Advances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study.

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Verification and Validation in Scientific Computing

Laboratory-based courses play a critical role in scientific education. Automation is changing the nature of these laboratories, and there is a long-running debate about the value of hands-on versus simulated laboratories. In addition, the introduction of remote laboratories adds a third category to the debate. Through a review of the literature related to these labs in education, the authors draw several conclusions about the state of current research. The debate over different technologies is confounded by the use of different educational objectives as criteria for judging the laboratories: Hands-on advocates emphasize design skills, while remote lab advocates focus on conceptual understanding. We observe that the boundaries among the three labs are blurred in the sense that most laboratories are mediated by computers, and that the psychology of presence may be as important as technology. We also discuss areas for future research.

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Hands-on, simulated, and remote laboratories: A comparative literature review

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

Effect of Al2O3–Cu/water hybrid nanofluid in heat transfer

Laboratory experimentation plays an essential role in engineering and scientific education. Virtual and remote labs reduce the costs associated with conventional hands-on labs due to their required equipment, space, and maintenance staff. Furthermore, they provide additional benefits such as supporting distance learning, improving lab accessibility to handicapped people, and increasing safety for dangerous experimentation. This paper analyzes the literature on virtual and remote labs from its beginnings to 2015, identifying the most influential publications, the most researched topics, and how the interest in those topics has evolved along the way. To do so, bibliographical data gathered from ISI Web of Science, Scopus and GRC2014 have been examined using two prominent bibliometric approaches: science mapping and performance analysis. Display Omitted Laboratory experimentation plays an essential role in engineering and sci-entific education.Virtual and remote labs are emerging as a valuable alternative to conven-tional hands-on labs.This paper analyzes the literature on virtual and remote labs from 1993 to 2015.4405 records retrieved from ISI Web of Science, Scopus and GRC2014 are processed.Two bibliometric approaches are applied: performance analysis and science mapping.

Virtual and remote labs in education

The following fundamental concepts of measurement science have been briefly reported in this tutorial article: 1) The result of a measurement provides only incomplete knowledge of the measurand, whose true value remains unknown and unknowable; 2) The uncertainty concept plays a key role in quantifying the incompleteness of the knowledge provided by the measurement result; and 3) A measurement result can be usefully employed only if the associated uncertainty is estimated and if it can be traced back to the appertaining standard; otherwise, it is a meaningless value. This tutorial has given a short review of the present practice for expressing and estimating uncertainty in measurement, based on the definitions and prescriptions given by the GUM.

Measurement uncertainty

The problem of evaluating power-quality is becoming an impelling need due to the proliferation of disturbing loads in the electric power systems. Unfortunately, the identification of the sources disturbing the system cannot generally be achieved by means of measurements in a single metering section of the electric network. This paper proposes a distributed measuring system to monitor a number of power-quality indices on every load connected to the same point of common coupling, to transmit the measured values to a master device that processes them in order to locate the sources of unbalance and harmonic distortion and to quantify the effects of such disturbances on power quality. The results of some experimental work performed on a low-voltage network are also reported.

A distributed system for electric power quality measurement

The good measurement practice requires that the measurement uncertainty is estimated and provided together with the measurement result. The practice today, which is reflected in the reference standard provided by the IEC-ISO "Guide to the expression of uncertainty in measurement," adopts a statistical approach for the expression and estimation of the uncertainty, since the probability theory is the most known and used mathematical tool to deal with distributions of values. However, the probability theory is not the only tool to deal with distributions of values and is not the most suitable one when the values do not distribute in a totally random way. In this case, a more general theory, the theory of the evidence, should be considered. This paper recalls the fundamentals of the theory of the evidence and frames the random-fuzzy variables within this theory, showing how they can usefully be employed to represent the result of a measurement together with its associated uncertainty. The mathematics is defined on the random-fuzzy variables, so that the uncertainty can be processed, and simple examples are given.

The random-fuzzy variables: a new approach to the expression of uncertainty in measurement

Uncertainty in Measurement.- Fuzzy Variables and Measurement Uncertainty.- The Theory of Evidence.- Random-Fuzzy Variables.- Construction of Random-Fuzzy Variables.- Fuzzy Operators.- The Mathematics of Random-Fuzzy Variables.- Representation of Random-Fuzzy Variables.- Decision-Making Rules with Random-Fuzzy Variables.- List of Symbols.

Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence

The availability of didactic experimental laboratories is of utmost importance for technical schools, mainly the engineering faculties. Experimental laboratories, however, represent a great investment, both in term of required money and space, that may become unbearable to the schools, especially when the number of students and the number of subjects that require lab classes increase. Remote laboratories are a very cost-effective solution to this problem, provided that they allow the students to access real instruments from any site connected to Internet without any need for buying commercial software. This paper shows how this goal has been achieved by implementing a Java-based client-server architecture that is presently used as a subsidiary laboratory for the measurement subjects in the Electrical Engineering curriculum at the Politecnico di Milano.

Simona Salicone

Papers

Measurement uncertainty

A distributed system for electric power quality measurement

The random-fuzzy variables: a new approach to the expression of uncertainty in measurement

Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence

ReMLab: a Java-based remote, didactic measurement laboratory