Modelling, Risk and Design for Reliability

About us


We conduct world-leading research in mathematical modelling in engineering. Members of the group have strong experience in:

  • advanced numerical analysis
  • probability and statistics
  • data analysis
  • modelling based on algebraic inequalities
  • computer programming
  • Monte Carlo simulation
  • operations research
  • design for reliability.

Abstract image - signal representing time series

Leadership

Dr Armando Coco

Senior Lecturer in Mathematical Modelling

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Professor Michael Todinov

Professor in Mechanical Engineering

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Dr Hooshang Izadi

Reader

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Research areas

  • Advanced numerical methods for solving differential and integral equations
  • Advanced statistical modelling involving medical data and engineering data
  • Generating new knowledge and optimising systems/processes by algebraic inequalities
  • Modelling repairable flow networks and stochastic networks
  • Methods for improving reliability and reducing risk by using domain-independent methods 


3 dimension graph of a two-variable mathematical function

Published research monographs

Risk and Uncertainty Reduction by Using Algebraic Inequalities

Covers the application of algebraic inequalities for reliability improvement and for risk and uncertainty reduction. Algebraic inequalities present a new generation, highly effective tool for dealing with deep uncertainty related to key parameters of systems and processes. They permit meaningful interpretation and inference of new knowledge and unknown properties related to real physical systems and processes. The method of algebraic inequalities transcends engineering and can be used in diverse domains of human activity.

Cover of the book "Risk and Uncertainty Reduction by Using Algebraic Inequalities"

Flow Networks

Develops the theory, algorithms and applications related to repairable flow networks and networks with disturbed flows. The results presented in the book lay the foundations of a new generation of ultra-fast algorithms for optimising the flow in networks after failures or congestion, and the high computational speed creates the possibility of optimal control of very large and complex networks in real time. 

Furthermore, the possibility for re-optimising the network flows in real time increases significantly the yield from real production networks and reduces to a minimum the flow disruption caused by failures.The potential application of repairable flow networks reaches across many large and complex systems, including active power networks, telecommunication networks, oil and gas production networks, transportation networks, water supply networks, emergency evacuation networks, and supply networks.

Cover of the book "Flow Networks"

Methods for Reliability Improvement and Risk Reduction

Introduces a new generation, domain-independent reliability improvement and risk reducing methods applicable in any area of human activity. Among these are the methods of separation, segmentation, self-reinforcement, inversion, permutation, substitution and comparative methods based on algebraic inequalities.

The domain-independent methods for reliability improvement and risk reduction do not rely on past failure data or knowledge of the failure mechanisms underlying the failure modes. Using the proven methods in the book, any company can greatly enhance the reliability of its products and operations.

Cover of the book "Methods for Reliability Improvement and Risk Reduction"

Images Credits:

  • Banner image Courtesy of the Federal Highway Administration