How to use Simulation to Improve the Engine Development Process for Carbon Neutral Fuels The need for clean, renewable energy sources requires exploring carbon neutral fuels and their combustion behaviors. This is typically done using single-cylinder (SC) engines. The advantages of this process are to make quick hardware changes such as replacing the head or […]
Why Cabin Modelling? Efficient cabin modelling has become crucial for the new age battery electric vehicles (BEVs) as every bit of energy that can be conserved will help increase the range of a BEV. The goal of a design engineer is to achieve an optimal balance between passenger comfort and the energy consumption of the […]
There are various challenges faced by an automotive engineer while designing a robust and optimized accessory drive system. Most original equipment manufacturers (OEMs) rely on different suppliers for their engine belt(s) and accessories (e.g. water pumps, alternators, A/C compressors, etc.). This leads to challenges in obtaining a comprehensive set of input data to incorporate in […]
Incorporating Dynamic Metamodeling Simulation To save computational time, engineers are persistently trying to speed up physical models, and some situations absolutely require faster simulation speeds. These situations might include more advanced co-simulation tasks, performing model-based optimization on a slower physical model, or the need to have a surrogate model for XiL (X-in-the-Loop) applications or to […]
Governmental Regulations Impacting Automotive OEMs In recent news, new vehicle tailpipe governmental regulations in the United States have softened for original equipment manufacturers (OEMs) development of electric vehicles (EVs) and hybrids (HEVs). The Department of Energy has significantly slowed the phase-out of existing rules that give automakers extra fuel-economy credit for electric and hybrid vehicles […]
Why Neural Networks are Effective in Machine Learning Neural networks are powerful machine learning [ML] models that can capture highly nonlinear relationships between inputs and outputs within a dataset while being computationally inexpensive to execute. The benefits of neural networks for modeling and simulation activities, using the simulation platform GT-SUITE, were covered in part 1 […]
Machine Learning and Modeling Simulation Machine learning [ML] models, such as neural networks and other types of metamodels, are fast-executing mathematical representations of data that serve a variety of modeling and simulation purposes, including: Replacing computationally expensive physics-based sub-systems in integrated simulation models (for instance, we are using GT-SUITE simulation models for the HVACR industry) […]
Gamma Technologies’ GT-SUITE paired with ThermoAnalytics’ TAITherm offer a robust solution for vehicle thermal management simulation. GT-SUITE is a powerful multi-physics tool that employs both 1D and 3D methodologies to solve complex integrated subsystem problems and TAITherm is an industry-leading thermal analysis code. By coupling these tools, simulation teams can now run robust simulations to […]
Gamma Technologies’ GT-SUITE paired with ThermoAnalytics’ TAITherm offer a robust solution for vehicle thermal management simulation. GT-SUITE is a powerful multi-physics tool that employs both 1D and 3D methodologies to solve complex integrated subsystem problems and TAITherm is an industry-leading thermal analysis code. By coupling these tools, simulation teams can now run robust simulations to […]
Simulating Battery Packs – Not All Cells are the Same When simulating a large battery module, typically we assume that all the cells in the module are going to be the same. However, that is not always the case. Factors such as the capacities and resistances of the cell can vary from cell-to-cell. This brings […]
