Carolin Schmidt

Uniform segment-wise body parametric convection heat transfer coefficient model for sitting posture in vehicles

In many industrial multi-physics engineering applications, models need to capture the heat transfer effects of spatial and temporal changes in conditions around the human body. For thermal comfort assessment, convection heat transfer coefficients (hc) form part of the heat balance equation of the human body. In many non-uniform flow conditions, due to the turbulently mixed or stratified environment, convection heat transfer varies significantly on the human body. Parametric, segment-wise applicable convection heat transfer correlations are seen as an alternative in order to bridge these scales and levels in space and time. Therefore, robust reduced-order convective heat transfer models are needed for predicting heat transfer between the human body and its surroundings. The main goal of this research is to develop a reduced order model database that provides the segment-wise convective heat transfer coefficients (hc) for typical indoor flow responses in multiple applications. In this article, a new parametric approach was detailed for estimating segment-wise body convection heat transfer coefficients for sitting posture in vehicles. The methodology follows a new strategy, i.e., in this application domain, here a car cabin, primarily relevant parameters are identified which affect the convective heat exchange. Following the sensitivity analysis of numerous computational fluid dynamics simulations with varying conditions, we identify relevant primary variables and heat transfer coefficients correlations and test the model robustness accordingly. A database-driven approach is developed in order to make correlations accessible during simulations, for example addressing energy performance. Finally, the experimentally investigated heat transfer analysis around the human body is presented and later compared with numerically reproduced data.

Energy-Efficient Climate Control in Electric Vehicles Through Innovative Sensor Technology and Novel Methods for Thermal Comfort Evaluation

The increasing emission of greenhouse gases caused by a growing global rate of motorization contributes substantially to global warming and climate change. Germany aims to cut CO2 emission by 80% by the end of 2050 (BMWi 2012). In order to reach this goal, the transportation sector has to make a significant contribution. The required energy for engines in electric vehicles can be harvested from regenerative energy sources, therefore offering an opportunity for the reduction of greenhouse gas emissions. This work introduces a system for intelligent thermal management and energy-efficient climate control in electric vehicles adopting a sensor-based evaluation of individual thermal comfort of each passenger. By deriving individual measures for each person, the overall vehicle air conditioning system operates at much lower energy levels, which results in a drastic reduction of energy consumption and hence an increase in the driving range of the vehicle.