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Cooling Airflow System Modeling in CFD Using Assumption of Stationary Flow

2011-11-29
Battery Electric Vehicles and Extended Range Electric Vehicles, like the Chevrolet Volt, can use electrical energy from the Grid to meet the majority of a driver�s transportation needs. This has the positive societal effects of displace petroleum consumption and associated pollutants from combustion on a well to wheels basis, as well as reduced energy costs for the driver. CO2 may also be lower, but this depends upon the nature of the grid energy generation. There is a mix of sources � coal-fired, gas -fired, nuclear or renewables, like hydro, solar, wind or biomass for grid electrical energy. This mix changes by region, and also on the weather and time of day. By monitoring the grid mix and communicating it to drivers (or to their vehicles) in real-time, electrically driven vehicles may be recharged to take advantage of the lowest CO2, and potentially lower cost charging opportunities.
Technical Paper

Cooling Airflow System Modeling in CFD Using Assumption of Stationary Flow

2011-09-13
2011-01-2182
Today CFD is an important tool for engineers in the automotive industry who model and simulate fluid flow. For the complex field of Underhood Thermal Management, CFD has become a very important tool to engineer the cooling airflow process in the engine bay of vehicles. To model the cooling airflow process accurately in CFD, it is of utmost importance to model all components in the cooling airflow path accurately. These components are the heat exchangers, fan and engine bay blockage effect. This paper presents CFD simulations together with correlating measurements of a cooling airflow system placed in a test rig. The system contains a heavy duty truck louvered fin radiator core, fan shroud, fan ring and fan. Behind the cooling module and fan, a 1D engine silhouette is placed to mimic the blockage done by a truck engine. Furthermore, a simple hood is mounted over the module to mimic the guiding of air done by the hood shape in an engine bay.
Technical Paper

Simplifications Applied to Simulation of Turbulence Induced by a Side View Mirror of a Full-Scale Truck Using DES

2018-04-03
2018-01-0708
In this paper, the turbulent flow induced by a production side-view mirror assembled on a full-scale production truck is simulated using a compressible k-ω SST detached eddy simulation (DES) approach -- the improved delayed DES (IDDES). The truck configuration consists of a compartment and a trailer. Due to the large size and geometric complexity of the configuration, some simplifications are applied to the simulation. A purpose of this work is to investigate whether the simplifications are suitable to obtain the reasonable properties of the flow near the side-view mirror. Another objective is to study the aerodynamic performances of the mirror. The configuration is simplified regarding two treatments. The first treatment is to retain the key exterior components of the truck body while removing the small gaps and structures. Furthermore, the trailer is shaped in an apex-truncated square pyramid.
Technical Paper

Multi-Objective Optimization of Fuel Consumption and NOx Emissions with Reliability Analysis Using a Stochastic Reactor Model

2019-04-02
2019-01-1173
The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low.
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