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Due to growing interest in hybrid and electric vehicles, li-ion battery modeling is receiving a lot of attention from designers and researchers. This paper presents a complete model for a li-ion battery pack. It starts from the Newman electrochemistry model to create the battery performance curves. Such information is then used for cell level battery equivalent circuit model (ECM) parameter identification. 28 cell ECMs are connected to create the module ECM. Four module ECMs are connected through a busbar model to create the pack ECM. The busbar model is a reduced order model (ROM) extracted from electromagnetic finite element analysis (FEA) results, taking into account the parasitic effects. Battery thermal performance is simulated first by computational fluid dynamics (CFD). Then, a thermal linear and time-invariant (LTI) ROM is created out of CFD solution. The thermal LTI ROM is then two-way coupled with the battery pack ECM to form a complete battery pack model.

Demands for higher power engines have led to higher pressures in fuel injectors. Internal nozzle flow plays a critical role in the near nozzle flow and subsequent spray pattern. The internal flow becomes more difficult to model when the injector pressure and internal shape make it more prone to cavitation. Two Bosch injectors, proposed for experimental and computational studies under the Engine Combustion Network (namely “Spray C” and “Spray D”) are modeled in the computational fluid dynamics code ANSYS Fluent. Both injectors operate with n-dodecane as fuel at 150 MPa inlet pressures. The computational model includes cavitation effects to characterize any cavitating regions. Including compressibility of both liquid and vapor is found to be critical. Also, due to high velocity gradients and stresses in the nozzle, turbulent viscous energy dissipation is considered along with pressure work resulting from significant pressure changes in the injector.

A linear parameter varying (LPV) reduced order model (ROM) is used to approximate the volume-averaged temperature of battery cells in one of the modules of the battery pack with varying mass flow rate of cooling fluid using uniform heat source as inputs. The ROM runs orders of magnitude faster than the original CFD model. To reduce the time it takes to generate training data, used in building LPV ROM, a divide-and-conquer approach is introduced. This is done by dividing the battery module into a series of mid-cell and end-cell units. A mid-cell unit is composed of a cooling channel sandwiched in between two half -cells. A half-cell has half as much heat capacity as a full-cell. An end-cell unit is composed of a cooling channel sandwiched in between full-cell and a half-cell. A mass flow rate distribution look-up-table is generated from a set of steady-state simulations obtained by running the full CFD model at different inlet manifold mass flow rate samples.

This paper introduces a model-based systems and embedded software engineering, workflow for the design of control systems. The interdisciplinary approach that is presented relies on an integrated set of tools that addresses the needs of various engineering groups, including system architecture, design, and validation. For each of these groups, a set of best practices has been established and targeted tools are proposed and integrated in a unique platform, thus allowing efficient communication between the various groups. In the initial stages of system design, including functional and architectural design, a SysML-based approach is proposed. This solution is the basis to develop systems that have to obey both functional and certification standards such as ARINC 653 (IMA) and ARP 4754A. Detailed system design typically requires modeling and simulation of each individual physical component of the system by various engineering groups (mechanical, electrical, etc.).

Battery thermal management for high power applications such as electrical/hybrid vehicles is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. While Computational Fluid Dynamics (CFD) has been used quite successfully for battery thermal management, CFD models can be too large and too slow for repeated transient thermal analysis especially for a battery module or pack. An accurate but much smaller battery thermal model using a state space representation is proposed. The parameters in the state space model are extracted from CFD results. The state space model is then shown to provide identical results as those from CFD under transient power inputs. While a CFD model may take hours to run depending on the size of the problem, the corresponding state space model runs in seconds.

This paper presents the current state of a three-layer surface icing model for ice crystal icing risk assessment in aircraft engines, being developed jointly by Ansys and Honeywell to account for possible heat transfer from inside an engine into the flow path where ice accretion occurs. The bottom layer of the proposed model represents a thin metal sheet as a substrate surface to conductively transfer heat from an engine-internal reservoir to the ice layer. The middle layer is accretion ice with a porous structure able to hold a certain amount of liquid water. A shallow water film layer on the top receives impinged ice crystals. A mass and energy balance calculation for the film determines ice accretion rate. Water wicking and recovery is introduced to transfer liquid water between film layer and porous ice accretion layer.

A methodology has been implemented to calculate local turbulent flame speeds for spark ignition engines accurately and on-the-fly in 3-D CFD modeling. The approach dynamically captures fuel effects, based on detailed chemistry calculations of laminar flame speeds. Accurately modeling flame propagation is critical to predicting heat release rates and emissions. Fuels used in spark ignition engines are increasingly complex, which necessitates the use of multi-component fuels or fuel surrogates for predictive simulation. Flame speeds of the individual components in these multi-component fuels may vary substantially, making it difficult to define flame speed values, especially for stratified mixtures. In addition to fuel effects, a wide range of local conditions of temperature, pressure, equivalence ratio and EGR are expected in spark ignition engines.

Due to the increasingly stringent environmental regulations all around the world confronted by exhaust emission and energy consumption, improving fuel economy has been the top priority for most automotive manufacturers. In this context, the basic process for vehicle shape development has evolved into optimizing the design to achieve better aerodynamic characteristics, especially drag reduction. Of all the optimization approaches, the gradient-based adjoint method has currently received extensive attention for its high efficiency in calculating the objective sensitivity with respect to geometry parameters, which is the first and foremost step for subsequent shape modification. In this work, the main goal is to explore the adjoint method through optimizing the vehicle shape for a lower drag based on a production SUV. Firstly, the influence of different mesh schemes was discussed on sensitivity prediction of aerodynamic drag.

Development of quick and efficient numerical tools is key to the design of industrial machines. While Computational Fluid Dynamics (CFD) techniques based on Navier Stokes (N-S) and Lattice Boltzman methods are becoming popular, predicting aeroacoustic behavior for complex geometries remains computationally intensive for design process and iteration. The goal of this paper is to evaluate application Navier-Stokes approach coupled with Ffowcs Williams and Hawkings (FW-H), and Broad-band Noise Model (BNS) to evaluate noise levels and predict design direction for industrial applications. Steady-state RANS based approaches are used to evaluate under-hood cooling performance and fan power demand. At each design iteration, noise levels and strength of noise source are evaluated using Gutin’s and broad-band noise models, respectively along with cooling performance. Each design feature selected for the final design has lower fan power and noise level with improved cooling.

Computational Fluid Dynamics (CFD) is widely used in vehicle aerodynamics development today, but typically used to study one vehicle shape at a time. In order to be used for aerodynamic shape exploration and optimization the CFD simulation process has to be able to study a large set of design alternatives (vehicle shape variants) within the short period of time typically available in the overall aerodynamics development process. This paper reports the development and testing of a process, referred to as the 50:50:50 Method, which is developed to study a large set of design alternatives in a highly automated way, while ensuring that each design alternative is simulated with a high fidelity CFD simulation.

Two main approaches are available when studying droplet dynamics for in-flight icing simulations: the Lagrangian approach, in which each droplet trajectory is integrated until it impacts the vehicle under study or when it leaves it behind without impact, and the Eulerian approach, where the droplet dynamics is solved as a continuum. In both cases, the same momentum equations are solved. Each approach has its advantages. In 2D, the Lagrangian approach is easy to code and it is very efficient, particularly when used in combination with a panel method flow solver. However, it is a far less practical approach for 3D simulations, particularly on complex geometries, as it is not an easy task to accurately determine the droplet seeding region without a great number of droplet trajectories, dramatically increasing the computing cost. Converting the impact locations into a water collection distribution is also a complex task, since droplet trajectories in 3D can follow convoluted paths.

A CFD simulation methodology is presented to evaluate the ice that sheds from rotating components. The shedding detection is handled by coupling the ice accretion and stress analysis solvers to periodically check for the propagation of crack fronts and possible detachment. A novel approach for crack propagation is highlighted where no change in mesh topology is required. The entire computation from flow to impingement, ice accretion and crack analysis only requires a single mesh. The accretion and stress module are validated individually with published data. The analysis is extended to demonstrate potential shedding scenarios on three complex industrially-relevant 3D cases: a helicopter blade, an engine fan blade and a turboprop propeller. The largest shed fragment will be analyzed in the context of FOD damage to neighboring aircraft/component surfaces.

With advances in computing power and Computational Fluid Dynamics (CFD) algorithms, the complexity of CutCell based simulation models has significantly increased. In this study three dimensional numerical simulations were created for steady incompressible flow around airfoil shape. The NACA-0012 airfoil was used for this study. Boundary layer thickness, mesh expansion ratio, and mesh density variation parameters were investigated. Drag and lift coefficients were compared to experimental data. Use of the CutCell method results in a good agreement between CFD results and experimental data.

A driver model in multibody dynamic analysis software is to run a vehicle dynamics model in various customer applications: handling events such as lane changes and circle turns, and durability events such as Belgian blocks, hill courses, driveways, and race tracks. Ansys Motion is a robust multibody dynamic analysis software for many applications including vehicle dynamics simulations. This paper discusses Ansys Driver development in Ansys Motion. It addresses developments of critical driver features: identification of vehicle handling capability, a path planning from complex road profiles, an analog filter design, and a longitudinal and lateral control of vehicle models. It also discusses how to achieve the robustness of the driver model for various customer simulation scenarios not affecting simulation output due to too much driver control. This study presents a couple of examples of handling and durability event simulations.

The thermal behavior of a fluid-cooled battery can be modeled using computational fluid dynamics (CFD). Depending on the size and complexity of the battery module and the available computing hardware, the simulation can take days or weeks to run. This work introduces a reduced-order model that combines proper orthogonal decomposition, capturing the variation of the temperature field in the spatial domain, and linear time-invariant system techniques exploiting the linear relationship between the resulting proper orthogonal decomposition coefficients and the uniform heat source considered here as the input to the system. After completing an initial CFD run to establish the reduction, the reduced-order model runs much faster than the CFD model. This work will focus on thermal modeling of a single prismatic battery cell with one adjacent cooling channel. The extension to the multiple input multiple output case such as a battery module will be discussed in another paper.

Thorough design exploration is essential for improving vehicle performance in various aspects such as aerodynamic drag. Shape optimization algorithms in combination with computational tools such as Computational Fluid Dynamics (CFD) play an important role in design exploration. The present work describes a Free-Form Deformation (FFD) approach implemented within a general purpose CFD code for parameterization and modification of the aerodynamic shape of real-life vehicle models. Various vehicle shape parameters are constructed and utilized to change the shape of a vehicle using a mesh morphing technique based on the FFD algorithm. Based on input and output parameters, a design of experiments (DOE) matrix is created. CFD simulations are run and a response surface is constructed to study the sensitivity of the output parameter (aerodynamic drag) to variations in each input parameter.

One of the best tools to explore complicated in-cylinder physics is computational fluid dynamics (CFD). In order to assess the accuracy and reliability of the CFD simulations, it is critical to perform validation studies over different engine operating conditions. Simulation-based design of SI engines requires predictive capabilities, where results do not need to be tuned for each operating condition. This requires the models adopted to simulate their respective engine physics to be reliable under a broad range of conditions. A detailed set of experimental data was obtained to validate the CFD predictions of SI engine combustion.

Gasoline Direct Injection (GDI) is a key technology in the automotive industry for improving fuel economy and performance of gasoline internal combustion engines. GDI engine performance and emission characteristics are mainly determined by the complex interaction of in-cylinder flow, mixture formation and subsequent combustion processes. In a GDI engine, mixture formation depends on spray characteristics. Spray evolution and mixture formation is critical to GDI engine operation. In this work, a multi-component surrogate fuel blend was used to represent the chemical and physical properties of the gasoline employed in the experimental engine tests. Multi-component spray models were also validated in this study against experimental spray injection measurements in a chamber. The spray-chamber data include spray-penetration lengths, transient spray velocities and droplet Sauter mean diameter (SMD) at different axial and radial distances from the spray tip, obtained using a PDPA system.

Computational Fluid Dynamics (CFD) is heavily used in automotive HVAC industry in order to reduce the time and cost in design, optimization, and development of different components [2]. Correct prediction of the aerodynamic characteristics of an HVAC blower is crucial in development of the accurate CFD models for the whole HVAC system. CFD models are extensively used in the optimization of both thermal and airflow characteristics of automotive HVAC [3, 4 and 5]. In this study we have performed CFD simulations for different blower operating conditions in order to assess the CFD results in prediction of the aerodynamic performance in an automotive HVAC forward curved (FC) centrifugal blower. The realizable k-ε turbulence model was used on the Reynolds Averaged Navier-Stokes approach to model complex flow field properly.

Increased energy costs make optimal aerodynamic design even more critical today as even small improvements in aerodynamic performance can result in significant savings in fuel costs. Energy conscious industries like transportation (aviation and ground based) are particularly affected. There have been a number of different optimization methods, some of which require geometrically parameterized models. For non-parameterized models (as it is the case often in reality where models and shapes are very complex). Shape optimization and adjoin solvers are some of the latest approaches. In our study we are focusing on generating best practices and investigating different strategies of employing the commercially available shape optimizer tool from ANSYS'CFD solver Fluent. The shape optimizer is based on a polynomial mesh-morphing algorithm. The simple case of a low speed, airfoil/flap combination is used as a case study with the objective being the lift to drag ratio.