Optimal Research Articles

Innovative silencer for window air inlets

This presentation introduces an innovative window air inlets silencer. The product is intended for exterior installation, tailored to match the standard dimensions of window air inlets, specifically the slot through the window. It is designed to complement internal window air inlet equipment. The silencer's design encompasses multiple functionalities. The main one is reducing external noise transmission (such as traffic, airport, and city center noise) from outside to inside the room equipped with the air inlet. Additionally, it minimally impacts air flow, ensures resistance to water and sunlight, offers washability for accumulated dust, and emphasizes aesthetics compared to other existing silencers. The development of the silencer involves a comprehensive approach, incorporating numerical simulations, optimizations, prototypes, and standard experimental tests. The performance of the developed silencer is compared with similar products already available in the market.

Improved order in Hilfer fractional differential systems: Solvability and optimal control problem for hemivariational inequalities

The main objective of this study is to analyze the optimal control results and solvability concerning Hilfer fractional differential hemivariational inequalities within the range of (1<ϱ<2). Initially, we explore the solvability of a mild solutions for the Hilfer fractional hemivariational inequality. Subsequently, we investigate the optimal control strategies for the given problems by employing cost functionals, cosine operators, mild solutions, the fixed point method for multivalued functions, and a generalized Clarke subdifferential approach. To illustrate our findings, we provide a practical example and a filter diagram.

Machine learning for locally periodic structure transmission modelling

This research aims to obtain an analytical transmission model for locally periodic structures with the approach of data-driven physics. So far, this task has been done mainly numerically for non-trivial geometries. From a dataset of Bloch phases generated for given frequency band and structure geometry parameters, it is possible to learn equations relating dispersion relation to axis-symmetric geometry. The dataset is transformed into a lower-dimensional space by applying Principal Component Analysis (PCA) to reduce the complexity of the problem. In the newly obtained coordinate system, the lower-dimensional patterns are extracted via symbolic regression. The resulting model is interpretable in terms of underlying physics and can be used, e.g., to propose an optimized design for a desired band gap width. Note that this has been so far possible only with numerical optimization repeatedly going back and forth from geometry to the dispersion relation, and hence, it significantly contributes to the overall readability of the system features.

Flexural properties and optimisation of hybrid composites reinforced by carbon, glass and flax fibres

This paper presents a study on the flexural properties of hybrid composites (HC) reinforced by carbon, glass, and flax fibres, utilising Finite Element Analysis (FEA). Possible optimal layups are identified through the application of design rules, and regression-based models are developed to predict flexural strength. With the developed models, multi-objective optimisation is done to minimise the cost and weight subjected to a given minimum required flexural strength. By integrating numerical simulation, regression analysis, and optimisation techniques, this research offers a practical approach to the design and fabrication of HC with enhanced mechanical performance and cost-efficiency.

Application-oriented numerical computation and optimization–a celebration of 60 years of the IJCM

Application-oriented numerical computation and optimization–a celebration of 60 years of the IJCM

Energy management method for battery swapping station combining reinforcement learning and mathematical optimization

Abstract The rapid proliferation of electric vehicles has spurred the expansion of scalable Battery Swapping Station (BSS) to cater to the demand for swift charging. However, the current energy management faces challenges in coping with the fluctuation of charging batteries in scalable BSS, the unpredictability of electricity prices and battery demand, as well as the intricacy of demand response. So, this paper presents a two-layer optimization framework for energy management in scalable BSS. The framework decomposes the power scheduling problem in BSS into two subproblems and solves them with deep reinforcement learning and mathematical optimization. The upper layer uses deep reinforcement learning to schedule the BSS’s total power, while the lower layer uses mathematical optimization to allocate the power to each charging pile. The two layers cooperate to achieve an efficient solution. The experiments demonstrate that the proposed method can reduce the operating cost of the BSS, satisfy the safety and user’s demand, and facilitate grid demand response, in BSSs of different sizes. The method is an effective solution for power scheduling of scalable BSS.

Thermal request optimization of a smart district heating system

This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.

Guest editorial to the Special Issue on Machine Learning and Mathematical Optimization in TOP-Transactions in Operations Research

Guest editorial to the Special Issue on Machine Learning and Mathematical Optimization in TOP-Transactions in Operations Research

Design, numerical optimisation and experimental validation of an innovative solar-powered tube heater with multiple air impingement jets

This research investigates a novel tube heater designed for the seamless integration of an innovative solar thermal system into the powder-based coating process to heat steel tube at a temperature of 240 °C. It incorporates a comprehensive numerical model developed and assessed using ANSYS FLUENT, concentrating on seven critical parameters that significantly influence the tube heater’s performance and size. These parameters include tube heater length, jets’ length, funnel height, Z/Djet, Y/Djet and X/Djet ratios, as well as jet diameter. The findings underline the critical role of tube heater length in enhancing heat transfer and maximising thermal efficiency, while reducing jet length and funnel height demonstrated negligible effects on thermal performance, promoting material economy. A lower Z/Djet ratio enhanced heat transfer uniformity, improving thermal performance, while optimal X/Djet and Y/Djet ratios were identified as 4, maintaining a balance between heat transfer rate and energy consumption. A smaller jet diameter proved beneficial since the potential core was not achieved, increasing heat transfer to the steel tubes. The experimental model, conducted to validate the novel tube heater’s performance, remarkably aligns with the numerical model, showing an R-squared value of 0.992. These results affirm the numerical setup’s accuracy and reliability in capturing the tube heater’s thermal behaviour. It is concluded that the novel tube heater stands as a highly efficient solution for the seamless integration of solar thermal systems into the powder-based coating process of steel tubes, promising significant emissions reduction.

Optimization of Joining Parameters in Pulsed Tungsten Inert Gas Weld Brazing of Aluminum and Stainless Steel Based on Response Surface Methodology

Combining aluminum and steel offers a promising solution for reducing structural weight and fuel consumption across various industries. Pulse in tungsten inert gas (TIG) weld brazing effectively suppresses interfacial brittle intermetallics and enhances joint strength by influencing pool stirring and heat input during aluminum-to-steel joining. However, optimizing the pulsed TIG weld brazing process is challenging due to its numerous welding parameters. This study established statistical models for Al/steel joint strength without reinforcement using response surface methodology (RSM) based on central composite design (CCD). The models’ adequacy and significance were verified through analysis of variance (ANOVA). The four welding parameters influence weld strength in the following descending order: pulse on time &gt; base current &gt; pulse current &gt; pulse frequency. Additionally, interactions between pulse current and pulse frequency, and between pulse on time and base current, were observed. Numerical optimization using RSM determined the optimal pulsed GTA weld brazing parameters for aluminum and stainless steel. With these optimized parameters, the joint strength reached 155.73 MPa, and the intermetallic compound (IMC) thickness was reduced to 3.4 μm.

Hepatitis C virus infection and Parkinson’s disease: insights from a joint sex-stratified BioOptimatics meta-analysis

Hepatitis C virus (HCV) infection poses a significant public health challenge and often leads to long-term health complications and even death. Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a proposed viral etiology. HCV infection and PD have been previously suggested to be related. This work aimed to identify potential biomarkers and pathways that may play a role in the joint development of PD and HCV infection. Using BioOptimatics-bioinformatics driven by mathematical global optimization-, 22 publicly available microarray and RNAseq datasets for both diseases were analyzed, focusing on sex-specific differences. Our results revealed that 19 genes, including MT1H, MYOM2, and RPL18, exhibited significant changes in expression in both diseases. Pathway and network analyses stratified by sex indicated that these gene expression changes were enriched in processes related to immune response regulation in females and immune cell activation in males. These findings suggest a potential link between HCV infection and PD, highlighting the importance of further investigation into the underlying mechanisms and potential therapeutic targets involved.

Convergence analysis of a primal–dual optimization-by-continuation algorithm

We present a numerical iterative optimization algorithm for the minimization of a cost function consisting of a linear combination of three convex terms, one of which is differentiable, a second one is prox-simple and the third one is the composition of a linear map and a prox-simple function. The algorithm’s special feature lies in its ability to approximate, in a single iteration run, the minimizers of the cost function for many different values of the parameters determining the relative weight of the three terms in the cost function. A proof of convergence of the algorithm, based on an inexact variable metric approach, is also provided. As a special case, one recovers a generalization of the primal–dual algorithm of Chambolle and Pock, and also of the proximal-gradient algorithm. Finally, we show how it is related to a primal–dual iterative algorithm based on inexact proximal evaluations of the non-smooth terms of the cost function.

Study on Production in Different Layers of Tight Reservoir

Abstract In rencent years, the tight oil blocks in our factory are all developed without injectio n and put into production by large-scale fracture network. There are problems of low initial pro duction and fast decline, and the development effect is poor. Because of the large inclination A ngle of the vertical well, it is impossible to obtain the test results of stratified fluid production b y conventional test methods. At the same time, the understanding of tight oil reservoir utilizatio n status after large-scale reconstruction is not clear enough. Therefore, the test of liquid product ion profile in inclined vertical Wells is explored. Meanwhile, the results of conventional liqui d production profile are applied to in-depth analysis of reservoir utilization status in convention al blocks and test blocks respectively, and the relationship between different reservoir types an d fracturing scale and liquid production capacity is determined. The influencing factors and app lication principles of CDFT test results were clarified, the understanding of different reservoi r utilization conditions was obtained, and the main direction of the optimal design of fracturin g program was clarified, which provided technical support for the subsequent design and optim ization of fracturing program in the new district.

LINEAR PROGRAMMING FOR ALLOCATING TRANSPORTATION CAPACITIES IN FREIGHT PLANNING

Effective allocation of transport capacities is vital for motor transport enterprises engaged in freight transportation. As the demand for goods transportation rises, optimizing resource utilization becomes essential for ensuring timely deliveries, minimizing costs, and enhancing overall operational efficiency. Linear programming, a powerful tool in mathematical optimization, plays a key role in facilitating the rational distribution of limited resources. By framing the allocation problem as a linear program, enterprises can systematically identify the optimal use of their transport capacities, taking into account critical factors such as vehicle availability, delivery schedules, and associated costs. In the realm of operational planning, linear programming offers a structured and analytical approach to addressing the complexities inherent in freight transportation. This study delves into both the theoretical underpinnings and the practical applications of linear programming, showcasing its potential to significantly improve resource allocation strategies, boost efficiency, and reduce operational costs. Through the analysis of real-world examples and empirical data, the study provides valuable insights that can aid practitioners and researchers alike in refining logistics optimization practices, ultimately contributing to more efficient and cost-effective freight transport operations. Keywords: linear programming, transportation capacities, operational planning, freight transportation, resource optimization.

Numerical modeling and optimization of fillet height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly

PurposeThis study aims to investigate the design configuration for an optimum solder height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly.Design/methodology/approachA multiphase finite volume model is developed for reflow soldering simulations to determine the fillet height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly. Different solders, namely SAC305-x, SAC305-xNiO and SAC305-xTi, with varying percentage weight compositions of nanoparticles (x = 0 Wt.%, 0.01 Wt.%, 0.05 Wt.%, 0.10 Wt.%, 0.15 Wt.%) are investigated. A reflow soldering experiment is also conducted, and the cross-sections of the reflowed packages are examined using a High-Resolution Transmission Electron Microscope (HRTEM). The optimum design configurations (nanoparticle composition and material) for the solder fillet height are investigated using the Taguchi orthogonal array method.FindingsGood correlations were recorded between the HRTEM micrographs and the numerical predictions of the nanoparticles' distribution in the molten solder. The numerical prediction of the fillet height also agrees with the experiment, with a maximum disparity of 5.43%. It was found that Ti nanoparticles, having the smallest density compared to NiO and, exhibit the highest buoyancy effect in the molten solder. The Taguchi analysis revealed that the nanoparticles' material factor is more significant than the Wt.% factor for an optimum fillet height. An optimum design configuration for fillet height was established as SAC 305–0.15 Wt.% Ti, corresponding to a 41.13% improvement of the plain SAC 305 solder.Practical implicationsThe fillet height of solder joints greatly influences the solder joint reliability of miniaturized electronic packages. Solder joint reliability of ultra-fine capacitors can be improved using this study's findings on the optimum design configuration for the capacitor's solder fillet. The study’s findings can be practically implemented in industries such as electronics manufacturing, where enhanced solder joint reliability is critical.Originality/valueInvestigation of the optimum design configuration for reinforced SAC305 solder fillet is almost nonexistent in the literature. This study explored the optimization of fillet height of reinforced SAC305 solder joints in miniaturized capacitor assembly.

Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm

The global spread of monkeypox across over 40 countries is a major health challenge. Its symptoms resemble those of chickenpox and measles, complicating early diagnosis. When PCR tests are unavailable, computational lesion detection offers a promising option. This research presents a non-invasive diagnostic method using the Sparrow Search Algorithm (SpaSA). SpaSA is applied to improve accuracy in medical imaging tasks. Deep learning, especially CNNs, effectively addresses challenges in medical imaging. By combining CNNs with optimization, we analyze large imaging datasets. This study evaluates several pre-trained models on two datasets: “Monkeypox 2022” and “Images of Monkeypox.” The method classifies patients as either “normal” or “pox” (including monkeypox, chickenpox, smallpox, cowpox, and measles). The novelty lies in using SpaSA to optimize pre-trained CNNs' performance. The Xception model achieves 97.66% accuracy without optimization. SpaSA enhances VGG19's performance, reaching 99.87% accuracy. With SpaSA, VGG19 reached 99.87% accuracy in 120 seconds. SpaSA had the fastest time (120s) and lowest failure rate (5%). It outperformed Grid Search, Random Search, Bayesian Optimization, WOA, GA, PSO, and ACO. This shows mathematical optimization's impact on medical imaging. It sets new standards in healthcare diagnostics using SpaSA and CNNs. This research shows the significant role of optimization in improving medical imaging. It sets new standards for healthcare diagnostics using SpaSA and CNNs.

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

Maximizing virtual power plant profit: A two-level optimization model for energy market participation

Managing dispersed generation via virtual power plants (VPPs) is crucial for maximizing profits in electricity markets. This paper presents a model aimed at maximizing VPP profit through participation in the energy market. The proposed model addresses grid and security constraints of units using deterministic programming, formulated as an equilibrium-constrained, two-level mathematical optimization model. The first level focuses on maximizing VPP profit, while the second optimizes social welfare. Applying duality theory transforms this two-level model into a mixed-integer linear programming model, further refined using Karush–Kuhn–Tucker (KKT) optimality conditions. Given the inherent conflict in these objectives, a novel algorithm employing water flow dynamics is proposed for solving the model. To enhance method performance, the Pareto criterion and fuzzy decision-making are incorporated. Model tests are conducted on a standard 24-bus IEEE grid, demonstrating its efficiency. For the single-objective problem without line congestion, the solving time was 12 s. Introducing line congestion increased the profit by 13.4 %, from $40,413.21 to $45,837.32. In the two-objective problem without congestion, the profit ranged between $36,928.72 and $42,813.28, and emissions ranged from 275.21 to 2,916.32 pounds. With congestion, the profit range increased by a maximum of 8.7 %, and emissions were reduced by up to 4.6 %.

Reverberation room design optimisation for low-frequency diffuse sound absorption testing

The reproducibility of sound absorption testing with the reverberation room method is a long-standing concern. Absorptive samples induce directionality in the nearfield, while the farfield depends on the room geometry below the Schroeder frequency. Nevertheless, when properly accounting for nearfield effects, the theoretical diffuse absorption coefficient of a sample still represents its average performance across an ensemble of different rooms, even at very low frequencies. Recent research found that particular reverberation room designs allow for an accurate measurement of the diffuse sound absorption coefficient of highly absorptive samples at low frequencies. Pinpointing such designs hence opens up a possibility to sustainably improve the low-frequency reproducibility of sound absorption testing in reverberation rooms. The present paper introduces a numerical optimisation framework that serves this purpose. Specific room shapes are parametrised and the geometrical room parameters are optimised so as to minimise the difference between the measured and the diffuse absorption coefficient under appropriate constraints. The sound absorption testing of a sample in a particular reverberation room is numerically simulated using a method that is both accurate and computationally efficient at low frequencies. The diffuse absorption is computed with a hybrid deterministic-statistical energy analysis approach that accounts for the detailed absorber properties, geometry, and boundary conditions, as well as the nearfield effects. The methodology is applied to both cuboidal and hexahedral room shapes. Certain optimised designs are found not only to provide an excellent match for the absorber that was used during the optimisation, but they also maintain their performance across a range of absorptive samples. Additionally, potential geometrical deviations are found to be well tolerated by these reverberation room designs.

On the Calibration of the Kennedy Model

The Kennedy model offers a robust framework for modeling forward rates, leveraging Gaussian random fields to accommodate emerging phenomena such as negative rates. In our study, we employ maximum likelihood estimations to determine the parameters of the Kennedy field, utilizing Radon–Nikodym derivatives for enhanced accuracy. We introduce an efficient simulation method for the Kennedy field and develop a Black–Scholes-like analytical pricing formula for diverse financial assets. Additionally, we present a novel parameter estimation algorithm grounded in numerical extreme value optimization, enabling the recalibration of parameters based on observed financial product prices. To validate the efficacy of our approach, we assess its performance using real-world par swap rates in the latter part of this article.