Vehicle Design Using High-fidelity Virtual Prototyping

1998 ◽  
Author(s):  
Jim Kozlowski ◽  
Chandra Prasad ◽  
Marc Serughetti
Keyword(s):  
Virtual Prototyping ◽  
Vehicle Design ◽  
High Fidelity

Multibody System for Virtual Prototyping

2009 ◽  
Author(s):  
Kevin Chang ◽  
Christopher Johnson
Keyword(s):  
Product Development ◽  
Modeling And Simulation ◽  
Virtual Environment ◽  
Interface Design ◽  
Multibody System ◽  
Virtual Prototyping ◽  
Electrical Power ◽  
Product Development Process ◽  
Vehicle Design ◽  
Human Factor Engineering

The Ground Systems (GS) business unit of BAE Systems Inc. develops and manufactures major ground combat vehicles for military. Because the development of ground-based combat vehicles is a complex process, it requires the coordinated effort of multiple engineering disciplines that include human factor engineering (HFE), product design, as well as modeling and simulation (M&S), to perform design analysis and to predict vehicle performance. In order to increase engineering efficiency and to reduce product development costs, GS has developed a virtual prototyping technology. Using this technology, it enables GS to perform vehicle design and requirement validation in a virtual environment prior to expensive and time consuming hardware prototyping. This technology also enables GS customers to be more involved in the product development cycle and makes the product development process more customer-centric. The development of this virtual environment requires integration of various technologies, including multibody dynamics, 3D computer graphics, networking, modeling and simulation, and the human-machine interface design. This paper describes how multibody system simulations are used in this virtual environment to support GS vehicle design in the areas of crew visibility studies, crew station design, vehicle interference checking, and electrical power management simulation.

Virtual Prototyping Simulation for Design of Mechanical Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 63-70 ◽  
Author(s):  
E. J. Haug ◽  
K. K. Choi ◽  
J. G. Kuhl ◽  
J. D. Wargo
Keyword(s):  
System Design ◽  
Mechanical System ◽  
Driving Simulator ◽  
Dynamic Performance ◽  
Virtual Prototyping ◽  
Mechanical Systems ◽  
Design Sensitivity ◽  
Vehicle Design ◽  
Design Environment ◽  
Analysis And Design

Developments in simulation technology that enable a qualitatively new virtual prototyping approach to design of mechanical systems are summarized and their integration into an engineering design environment is illustrated. Simulation tools and their enabling technologies are presented in the context of vehicle design, with references to the literature provided. Their implementation for design representation, real-time driver-in-the-loop simulation, dynamic performance simulation, dynamic stress and life prediction, maintainability analysis, design sensitivity analysis, and design optimization is outlined. A testbed comprised of computer aided engineering tools and a design level of fidelity driving simulator that has been developed to demonstrate the feasibility of virtual prototyping simulation for mechanical system design is presented. Two 1994 demonstrations of this capability for vehicle design are presented, to illustrate the state of the technology and to identify challenges that remain in making virtual prototyping simulation an integral part of mechanical system design in US industry.

Virtual Prototyping Simulation for Design of Mechanical Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 63-70 ◽  
Author(s):  
E. J. Haug ◽  
K. K. Choi ◽  
J. G. Kuhl ◽  
J. D. Wargo
Keyword(s):  
System Design ◽  
Mechanical System ◽  
Driving Simulator ◽  
Dynamic Performance ◽  
Virtual Prototyping ◽  
Mechanical Systems ◽  
Design Sensitivity ◽  
Vehicle Design ◽  
Design Environment ◽  
Analysis And Design

Developments in simulation technology that enable a qualitatively new virtual prototyping approach to design of mechanical systems are summarized and their integration into an engineering design environment is illustrated. Simulation tools and their enabling technologies are presented in the context of vehicle design, with references to the literature provided. Their implementation for design representation, real-time driver-in-the-loop simulation, dynamic performance simulation, dynamic stress and life prediction, maintainability analysis, design sensitivity analysis, and design optimization is outlined. A testbed comprised of computer aided engineering tools and a design level of fidelity driving simulator that has been developed to demonstrate the feasibility of virtual prototyping simulation for mechanical system design is presented. Two 1994 demonstrations of this capability for vehicle design are presented, to illustrate the state of the technology and to identify challenges that remain in making virtual prototyping simulation an integral part of mechanical system design in US industry.

Possibility-Based Multidisciplinary Optimisation For Electric-Powered Unmanned Aerial Vehicle Design

2015 ◽  
Vol 119 (1221) ◽  
pp. 1397-1414 ◽  
Author(s):  
N. V. Nguyen ◽  
J.-W. Lee ◽  
M. Tyan ◽  
D. Lee
Keyword(s):  
Design Space ◽  
Point Of View ◽  
Vehicle Design ◽  
High Fidelity ◽  
Performance Parameters ◽  
Piston Engine ◽  
Design Variables ◽  
Aerial Vehicle ◽  
And Performance ◽  
System Point

AbstractThis paper describes a possibility-based multidisciplinary optimisation for electric-powered unmanned aerial vehicles (UAVs) design. An in-house integrated UAV (iUAV) analysis program that uses an electric-powered motor was developed and validated by a Predator A configuration for aerodynamics, weight, and performance parameters. An electric-powered propulsion system was proposed to replace a piston engine and fuel with an electric motor, power controllers, and battery from an eco-system point of view. Moreover, an in-house Possibility-Based Design Optimisation (iPBDO) solver was researched and developed to effectively handle uncertainty variables and parameters and to further shift constraints into a feasible design space. A sensitivity analysis was performed to reduce the dimensions of design variables and the computational load during the iPBDO process. Maximising the electric-powered UAV endurance while solving the iPBDO yields more conservative, but more reliable, optimal UAV configuration results than the traditional deterministic optimisation approach. A high fidelity analysis was used to demonstrate the effectiveness of the process by verifying the accuracy of the optimal electric-powered UAV configuration at two possibility index values and a baseline.

CALIBRATION, REGISTRATION, AND PREPARATION OF AN AUGMENTED REALITY ENVIRONMENT FOR VIRTUAL PROTOTYPING OF DYNAMIC SYSTEMS

2002 ◽  
Vol 01 (01) ◽  
pp. 19-36 ◽  
Author(s):  
C. P. HUANG ◽  
S. AGARWAL ◽  
F. W. LIOU
Keyword(s):  
Augmented Reality ◽  
Computer Model ◽  
Dynamic Systems ◽  
Virtual Prototyping ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
New Materials ◽  
Reliable System ◽  
3D Tracking ◽  
Augmented Reality Technology

The aim of this research is develop an effective virtual prototyping system for product development using augmented reality technology. Before a virtual environment is put into work for design and development, some way of quantifying errors or uncertainties in the computer model is needed so that a robust and reliable system can be achieved. This paper presents the calibration, registration, and preparation of an augmented reality environment with 3D tracking and dynamic simulation technologies for studying dynamic systems, such as parts orientation devices. With such virtual prototyping techniques, engineers can run high-fidelity simulation to test new materials, components, and systems before investing valuable resources in construction of physical prototypes.

scholarly journals MULTI-CRITERIA OPTIMIZATION AS KEY TO YACHT PERFORMANCE IMPROVEMENT

2019 ◽  
Author(s):  
K Hochirch ◽  
S Deucker ◽  
V Bertram
Keyword(s):  
Energy Efficiency ◽  
Performance Improvement ◽  
Virtual Prototyping ◽  
High Fidelity ◽  
Design Exploration ◽  
Concept Exploration ◽  
Industry Practice ◽  
Key Techniques ◽  
Short Time ◽  
Formal Optimization

The paper describes the application of multi-criteria optimization to monohull and multihull yacht configurations. The paper outlines the process (involving concept exploration, formal optimization and simulations), key techniques, employed software tools and results. The analyses combine efficient low-fidelity design exploration with high-fidelity CFD simula-tions for accurate results. The process allows advancing unconventional designs in relatively short time exploiting the power of parallel computing and virtual prototyping. For yachts, “performance” improvement encompasses besides energy efficiency also aspects of passenger comfort. Examples, sometimes anonymized, from industry practice are used to illus-trate approach and possible achievements.

Research on a Multi-Fidelity Surrogate Model Based Model Updating Strategy

2018 ◽  
Author(s):  
Ping Wang ◽  
Qingmiao Wang ◽  
Xin Yang ◽  
Zhenfei Zhan
Keyword(s):  
Surrogate Model ◽  
Model Simulation ◽  
Side Impact ◽  
Computational Time ◽  
Vehicle Design ◽  
Surface Model ◽  
High Fidelity ◽  
Fe Model ◽  
Simulation Data ◽  
Full Vehicle

In vehicle design modeling and simulation, surrogate model is commonly used to replace the high fidelity Finite Element (FE) model. A lot of simulation data from the high-fidelity FE model are utilized to construct an accurate surrogate model requires. However, computational time of FE model increases significantly with the growing complexities of vehicle engineering systems. In order to attain a surrogate model with satisfactory accuracy as well as acceptable computational time, this paper presents a model updated strategy based on multi-fidelity surrogate models. Based on a high-fidelity FE model and a low-fidelity FE model, an accurate multi-fidelity surrogate model is modeled. Firstly, the original full vehicle FE model is simplified to get a sub-model with acceptable accuracy, and it is able to capture the essential behaviors in the vehicle side impact simulations. Next, a primary response surface model (RSM) is built based on the simplified sub-model simulation data. Bayesian inference based bias term is modeled using the difference between the high-fidelity full vehicle FE model simulation data and the primary RSM running results. The bias is then incorporated to update the original RSM. This method can enhance the precision of surrogate model while saving computational time. A real-world side impact vehicle design case is utilized to demonstrate the validity of the proposed strategy.

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