Probabilistic Inverse Simulation and its Application in Vehicle Accident Reconstruction

2013 ◽  
Author(s):  
Xiaoyun Zhang ◽  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Traffic Accident ◽  
Joint Probability ◽  
Accident Reconstruction ◽  
Direct Simulation ◽  
Inverse Simulation ◽  
Vehicle Accident ◽  
Simulation Output ◽  
Simulation Input ◽  
Input Variables ◽  
Joint Probability Density

Inverse simulation is an inverse process of direct simulation. It determines unknown input variables of the direct simulation for a given set of simulation output variables. Uncertainties usually exist, making it difficult to solve inverse simulation problems. The objective of this research is to account for uncertainties in inverse simulation in order to produce high confidence in simulation results. The major approach is the use of the maximum likelihood methodology, which determines not only unknown deterministic input variables but also the realizations of random input variables. Both types of variables are solved on the condition that the joint probability density of all the random variables is maximum. The proposed methodology is applied to a traffic accident reconstruction problem where the simulation output (accident consequences) is known and the simulation input (velocities of the vehicle at the beginning of crash) is sought.

Probabilistic Inverse Simulation and Its Application in Vehicle Accident Reconstruction

2013 ◽  
Vol 135 (12) ◽  
Author(s):  
Xiaoyun Zhang ◽  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Probability Density ◽  
Joint Probability ◽  
Accident Reconstruction ◽  
Direct Simulation ◽  
Maximum Probability ◽  
Inverse Simulation ◽  
Vehicle Accident ◽  
Simulation Output ◽  
Simulation Input ◽  
Input Variables

Inverse simulation is an inverse process of direct simulation. It determines unknown input variables of the direct simulation for a given set of simulation output variables. Uncertainties usually exist, making it difficult to solve inverse simulation problems. The objective of this research is to account for uncertainties in inverse simulation in order to produce high confidence in simulation results. The major approach is the use of the maximum probability density function (PDF), which determines not only unknown deterministic input variables but also the realizations of random input variables. Both types of variables are solved on the condition that the joint probability density of all the random variables is maximum. The proposed methodology is applied to a traffic accident reconstruction problem where the simulation output (accident consequences) is known and the simulation input (velocities of the vehicle at the beginning of crash) is sought.

Study of the Random Vibration of Nonlinear Systems by the Gaussian Closure Technique

1978 ◽  
Vol 45 (2) ◽  
pp. 393-399 ◽  
Author(s):  
R. N. Iyengar ◽  
P. K. Dash
Keyword(s):  
Nonlinear Systems ◽  
Random Vibration ◽  
Joint Probability ◽  
Statistical Linearization ◽  
Closure Technique ◽  
Input Variables ◽  
Non Gaussian ◽  
Joint Probability Density ◽  
Gaussian Closure ◽  
Good Agreement

A technique is developed to study random vibration of nonlinear systems. The method is based on the assumption that the joint probability density function of the response variables and input variables is Gaussian. It is shown that this method is more general than the statistical linearization technique in that it can handle non-Gaussian excitations and amplitude-limited responses. As an example a bilinear hysteretic system under white noise excitation is analyzed. The prediction of various response statistics by this technique is in good agreement with other available results.

A Reliability Approach to Inverse Simulation Under Uncertainty

2012 ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
High Efficiency ◽  
Probability Distributions ◽  
Random Parameters ◽  
Inverse Simulation ◽  
First Order ◽  
Reliability Method ◽  
Simulation Input ◽  
Input Variables ◽  
Impact Problem

Inverse simulation is an inverse process of a direct simulation. During the process, the simulation input variables are identified for a given set of simulation output variables. Uncertainties such as random parameters may exist in engineering applications of inverse simulation. A reliability method is developed in this work to estimate the probability distributions of unknown simulation input. The First Order Reliability Method is employed and modified so that the inverse simulation is embedded within the reliability analysis algorithm. This treatment avoids the separate executions of reliability analysis and inverse simulation and consequently maintains high efficiency. In addition, the means and standard deviations of unknown input variables can also be obtained. A particle impact problem is presented to demonstrate the proposed method for inverse simulation under uncertainty.

Inverse Simulation Under Uncertainty by Optimization

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
High Efficiency ◽  
Probability Distributions ◽  
Optimization Method ◽  
Unknown Input ◽  
Random Parameters ◽  
Inverse Simulation ◽  
Reliability Method ◽  
Simulation Input ◽  
Input Variables

Inverse simulation is an inverse process of a direct simulation. During the process, unknown simulation input variables are identified for a given set of known simulation output variables. Uncertainties such as random parameters may exist in engineering applications of inverse simulation. An optimization method is developed in this work to estimate the probability distributions of unknown input variables. The first order reliability method is employed and modified so that the inverse simulation is embedded within the reliability analysis. This treatment avoids the separate executions of reliability analysis and inverse simulation and consequently maintains high efficiency. In addition, the means and standard deviations of the unknown input variables can also be obtained. A particle impact problem is presented to demonstrate the proposed method for inverse simulation under uncertainty.

scholarly journals Metamodels of a gas turbine powered marine propulsion system for simulation and diagnostic purposes

2015 ◽  
Vol 12 (1) ◽  
pp. 1-14 ◽  
Author(s):  
U. Campora ◽  
M. Capelli ◽  
C. Cravero ◽  
R. Zaccone
Keyword(s):  
Gas Turbine ◽  
Response Surfaces ◽  
Propulsion System ◽  
Direct Simulation ◽  
Analysis Method ◽  
Simulation Code ◽  
Marine Propulsion ◽  
Performance Reduction ◽  
Input Variables ◽  
Marine Propulsion System

The paper presents the application of artificial neural network for simulation and diagnostic purposes applied to a gas turbine powered marine propulsion plant. A simulation code for the propulsion system, developed by the authors, has been extended to take into account components degradation or malfunctioning with the addition of performance reduction coefficients. The above coefficients become input variables to the analysis method and define the system status at a given operating point. The simulator is used to generate databases needed to perform a variable selection analysis and to tune response surfaces for both direct (simulation) and inverse (diagnostic) purposes. The application of the methodology to the propulsion system of an existing frigate version demonstrate the potential of the approach.

scholarly journals Determination of Selected Crash Parameters in Head-on Vehicle Collision with Rollover

2016 ◽  
Vol 28 (1) ◽  
pp. 71-79
Author(s):  
Tomáš Coufal ◽  
Marek Semela
Keyword(s):  
Traffic Accident ◽  
Crash Test ◽  
Accident Reconstruction ◽  
Vehicle Collision ◽  
Reconstruction Software ◽  
Vehicle Rollover ◽  
Head And Neck Injury ◽  
Small Overlap ◽  
Crush Zone

The paper presents complete results of the head-on small overlap crash test of vehicle with driver moving at a speed of approximately 12 m/s against stationary vehicle with post-crash rollover. When a crash does not involve the main crush-zone structures, the occupant compartment is not well protected. The emphasis in the paper was put on determination and presentation of crash parameters for the application in traffic accident analyses and for simulation with the help of software for accident reconstruction. The experimentally measured data from the crash test were analysed and important crash parameters which are necessary for accident reconstruction were obtained. The crash test was specific because of rollover of the impacting vehicle resulting from small overlap. The results have shown that small overlap accident is extremely dangerous for the crew with the possibility of vehicle rollover and occupant head and neck injury. Also in this case, at relative low speed, the driver suffered light neck and head injury in the following days and the longitudinal damage was relatively large. The input parameters for accident reconstruction software as the result of performed crash test were gained.

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