Performance Of Optimization Method Research Articles

Recent development of integrated design and improving methods of waverider and inlet

AbstractFor its outstanding aerodynamic capabilities, the integrated design of the waverider and inlet is widely accepted in hypersonic aircraft. Three aspects of the integrated design of the waverider and inlet since 2020 are mainly described in this paper. Firstly, the advantages and shortcomings of the conventional integrated design are examined. Then, different layouts of integrated design are proposed, and the aerodynamic performance optimization methods of the waverider, inlet, and their coupling are introduced. Besides, a series of methods for increasing the calculating accuracy of the waverider and inlet are presented, and the focus of attention is to consider the lateral flow problem.

Method for Locating Secondary Circuit Faults in Substations Based on Graph Neural Networks

To improve the accuracy and portability of fault location in secondary circuits of smart substations, a fault location method for secondary circuits based on graph neural networks is proposed. First, a fault analysis of the secondary circuits in smart substations is conducted, and a graph database model of the secondary circuits is constructed, revealing the connection relationships between the secondary devices. Then, the fault location problem in secondary circuits is defined as a graph classification problem, and a fault graph generation model is proposed based on a representation method for fault information in secondary circuits. Finally, a fault location model for secondary circuits based on graph neural networks is constructed, and a performance optimization method based on binary cross-entropy is proposed to achieve accurate fault location in substation secondary circuits. Using the secondary circuits of a 220 kV smart substation as a reference, different case studies of secondary circuit faults are generated by altering the network topology, connection relationships, and network configurations through the fault graph generation model. Experiments comparing the proposed location method with other models show that this method has higher location accuracy and robustness.

Desensitization design method of an off-axis three-mirror anastigmatic optical system based on the nodal aberration theory

A design method for an off-axis three-mirror anastigmatic optical system with low misalignment sensitivity is proposed. Based on the transformed pupil coordinate and nodal aberration theory, the analytical expressions between the optical parameters and misalignments are derived and an as-built performance evaluation model is established. The effects of mirror spacings and the off-axis magnitude value on the misalignment sensitivity are analyzed. With the desensitization design method proposed in this paper, a low-sensitivity off-axis TMA optical system can be designed. The simulation experiments show that, the misalignment sensitivity of the off-axis TMA optical system obtained by our method is approximately 60% that of the system obtained by nominal performance optimization method, and the as-built performance is about 1.49∼1.54 times that of the nominal performance optimization method.

A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. Data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account; from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6.7% of the literature focus on the impacts of human behavior and climate change on building retrofits, respectively. In the future, it is necessary to give further consideration to user behaviors and long-term climate change in the retrofit process, in addition to improving the accuracy of optimization models and exploring the generalization and migration capabilities of surrogate models.

Differences in Musculoskeletal Injury and Disability Rates in U.S. Navy Sailors Aboard Aircraft Carrier and Amphibious Assault Ships.

Musculoskeletal injuries (MSKI) are the most common clinical condition in the military that affect medical readiness. Evaluation of MSKI burden and the effects of these injuries on readiness in large deck Navy ships is warranted. A retrospective cohort study assessing population-level MSKI rates, short-term disability (restricted duty), and long-term disability episode counts of all Sailors assigned to U.S. Navy Aircraft Carriers (CVNs) and Amphibious Assault Ships (LHA/LHD) from November 2016 to February 2023 was extracted from the Musculoskeletal Naval Epidemiological Surveillance Tool. A negative binomial regression and general additive (Gaussian) models evaluate the association of ship platform, deployment status, days underway, and sex with MSKI rates and the proportion of cases that resulted in short-term disability, returned-to-duty following restricted duty, or progressed to long-term disability. Sailors attached to CVNs contributed a mean 17,893.8 ± 23,280.6 person-months, with those attached to LHA/LHDs contributing an average 5,981.8 ± 8,432.7 person-months. Aboard CVNs, MSKI occurred at a rate of 0.30 ± 0.16/1,000 person-months while deployed and 0.64 ± 0.31/1,000 person-months in homeport. Aboard LHA/LHDs, Sailors incurred MSKI at a rate of 0.59 ± 0.58/1,000 person-months while on deployment and 1.24 ± 0.68/1,000 person-months in homeport. Among Sailors aboard CVNs, short-term disability occurred in 7.95 ± 7.75% of MSKI cases while deployed and 5.13 ± 5.26% while in homeport. Aboard LHA/LHDs, 8.57 ± 13.42% of MSKI cases were placed on short-term disability while deployed and 4.95 ± 5.27% while in homeport. In the multivariable assessment of short-term disability, being deployed underway was a significant factor (B = 3.62 P = .03, variance explained = 3.86%). Sailors that were female and served aboard LHA/LHDs returned to full duty at a significantly greater frequency compared to their male counterparts and Sailors serving aboard CVNs. None of the independent variables evaluated were associated with long-term disability. The findings in the current study demonstrate the substantial burden of MSKI aboard large deck ships, both in homeport and while deployed. Inclusion of a physical therapist aboard LHA/LHDs, like the CVN, may help to prevent and mitigate the effects of MSKI through early access to specialized care and integral injury prevention and performance optimization methods.

Performance optimization of VPP in fast frequency control ancillary service provision

This paper proposes a computation offloading method for performance optimization in the fast frequency control ancillary service (FFCAS) provision of a virtual power plant (VPP). VPP aggregates massive demand-side resources to provide power systems with the FFCAS. It faces excessive communication and computation tasks. Edge computing addresses these issues through computation offloading. Heavy tasks can be partially offloaded to the edge to relieve the burden of the central server. Existing offloading methods, however, are not specific for the VPP. To fill the gap, first, an age of information (AoI) model is introduced to characterize the data flow of an edge-enabled VPP. Next, an AoI-based FFCAS performance evaluation model is proposed considering the impacts of communication delay, communication failures, and computational delay. Then an FFCAS performance optimization model is formulated. It aims to maximize VPP’s profit through communication offloading and the DSR portfolio determination. Simulation results show that the proposed method can efficiently improve VPP’s profit.

The Design and Real-Time Optimization of an EtherCAT Master for Multi-Axis Motion Control

To address the issues of low bandwidth, weak real-time performance, and poor synchronization in traditional fieldbuses for multi-axis motion control, a solution for the implementation of an EtherCAT master based on the IgH EtherCAT Master open-source software framework and an embedded hardware platform is proposed. On a hardware platform centered around the AM64x Sitara processor, a Linux real-time operating system based on the Xenomai real-time kernel is constructed, and the IgH master framework is ported to realize a high-performance EtherCAT master. The configuration process of the EtherCAT bus is detailed, a master application program is developed, and methods for the real-time performance optimization of the master—such as exclusive CPU usage by the master process and the optimization of the network card driver—are proposed. Finally, experiments are conducted on a six-axis servo control platform, with the packet analysis of the periodic EtherCAT data frames sent by the master. The experimental results show that the optimized master, under a high-speed communication cycle of 500 microseconds, maintains maximum jitter within 20 microseconds and average jitter within 1 microsecond, meeting the requirements for high-precision multi-axis motion control.

Multi-morphological design of TPMS-based microchannels for thermal performance optimization

Higher heat flux density in more compact and powerful electronic systems calls for further innovative solutions. Microchannel heat sinks based on triply periodic minimal surface (TPMS) can help dissipate highly concentrated heat, ensuring the system’s safety and longevity. Some researches indicate that compared to traditional TPMS structures, multi-morphology TPMS structures can further improve performances and adapt to local functional requirements. However, the morphology distribution has not been analyzed from a thermal performance perspective. In this study, a multi-morphological TPMS-based microchannel design method for thermal performance optimization is proposed. Firstly, the thermal properties of different TPMS structures are analyzed using the homogenization method, which provides a theoretical basis for TPMS distributions and their transition boundaries. Then, a beta growth algorithm is proposed to smoothly transition these regions with complex transition boundaries. A series of designed microchannels show that this method can not only greatly increase design freedom but also automatically guarantee the geometric continuity of heterogeneous TPMS structures. Finally, the optimized microchannel and the traditional gradient microchannel are compared in a finite element analysis simulation. And the results show that the optimized structure’s cooling efficiency increased by 4.4%, and its cooling uniformity improved by 15%, proving that properly arranging the TPMS morphology can further enhance thermal performance.

Analysis of Reliable Transmission Performance Optimization Methods for Satellite-to-Ground Laser Communication Links

The satellite-to-ground laser communication link plays a crucial role in addressing the technical bottleneck of high-speed information transmission between satellite and ground networks. However, the impact of atmospheric channel conditions poses challenges to the link performance. To overcome these challenges, this paper presents a comprehensive analysis of the characteristics and influencing factors of the satellite-to-ground laser communication link. It summarizes methods for optimizing the transmission performance of the link and proposes approaches for optimizing, monitoring, and predicting the link performance through factors such as the optimization of ground station quantity and location, link availability, link transmission quality, and adaptive coding and modulation. These methods enable the optimization of the transmission performance of the satellite-to-ground laser communication link, thus significantly contributing to the realization of high-speed and reliable laser communication between satellite and ground networks.

Adaptive Kriging-Based Heat Production Performance Optimization for a Two-Horizontal-Well Geothermal System

Optimizing heat generation capacity is crucial for geothermal system design and evaluation. Computer simulation is a valuable approach for determining the influence of various parameter combinations on a geothermal system’s ability to produce heat. However, computer simulation evaluations are often computationally demanding since all potential parameter combinations must be examined, posing significant hurdles for heat generation performance evaluation and optimization. This research proposes an adaptive Kriging-based heat generation performance optimization method. Firstly, a two-horizontal-well geothermal system with rectangular multi-parallel fractures is constructed. The heat production performance optimization problem is then established, and the temperature and enthalpy of the outlet water are calculated using computer simulation and Kriging. A parameterized lower confidence bounding sampling scheme (PLCB) is developed to adaptively update Kriging in order to strike a compromise between optimization accuracy and computation burden. The outcomes of the optimization are compared to those of the Kriging-based optimization approach and other common infill options to demonstrate the efficiency of the proposed method. The outlet temperature curve obtained with PLCB-AKO-1 rose for a longer time and the heat generation power curve reached a stable output without a downward trend. According to the Friedman and Wilcoxon signed ranks tests, the PLCB-1-AKO technique is statistically superior to alternative strategies.

Peltier-based temperature regulation: A method for performance optimization in solid-state lasers

This article presents a direct method for temperature control in solid-state lasers, where temperature stability is crucial for optimizing the performance and reliability of such lasers. The proposed method utilizes Peltier chips for both cooling and heating the laser crystal to achieve precise temperature regulation.The system design is based on the step response of the open-loop thermal system and employs a proportional-integral (PI) controller for closed-loop temperature control. Comprehensive testing on a femtosecond Titanium-Sapphire Laser (Ti:Sapphire laser) demonstrated that the system is capable of maintaining the desired operating temperature with remarkable stability and efficiency, highlighting its practicality for real-world applications.Method Outline:•Utilization of Peltier chips for precise temperature control.•Estimation of first-order transfer function based on step response.•Implementation of a proportional-integral (PI) controller for closed-loop temperature regulation.

Performance optimization of cement calciner based on CFD simulation and machine learning algorithm

In order to improve the combustion efficiency and decomposition rate of the cement calciner and reduce pollutant emission, a performance optimization method based on Computational Fluid Dynamics (CFD) numerical simulation integrated with machine learning is proposed. The Multiphase Particle-in-cell (MP-PIC) method and the chemical reaction models are employed to simulate the coal combustion and CaCO3 decomposition process, whose calculation results are combined with the industrial practical data of the cement plant, so as to establish a more comprehensive training database. On this basis, a novel Topology Particle Swarm Optimization algorithm integrating with Convolutional Neural Network and Long Short-Term Memory (RITPSO–CNN–LSTM) algorithm model is established to predict combustion efficiency, decomposition rate, and NOx emission, respectively. Results show that compared with two other relative basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.045 %, 0.038 %, and 0.021 %, respectively. The addition of CFD simulation data makes the prediction model more applicable with higher stability and accuracy. Based on the prediction results, Grey Wolf Optimizer (GWO) algorithm is employed to optimize operating parameters, and finally the average optimization amount of combustion efficiency, decomposition rate, and NOx emission are 2.17 %, 2.24 %, and 6.15 ppm, respectively, which meet the optimization requirements.

Amphibious vehicle's resistance optimization through neural networks and genetic algorithms

Amphibious vehicles, as a new type of aquatic and terrestrial transport platform, are increasingly involved in the existing transportation system. Resistance is a key factor that affects the efficiency and energy consumption of vehicles in aquatic sailing. Resistance reduction optimization design is a focal point and challenge in the design process of amphibious vehicles. In this paper, a resistance performance optimization method has been proposed based on neural networks and genetic algorithms. First, key parameters for the shape design are extracted based on a thorough understanding of the vehicle's performance. These parameters are used to construct a parameterized design space. Second, a training set is obtained based on the Latin hypercube sampling method and numerical calculation methods, and a test set is randomly generated. To achieve better resistance prediction performance, a method based on the genetic algorithm-optimized backpropagation Neural Network is proposed. Next, the resistance performance of the two operating conditions is optimized through the non-dominated sorting genetic algorithm II, and optimized configuration parameters are obtained, which has a 22.71% energy-saving ratio at cruising speed. Finally, the optimized configuration is analyzed using numerical calculation methods to validate the resistance prediction and optimization methods.

Current Study on A* Algorithm in Autonomous Obstacle Avoidance

The A* algorithm is a heuristic search algorithm widely used in the fields of path planning and graph search. This paper aims to delve deeply into the principles, implementation, and performance optimization of the A* algorithm in practical applications. Firstly, we elaborate on the basic principles of the A* algorithm, including the definitions of cost function and heuristic function, as well as how to achieve efficient search by maintaining open lists and closed lists. Secondly, we analyze the application of the A* algorithm in path planning problems. By constructing environmental models and designing appropriate heuristic functions, we achieve optimal path search from the start point to the end point. Additionally, we explore the performance optimization methods of the A* algorithm, including strategies such as utilizing priority queues to manage nodes to be searched and dynamically adjusting heuristic functions, to improve the execution efficiency and search accuracy of the algorithm. Finally, through a series of experiments and case studies, we verify the effectiveness and superiority of the A* algorithm in path planning problems. The research results of this paper provide beneficial references and insights for the application of the A* algorithm in related fields.

Performance evaluation of conductive materials in conductive mortar based on machine learning

Conductive mortar auxiliary anode (CMAA) is one of the main materials to realize stable operation of cathodic protection system. However, the conductive material in CMAA is complex and diverse, and its basic physical properties have a complex functional relation with the type and content of conductive material. At present, there is no uniform evaluation criterion, which can realize the evaluation of the action properties of different conductive substances. To address this issue, machine learning was proposed to establish predictive models between input and output factors. The SHAP-value was used to analyze the model's global and local features, and the partial dependency of prediction results. The results show that: on the basis of 241*3 groups of databases, the predictive performance analysis of RFR, GB, XGBoost and LGBM models shows that LGBM is the optimal model, and the determination coefficients of its training set and test set are 0.98 and 0.97, respectively. The LGBM model, combined with SHAP-Value analysis and PDP analysis, reveals the importance ranking of the input features that affect the compressive strength, and can also provide a quantitative evaluation of the effect of the input factors against the compressive strength, providing a new analysis method for material design and performance optimization.

Display performance optimization method for light field displays based on a neural network

Crosstalk between adjacent views, lens aberrations, and low spatial resolution in light field displays limit the quality of 3D images. In the present study, we introduce a display performance optimization method for light field displays based on a neural network. The method pre-corrects the encoded image from a global perspective, which means that the encoded image is pre-corrected according to the light field display results. The display performance optimization network consists of two parts: the encoded image pre-correction network and the display network. The former realizes the pre-correction of the original encoded image (OEI), while the latter completes the modeling of the display unit and realizes the generation from the encoded image to the viewpoint images (VIs). The pre-corrected encoded image (PEI) obtained through the pre-correction network can reconstruct 3D images with higher quality. The VIs are accessible through the display network. Experimental results suggest that the proposed method can reduce the graininess of 3D images significantly without increasing the complexity of the system. It is promising for light field displays since it can provide improved 3D display performance.

Study on the Permeability and Mechanical Properties of Sandy Soil Under Carbon Fiber-Based Urease Mineralization

In recent years, soil solidification by EICP method is a green and environmentally friendly foundation treatment technology that has become popular in the field of geotechnical engineering. Compared with the traditional chemical induction method and the MICP method, the EICP method is not easily restricted by environmental factors associated with engineering geological problems and is green and environmentally friendly with lower cost. In this paper, a study was carried out to improve the seepage-mechanical properties of sandy soils by the EICP method. First, soybean urease solution extraction and its performance optimization methods were explored. Tests were conducted to compare urease extraction and detect activity. The activity of urease was 7.1% higher in deionized water than Laoshan mineral water. Second, the EICP grouting mineralization test was performed by using the carbon fiber reinforcement method. The CaCO3 content, unconfined compressive strength, porosity, permeability and other indicators of the sandy soil were systematically analyzed, and microscopic tests were conducted. The test results showed that after carbon fibers were added to the sand, the mineralization effect of EICP led to the precipitation of CaCO3 that adhered to the surfaces of sand grains and fibers and filled and cemented the pores between sand grains, which improved the integrity of the spatial structure and formed a stable solid sand-fiber monolithic composite structure. When the carbon fiber content was 1.2%, all aspects of performance were optimum. The above test results showed that the carbon fiber-based EICP mineralization method could effectively inhibit the brittle failure and improve the permeability and mechanical properties of sandy soil. This method has good application prospects and development potential for use in engineering geology.

A New Global Gas Dynamics Performance Optimization Method for Refrigeration Centrifugal Compressor Stage Based on the Immune Algorithm

Abstract This study proposes a new global gas dynamics optimization method, which was applied to a multi-objective optimization task of centrifugal compressor performance with the aim of determining the improvement probability for achieving high efficiency across a wide operating range. Initially, the original nondominated neighbor immune algorithm (NNIA) was extended to solve constrained multi-objective optimization problems for the first time, which mainly incorporated a procedure for handling inequality and equality constraints without additional parameters. Subsequently, an adaptive topological back-propagation multilayer feed-forward artificial neural network (BP-MLFANN) was trained using the modified NNIA to quickly evaluate the fitness value of the centrifugal compressor stage performance during the optimization. The feasibility of the method was validated using the first stage of a refrigeration centrifugal compressor. The results indicated a substantial enhancement in the stage efficiency of the optimized impeller at the Near-stall, Design, and Near-choke operating points, with increasement of 1.8%, 1.9%, and 4%, respectively, as compared to the baseline stage. The flow field analysis shows that the impact loss at impeller leading edge and flow separation in the passage reduced greatly, the mixing process between the leakage flow and mainstream in the channel is significantly weakened, thus the flow field becomes more uniform after optimization. The new global gas dynamics optimization method provides a reference for the development of efficient and rapid optimization techniques for centrifugal compressor.

HK-SPSA based performance optimization method for steam generator liquid level control

Steam generator level control systems play a crucial role in ensuring safe, economical, and stable operation of nuclear power plants. However, traditional control systems exhibit limited efficiency in commissioning and are susceptible to human error. Additionally, traditional parameter tuning methods are typically experienced-based, cumbersome, and time-consuming, making it challenging to obtain optimal parameters. To address these issues, we propose a hybrid knowledge-guided improved simultaneous perturbation stochastic approximation algorithm (HK-SPSA) to optimize the control parameters of steam generator level control systems. Firstly, the algorithm utilizes iteration point adjacency information to approximate the current optimization process status and guide the adaptive adjustment of the iteration step size. Secondly, it utilizes composite gradient information generated by the estimated historical and current gradients to guide the optimization direction and the tuning of iteration step size. Simulation experiments have shown that HK-SPSA improves optimization performance and reduces the costs of optimization and adjustment compared to traditional methods.

CBF‐guided iterative learning control performance optimization for switched systems

AbstractThis article studies the control barrier function (CBF) guided tracking performance optimization for switched systems under iterative learning control (ILC). CBF is an effective method for handling control system state safety. If the safe set is designed as expected performance, and the unsafe set is not expected, then this may be a good choice for optimizing control system performance. We propose a tracking performance optimization method based on CBF to speed up the convergence of the tracking error. A safe set is used to describe the expected tracking performance, by utilizing CBF, safety is further transformed into a set of inequality conditions that constrain ILC controller gains, which ensures that the tracking error is always within the safe set. In addition, an event‐triggering mechanism (ETM) is considered to reduce unnecessary data transmission, and sufficient conditions for exponential stability of the tracking error of switched systems and ILC controllers design are obtained. Finally, a simulation example is given to verify the feasibility of the proposed method.