Search Results

Accurate assessment of thermal comfort requires comprehensive analysis of the environmental effects contributing to the heat transfer to and from the human body. A common comfort evaluation approach (e.g. PMV/PPD, Equivalent Temperature) is to find a direct correlation of comfort to environmental conditions (e.g. air temperature, relative humidity, clothing), thus implicitly accounting for the relationship between physiological response and thermal comfort. An alternate approach (e.g. Berkeley Comfort Model, Fiala's DTS) is to explicitly correlate comfort to basic physiological response (e.g. skin and core temperature), thereby separating the thermal analysis portion of the problem from the more subjective comfort analysis portion. While it has been shown that comparable results can be obtained between environment-based comfort metrics and physiology-based comfort metrics, the latter should be employed for optimal prediction accuracy.

Delphi Harrison Thermal System's comfort model can be used to predict the local thermal comfort level of an occupant in the highly non-uniform thermal environment of a vehicle cabin. This model is based on the concept of Equivalent Homogeneous Temperature (EHT) to assess the local comfort of 16 body segments as a function of air temperature, surrounding surface temperatures, air velocity, humidity, direct solar flux, as well as the level of activity and clothing type of each individual. Although EHT has been accepted by some European automotive industries, OEMs in North America have their own comfort scales. In the present study, we developed a model to correlate our EHT scale to an OEM's comfort scale. The current comfort model based on EHT produced excellent agreements with human subject data based on an OEM's comfort scale for both summer and winter rides.

Simulation of cabin climatic conditions is becoming increasingly important as a complement to wind tunnel and field testing to help achieve improved thermal comfort while reducing vehicle development time and cost. Delphi developed the Virtual Thermal Comfort Engineering (VTCE) process to explore different climate control strategies as they relate to occupant thermal comfort in a quick and inexpensive manner. The comfort model has the ability to predict the local thermal comfort level of an occupant in a highly non-uniform thermal environment as a function of air temperature, surrounding surface temperatures, air velocity, humidity, direct solar flux, as well as the level of activity and clothing type of each individual.

Understanding the flow characteristics and, especially, how the aerodynamic forces are influenced by the changes in the vehicle body shape, are very important in order to improve vehicle aerodynamics. One specific goal of aerodynamic shape optimization is to predict the local shape sensitivities for aerodynamic forces. The availability of a reliable and efficient sensitivity analysis method will help to reduce the number of design iterations and the aerodynamic development costs. Among various shape optimization methods, the Adjoint Method has received much attention as an efficient sensitivity analysis method for aerodynamic shape optimization because it allows the computation of sensitivity information for a large number of shape parameters simultaneously.

Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

Three-dimensional flow and temperature distributions in a passenger compartment are very important for evaluating passenger comfort and improving A/C system design. In the present study, the Reynolds-averaged Navier-Stokes equations and the energy transport equation were solved, by both quasi-steady and full transient approaches, to simulate a passenger compartment cooling process. By comparing the predictions with experimental results for a simplified GM-10 passenger compartment, the accuracy of the simulation was assessed. Throughout the 800-second period, good agreement was observed between the measured breath-level air temperatures and the prediction of the transient simulation. The quasi-steady simulation underpredicted air temperatures at the very early stage of the cooling process. However, after 200 seconds of cool down, the quasi-steady simulation predicted air temperatures equally as well as the full transient simulation.

The Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project is a U.S. Department of Energy-funded, multi-year project which is aimed at developing a complete CAE tool set for the automotive battery pack design. This paper reports the application of the full field approach of the CAEBAT which is developed by the General Motors-led industry team, for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The simulation results using the Full Field Approach are found to have a very good agreement with the measurement data.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

Virtual simulation of passenger compartment climatic conditions is becoming increasingly important as a complement to the wind tunnel and field testing to achieve improved thermal comfort while reducing the vehicle development time and cost. The vehicle cabin is subjected to various thermal environments. At the same time many of the design parameters are dependent on each other and the relationship among them is quite complex. Therefore, an experimental parametric study is very time consuming. The present 3-D RadTherm analysis coupled with the 3-D CFD flow field analysis takes into account the geometrical configuration of the passenger compartment which includes glazing surfaces and pertinent physical and thermal properties of the enclosure with particular emphasis on the glass properties. Virtual Thermal Comfort Engineering (VTCE) is a process that takes into account the cabin thermal environment coupled with a human physiology model.

The main objective of this work is to present an adjoint-based methodology to address combined optimization of drag force and cooling flow rate of an industrial vehicle. In order to cope with cooling effect, the volumetric flow rate is treated through a newly introduced cost function and the corresponding adjoint source term is derived. Also an alternative strategy is presented to tackle aerodynamic vehicle design improvement that relies on a so-called indirect force computation. The overall optimization is treated as a Multi-Objective problem and an original approach, called Optimize Both Favor One (OBFO), is introduced that allows selective emphasis on one or another objective without resorting to artificial cost function balancing. Finally, comparative results are presented to demonstrate the merit of the proposed methodology.

The major challenge in diesel NOx aftertreatment systems using NOx adsorbers is their susceptibility to sulfur poisoning. A new computational model has been developed for the thermal management of NOx adsorber desulfation and describes the exothermic reaction mechanisms on the catalyst surface in the diesel NOx trap. Sulfur, which is present in diesel fuel, adsorbs as sulfates and accumulates at the same adsorption sites as NOx, therefore inhibiting the ability of the catalyst to adsorb NOx. Typically, a high surface temperature above 650 °C is required to release sulfur rapidly from the catalyst [1]. Since the peak temperatures of light-duty diesel engine exhaust are usually below 400 °C, additional heat is required to remove the sulfur. This report describes a new mathematical model that employs Navier-Stokes equations coupled with species transportation equations and exothermic chemical reactions.

Passive sensor (HVAC) manikins have been developed to obtain high-resolution measurements of environmental conditions across a representative human body form. These manikins incorporate numerous sensors that measure air velocity, air temperature, radiant heat flux, and relative humidity. The effect of a vehicle’s climate control system on occupant comfort can be characterized from the data collected by an HVAC manikin. Equivalent homogeneous temperature (EHT) is often used as a first step in a cabin comfort analysis, particularly since it reduces a large data set to a single intuitive number. However, the applicability of the EHT for thermal comfort assessment is limited since it does not account for human homeostasis, i.e., that the human body actively counter-balances heat flow with the environment to maintain a constant core temperature. For this reason, a thermo-physiological human model is required to accurately simulate the body’s dynamic response to a changing environment.

In today's highly competitive marketplace and ever-shrinking vehicle development cycle, automotive HVAC engineers are constantly challenged to develop systems that deliver adequate thermal comfort at lower cost and higher fuel-efficiency. However, in the absence of a reliable predictive tool that is capable of quantifying thermal comfort early in the vehicle development program, OEMs employ expensive and time-consuming human subject test rides to assess HVAC system's performance. In order to address this issue, a Computational Fluid Dynamics (CFD) model of a virtual thermal manikin in a passenger cabin is developed in this study. The model is capable of providing thermal sensations, as experienced by humans, under any arbitrary thermal environment. Additionally, the model can provide sensitivity of various critical parameters, such as solar load, A/C discharge outlet air flow-rate and temperature, A/C outlet locations, various HVAC modes etc., to thermal sensation.

Spot, or distributed, cooling and heating is an energy efficient way of delivering comfort to an occupant in the car. This paper describes an approach to distributed cooling in the vehicle. A two passenger CFD model of an SUV cabin was developed to obtain the solar and convective thermal loads on the vehicle, characterize the interior thermal environment and accurately evaluate the fluid-thermal environment around the occupants. The present paper focuses on the design and CFD analysis of the energy efficient HVAC system with spot cooling. The CFD model was validated with wind tunnel data for its overall accuracy. A baseline system with conventional HVAC air was first analyzed at mid and high ambient conditions. The airflow and cooling delivered to the driver and the passenger was calculated. Subsequently, spot cooling was analyzed in conjunction with a much lower conventional HVAC airflow.

A test protocol previously developed for automotive applications was adapted to evaluate the performance of a climate control system for a heavy-duty truck. Human subjects, as well as a test system composed of a high-resolution passive sensor manikin and a human thermal model, were employed to evaluate thermal comfort perception. Testing was performed in a climate-controlled wind tunnel equipped with a dynamometer. The truck’s HVAC system performance was evaluated in a −10 °C environment. Additionally, the test protocol was designed to explore a large range of thermal sensation and comfort states. Subjective responses, including thermal sensation and comfort, as well as thermo-physiological state information, quantified by skin temperatures measured across the body, were obtained from the human test participants and compared to that which was indicated by the test system.

In a previous study, a passive sensor (HVAC) manikin coupled with a human thermal model was used to predict the thermal comfort of human test participants. The manikin was positioned among the test participants while they were collectively exposed to a mild transient heat up within a thermally asymmetric chamber. Ambient conditions were measured using the HVAC manikin’s distributed sensor system, which measures air velocity, air temperature, radiant heat flux, and relative humidity. These measurements were supplied as input to a human thermal model to predict thermophysiological response and subsequently thermal sensation and comfort. The model predictions were shown to accurately reproduce the group trends and the “time to comfort” at which a transition occurred from a state of thermal discomfort to comfort. In the current study, the effectiveness of using a coupled HVAC manikin-model system to evaluate a vehicle climate control system was investigated.

The interaction between the deploying airbag and the Out-Of-Position (OOP) occupants remains a challenge in occupant protection system simulations. The integration of Computational Fluid Dynamics (CFD) analysis into Finite Element (FE) airbag model is a helpful and important tool to address this challenge. Three major commercial crash simulation software packages widely used in the automotive safety industry, LS-DYNA, MADYMO and PAM-CRASH are in the process of implementing different approaches for airbag CFD simulation. In this study, an attempt was made to evaluate and compare the CFD integrated airbag models in these software packages. Specially designed tests were conducted to study and capture the pressure distribution inside a flat airbag and the test results were used for the evaluation. Strengths and limitations of each software package are discussed in this paper.

The growing applications of Computer-Aided Engineering (CAE) tools have been motivated by the need to create more effective product development processes. Computational Fluid Dynamics (CFD), as one of the CAE tools, has enjoyed growing popularity for analysis of many airflow situations, including road vehicle aerodynamics. In many cases, these applications have been limited by the level of predictive accuracy that is possible with CFD codes today. In the present exercise, simplified representations of three vehicle models (1:12 scale) were chosen to assess the overall level of predictability of the GMTEC CFD code, using detailed measurements that were made in a scale-model wind tunnel. The CFD computations used two turbulence models (standard k-εand RNG k-ε) and were matched to the experimental geometry and test conditions.

Traditional vehicle air conditioning systems condition the entire cabin to a comfortable range of temperature and humidity regardless of the number of passengers in the vehicle. The A/C system is designed to have enough capacity to provide comfort for transient periods when cooling down a soaked car. Similarly for heating, the entire cabin is typically warmed up to achieve comfort. Localized heating and cooling, on the other hand, focuses on keeping the passenger comfortable by forming a micro climate around the passenger. This is more energy efficient since the system only needs to cool the person instead of the entire cabin space and cabin thermal mass. It also provides accelerated comfort for the passenger during the cooling down periods of soaked cars. Additionally, the system adapts to the number of passengers in the car, so as to not purposely condition areas that are not occupied.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.