This SAE standard outlines the steps and known accepted methodologies and standards for linking Model V&V with model based product reliability assessments. The standard's main emphasis is that quantified values for Model-based product reliability must be accompanied by a quantified confidence value if the users of the model wish to claim use of a "Verified and Validated" model, and if they wish to further link into business and investment decisions that are informed by quantitative second-order risk and benefit cost considerations. SAE has numerous standards relating to the use of models6567,70,71, and product reliability6069. Other professional organizations (AIAA1, ASME5,6, DoD50, NASA58, etc) have recent standards for Model Verification & Validation (V&V). Lacking, however, is a standard relating the increasing use of numerical and computer Model V&V to quantitative design assessments of product reliability. It is the intent of SAE J2940 to provide such a standard.
Created to elevate expertise in testing, verification, and validation with industry-specific terminology, readers are empowered to navigate the complex world of quality assurance. From foundational concepts to advanced principles, each entry provides clarity and depth, ensuring the reader becomes well-versed in the language of precision. This dictionary is an indispensable companion for both professionals and students seeking to unravel the nuances of testing methodologies, verification techniques, and validation processes. Readers will be equipped with the tools to communicate effectively, make informed decisions, and excel in projects. In addition, references to SAE Standards are included to direct the read to additional information beyond a practical definition. (ISBN 9781468605907, ISBN 9781468605914, ISBN 9781468605921, DOI 10.4271/9781468605914)
The concepts of Verification and Validation (V & V) can be oversimplified in a succinct manner by saying that 'verification is doing things right' and 'validation is doing the right thing'. In the world of the Finite Element Method (FEM) and computational analysis, it is sometimes said that 'verification means solving the equations right' and 'validation means solving the right equations'. In other words, if one intends to give an answer to the equation '2+2=', then one must run the resulting code to assure that the answer '4' results. However, if the nature of the physics or engineering problem being addressed with this code is multiplicative rather than additive, then even though Verification may succeed (2+2=4 etc), Validation may fail because the equations coded are not those needed to address the real world (multiplicative) problem. We have previously provided a 4-step 'ABCD' quantitative implementation for a quantitative V & V process: (A) Plan the analyses and validation testing that may be needed along the way. Assure that the code[s] chosen have sufficient documentation of software quality and Code Verification (i.e., does 2+2=4?). Perform some calibration analyses and calibration based sensitivity studies (these are not validated sensitivities but are useful for planning purposes). Outline the data and validation analyses that will be needed to turn the calibrated model (and calibrated sensitivities) into validated quantities. (B) Solution Verification: For the system or component being modeled, quantify the uncertainty and error estimates due to spatial, temporal, and iterative discretization during solution. (C) Validation over the data domain: Perform a quantitative validation to provide confidence-bounded uncertainties on the quantity of interest over the domain of available data. (D) Predictive Adequacy: Extend the model validation process of 'C' out to the application domain of interest, which may be outside the domain of available data in one or more planes of multi-dimensional space. Part 'D' should provide the numerical information about the model and its predictive capability such that given a requirement, an adequacy assessment can be made to determine of more validation analyses or data are needed.
This third volume of eight from the IMAC - XXXII Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Linear Systems Substructure Modelling Adaptive Structures Experimental Techniques Analytical Methods Damage Detection Damping of Materials & Members Modal Parameter Identification Modal Testing Methods System Identification Active Control Modal Parameter Estimation Processing Modal Data
Model verification and validation (V & V) is an enabling methodology for the development of computational models that can be used to make engineering predictions with quantified confidence. Model V & V procedures are needed by government and industry to reduce the time, cost, and risk associated with full-scale testing of products, materials, and weapon systems. Quantifying the confidence and predictive accuracy of model calculations provides the decision-maker with the information necessary for making high-consequence decisions. The development of guidelines and procedures for conducting a model V & V program are currently being defined by a broad spectrum of researchers. This report reviews the concepts involved in such a program. Model V & V is a current topic of great interest to both government and industry. In response to a ban on the production of new strategic weapons and nuclear testing, the Department of Energy (DOE) initiated the Science-Based Stockpile Stewardship Program (SSP). An objective of the SSP is to maintain a high level of confidence in the safety, reliability, and performance of the existing nuclear weapons stockpile in the absence of nuclear testing. This objective has challenged the national laboratories to develop high-confidence tools and methods that can be used to provide credible models needed for stockpile certification via numerical simulation. There has been a significant increase in activity recently to define V & V methods and procedures. The U.S. Department of Defense (DoD) Modeling and Simulation Office (DMSO) is working to develop fundamental concepts and terminology for V & V applied to high-level systems such as ballistic missile defense and battle management simulations. The American Society of Mechanical Engineers (ASME) has recently formed a Standards Committee for the development of V & V procedures for computational solid mechanics models. The Defense Nuclear Facilities Safety Board (DNFSB) has been a proponent of model V & V for all safety-related nuclear facility design, analyses, and operations. In fact, DNFSB 2002-1 recommends to the DOE and National Nuclear Security Administration (NNSA) that a V & V process be performed for all safety related software and analysis. Model verification and validation are the primary processes for quantifying and building credibility in numerical models. Verification is the process of determining that a model implementation accurately represents the developer's conceptual description of the model and its solution. Validation is the process 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. Both verification and validation are processes that accumulate evidence of a model's correctness or accuracy for a specific scenario; thus, V & V cannot prove that a model is correct and accurate for all possible scenarios, but, rather, it can provide evidence that the model is sufficiently accurate for its intended use. Model V & V is fundamentally different from software V & V. Code developers developing computer programs perform software V & V to ensure code correctness, reliability, and robustness. In model V & V, the end product is a predictive model based on fundamental physics of the problem being solved. In all applications of practical interest, the calculations involved in obtaining solutions with the model require a computer code, e.g., finite element or finite difference analysis. Therefore, engineers seeking to develop credible predictive models critically need model V & V guidelines and procedures. The expected outcome of the model V & V process is the quantified level of agreement between experimental data and model prediction, as well as the predictive accuracy of the model. This report attempts to describe the general philosophy, definitions, concepts, and processes for conducting a successful V & V program. This objective is motivated by the need for highly accurate numerical models for making predictions to support the SSP, and also by the lack of guidelines, standards and procedures for performing V & V for complex numerical models.
Helps you ensure that your simulations are appropriate representations of real-world systems. The book concentrates on the differentiation between the assessment of a simulation tool and the verification and validation of general software products. It is a systematic, procedural, practical guide that you can use to enhance the credibility of your simulation models. In addition, it is a valuable reference book and a road map for software developers and quality assurance experts, or as a text for simulation methodology and software engineering courses. This book details useful assessment procedures and phases, discusses ways to tailor the methodology for specific situations and objectives, and provides numerous assessment aids. The reader can use these aids to support ongoing assessments over the entire life cycle of the model.
The developed confidence-based model validation will provide a conservative RBDO optimum design at the target confidence level. However, it is challenging to obtain steady convergence in the RBDO process with confidence-based model validation because the feasible domain changes as the design moves (i.e., a moving-target problem). To resolve this issue, a practical optimization procedure, which terminates the RBDO process once the target reliability is satisfied, is proposed. In addition, the efficiency is achieved by carrying out deterministic design optimization (DDO) and RBDO without model validation, followed by RBDO with the confidence-based model validation. Numerical examples are presented to demonstrate that the proposed RBDO approach obtains a conservative and practical optimum design that satisfies the target reliability of designed product given a limited number of experimental output data. Thus far, while the simulation model might be biased, it is assumed that we have correct distribution models for input variables and parameters. However, in real practical applications, only limited numbers of test data are available (parameter uncertainty) for modeling input distributions of material properties, manufacturing tolerances, operational loads, etc. Also, as before, only a limited number of output test data is used. Therefore, a reliability needs to be estimated by considering parameter uncertainty as well as biased simulation model. Computational methods and a process are developed to obtain confidence-based reliability assessment. The insufficient input and output test data induce uncertainties in input distribution models and output distributions, respectively. These uncertainties, which arise from lack of knowledge - the insufficient test data, are different from the inherent input distributions and corresponding output variabilities, which are natural randomness of the physical system.
Striking a balance between the use of computer-aided engineering practices and classical life testing, this reference expounds on current theory and methods for designing reliability tests and analyzing resultant data through various examples using Microsoft® Excel, MINITAB, WinSMITH, and ReliaSoft software across multiple industries. The book disc
Are you buying a car or smartphone or dishwasher? We bet long-term, trouble-free operation (i.e., high reliability) is among the top three things you look for. Reliability problems can lead to everything from minor inconveniences to human disasters. Ensuring high reliability in designing and building manufactured products is principally an engineering challenge–but statistics plays a key role. Achieving Product Reliability explains in a non-technical manner how statistics is used in modern product reliability assurance. Features: Describes applications of statistics in reliability assurance in design, development, validation, manufacturing, and field tracking. Uses real-life examples to illustrate key statistical concepts such as the Weibull and lognormal distributions, hazard rate, and censored data. Demonstrates the use of graphical tools in such areas as accelerated testing, degradation data modeling, and repairable systems data analysis. Presents opportunities for profitably applying statistics in the era of Big Data and Industrial Internet of Things (IIoT) utilizing, for example, the instantaneous transmission of large quantities of field data. Whether you are an intellectually curious citizen, student, manager, budding reliability professional, or academician seeking practical applications, Achieving Product Reliability is a great starting point for a big-picture view of statistics in reliability assurance. The authors are world-renowned experts on this topic with extensive experience as company-wide statistical resources for a global conglomerate, consultants to business and government, and researchers of statistical methods for reliability applications.
