Simulation Based Probabilistic Risk Assessment (SIMPRA): Risk Based Design

TL;DR

This paper introduces SIMPRA, a risk-based design framework that uses simulation and probabilistic assessment to identify and mitigate risks in complex, sensitive systems, improving safety and reducing over-design.

Contribution

It proposes a novel simulation-based probabilistic risk assessment methodology with an efficient planner module for risk-informed system design.

Findings

SIMPRA effectively identifies risk scenarios in complex systems.

The approach reduces the need for biased Monte Carlo simulations.

Application to satellite system demonstrates practical utility.

Abstract

The classical approach to design a system is based on a deterministic perspective where the assumption is that the system and its environment are fully predictable, and their behaviour is completely known to the designer. Although this approach may work fairly well for regular design problems, it is not satisfactory for the design of highly sensitive and complex systems where significant resources and even lives are at risk. In addition it can results in extra costs of over-designing for the sake of safety and reliability. In this paper, a risk-based design framework using Simulation Based Probabilistic Risk Assessment (SIMPRA) methodology is proposed. SIMPRA allows the designer to use the knowledge that can be expected to exist at the design stage to identify how deviations can occur; and then apply these high-level scenarios to a rich simulation model of the system to generate detailed scenarios and identify the probability and consequences of these scenarios. SIMPRA has three main modules including Simulator, Planner and Scheduler, and it approach is much more efficient in covering the large space of possible scenarios as compared with, for example, biased Monte Carlo simulations because of the Planner module which uses engineering knowledge to guide the simulation process. The value-added of this approach is that it enables the designer to observe system behaviour under many different conditions. This process will lead to a risk-informed design in which the risk of negative consequences is either eliminated entirely or reduced to an acceptable range. For illustrative purposes, an earth observation satellite system example is introduced.