Machine Learning-based Optimization Research Articles

Mathematical modeling and machine learning-based optimization for enhancing biofiltration efficiency of volatile organic compounds

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic (α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}-pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}-pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}-pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

Machine learning based analysis of entropy production and wall shear stress in blood flow through stenosed artery

The building up of cholesterol and various substances on the inner boundary of arterial walls forms a plaque (stenosis), which results in the narrowing of walls of arteries, and this exerts additional stress on the walls. Shear stress at the wall of arteries may cause the formation of plaque, plaque growth, and expansive arterial remodeling, and it may also result in an increased risk of plaque rupture. The purpose of this research work is to use machine learning approaches to analyze the wall shear stress and entropy generation through irreversible processes of blood flow in a porous stenosed artery within the context of magnetohydrodynamic consideration. As a way of illustrating blood’s non-Newtonian behavior, the Casson fluid model is used. A blood flow model in the context of mild stenosis has been developed, and it has subsequently been computationally addressed using an explicit finite difference method. The velocity, temperature, entropy generation, Bejan number, and wall shear stress are visualized for different values of pertinent variables. The result reflects the increased velocity in the stenosed region. A hybrid machine learning method using artificial neural network and particle swarm optimization is implemented to optimize the entropy generation and wall shear stress. The network is trained using Stochastic gradient–descent training procedure as well as Levenberg–Marquardt training procedure, and the outcomes of both training procedures are compared. Using this machine learning process, the minimum entropy production has been found for specific values of the Hartmann number, dimensionless temperature difference, and pulsatile component of pressure gradient. This machine learning-based optimization approach enhances the ability to forecast quantitative changes in affecting parameters, which will contribute to minimized damage to the arterial wall, improved blood flow efficiency and the delivery of necessary components to tissues.

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study

BackgroundDiabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial. ObjectiveTo develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning. MethodologyA retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability. ResultsOf the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R2 score of 0.97. ConclusionMachine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.

Multi-Objective Bayesian Optimization for Laminate-Inspired Mechanically Reinforced Piezoelectric Self-Powered Sensing Yarns.

Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF-TrFE) yarn structures are efficiently optimized. By leveraging a multi-objective Bayesian optimization-based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high-performance piezoelectric P(VDF-TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self-powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real-time signal detection.

Machine learning-based optimization and 4E analysis of renewable-based polygeneration system by integration of GT-SRC-ORC-SOFC-PEME-MED-RO using multi-objective grey wolf optimization algorithm and neural networks

This study introduces a new solar-methane-driven setup that recovers waste heat from flue gases to produce electricity, hydrogen, oxygen, fresh water (FW), and domestic hot water (DHW) while reducing the environmental impact of gas turbines. This integration includes the Brayton cycle and solid oxide fuel cell as motive cycles, the steam and organic Rankine cycle, a parabolic trough solar collector (PTC), a proton exchange membrane electrolyzer, multi-effect distillation, and reverse osmosis. Using Engineering Equation Solver software, analyses in energy, exergy, exergoeconomic, and exergoenvironment (4E) are conducted to identify critical system evaluation parameters in thermodynamics, economics, and the environment. Additionally, the proposed system is optimized using an artificial neural network and the multi-objective grey wolf optimizer algorithm, utilizing decision variables identified through sensitivity analysis to enhance system performance. The results of six-objective optimization show energy and exergy efficiencies at 49.48 % and 47.21 %, with a net power production of 133 MW and a total cost rate of $7903/h. Among all the components, the combustion chamber and PTC have the maximum exergy destruction, with values of 58.87 MW and 17.29 MW, respectively. Also, hydrogen, FW, and DHW production rates are 201.6 kg/h, 43.88 kg/s, and 40.98 kg/s, respectively. Moreover, the costs of hydrogen, FW, and DHW are $953.64/h, $57.35/h, and $82.48/h, respectively. Finally, Bloemfontein, Las Vegas, and Tunis are the most favorable regions economically, with a low levelized cost of electricity of $0.05931, $0.06006, and $0.05932 per kWh, along with payback periods of 0.6144, 0.5524, and 1.069 years, respectively.

Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty.

Machine learning-based optimal design of an acoustic black hole metaplate for enhanced bandgap and load-bearing capacity

This paper introduces a novel machine learning-based optimization strategy for multi-functional acoustic black hole (ABH) metaplates. The primary objective is to achieve a multi-functional metaplate with excellent performance in elastic wave attenuation and load-bearing capacity simultaneously. The paper begins by describing the design of nanocomposite ABH metaplates, presenting a new pathway to realize multi-functional metaplates. Then, a semi-analytical method, based on plate theory and the Bloch–Floquet theorem, is introduced to consider the band structure of the nanocomposite metaplates. Through systematic analysis, the impacts of the ABH effect, nanocomposite reinforcements, and the viscoelastic damping layer on the bandgaps and strain energy compliance are highlighted. Meanwhile, two optimization objectives representing bandgap characteristics and in-plane stiffness are derived respectively. Subsequently, a deep learning surrogate model is employed to establish a relationship involving significant parameters with the optimization objectives. The performance evaluation confirms accuracy and computational speed of the surrogate model. Finally, an optimization strategy based on deep reinforcement learning is proposed to obtain multi-functional metaplates with superior bandgaps, enhanced in-plane stiffness, or both. The robustness and efficiency of the strategy are demonstrated under different tests. The results show that the proposed strategy can achieve identical results as the genetic algorithm and nondominated sorting genetic algorithm-II, while surpassing them in computational efficiency and balancing multiple objectives. The findings of this study serve as valuable references for the future development and application of multi-functional advanced metamaterials.

Feature Optimization-Based Machine Learning Approach for Czech Land Cover Classification Using Sentinel-2 Images

The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and the Czech Republic. This study aims to develop a practical approach for land cover classification in the Czech Republic, with the goal of efficiently acquiring spatial distribution information regarding its forest resources. This approach is based on multi-level feature extraction and selection, integrated with advanced machine learning or deep learning models. To accomplish this goal, the study concentrated on two typical experimental regions in the Czech Republic and conducted a series of classification experiments, using Sentinel-2 and DEM data in 2018 as the main data sources. Initially, this study extracted various features, including spectral, vegetation, and terrain features, from the study area, then assessed and selected key features based on their importance. Additionally, this study also explored multi-level spatial contextual features to improve classification performance. The extracted features include texture and morphological features, as well as deep semantic information learned by utilizing a deep learning model, 3D CNN. Finally, an AdaBoost ensemble learning model with the random forest as the base classifier is designed to produce land cover classification maps, thus obtaining the spatial distribution of forest resources. The experimental results demonstrate that feature optimization significantly enhances the extraction of high-quality features of surface objects, thereby improving classification performance. Specifically, morphological and texture features can effectively enhance the discriminability between different features of surface objects, thereby improving classification accuracy. Utilizing deep learning networks enables more efficient extraction of deep feature information, further enhancing classification accuracy. Moreover, employing an ensemble learning model effectively boosts the accuracy of the original classification results from different individual classifiers. Ultimately, the classification accuracy of the two experimental areas reaches 92.84% and 93.83%, respectively. The user accuracies for forests are 92.24% and 93.14%, while the producer accuracies are 97.71% and 97.02%. This study applies the proposed approach for nationwide classification in the Czech Republic, resulting in an overall classification accuracy of 90.98%, with forest user accuracy at 91.97% and producer accuracy at 96.2%. The results in this study demonstrate the feasibility of combining feature optimization with the 3D Convolutional Neural Network (3DCNN) model for land cover classification. This study can serve as a reference for research methods in deep learning for land cover classification, utilizing optimized features.

Machine learning-based optimization of a multi-step ion exchange chromatography for ternary protein separation

Ion-exchange chromatography is an essential but complicated step in the biopharmaceutical downstream process, with multiple factors affecting the separation efficiency. Model-based optimization can help expedite process developments with limited time and resource investments. To address the mechanistic model's high computational complexity, a machine learning (ML)-based optimization framework was introduced. Specifically, Gaussian Process Regression models were utilized as substitutes for the mechanistic model in calculating the constraints and objective function. To further reduce the required sampling, feasibility and optimization stages were incorporated into the framework, with a penalty introduced at each stage to guide the search. This ML-based framework was applied to a case study to separate a protein mixture. Compared to the results obtained through genetic algorithm, this approach enhanced productivity by 50.1 % and reduced computation time by 70.8 % simultaneously. The effects of peak cutting criteria on the optimal results were examined, followed by a detailed analysis of design space.

Advanced process integration and machine learning-based optimization to enhance techno-economic-environmental performance of CO2 capture and conversion to methanol

Developing economically sustainable CO2 capture and conversion processes is essential to realize carbon neutrality. This study proposed an integrated process for CO2 capture and conversion-to-methanol (CCTM) and applied machine learning-based optimization to enhance techno-economic-environmental performance. After validating CO2 capture and CO2-to-methanol sections, an advanced CCTM design was developed and compared with conventional one regarding techno-economic-environmental performance across various operating scenarios. The advanced CCTM exhibited significant improvements in energy consumption (14.73–16.30%), production cost (0.81–1.28%), and net CO2 reduction (3.13–3.38%) owing to efficiently reusing waste heat, off-gas, and water resources. The one-at-a-time sensitivity analysis revealed roles of each variable and nonlinear variable-performance tendencies among operating variables in the advanced CCTM process. Subsequently, a well-developed deep neural network (DNN) model precisely formulated the relationship between key variables and performances. The DNN-based optimization provided optimum operating conditions within a minute, resulting in an 8.21 $/tMeOH (∼0.81%) reduction in production cost compared to base case of CCTM. Notably, the total CO2 capture rate of 92.53% at an optimal condition highlighted the significant contribution of advanced CCTM to carbon neutrality. The findings provide a viable reference for the effective and sustainable design and operation of an integrated CCTM process.

GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

Invited for this month's cover are the collaborating groups of Dr. Ryohei Kakuchi and Ms. Kiho Matsubara at Gunma University, Japan, Prof. Kei Takahashi at Fukuoka Institute of Technology and The Institute of Statistical Mathematics, Japan, Prof. Takeshi Matsuda at Hannan University, Japan, Dr. Noriaki Seko and Dr. Yuji Ueki at National Institutes for Quantum Science and Technology, Japan. The cover picture shows the machine learning-based optimization and interpretation of radiation-induced graft polymerizations under emulsion conditions based on realistic information for monomers calculated by the state-of-the-art semiempirical method. More information can be found in the Research Article by Kiho Matsubara, Kei Takahashi, Ryohei Kakuchi, and co-workers.

Solving real-world optimization tasks using physics-informed neural computing

Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development. Recently, machine learning applications, including deep reinforcement learning (RL) and genetic algorithms (GA), have emerged in these real-world optimization tasks. We introduce a new machine learning-based optimization scheme that incorporates physics with the operational objectives. This physics-informed neural network (PINN) could find the optimal path in well-defined systems with less exploration and also was capable of obtaining narrow and unstable solutions that have been challenging with bottom-up approaches like RL or GA. Through an objective function that integrates governing laws, constraints, and goals, PINN enables top-down searches for optimal solutions. In this study, we showcase the PINN applications to various optimization tasks, ranging from inverting a pendulum, determining the shortest-time path, to finding the swingby trajectory. Through this, we discuss how PINN can be applied in the tasks with different characteristics.

Machine Learning Aided Optimization of Non-Metallic Seals in Downhole Tools

Machine learning-based optimization analysis is conducted to improve the performance and reliability of non-metallic seals that undergo dynamic loading. Non-metallic seals, mostly made from elastomeric materials such as rubber, are widely used in the oil and gas industry due to their combination of hyper-elastic behavior and lack of plastic deformation under pressure and temperature cycles. However, these elastomeric materials could soften at elevated temperatures or during cyclic loading which makes them more prone to extrude from the seal region. For simple seals such as O-rings, thermoplastic anti-extrusion backup rings can be used to resist rubber extrusion. For more complex molded or bonded seals subject to cyclic loads and temperature cycles, minimizing the rubber damage and maintaining the seal integrity becomes more challenging as there may be inadequate room for backup rings. Additionally, the rubber may also experience repeated long stroke lengths during service which can cause surface abrasion. Non-metallic seals are extensively used in Sand Control Tools, a class of completion equipment. In the development of these molded and bonded seals, advanced optimization analysis techniques are important to make the seal design sufficiently robust to meet the stringent requirements of industry codes and regulations. Machine learning-based optimization analysis allows for increasing the seal performance and reliability. The Mullins effect is included in the elastomeric material model to capture the material softening effects under cyclic loading. Machine learning-based optimization starts with a feasible design space that is defined based on the controlling design parameters related to the geometry of the seal. Comprehensive Finite Element Analysis is performed on each design sample under the target cyclic load and temperature cycles. The key response parameters of the non-metallic seal are selected as the principal strain and the contact stress in the rubber which together indicate the damage and seal-ability states of the design. A Performance Objective Index is defined as a function of the key response parameters. The FEA results covering the entire design space provide the simulation-driven training data to build the surrogate model using multi-layer neural networks. The optimum design solution is found from the surrogate model which correlates the Performance Objective Index to the design parameters. The results of this study reveal that the performance of the seal is greatly affected by the ratio of the elastomeric seal volume to the gland volume into which the seal is molded. As opposed to the base design, the optimum design shows a solid performance with no signs of extrusion when the principal strain of the rubber is kept below the elongation limit throughout the load cycles, and when sufficient contact stress is developed to maintain the seal.

A Survey On Non – linear Optimization And Global Optimization Methods

bjective functions and constraints are not linear. Non-linear optimization involves finding the best solution in the presence of non-linear relationships among the variables. These problems arise in various fields such as engineering design, economics, machine learning, and operational research. The primary challenge in non-linear optimization is the potential existence of multiple local optima, which can make it difficult to identify the global optimum. Global optimization methods are designed to overcome this challenge by searching for the global optimum, which is the best possible solution among all local optima. These methods can be broadly categorized into deterministic and stochastic approaches. Deterministic methods, such as branch and bound, interval analysis, and cutting-plane methods, systematically explore the search space and provide guarantees of finding the global optimum or bounds on the global optimum. These methods are often rigorous but can be computationally expensive, especially for high-dimensional problems. On the other hand, stochastic methods, such as genetic algorithms, simulated annealing, and particle swarm optimization, use probabilistic rules to explore the search space. These methods are inspired by natural processes and heuristics, offering flexibility and often being more efficient for large-scale problems. While they do not guarantee finding the global optimum, they are widely used due to their ability to escape local optima and explore the search space effectively. Hybrid methods that combine deterministic and stochastic approaches have also gained popularity. These methods leverage the strengths of both approaches, enhancing the robustness and efficiency of the optimization process. For example, a hybrid algorithm might use a stochastic method to explore the search space broadly and then apply a deterministic method to refine the search around promising areas. Recent advancements in computational power and algorithmic development have significantly improved the efficiency and applicability of non-linear and global optimization methods. Techniques such as machine learning-based optimization and parallel computing have enabled the handling of more complex and larger-scale optimization problems. Moreover, these advancements have facilitated the integration of optimization methods into real-time and adaptive systems, further broadening their applications.

A Survey On Non – linear Optimization And Global Optimization Methods

Non-linear optimization and global optimization methods are crucial in solving complex real-world problems where the objective functions and constraints are not linear. Non-linear optimization involves finding the best solution in the presence of non-linear relationships among the variables. These problems arise in various fields such as engineering design, economics, machine learning, and operational research. The primary challenge in non-linear optimization is the potential existence of multiple local optima, which can make it difficult to identify the global optimum. Global optimization methods are designed to overcome this challenge by searching for the global optimum, which is the best possible solution among all local optima. These methods can be broadly categorized into deterministic and stochastic approaches. Deterministic methods, such as branch and bound, interval analysis, and cutting-plane methods, systematically explore the search space and provide guarantees of finding the global optimum or bounds on the global optimum. These methods are often rigorous but can be computationally expensive, especially for high-dimensional problems. On the other hand, stochastic methods, such as genetic algorithms, simulated annealing, and particle swarm optimization, use probabilistic rules to explore the search space. These methods are inspired by natural processes and heuristics, offering flexibility and often being more efficient for large-scale problems. While they do not guarantee finding the global optimum, they are widely used due to their ability to escape local optima and explore the search space effectively. Hybrid methods that combine deterministic and stochastic approaches have also gained popularity. These methods leverage the strengths of both approaches, enhancing the robustness and efficiency of the optimization process. For example, a hybrid algorithm might use a stochastic method to explore the search space broadly and then apply a deterministic method to refine the search around promising areas. Recent advancements in computational power and algorithmic development have significantly improved the efficiency and applicability of non-linear and global optimization methods. Techniques such as machine learning-based optimization and parallel computing have enabled the handling of more complex and larger-scale optimization problems. Moreover, these advancements have facilitated the integration of optimization methods into real-time and adaptive systems, further broadening their applications.

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Machine learning-based optimization of storage ring injection efficiency

Since the DELTA accelerator facility does not use a White circuit-driven fast topping-up mode, each software-driven injection ramp cycle takes about 7 seconds. Depending on the injection efficiency, 150 to 200 ramp cycles are required to reach the maximum beam current of 130 mA in the storage ring. Thus, for fast post-injection, a high electron transfer rate is crucial. During the injection process, a large number of parameters (e. g., magnet settings, timings of pulsed elements) have to be adjusted manually. The injection efficiency depends mainly on the settings of the booster extraction elements, the T2 transfer line magnets, and the storage ring injection components. In order to automate the injection procedure and to improve the electron transfer efficiency, the application of innovative machine learning concepts was studied.

Accelerating Battery Electrolyte Design Process Via Closed-Loop Optimization of Simulations and Experiments

Designing and discovering new material combinations for battery applications has been and remains to be a slow and expensive process. It is a challenging task to develop battery electrolytes that are safe, high-performant, and cost-effective, as there are many design parameters to consider. There are many recent efforts to expedite such a process, ranging from the use of machine learning optimization techniques and lab automation to utilizing computer simulations.We propose an automated, closed-loop optimization strategy to design electrolytes by integrating machine learning-based optimization algorithms, autonomous robotics, and molecular simulations. In particular, we demonstrate the application of our approach for designing room-temperature ionic liquids (RTILs) for Li-ion batteries, which consist of multiple salts, cations, and anions. The composition space is too vast to explore via a conventional approach. Thus, we use intelligent decision-making algorithms for selecting which composition to test next, which is carried out by the in-house robotic platform. The molecular simulations are also used to provide an in-depth understanding of the experimentally observed behavior, and the simulation results are also fed into the optimization algorithm. The presented approach is designed to be general, so it can be applied to other electrochemical applications with minimal modifications to the algorithms or the experimental setup.

An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions

Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions. Plenty of existing literature has considered two-dimensional infinite airfoil optimization, while three-dimensional finite wing optimizations are subject to limited study because of high computational costs. Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms, which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions. This methodology unfolds in three stages: radial basis function interpolated wing generation, collection of inputs from computational fluid dynamics simulations, and deep neural network that constructs the surrogate model for the optimal wing configuration. It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations. It also has the potential to optimize various aerial vehicles undergoing different mission environments, loading conditions, and safety requirements.

Loading pattern optimization using simulated annealing and binary machine learning pre-screening

We introduce a creative approach combining machine learning with optimization techniques to enhance the optimization of the loading pattern (LP). Finding the optimal LP is a critical decision that impacts both the reload safety and the economic feasibility of the nuclear fuel cycle. While simulated annealing (SA) is a widely accepted technique to solve the LP optimization problem, it suffers from the drawback of high computational cost since LP optimization requires three-dimensional depletion calculations. In this note, we introduce a technique to tackle this issue by leveraging neural networks to filter out inappropriate patterns, thereby reducing the number of SA evaluations. We demonstrate the efficacy of our novel approach by constructing a machine learning-based optimization model for the LP data of the Korea Standard Nuclear Power Plant (OPR-1000).