Design Optimization for Spatial Arrangement of Used Nuclear Fuel Containers

Canada's proposed deep geological repository is a multiple-barrier system designed to isolate used nuclear fuel containers (UFCs) indefinitely with no release of radionuclides for at least one million years. Placing UFCs together as densely as possible is ideal for mitigating repository size and cost. However, due to heat generation from radioactive decay and material limitations, a key design criterion is that the maximum temperature inside the repository must not exceed 100 °C. To satisfy that criterion, design optimization for the spatial arrangement of UFCs in a crystalline rock repository is performed. Spatial arrangement pertains to: (i) the spacing between UFCs, (ii) the separation between placement rooms underground, and (iii) the locations of variously aged UFCs that generate heat at different rates. Most studies have considered UFCs to be identical in age during placement into the repository. Parameter analyses have also been performed to evaluate repository performance under probable geological conditions. In this work, the various ages of UFCs and the uncertainties in spacing-related design variables are of focus. Techniques for the actual placement of UFCs in the deep geological repository based on their age and methods for repository risk analysis using yield optimization are developed. The thermal evolution inside the deep geological repository is simulated using a finite element model. With many components inside the massive repository planned for upwards of 95,000 UFCs, direct optimization of the model is impractical or even infeasible due to it being computationally expensive to evaluate. Surrogate optimization is used to overcome that burden by reducing the number of detailed evaluations required to reach the optimal designs. Two placement cases are studied: (i) UFCs all having been discharged from a Canadian Deuterium Uranium reactor for 30 years, which is a worst-case scenario, and (ii) UFCs having been discharged between 30 and 60 years. Design options that have UFC spacing 1–2 m and placement room separation 10–40 m are explored. The placement locations of the variously aged UFCs are specified using either sinusoidal (cosine) functions or Kumaraswamy probability density functions. Yield optimization under assumed design variable tolerances and distributions is performed to minimize the probability of a system failure, which occurs when the maximum temperature constraint of 100 °C is exceeded. This method allows variabilities from the manufacturing and construction of the repository components that affect the design variables to be taken into account, incorporating a stochastic aspect into the design optimization that surrogate optimization would not include. Several distributions for the design variables are surveyed, and these include uniform, normal, and skewed distributions—all of which are approximated by Kumaraswamy distributions

Towards a more efficient use of computational budget in large-scale black-box optimization

Evolutionary algorithms are general purpose optimizers that have been shown effective in solving a variety of challenging optimization problems. In contrast to mathematical programming models, evolutionary algorithms do not require derivative information and are still effective when the algebraic formula of the given problem is unavailable. Nevertheless, the rapid advances in science and technology have witnessed the emergence of more complex optimization problems than ever, which pose significant challenges to traditional optimization methods. The dimensionality of the search space of an optimization problem when the available computational budget is limited is one of the main contributors to its difficulty and complexity. This so-called curse of dimensionality can significantly affect the efficiency and effectiveness of optimization methods including evolutionary algorithms. This research aims to study two topics related to a more efficient use of computational budget in evolutionary algorithms when solving large-scale black-box optimization problems. More specifically, we study the role of population initializers in saving the computational resource, and computational budget allocation in cooperative coevolutionary algorithms. Consequently, this dissertation consists of two major parts, each of which relates to one of these research directions. In the first part, we review several population initialization techniques that have been used in evolutionary algorithms. Then, we categorize them from different perspectives. The contribution of each category to improving evolutionary algorithms in solving large-scale problems is measured. We also study the mutual effect of population size and initialization technique on the performance of evolutionary techniques when dealing with large-scale problems. Finally, assuming uniformity of initial population as a key contributor in saving a significant part of the computational budget, we investigate whether achieving a high-level of uniformity in high-dimensional spaces is feasible given the practical restriction in computational resources. In the second part of the thesis, we study the large-scale imbalanced problems. In many real world applications, a large problem may consist of subproblems with different degrees of difficulty and importance. In addition, the solution to each subproblem may contribute differently to the overall objective value of the final solution. When the computational budget is restricted, which is the case in many practical problems, investing the same portion of resources in optimizing each of these imbalanced subproblems is not the most efficient strategy. Therefore, we examine several ways to learn the contribution of each subproblem, and then, dynamically allocate the limited computational resources in solving each of them according to its contribution to the overall objective value of the final solution. To demonstrate the effectiveness of the proposed framework, we design a new set of 40 large-scale imbalanced problems and study the performance of some possible instances of the framework

Comparison of the MATSuMoTo Library for Expensive Optimization on the Noiseless Black-Box Optimization Benchmarking Testbed

International audienceNumerical black-box optimization problems occur frequently in engineering design, medical applications, finance, and many other areas of our society's interest. Often, those problems have expensive-to-calculate objective functions for example if the solution evaluation is based on numerical simulations. Starting with the seminal paper of Jones et al. on Efficient Global Optimization (EGO), several algorithms tailored towards expensive numerical black-box problems have been proposed. The recent MATLAB toolbox MATSuMoTo (short for MATLAB Surrogate Model Toolbox) is the focus of this paper and is benchmarked within the Black-box Optimization Benchmarking framework BBOB. A comparison with other already previously benchmarked algorithms for expensive numerical black-box optimization with the default setting of MATSuMoTo highlights the strengths and weaknesses of MATSuMoTo's cubic radial basis functions surrogate model in combination with a Latin Hypercube initial design in the range of 50 times dimension many function evaluations

Comparison of the MATSuMoTo Library for Expensive Optimization on the Noiseless Black-Box Optimization Benchmarking Testbed

International audienceNumerical black-box optimization problems occur frequently in engineering design, medical applications, finance, and many other areas of our society's interest. Often, those problems have expensive-to-calculate objective functions for example if the solution evaluation is based on numerical simulations. Starting with the seminal paper of Jones et al. on Efficient Global Optimization (EGO), several algorithms tailored towards expensive numerical black-box problems have been proposed. The recent MATLAB toolbox MATSuMoTo (short for MATLAB Surrogate Model Toolbox) is the focus of this paper and is benchmarked within the Black-box Optimization Benchmarking framework BBOB. A comparison with other already previously benchmarked algorithms for expensive numerical black-box optimization with the default setting of MATSuMoTo highlights the strengths and weaknesses of MATSuMoTo's cubic radial basis functions surrogate model in combination with a Latin Hypercube initial design in the range of 50 times dimension many function evaluations

Machine learning applications for noisy intermediate-scale quantum computers

Quantum machine learning (QML) has proven to be a fruitful area in which to search for applications of quantum computers. This is particularly true for those available in the near term, so called noisy intermediate-scale quantum (NISQ) devices. In this thesis, we develop and study QML algorithms in three application areas. We focus our attention towards heuristic algorithms of a variational (meaning hybrid quantum-classical) nature, using parameterised quantum circuits as the underlying quantum machine learning model. The variational nature of these models makes them especially suited for NISQ computers. We order these applications in terms of the increasing complexity of the data presented to them. Firstly, we study a variational quantum classifier in supervised machine learning, and focus on how (classical) data, feature vectors, may be encoded in such models in a way that is robust to the inherent noise on NISQ computers. We provide a framework for studying the robustness of these classification models, prove theoretical results relative to some common noise channels, and demonstrate extensive numerical results reinforcing these findings. Secondly, we move to a variational generative model called the Born machine, where the data becomes a (classical or quantum) probability distribution. Now, the problem falls into the category of unsupervised machine learning. Here, we develop new training methods for the Born machine which outperform the previous state of the art, discuss the possibility of quantum advantage in generative modelling, and perform a systematic comparison of the Born machine relative to a classical competitor, the restricted Boltzmann machine. We also demonstrate the largest scale implementation (28 qubits) of such a model on real quantum hardware to date, using the Rigetti superconducting platform. Finally, for our third QML application, the data becomes purely quantum in nature. We focus on the problem of approximately cloning quantum states, an important primitive in the foundations of quantum mechanics. For this, we develop a variational quantum algorithm which can learn to clone such states, and show how this algorithm can be used to improve quantum cloning fidelities on NISQ hardware. Interestingly, this application can be viewed as either supervised or unsupervised in nature. Furthermore, we demonstrate how this can algorithm can be used to discover novel implementable attacks on quantum cryptographic protocols, focusing on quantum coin flipping and key distribution as examples. For the algorithm, we derive differentiable cost functions, prove theoretical guarantees such as faithfulness, and incorporate state of the art methods such as quantum architecture search

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin