On the Effects of Parallelization on the Flow Prediction around a Fastback DrivAer Model at Different Attitudes

Misar, Adit; Bounds, Charles Patrick; Ahani, Hamed; Zafar, Muhammad Usman; Uddin, Mesbah

doi:10.4271/2021-01-0965

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…[1,22,62]). To minimize the effects of domain decomposition in CFD predictions, all simulations were run on UNC Charlotte High Performance Computing (HPC) clusters using 144 processors across 3 nodes having 48 processors each [63].…”

Section: Cfd Simulation Process Detailsmentioning

confidence: 99%

Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

et al. 2023

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This paper attempts to develop a Dynamic Mode Decomposition (DMD)-based Reduced Order Model (ROMs) that can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle’s trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time dynamics. This ROM can then also be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7×106. The high-dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using the Menter’s Shear Stress Transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in the literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper, which includes: (a) a requirement of higher sampling rate to obtain a higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high-Reynolds-number, separation-dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and they were found to offer significant improvement.

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Section: Cfd Simulation Process Detailsmentioning

confidence: 99%

Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

et al. 2023

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“…The authors have previously observed a significant variation in the aerodynamic coefficient predictions from the CFD of a road vehicle when simulations were carried out using a Message Passing Interface (MPI) as the parallelization tool. Thus, care was taken to maintain the same parallelization schemes and hardware consistency throughout this study [24]. All simulations were run on UNC Charlotte's High-Performance Computing clusters using 144 processors across three nodes having 48 processors each.…”

Section: Computational Resourcesmentioning

confidence: 99%

“…Substantial review papers are available in the published literature and the interested reader is directed to these references for further details [19][20][21][22][23]. Based upon the prior experience of the authors with a NASCAR geometry, all the CFD cases presented in this paper use the Shear Stress Transport (SST) k − ω turbulence model developed by Menter [1,2,8,24,25].…”

Section: Introductionmentioning

confidence: 99%

On the Effectiveness of Scale-Averaged RANS and Scale-Resolved IDDES Turbulence Simulation Approaches in Predicting the Pressure Field over a NASCAR Racecar

Misar

Davis

Uddin

2023

Fluids

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Racecar aerodynamic development requires well-correlated simulation data for rapid and incremental development cycles. Computational Fluid Dynamics (CFD) simulations and wind tunnel testing are industry-wide tools to perform such development, and the best use of these tools can define a race team’s ability to compete. With CFD usage being limited by the sanctioning bodies, large-scale mesh and large-time-step CFD simulations based on Reynolds-Averaged Navier–Stokes (RANS) approaches are popular. In order to provide the necessary aerodynamic performance advantages sought by CFD development, increasing confidence in the validity of CFD simulations is required. A previous study on a Scale-Averaged Simulation (SAS) approach using RANS simulations of a Gen-6 NASCAR, validated against moving-ground, open-jet wind tunnel data at multiple configurations, produced a framework with good wind tunnel correlation (within 2%) in aerodynamic coefficients of lift and drag predictions, but significant error in front-to-rear downforce balance (negative lift) predictions. A subsequent author’s publication on a Scale-Resolved Simulation (SRS) approach using Improved Delayed Detached Eddy Simulation (IDDES) for the same geometry showed a good correlation in front-to-rear downforce balance, but lift and drag were overpredicted relative to wind tunnel data. The current study compares the surface pressure distribution collected from a full-scale wind tunnel test on a Gen-6 NASCAR to the SAS and SRS predictions (both utilizing SST k−ω turbulence models). CFD simulations were performed with a finite-volume commercial CFD code, Star-CCM+ by Siemens, utilizing a high-resolution CAD model of the same vehicle. A direct comparison of the surface pressure distributions from the wind tunnel and CFD data clearly showed regions of high and low correlations. The associated flow features were studied to further explore the strengths and areas of improvement needed in the CFD predictions. While RANS was seen to be more accurate in terms of lift and drag, it was a result of the cancellation of positive and negative errors. Whereas IDDES overpredicted lift and drag and requires an order of magnitude more computational resources, it was able to capture the trend of surface pressure seen in the wind tunnel measurements.

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Development of a Computational Fluid Dynamics Simulation Framework for Aerothermal Analyses of Electric Vehicle Battery Packs

Misar,

Jain,

et al. 2023

Journal of Electrochemical Energy Conversion and Storage

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The rise of electric vehicles has driven the extensive adoption of Lithium-ion Batteries (LIBs) due to their favorable attributes—- compactness, low resistance, high power density, and minimal self-discharge. To enhance LIB reliability, an efficient battery thermal management system is imperative. This paper introduces a finite volume-based aerothermal analysis framework for a 32-cell high-energy density LIB pack. We also explore the effectiveness of various turbulence models in capturing local hotspots, discharge rates, and current levels across different geometries and inlet velocities. Our approach involves modeling the battery using Simcenter Battery Design Studio, and importing it into Simcenter STARCCM+ for aerothermal simulations in which temperature distribution, discharge rates, current levels, and maximum temperature across are monitored for aligned, cross, and staggered configurations of the battery-pack under varying inlet velocities. Our findings highlight the significant impact of boundary condition modeling on simulation stability. Also we observed that the standard k - e model provides the most accurate predictions, with prediction accuracy within 3–10% of experimental data. Moreover, we identify substantial dependencies between heat generation and discharge current, as well as thermal gradients and inlet velocity. Finally, we conclude that he aligned cell arrangement offers the best thermal uniformity and cooling efficiency.

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On the Effects of Parallelization on the Flow Prediction around a Fastback DrivAer Model at Different Attitudes

Cited by 10 publications

References 25 publications

Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle

On the Effectiveness of Scale-Averaged RANS and Scale-Resolved IDDES Turbulence Simulation Approaches in Predicting the Pressure Field over a NASCAR Racecar

Development of a Computational Fluid Dynamics Simulation Framework for Aerothermal Analyses of Electric Vehicle Battery Packs

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