Virtual Test of Injector Design Using CFD

2014 ◽  
Author(s):  
Meng-Huang Lu ◽  
Figen Lacin ◽  
Daniel McAninch ◽  
Frank Yang
Keyword(s):  
Virtual Test ◽  
Injector Design

Virtual Test of Manufacturing Process Effect on Injector Design

2015 ◽  
Author(s):  
Meng-Huang Lu ◽  
Figen Lacin ◽  
Daniel McAninch ◽  
Frank Yang
Keyword(s):  
Manufacturing Process ◽  
Virtual Test ◽  
Injector Design

In-Plane Flexible Ring Tire Model—Part 1: Modeling and Parameter Identification

2018 ◽  
Vol 46 (3) ◽  
pp. 174-219 ◽  
Author(s):  
Bin Li ◽  
Xiaobo Yang ◽  
James Yang ◽  
Yunqing Zhang ◽  
Zeyu Ma
Keyword(s):  
Static Load ◽  
Model Parameters ◽  
Tire Model ◽  
Test Results ◽  
Lumped Mass ◽  
Virtual Test ◽  
Proposed Model ◽  
Particle Swarm Method ◽  
Vehicle Dynamic Simulation ◽  
Flexible Ring

ABSTRACT The tire model is essential for accurate and efficient vehicle dynamic simulation. In this article, an in-plane flexible ring tire model is proposed, in which the tire is composed of a rigid rim, a number of discretized lumped mass belt points, and numerous massless tread blocks attached on the belt. One set of tire model parameters is identified by approaching the predicted results with ADAMS® FTire virtual test results for one particular cleat test through the particle swarm method using MATLAB®. Based on the identified parameters, the tire model is further validated by comparing the predicted results with FTire for the static load-deflection tests and other cleat tests. Finally, several important aspects regarding the proposed model are discussed.

A novel approach for experimental identification of vehicle dynamic parameters

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2634-2648
Author(s):  
Di Yao ◽  
Philipp Ulbricht ◽  
Stefan Tonutti ◽  
Kay Büttner ◽  
Prokop Günther
Keyword(s):  
Parameter Identification ◽  
Dynamic Modeling ◽  
Trajectory Optimization ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Test Rig ◽  
Vehicle Simulation ◽  
Virtual Test ◽  
Identification Approach ◽  
Measuring Machine

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

Virtual test-driving: The impact of simulated products on purchase intention

2014 ◽  
Vol 21 (5) ◽  
pp. 877-887 ◽  
Author(s):  
Savvas Papagiannidis ◽  
Eric See-To ◽  
Michael Bourlakis
Keyword(s):  
Purchase Intention ◽  
Virtual Test ◽  
The Impact

Design Scheme and Simulation on Automatic Transmission Travel System of Engineering Vehicles Hydraulic Chassis

2010 ◽  
Vol 156-157 ◽  
pp. 137-141 ◽  
Author(s):  
Hui Xian Han ◽  
Pi Shun Ren ◽  
Xian Li Cao ◽  
Mao Fu Liu
Keyword(s):  
Control Strategy ◽  
Hydraulic System ◽  
Automatic Transmission ◽  
Travel Speed ◽  
Transmission System ◽  
Control Effect ◽  
Actual System ◽  
Virtual Test ◽  
Design Scheme ◽  
Software And Hardware

This study analysis the use requirement of engineering vehicles travel system, introduces the hydraulic system and the design scheme of automatic transmission system, discusses the control strategy and method of engineering vehicles travel speed, modeling and simulating based on the actual system software and hardware and the control Strategy and method of automatic transmission system, verifies the vehicle control effect through virtual test, and draws the full-text conclusion at last.

Low‐energy injector design for SSC

1992 ◽  
Vol 63 (4) ◽  
pp. 2738-2740 ◽  
Author(s):  
O. A. Anderson ◽  
C. F. Chan ◽  
K. N. Leung ◽  
L. Soroka ◽  
R. P. Wells
Keyword(s):  
Low Energy ◽  
Injector Design

Factor of fuel pyrolysis in injector design.

AIAA Journal ◽  
1973 ◽  
Vol 11 (1) ◽  
pp. 99-100
Author(s):  
LEONARD DAUERMAN ◽  
G. E. SALSER
Keyword(s):  
Fuel Pyrolysis ◽  
Injector Design

High-diodicity impinging injector design for rocket propulsion enabled by additive manufacturing

2022 ◽  
Author(s):  
Alex R. Keller ◽  
Joel Otomize ◽  
Anil P. Nair ◽  
Nicolas Q. Minesi ◽  
Raymond M. Spearrin
Keyword(s):  
Additive Manufacturing ◽  
Rocket Propulsion ◽  
Impinging Injector ◽  
Injector Design

Improving correlation accuracy of crashworthiness applications by combining the CORA and MADM methods

2021 ◽  
pp. 095440702110696
Author(s):  
Christophe Bastien ◽  
Alexander Diederich ◽  
Jesper Christensen ◽  
Shahab Ghaleb
Keyword(s):  
Numerical Models ◽  
Monotonic Functions ◽  
Virtual Test ◽  
Industry Standard ◽  
Discrepancy Method ◽  
Original Equipment Manufacturers ◽  
The Relationship ◽  
Correlation Accuracy ◽  
New Framework ◽  
Selection Of

With the increasing use of Computer Aided Engineering, it has become vital to be able to evaluate the accuracy of numerical models. This research poses the problem of selection of the most accurate and relevant correlation solution to a set of corridor variations. Specific methods such as CORA, widely accepted in industry, are developed to objectively evaluate the correlation between monotonic functions, while the Minimum Area Discrepancy Method, or MADM, is the only method to address the correlation of non-injective mathematical variations, usually related to force/acceleration versus displacement problems. Often, it is not possible to differentiate objectively various solutions proposed by CORA, which this paper proposes to answer. This research is original, as it proposes a new innovative correlation optimisation framework, which can select the best CORA solution by including MADM as a subsequent process. The paper and the methods are rigorous, having used an industry standard driver airbag computer model, built virtual test corridors and compared the relationship between different CORA and MADM ratings from 100 Latin Hypercube samples. For the same CORA value of ‘1’ (perfect correlation), MADM was capable to objectively differentiate between 13 of them and provide the best correlation possible. The paper has recommended the MADM settings n = 1; m = 2 or n = 3; m = 2 for a congruent relationship with CORA. As MADM is performed subsequently, this new framework can be implemented in already existing industrial processes and provide automotive manufacturers and Original Equipment Manufacturers (OEM) with a new tool to generate more accurate computer models.

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