The uncertainty of any measurement is the interval in which one believes the actual error lies. Particle Image Velocimetry (PIV) measurement error depends on the PIV algorithm used, a wide range of user inputs, flow characteristics, and the experimental setup. Since these factors vary in time and space, they lead to nonuniform error throughout the flow field. As such, a universal PIV uncertainty estimate is not adequate and can be misleading. This is of particular interest when PIV data are used for comparison with computational or experimental data. A method to estimate the uncertainty due to the PIV calculation of each individual velocity measurement is presented. The relationship between four error sources and their contribution to PIV error is first determined. The sources, or parameters, considered are particle image diameter, particle density, particle displacement, and velocity gradient, although this choice in parameters is arbitrary and may not be complete. This information provides a four-dimensional "uncertainty surface" for the PIV algorithm used. After PIV processing, our code "measures" the value of each of these parameters and estimates the velocity uncertainty for each vector in the flow field. The reliability of the methodology is validated using known flow fields so the actual error can be determined. Analysis shows that, for most flows, the uncertainty distribution obtained using this method fits the confidence interval. The method is general and can be adapted to any PIV analysis.

Phenomenological material models such as Johnson-Cook plasticity are often used in finite element simulations of large deformation processes at different strain rates and temperatures. Since the material constants that appear in such models depend on the material, experimental data, fitting method, as well as the mathematical representation of strain rate and temperature effects, the predicted material behavior is subject to uncertainty. In this dissertation, evidence theory is used for modeling uncertainty in the material constants, which is represented by separate belief structures that are combined into a joint belief structure and propagated using impact loading simulation of structures. Yager’s rule is used for combining evidence obtained from more than one source. Uncertainty is quantified using belief, plausibility, and plausibility-decision functions. An evidence-based design optimization (EBDO) approach is presented where the nondeterministic response functions are expressed using evidential reasoning. The EBDO approach accommodates field material uncertainty in addition to the embedded uncertainty in the material constants. This approach is applied to EBDO of an externally stiffened circular tube under axial impact load with and without consideration of material field uncertainty caused by spatial variation of material uncertainties due to manufacturing effects. Surrogate models are developed for approximation of structural response functions and uncertainty propagation. The EBDO example problem is solved using genetic algorithms. The uncertainty modeling and EBDO results are presented and discussed.

Uncertainty is prevalent throughout the design, analysis, and optimization of aerospace products. When scientific computing is used to support these tasks, sources of uncertainty may include the freestream flight conditions of a vehicle, physical modeling parameters, geometric fidelity, numerical error, and model-form uncertainty, among others. Moreover, while some uncertainties may be treated as probabilistic, aleatory sources, other uncertainties are non-probabilistic and epistemic due to a lack of knowledge, and cannot be rigorously treated using classical statistics or Bayesian approaches. An additional complication for propagating uncertainty is that many aerospace scientific computing tools may be computationally expensive; for example, a single high-fidelity computational fluid dynamics solution may require several days or even weeks to complete. It is therefore necessary to employ uncertainty propagation strategies that require as few solutions as possible. The Non-Intrusive Polynomial Chaos (NIPC) method has grown in popularity in recent decades due to its ability to propagate both aleatory and epistemic parametric sources of uncertainty in a computationally efficient manner. While traditional Monte Carlo methods might require thousands to millions of function evaluations to achieve statistical convergence, NIPC typically requires tens to hundreds for problems with similar numbers of uncertain dimensions. Despite this efficiency, NIPC is limited in one important aspect: it can only propagate uncertainty at a particular point in a design space or flight envelope. For optimization or aerodynamic database problems that require uncertainty estimates at many more than one point, the use of NIPC quickly becomes computationally intractable. This dissertation introduces a new method entitled Spatially Accurate Polynomial Chaos (SAPC) that extends the original NIPC approach for the spatial regression of aleatory and epistemic parametric sources of uncertainty. Throughout the dissertation, the SAPC method is applied to various aerospace problems of interest. These include the regression of aerodynamic force and moment uncertainties throughout the flight envelope of a commercial aircraft, the design under uncertainty of a two-stream propulsive mixer device, and the robust design of a low-boom supersonic demonstrator aircraft. Collectively the results suggest that SAPC may be useful for a large variety of engineering applications. / Doctor of Philosophy / Uncertainty is prevalent throughout the design, analysis, and optimization of aerospace products. When scientific computer simulations are used to support these tasks, sources of uncertainty may include the speed of an aerospace vehicle, the direction of the wind, physical modeling constants or assumptions, and the vehicle shape, among others. As a result of these sources uncertainty, assessments of vehicle performance are also uncertain. For example, if the speed of a vehicle is not known precisely, then computer simulations will predict a lift force which is also imprecisely known. A challenge when assessing the uncertainty in aerospace vehicle performance is that the computer simulations which predict performance may take a long time to run, even on state-of-the-art super computers. Traditional statistical methods may require thousands or millions of simulations for the prediction of uncertainty, which does not fit within the computational budget of most aerospace analyses. A newer method called Non-Intrusive Polynomial Chaos (NIPC) is more efficient, typically requiring only tens to hundreds of simulations; however, NIPC only provides uncertainty estimates at a single point in an aircraft flight envelope or design condition. In this dissertation, a new method called Spatially Accurate Polynomial Chaos (SAPC) is developed. The SAPC method combines desirable features of NIPC with regression methods for an efficient estimation of uncertainty throughout a vehicle flight envelope or design space. Throughout the dissertation, the SAPC method is applied to various aerospace problems of interest. These include the regression of aerodynamic force and moment uncertainties throughout the flight envelope of a commercial aircraft, the design under uncertainty of a two-stream propulsive mixer device, and the robust design of a low-boom supersonic demonstrator aircraft. Collectively the results suggest that SAPC may be useful for a large variety of engineering applications.

Экономическая среда становится все более неопределенной. Особенно заметно это становится в условиях цифровизации, когда скорость взаимодействия с информацией значительно возросла. Чтобы сохранять и увеличивать свою конкурентоспособность компаниям приходится применять различные методы управления и модернизаций для достижения лидирующих позиций на рынке. В результате все больше компаний стремятся к цифровизации, которая позволяет оптимизировать процессы. Однако подобные изменения влекут за собой еще большую неопределенность. Поэтому компаниям становится особенно важно грамотно управлять и принимать решения в условиях неопределенности. Цель работы – разработка методических рекомендаций по управлению неопределенностью на современном этапе цифровизации экономики. Объектом исследования является совокупность подходов к управлению неопределенностью. Практическая значимость состоит в разработке алгоритма внедрения предложенных методических рекомендаций по управлению цифровой неопределенностью в деятельность компании. / The economic environment is becoming increasingly uncertain. This is especially noticeable in the conditions of digitalization, when the speed of interaction with information has increased significantly. In order to maintain and increase their competitiveness, companies have to apply various management methods and upgrades to achieve leading positions in the market. As a result, more and more companies are striving for digitalization, which allows optimizing processes. However, such changes entail even greater uncertainty. Therefore, it becomes especially important for companies to manage competently and make decisions in conditions of uncertainty. The purpose of the work is to develop methodological recommendations for managing uncertainty at the current stage of the economy digitalization. The object of the study is a set of approaches to uncertainty management. The practical significance lies in the development of an algorithm for implementing the proposed methodological recommendations for managing digital uncertainty in the company's activities.

The purpose of this study is to examine the impact of perception and uncertainty among Generation Z and Generation Y Swedish consumers when making purchase decisions from online Chinese retailersQualitative method in the form of focus groups was used to collect the data in order to answer the research question. This study explored perception and uncertainty in Swedish Gen Z and Gen Y consumers when purchasing from Chinese online retailers. The results revealed that Gen Y prioritizes eco-friendliness and domestic retailers, while Gen Z is cost-conscious and prefers Chinese retailers. Income levels influence these preferences, with Gen Y valuing sustainability and Gen Z seeking competitive prices.

Safety and risk assessment are key priorities for nuclear power plants. Probabilistic risk assessment (PRA) is a method for quantitative evaluation of accident risk, in particular severe nuclear core damage and the associated release of radioactive materials into the environment. The reliability and certainty of PRA have at times been questioned, especially when real-world observations have indicated that the frequency of nuclear accidents is higher than the probabilities predicted by PRA. This thesis provides a philosophical review of the epistemic uncertainties in PRA, using the Fukushima Daiichi accident of March 2011 as a case study. The thesis provides an overview of the PRA model structure, its key elements, and possible sources of uncertainty, in an attempt to understand the deviation between the real frequency of nuclear core-melt accidents and the probabilities predicted by PRA.The analyses in this thesis address several sources of epistemic uncertainty in PRA. Analyses of the PRA approach reveal the difficulty involved in covering all possible initiating events, all component and system failures, as well as their possible combinations in the risk evaluations. This difficulty represents the source of a characteristic epistemic uncertainty, referred to as completeness uncertainty. Analyses from the case study (the Fukushima Daiichi accident) illustrate this difficulty, as the PRA failed to identify a combined earthquake and tsunami, with the resultant flooding and consequent power failure and total blackout, as an initiating causal event in its logic structure.The analyses further demonstrate how insufficient experience and knowledge, as well as a lack of empirical data, lead to incorrect assumptions, which are used by the model as input parameters to estimate the probabilities of accidents. With limited availability of input data, decision-makers rely upon the subjective judgements and individual experiences of experts, which adds a further source of epistemic uncertainty to the PRA, usually referred to as input parameter uncertainty. As a typical example from the case study, the Fukushima Daiichi accident revealed that the PRA had underestimated the height of a possible tsunami. Consequently, the risk mitigation systems (e.g. the barrier seawalls) built to protect the power plant were inadequate due to incorrect input data.Poor assumptions may also result in improper modeling of failure modes and sequences in the PRA logic structure, which makes room for an additional source of epistemic uncertainty referred to as model uncertainty. For instance, the Fukushima Daiichi accident indicated insufficient backup of the power supply, because the possibility of simultaneous failure of several emergency diesel generators was assumed to be negligibly small. However, that was exactly what happened when 12 out of the 13 generators failed at the same time as a result of flooding.Furthermore, the analyses highlight the difficulty of modeling the human interventions and actions, in particular during the course of unexpected accidents, taking into account the physiological and psychological effects on the cognitive performance of humans, which result in uncertain operator interventions. This represents an additional source of epistemic uncertainty, usually referred to as uncertainty in modeling human interventions. As a result, there may be an increase in the probability of human error, characterized by a delay in making a diagnosis, formulating a response and taking action. Even this statement confirms the complexity of modelling human errors. In the case of the Fukushima Daiichi accident, lack ofvsufficient instructions for dealing with this "unexpected" accident made the coordination of operators' interventions almost impossible.Given the existence of all these sources of epistemic uncertainty, it would be reasonable to expect such a detected deviation between the real frequency of nuclear core-melt accidents and the probabilities predicted by PRA.It is, however, important to highlight that the occurrence of the Fukushima Daiichi accident could lie within the uncertainty distribution that the PRA model predicted prior to the accident. Hence, from the probabilistic point of view, the occurrence of a single unexpected accident should be interpreted with care, especially in political and commercial debates. Despite the limitations that have been highlighted in this thesis, the model still can provide valuable insights for systematic examination of safety systems, risk mitigation approaches, and strategic plans aimed at protecting the nuclear power plants against failures. Nevertheless, the PRA model does have development potentials, which deserves serious attention. The validity of calculated frequencies in PRA is restricted to the parameter under study. This validity can be improved by adding further relevant scenarios to the PRA, improving the screening approaches and collecting more input data through better collaboration between nuclear power plants world-wide. Lessons learned from the Fukushima Daiichi accident have initiated further studies aimed at covering additional scenarios. In subsequent IAEA safety report series, external hazards in multi-unit nuclear power plants have been considered. Such an action shows that PRA is a dynamic approach that needs continuous improvement toward better reliability.

No description available.

No description available.

No description available.

No description available.