Modelling, verification and validation

Text Box: Modelling, verification, validation and quality management Short course Introduction A model yielding erroneous results may engender serious consequences, not least in terms of cost, safety and legal liability. This course delivers best-practice techniques for minimising the opportunity for error in a generic engineering simulation project. The course begins by carefully reviewing the modelling, simulation and analysis process and identifying the sources of error potentially arising at each stage. The simulation process invariably commences by describing the problem to be analysed, followed by an idealisation of the problem's behavioural characteristics. The idealisation assumptions made at this stage will directly impact on the validity of the model and need to be clearly identified. Moreover, the model data needs to be of sufficient accuracy to support the required solution accuracy. Techniques to estimate the required accuracy of models and data will be discussed. Computational modelling is the next and most obvious step in the simulation process, whereby the idealised model is numerically approximated in analysis software. Numerical error and solution accuracy are discussed along with best-practice techniques for minimising solution error. Model verification is discussed in the context of determining whether a computational model accurately represents the underlying mathematical model and its solution. Model validation is discussed in the context of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. The course proceeds to introduce and explain the basic requirements of Quality Management System ISO 9001 and NAFEMS QSS 001 (a supplement to ISO 9001 for engineering simulation), in the context of computational modelling. This section of the material focuses on gaining an understanding of the concepts of the ISO 9001 and QSS 001 process-based quality models and their practical application to managing an engineering simulation project. The development of engineering simulation procedures suitable for implementation into a quality management system will be discussed and examples provided. Formal quality certification requirements will be briefly discussed along with issues relating to the effect of an implemented quality system on simulation product quality, and on the working practices and culture of the organisation. Course content Introduction Modelling, verification, validation and quality management The simulation process Behavioural model Analytical model Computational model Solution Verification Validation Behavioural modelling Problem description Accuracy requirements Behavioural abstraction Analytical modelling Idealisation error Data error Sensitivity studies and estimation Computational modelling Approximation error Computation error Error estimation and treatment Solution verification Verification requirements Approaches to solution verification Solution verification procedures Model validation Validation requirements Approaches to model validation Model validation procedures Quality system ISO 9001 and NAFEMS QSS ISO 9000 family of standards Quality management principles Process based quality management NAFEMS QSS 001 Quality requirements Quality management system Management responsibility Resource management Product realisation Measurement, analysis and improvement Quality management of simulation projects Simulation project management Human and computer resources Simulation procedures Conclusions Simulation management in practice Design, implementation and maintenance of the management system Certification and audit of the management system

Presenter: Jonathan Smith

Duration: One day

Synopsis

Computational models need to be fit for their intended use.

This course identifies the common sources of error, uncertainty and mistakes that inevitably arise in the computational modelling process, and discusses best practice techniques for modelling, verification and validation in the context of systematic simulation management .