizes the route, the charge plan and the driven speed provides gains in energy and travel time, thus helps to reduce the range anxiety expressed by EVs users.

Simulation results have shown promising energy gains by optimizing the speed profile. Eco-charging, by both optimizing the charge plan and the average speed, could provide gains up to 5%. Eco-driving by optimizing the instantaneous speed profile could reduce the energy consumption by 3.5% in average up to 13%.

Experimental validation of the developed connected strategy, enhancing the eco-driving feature, shows promising results with average gains of 10% and demonstrate the technical feasibility of the proposed approach.

Acknowledgement

Figure 1 :

 1 Figure 1: Dependency between available charging power, energy demand and trip time for a long distance trip

Figure 2 :

 2 Figure 2: Mission profile between Aachen (Germany) and Deauville (France) (© 2022 HERE)

Figure 5 :Figure 6 :

 56 Figure 5: Normalized battery energy consumption according to the total travel time

Figure 8 :

 8 Figure 8: Ford Transit BEV demonstration vehicle

Figure 10 :

 10 Figure 10: Speed limit share for the two directions of the Ford ECO-driving evaluation routeThe rural route is a connection between two urban zones. It has a few stop lights and a rather smooth traffic. The urban route is the main road through a suburb of Aachen. It has more stop lights and crossings. At the time the routes were driven the traffic was moderate, with large shares of the trajectory with no vehicle in front. One could imagine such a route being driven by van drivers, for example from delivery vehicles or craftsmen. The route with the more rural speed profile was driven in both directions, called trip 1 and 2 in the following. The length of the trajectory is 4,25 km for trip 1 and 4,75 km for trip 2.The route starts for less than 100m in a 50 km/h street followed by a ring road where 70 km/h is allowed. At the end, the trajectory turns into a village with a 50 km/h speed limit, to finally find its endpoint in a 30 km/h shopping park zone. On the way back, the ring road contains a share with a speed limit of 100 km/h. After the exit of the ring road, the last part is a 50 km/h street. This part is about 400m longer than in the other direction. The fact that the exit from the ring road is 400m further away from the start point than the entrance explains most of the difference in length for both directions. Further, a second route with a more urban speed profile was added to the evaluation tests. This route was driven in both directions as well which will be called trip 3 and trip 4. The length for both trajectories is 2,97 km. This route has a 50 km/h speed limit, apart from the last part where it ends also in the shopping park zone. All the trips have been driven repeatedly, whereby the target was three times for each case. The number of drives per case is shown in Figure11. The repetition of the routes is needed for plausibility of the results, also because the traffic lights influence strongly the speed profile.

Figure 11 :

 11 Figure 11: Number of drive repetition with and without ECO-driving With the advisory function on, the driver attempts to follow the speed advice. No data about the real-time

Figure 12 :

 12 Figure 12: Urban route with and without ECO-driving

Figure 14 :

 14 Figure 14: Battery energy consumption with and without ECO-driving for the two directions of the trip

Table 1 : Eco-charging performance with average speed as a decision variable No speed With average speed Trip time difference 7h38 6h54

 1

	Number of charging stops	4	3
	Energy consumption [kWh]	/	-3.9 %
	Recharged energy [kWh]	/	-11.1 %
	2.3 Eco-driving
	The potential energy gain from the eco-driving
	strategy is evaluated on a 19 km trip repeated 24
	times in Cologne near Ford's facilities at off-peak
	hours.

The mission profile is divided into three parts: (1) the highway part for routes with 𝑣 𝑚𝑎𝑥 ≥ 70 𝑘𝑚/ℎ, (2) the extra-urban part with 50 < 𝑣 𝑚𝑎𝑥 < 70 𝑘𝑚/ℎ, and (3) the urban part where 𝑣 𝑚𝑎𝑥 ≤ 50 𝑘𝑚/ℎ. The split is detailed in Figure

This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 824295, CEVOLVER (Connected Electric Vehicle Optimized for Life, Value, Efficiency and Range).


Abdelghani Chahmi
email: chahmi.abdelghani@gmail.

Study and realization of a torque measurement sensor based on strain gauges dedicated to bending and torsion

Keywords: strain gauges, bending and torsion, sensor, diameter

This We have chosen the most popular tool which is the strain gauge, which is the most suitable for the torsion and bending tests we performed. We have detailed the theoretical operation of torque sensors based on strain gauges as well as some difficulties related to this method of measurement. Then we analyzed the bending and torsion tests. A design of the torque sensor has been made.

In the world of design and manufacturing, torque measurement is essential for defining assembly specifications and validating product characteristics such as motors. However, measuring torque is relevant for some applications especially with the improvement of mechanical equipment performance where the need to choose the measurement method that fits with the imposed specifications is critical. Therefore, many technological advances in the applications concerned require innovation in torque measurement where research and development in this area is ongoing and many advances have been made in the design of torque sensors [START_REF] Hammal | Design and realization of sensors for the instrumentation of a traction machine traction machine[END_REF] [START_REF] Liamine | Contribution to the study of deformations and stresses of a thin cylindrical wall cylindrical thin wall tests on gunt machine[END_REF]. Thus, the objective of the work carried out is to select and build a torque sensor with good sensitivity, which can measure up to 2N.m [START_REF] Konaté | Study of the behavior of adhesive films: application to strain gauge sensors. deformation sensors[END_REF][4] [START_REF] Dolev | ISHAI Mechanical characterization of Adhesive Layer in situ and as Bulk Material[END_REF]. Firstly, a theoretical study of the adapted measurement technology will be presented. Finally, we start the practical part which consists in designing the torque sensor based on strain gauges by detailing the various stages carried out which enables us thereafter to carry out a prototype by exploiting the case of flexion and torsion. Torque is the tendency of a force to rotate an object around an axis; its measurement is a technology that began in the 17th century and has continued to develop to the present day [START_REF] Dolev | ISHAI Mechanical characterization of Adhesive Layer in situ and as Bulk Material[END_REF]. This concept is present in the manufacture of a large number of products used daily. However, it is essential for manufacturers to measure the torque. For this purpose, there are several measurement methods: direct and indirect; static and dynamic. Four approaches can be used, including: -Torque measurement by gauges; -Torque measurement by torsion; -Torque measurement by reaction; -Torque measurement by current. Now that the advantages of using a strain gauge based torque transducer have been highlighted, we will present in detail its theoretical principle of operation as well as the origin of the faults appearing in practice.

I. TORQUE MEASUREMENT IN AN INDISTRUAL FIELD

A. Torque measurement by gauge

Torque measurement can be done using strain gauges, by inserting a mechanical part between the part on which a torque is applied and another part that receives this torque [START_REF] Yorozu | Electron spectroscopy studies on magneto-optical media and plastic substrate interface[END_REF]. The measured stresses are proportional to the torque between the two elements connected to this mechanical part. Generally, this system does not allow for much rotation, as the strain gauges must be connected to an acquisition system (using a brush and ring system to transmit the gauge signals) [START_REF] Gali | ISHAI An Effective stress; strain Concept in the Mechanical[END_REF].

B. Torque measurement by torsion

The measurement of torque by torsion is based on the torsion undergone by a shaft subjected to a torque [START_REF] Young | The Technical Writer's Handbook[END_REF] [START_REF] Guyott | Evaluation of the cohesive properties of adhesive joints using ultrasonic spectroscopy[END_REF]. At each end of the shaft are installed metal gears and two d ínductive proximity detectors figure(II.3).When the shaft rotates and no torque is present, there is no torsion and the phase shift between the two detector outputs is equal to 0 •. When the shaft experiences torqueT , the shaft experiences torsion and a phase shift occurs between the two detector outputs. The relationship between the torque T (Newton-meter) and the phase shift θ (in radians) is

T = GJθ/ IN (I-1)
Where T is the shear modulus of the shaft material; J is its moment of inertia; l is the distance between the sensors; and N is the number of teeth of the metal gears. What is interesting about this approach is that the measurement can be made on a rotating machine.

II. TORQUE MEASUREMENT METHOD USING STRAIN GAUGES

Now that the interest of using a strain gauge based torque transducer has been highlighted, we will present the theoretical principle of operation. Afterwards, we will explain some basic notions useful to clarify our topic. This allows us to start the practical part in the following section.

A. Definition of the strain gauge

Gauges consist of a resistive wire bent in an accordion-like fashion and are usually made from a thin metal foil (μm) and an insulator, which is treated like a printed circuit board. The strain gauge (often called a strain gauge) figure ( 3) is based on the principle of elongation or shortening of a resistive wire attached to the deforming support. In a more detailed way, a strain gauge is a passive sensor that translates its own deformation into a variation of resistance: By acting by traction or compression on the wire, it lengthens or shortens. This variation in length modifies its electrical resistance. This change in resistance is measured between the resting state and the stressed state [START_REF] Srivaten | SUDARSHAN Electromagnetic Measurement of Damping Capacity to Detect Damage in Adhesively Bonded Material[END_REF] [START_REF]DIB Study of the failure of adhesive bonded metal assemblies[END_REF]. The wire is arranged in turns so that the deformation acts simultaneously on several sections of the wire to amplify the change in resistance electrical given by equation (II.1) R = ρL/ S (II.1)

Where ρ, L and S are the resistivity, length and crosssectional area of the conductor. A change in length causes a change in resistance according to the essential formula of extensometry.

Kε = ∆R/ R (II.2)
Where k is the gauge factor whose value is close to 2 and ∆R/R is the relative change in resistance.

B. The basic circuit, the Wheatstone bridge

The basic circuit, the Wheatstone bridge A Wheatstone bridge is a measuring device invented by Samuel Hunter Christie. It is used to measure an unknown electrical resistance by balancing two legs of a bridge circuit, with one leg containing the unknown component. The bridge consists of four resistors R 1 , R 3 , R 2 which is a variable resistor and an unknown resistor noted Rx. Between points B and C, by symmetry, there is a zero potential difference. A measuring instrument, for example a G galvanometer, does not deviate when the symmetry is perfect (the value of R x is fixed by varying the resistance R 2 to have V BC = 0). If the resistance R 1 varies slightly, the equilibrium is destroyed. We can either measure the variation of resistance by the current of the unbalance voltage, or restore the equilibrium by a known variation of one of the 3 other resistances. We can easily verify that R 1 and R 3 act in the same direction, while R 2 and Rx act in the opposite direction, hence the first fundamental principle of extensometry : Two adjacent resistors act in opposite directions, while two opposite resistors act in the same direction. In practice, either one or more of these four resistors are gauges, the others being pure resistors. In measuring strain, R 1 is an active gauge glued to the structure, R 2 is an identical gauge glued to a part of the same metal, but not stressed. R 3 and Rx are pure resistances. Under these conditions, the thermal effects are eliminated and only the deformations linked to the stresses are detected, the latter is interpreted in the form of a variation of potentials, which will then be used to measure the force or the torque [START_REF] Dubois | Calibration of dynamometers and scales at the Modane-Avrieux test center[END_REF] [START_REF] Dubois | Experimental study of high precision dynamometers with extensometric gauges at the Modane-Avrieux test center of the O.N.E.R.A. Communication[END_REF].

Note: -Before carrying out a strain gage measurement, a choice must be made between different mounting and wiring solutions.-Full bridge, half bridge and quarter bridge mountings are not used under the same conditions and do not all require the same precautions.-In all cases, the wiring plays an essential role. A Wheatstone bridge for strain gauges is configured as either: -one (01) strain gauge en 1/4 bridge; -two (02) strain gauges en 1/2 bridge; -four (04) strain gauges in full bridge ; and in accordance with the measurement objective [START_REF] Dubois | Measurement of the thermoelastic coefficients of metals using strain gages.strain gauges[END_REF].

This step allowed us to choose the Wheatstone bridge as the ideal candidate for the gauge conditioning circuit and to define the arrangement of the gauges in it. Then we analyzed the bending and torsion cases, which allowed us to start the experimental part.

III. TORQUE TRANSDUCER DESIGN BASED ON STRAIN GAUGES

The objective of this section is to describe the design of a sensor dedicated to the measurement of the torque based on strain gauges, to realize it, two principals were exploited: the first one consists in using a test body equipped with strain gauges which contributes to measure the bending force and the second one is based on the torsion where strain gauges will be glued on an Aluminium rod to detect it. In order to evaluate the performance of the sensor realized, a comparison of the obtained results must be carried out, for this reason we used this type of sensor at the level of the laboratory of the solid and structure at the university as a reference of validation of our results. Finally, we will conclude with the exposition of the experimental results obtained.

A. Measurement chains

B. The approach followed B.1 Bending case

A sensor based on strain gauges has been made to measure the bending, this force will be exploited later to find the values of the applied torque. In this case, we have a test body in rectangular form whose strain gauges are mounted in a complete bridge; the latter is connected to a module HX711 to amplify the output signal which will be connected in turn to an arduino board to make the acquisition.

In this part, we describe the different steps to design the desired sensor: We start by connecting the HX711 module on the one hand, to the arduino board and on the other hand to the test body equipped with strain gauges. As it is illustrated in the figure (4), load cell is equipped with four wires red, black, green and white, constituting a complete bridge of Wheatstone these last ones will be connected to the module HX711.

The strain gauges are placed on the upper and lower part of the test body in order to maximize the sensitivity of measurement and linearize the bridge, these are mounted in a region where the deformation is maximum and which is not affected by the phenomena of extremity so that all the gauges undergo the same deformation simultaneously such that those which are opposite undergo the same phenomenon of traction or compression (R 1 and R 3 ) or (R 2 and R 4 ). Once we connect this assembly to the PC, we can easily obtain the variation of the resistance of the strain gauges.

C. Torsion case

In this case, the body of test is a hollow cylinder in Aluminum, or the gauges of constraints are stuck to 45 • with regard to its axis constituting a complete bridge which allows the conversion of the deformations in variation of the electrical resistances. Figure [START_REF] Gali | ISHAI An Effective stress; strain Concept in the Mechanical[END_REF]. once this step is completed which is the most delicate task of the process, we connect our test body to a HX711 module which allows amplifying the level of the output signal, and then we connect it to an arduino board in order to obtain torque measurements in real time. In order to evaluate the performance of this sensor, we use the one at the level of solid mechanic's laboratory to validate our results. By using a complete bridge of strain gauges we ensure: -A linearity of the measuring bridge; -A better sensitivity to the torque to measure; -Compensation of the effect of parasitic forces.

The figure below shows the rod and the support used: As in the case of bending, we connected the four wires of the strain gauges to the terminals A+, A-, E-and E+ of the HX711 module so as to form a Wheatstone bridge, and the terminals Gnd, Sck, Dt and V cc to the arduino board as previously explained.

The figure below illustrates the final assembly obtained The solid mechanics laboratory reference device is made up of two parts: the first is the torsion one where the gauges are mounted on the cylinder at 45 • and the second presents the bending case, connecting it to an acquisition card we can read directly the deformation value that corresponds to the force or torque applied. It has a maximum weight of 0.5 Kg for this value the acquisition card displays a value of deformation of 30 which presents the calibration of the system. In this section, we were able to study and design a sensor to measure the torque based on strain gauges by exploiting the case of bending and torsion. First, we presented and described the hardware used, such as the arduino board and the HX711 module that allows amplifying the output signal of the Wheatstone bridge, and then we explained the approach followed by describing the steps of realization of the sensor in the two cases mentioned. Finally, we ended by exposing the device of reference that of laboratory of mechanics of solid and structure by specifying its calibration.

Figure 1

 1 Figure 1 the force is transmitted by the two perforated metal plates connecting the two sections of the part.

Figure 1 .

 1 Figure1. Mechanical part for torque measurement by gauge[START_REF] Young | The Technical Writer's Handbook[END_REF]

Figure 2 .

 2 Figure 2. Torque measurement by torsion [9].

Figure 3 .

 3 Figure 3.Wheatstone bridge[11].

Figure 4 .

 4 Figure 4. Arduino uno used cards.

Figure 5 .

 5 Figure 5. Wheatstone Bridge

Figure 6 .

 6 Figure 6. HX711 Module wiring

Figure 7 .

 7 Figure 7. The stem and the support used

Figure 8 .

 8 Figure 8. The final assembly

Figure 9 .

 9 Figure 9. The stem and the support used.

2nd International Conference on Advanced Electrical Engineering (ICAEE) | 978-1-6654-1741-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICAEE53772.2022.9961981 Authorized licensed use limited to: USTHB Alger. Downloaded on December 06,2022 at 13:33:53 UTC from IEEE Xplore. Restrictions apply.

Authorized licensed use limited to: USTHB Alger. Downloaded on December 06,2022 at 13:33:53 UTC from IEEE Xplore. Restrictions apply.

IV. CONCLUSION

We have chosen the strain gauge method which is the most suitable for the torsion and bending tests we have performed. We detailed thereafter the theoretical operation of the sensors of torque based on the strain gauges. Thereafter we analyzed the tests of flexion and torsion; that allowed us to begin the experimental part. A design of the torque transducer was made.