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Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models

Bernard Munyazikwiye at the Faculty of Engineering and Science has submitted his thesis entitled “Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models”, and will defend the thesis for the PhD-degree Tuesday 31 March 2020. (Photo: Private)

The models were finally used to reconstruct the real crash signal. The crash analysis simulation can be used to assess the crashworthiness and investigate alternative ways to improve car design. The simulation can also be used to assess the injury of an occupant during a crash event.

Bernard Munyazikwiye

PhD Candidate and lecturer

The disputation will be held digitally, because of the Corona covid-19-situation. Spectators may follow the disputation digitally – link and thesis is available below.

Bernard Munyazikwiye at the Faculty of Engineering and Science has submitted his thesis entitled “Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Modelsand will defend the thesis for the PhD-degree Friday 29 May 2020.

He has followed the PhD-programme at the Faculty of Engineering and Science with Spesialisation in Mechatronics.

Summary of the thesis by Bernard Munyazikwiye:

Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models

It is a difficult task to understand fully the dynamic of a car crash.

Finding the crash pulse

To determine the crash pulse or the acceleration signal during an accident, crash reconstruction experts perform a reverse engineering process by starting from the final scene of the crash and work backward to identify the parameters of the car structure, a process known as system identification. This process is often very complex.

Hence, solving such a complex problem is crucial and requires precise data acquisition and the use of the right tools.

Crash reconstruction

In most frontal crashes, the occupant is the victim of the scenario then it was worth investigating the response of the occupant.

A vehicle to vehicle crash scenario was also investigated.

The accurate crash reconstruction was achieved with the help of a nature-inspired algorithm, which is a genetic algorithm and the frontal structure of the car involved in the crash was modeled as a piecewise function of displacement and velocity, respectively.

Reduces need for physical crash tests

The project is of interest to the community including end-users, car manufacturers, and car designers since the crash event can be predicted before performing a physical crash test.

This will reduce the cost and will allow designers to predict the behavior of the car when subjected to impact.

A full-scale crash test is conventionally used for vehicle crashworthiness analysis.

However, this approach is expensive and time-consuming. Vehicle crash reconstructions using different numerical modelling approaches can predict vehicle behavior and reduce the need for multiple full-scale crash tests, thus research on crash reconstruction has received great attention in the last few decades.

Math models for crash analysis

In this work, the author has developed simple tools (mathematical models) that could reconstruct the crash events (vehicle-to-barrier, vehicle-occupant, and vehicle-to-vehicle crashes) with sufficient accuracy.

The frontal crash is investigated because it is the most cause of injury and death as compared to other types of crash events. The vehicle frontal structure is the most affected zone of the car during a crash scenario.

In this work, the frontal structure of the car is estimated as spring-dampers connected to a mass or in the context of this work, these combinations are called Lumped parameters Models.

The models were finally used to reconstruct the real crash signal. The crash analysis simulation can be used to assess the crashworthiness and investigate alternative ways to improve car design.

The simulation can also be used to assess the injury of an occupant during a crash event. Within this framework, the advantages of the proposed methods are explained in detail, and suggested solutions are presented to address the challenges in the study.

Disputation facts:

The Candidate: Bernard Munyazikwiye (Masisi, Republic Democratic of Congo (1971), nationality: Rwanda) Electromechanical Engineering (BSc), Kigali Institute of Science and Technology, Rwanda (2004), Mechanical Engineering (MSc), Jomo Kenyatta University of Agriculture and Technology, Kenya (2010) From December  2004 to Present: Tutorial Assistant, Assistant Lecturer, Lecturer at the University of Rwanda.

The trial lecture and the public defence will take place at internet, via the Zoom conferencing app (link below) Friday 29 May 2020.

Professor Tom Viggo Nilsen,  Department of Engineering sciences, UiA, will chair the disputation.

Trial lecture at 10:15 a.m.

Public defense at 12:15 p.m.

Given topic for trial lecture“Artificial Intelligence and Machine Learning as a tool for improving Vehicle Safety and Crashworthiness”

Thesis Title: “Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models

Search for the thesis in AURA - Agder University Research Archive, a digital archive of scientific papers, theses and dissertations from the academic staff and students at the University of Agder.

Opponents:

First opponent: Professor Ole Balling, Aarhus Universitet, Danmark

Second opponent: Associate Professor Bjørn Haugen, NTNU Trondheim, Norway

Associate Professor Rune Strandberg, Department of Engineering sciences, UiA, is appointed as the administrator for the assessment commitee.

Supervisors were Professor Kjell Gunnar Robbersmyr, UiA (main supervisor), Professor Hamid Reza Karimi now at the Politecnico di Milano, Italy and Associate Professor Dmitry Vysoschinskiy, UiA (co-supervisors)

What to do as an audience member:

The disputation is open to the public, but to follow the trial lecture and the public defence you have to click in as an audience member on this ink: https://uia.pameldingssystem.no/disputas-bernard-munuyazikwiye  A Zoom-link will be returned to you.

We ask audience members to join the virtual trial lecture at 10:05 at the earliest and the public defense at 12:05 at the earliest. After these times, you can leave and rejoin the meeting at any time. Further, we ask audience members to turn off their microphone and camera and keep them turned off throughout the event. You do this at the bottom left of the image when in Zoom. We recommend you use ‘Speaker view’. You select that at the top right corner of the video window when in Zoom.

Opponent ex auditorio:

The chair invites members of the public to pose questions ex auditorio in the introduction to the public defense, with deadlines. Questions can be submitted to the chair Tom Viggo Nilsen at e-mail tom.v.nilsen@uia.no.

The thesis is available here:

Link to the PhD defense which is transmitted via the Zoom conferencing app:

The disputation is open to the public, but to follow the trial lecture and the public defence you have to click in as an audience member on this ink: https://uia.pameldingssystem.no/disputas-bernard-munuyazikwiye  A Zoom-link will be returned to you.

Here are introductions for how to use Zoom: support.zoom.us if you cannot join by clicking on the link.