Harnessing the Power of Supercomputers: Solving Complex Mathematical Problems and Queueing Theory

Approximation models (also known as metamodels) have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the sampling strategies used. Our goal in this paper is to investigate the general applicability of sequential sampling for creating global metamodels. Various sequential sampling approaches are reviewed and new approaches are proposed. The performances of these approaches are investigated against that of the one-stage approach using a set of test problems with a variety of features. The potential usages of sequential sampling strategies are also discussed.

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples.

In the field of urban and green mobility, electric car-sharing networks have recently emerged as one of the promising alternatives, dealing with both environmental and transportation issues. Despite the increasing success of the shared mobility concept over the world, the design and management of such urban mobility systems implies strategic, tactical and operational challenges. To support designers and operators, all these issues constitute an emerging and challenging research topic, aiming to develop techniques and tools for modeling, analysis and optimization of such complex systems. In this contribution, based on the modeling and analysis power of stochastic timed Petri nets, a discrete event simulation approach is developed, taking into account their dynamic behavior complexities due to their self-service mode and characteristics including capacities of the stations, energy availability, rebalancing and maintenance activities. A real-life application is presented, to demonstrate the applicability and the potential of the proposed modeling and simulation analysis approach. <i>Note to Practitioners</i>&#x2014;To support designers, providers and managers of shared mobility programs, performance and optimization models and decision-making tools are needed. In this contribution, we deals with a powerful and modular stochastic timed Petri net methodology suitable for performance modeling and simulation analysis of such complex dynamical systems. The authors believe that this discrete event dynamic approach has significant promise for the future and to influence economic viability and operational efficiency of such emerging urban transportation systems.

Use of support vector regression in structural optimization: Application to vehicle crashworthiness design

The high selectivities of liquid chromatography and mass spectrometry make liquid chromatography-mass spectrometry one of the most popular tools for quantitative analysis in complex chemical, biological, and environmental systems, while the potential mathematical selectivity of liquid chromatography-mass spectrometry is rarely investigated. This work discussed the mathematical selectivity of liquid chromatography-mass spectrometry by three-way calibration based on the trilinear model, with an application to quantitative analysis of coeluting aromatic amino acids in human plasma. By the trilinear decomposition of the constructed liquid chromatography-mass spectrometry-sample trilinear model and individual regression of the decomposed relative intensity versus concentration, the proposed three-way calibration method successfully achieved quantitative analysis of coeluting aromatic amino acids in human plasma, even in the presence of uncalibrated interferent(s) and a varying background. This analytical method can ease the requirements for sample preparation and complete chromatographic separation of components, reduce the use of organic solvents, decrease the time of chromatographic separation, and increase the peak capacity of liquid chromatography-mass spectrometry. As a "green analytical method", the liquid chromatography-mass spectrometry three-way calibration method can provide a promising tool for direct and fast quantitative analysis in complex systems containing uncalibrated spectral interferents, especially for the situation where the coelution problem is difficult to overcome.

The Department of Energy envisions the next generation very high temperature gas-cooled reactor (VHTR) as a single-purpose or dual-purpose facility that produces hydrogen and electricity. The Ministry of Science and Technology (MOST) of the Republic of Korea also selected VHTR for the Nuclear Hydrogen Development and Demonstration (NHDD) Project. The report will address the evaluation of hydrogen and electricity production cycle efficiencies for such systems as the VHTR and NHDD, and the optimization of system configurations. Optimization of such complex systems as VHTR and NHDD will require a large number of calculations involving a large number of operating parameter variations and many different system configurations. The research will produce (a) the HyPEP which is specifically designed to be an easy-to-use and fast running tool for the hydrogen and electricity production evaluation with flexible system layout, (b) thermal hydraulic calculations using reference design, (c) verification and validation of numerical tools used in this study, (d) transient analyses during start-up operation and off-normal operation. This project will also produce preliminary cost estimates of the major components.

The need for an efficient assessment of the occupational risk of the employees involved in the construction of buildings and structures was established. A study was conducted for substantiating the system for ensuring the safety of personnel in the construction complex using quantitative risk criteria developed based on of mathematical modeling. Technological processes at the construction sites and in the organizations are considered. The methods of complex (integral) system analysis, process analysis, aggregation and optimization of risk parameters were also applied. Based on the systematic studies of the technological processes at facilities under construction, a methodology for assessing occupational risk was developed. The ways and means of achieving the strategic goal of the occupational safety, which is to ensure the safe professional activity of personnel, are considered. The possibility of forming a set of criteria for the quantitative assessment of occupational risk based on the mathematical modeling is shown. The key to obtaining efficient evaluative expressions is the use of real statistical data in the numerical form. The importance is noted considering the interaction of various harmful and hazardous production factors that affect personnel and are formed according to the principle of systems emergence. The developed criteria for the quantitative assessment of occupational risk are an objective and reliable basis for the development of an efficient system for ensuring the safety of construction employees. The described technique is based on the fundamental provisions of the physical and mathematical theory, the scientific discipline «Life Safety», the theory of prognostic and experimental research. It can be used to develop measures for creating a safe working environment in all the industries.

He co-edited two books published in Taylor & Francis and MDPI AG. Moreover, he has published more than 160 peer-reviewed papers in international journals and conferences, including IEEE Transactions on Industrial Informatics, IEEE Transactions on Multimedia, IEEE Transactions on Image Processing, Processes, IEEE SMC, and IEEE IJCNN. Among his published papers, there are 12 ESI Top 1% Highly Cited Papers and 2 ESI Top 0.

A Trailing Paper Trail

For complex mechanical equipment containing N units the optimal model to preventive maintenance cycle policy is proposed to minimize the maintenance cost rate, the model is subject to the availability and the reliability. And optimal variable is preventive maintenance cycle. The recursion relationship of failure rate before and after imperfect preventive maintenance was built up concerning with the concept of age reduction factor. Finally, immunity particle swarm algorithm is used to solve the model; the result of an example proves the model valid and effective and the cost rate decreases 10.2% than single parts maintenance policy under identical restraint condition.

Modelling and optimization of complex systems: Proceedings of the IFIP-TC 7 working conference, Novosibirsk, USSR, 1978: G.I. Marchuk (Ed.) Volume 18 in: Lecture Notes in Control and Information Sciences, Springer, Berlin, 1979, vi + 293 pages, DM 28.50

Simulation optimisation offers great opportunities in the design and optimisation of complex systems. In the presence of multiple objectives, there is usually no single solution that performs best on all objectives. Instead, there are several Pareto-optimal (efficient) solutions with different trade-offs which cannot be improved in any objective without sacrificing performance in another objective. For the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via simulation, we consider the problem of efficiently identifying the Pareto-optimal designs out of a (small) given set of alternatives. We present a simple myopic budget allocation algorithm for multi-objective problems and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. This paper shows how the algorithm works in bi-objective problems under different settings. Empirical tests show that our algorithm can significantly reduce the necessary simulation budget.

Vehicular digital twin networks (VDTN) is an emerging paradigm integrating physical vehicular networks with their virtual digital twins (DT) mirror, enabling real-time mapping, simulation, and optimization of complex systems. However, constrained resources, high data synchronization costs, and dynamic network conditions in vehicular networks may degrade the performance of DT. We consider the interaction between task performance guarantees and node resource constraints to adaptively determine collaborative task computation transfer optimization in VDTN. In this paper, we design a semantic-aware multi-task vehicular digital twin network model, where vehicles extract semantic representations to achieve lightweight data transmission and efficient DT synchronization. We formulate a problem of maximizing the average utility of DT tasks by jointly considering the synchronization performance of DT tasks and the resource consumption among heterogeneous nodes. To solve the formulated problem, we develop a dynamic collaborative task computation transfer algorithm involving the high mobility of vehicles and heterogeneous resources of nodes. The algorithm is optimized in two phases to maximize average utility. A coarse-grained policy space is first obtained through an adaptive multi-agent deep reinforcement learning approach, aiming to alleviate the policy space explosion caused by dynamic task requirements and heterogeneous node collaboration. Subsequently, a fine-grained policy is derived via a resource-aware refinement mechanism. Numerical results validate the effectiveness and robustness of our proposed algorithm.

Data analytics is related to extracting information from observations, measurements, or experiments about a phenomenon or subject of interest for different purposes, such as interpretation of the data, decision making, diagnosis, and prediction [1]. A classification of the entire field of big data analytics is difficult to make and can be based on a variety of criteria. One classification related to the depth of the analysis is presented by Blackett [2], identifying three levels: descriptive, predictive, and prescriptive analytics. Descriptive analytics uses past data to analyze what has occurred, while predictive analytics focuses on forecasting future trends and determining probabilities of occurrence mainly through the application of different statistical techniques or data mining algorithms for pattern extraction. Prescriptive analytics facilitates decision making and optimization of complex systems.

Controlling product complexity turned into an important issue for product development. This paper introduces a methodology for analysis, interpretation, and optimization of complex systems comprising of interdependencies between several domains. Therefore, the multi-domain matrix is introduced as an extension of known matrix approaches. Furthermore, analysis criteria for system structures are defined that base on graph theory.