Particle Swarm Optimization Neural Network Research Articles

Heat transfer performance optimization of turbine blade internal cooling channel with pin-fins and recessed-extruded endwalls

This paper involves implementing and optimizing a novel recessed-extruded endwall design for a rectangular cooling channel equipped with pin-fins. The study encompasses an in-depth exploration of the flow dynamics, pondering the generation and sustenance of vortices within the channel, using Reynolds-averaged Navier-Stokes analysis. The study delves into the evaluation of heat transfer characteristics, considering crucial parameters such as Nusselt number, friction factor, and overall heat transfer performance. These attributes are contrasted with the corresponding values observed in cases featuring flat endwalls, concerning Reynolds numbers spanning from 7400 to 36,000. A parametric study of the channel with recessed-extruded endwalls is conducted, aiming to comprehend the influence of geometric factors on channel heat transfer. A single-objective optimization process focusing on enhancing heat transfer effectiveness, quantified through the heat transfer efficiency index (HTEI), is conducted with three design variables. The results indicate that the new endwall configuration significantly enlarges the high heat transfer areas near the pin-fins compared to the flat endwall across all Reynolds numbers. This novel endwall design substantially elevates the heat transfer levels adjacent to the pin-fins, showcasing an impressive 62.4% increase in HTEI at Re = 21,500 compared to the flat endwall, with a protrusion height of 2.5% of the channel height. Furthermore, by increasing the height, the recessed-extruded endwall configuration can achieve a remarkable 91.1% increase in HTEI compared to the flat endwall. This achievement is reached with a protrusion height equivalent to 5% of the channel height at Re = 7400. Through design optimization using the radial basis neural network (RBNN) surrogate model and particle swarm optimization (PSO) technique, the HTEI of the recessed-extruded endwall design shows enhancements of 91.7% and 18.0% compared with the flat endwall and reference designs, respectively, at a Reynolds number of 21,500. The present work demonstrates the potential for enhancing the heat transfer of pin-fins channels by improving endwall design.

Rapid and precise calibration of soil microparameters for high-fidelity discrete element models in vehicle mobility simulation

In the realm of numerical simulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to align with the mechanical responses of actual soil. In pursuit of a rapid and precise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models. This method calibrates the corresponding discrete element microparameters based on the macroscopic Mohr–Coulomb parameters derived from actual soil direct shear tests. The distinct contribution lies in the creation of a dataset that bridges the soil microparameters and macroparameters through simulated direct shear tests, which serves as training data for machine learning algorithms. Additionally, an adaptive particle swarm optimization neural network algorithm is proposed to represent the nonlinear relationships among parameters within the dataset, thus achieving intelligent calibration. To verify the reliability of the proposed soil calibration model in the context of vehicle mobility simulations, a co-simulation is conducted using a vehicle multibody dynamics simulation model and the calibrated soil model, with validation conducted across multiple criteria.

Modeling method of photovoltaic power generation grid connection based on particle swarm optimization neural network

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, Power-Reactive Power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances.

Control of Thermal Uniformity in Microwave Heating Process by BPNN and Adaptive Particle Swarm Optimization

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive Particle Swarm Optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of Particle Swarm Optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature closely follows the preset temperature curve. Experimental results demonstrate that the actual output value of the system closely aligns with the original preset temperature curve, with a root mean square error of only 0.74. This designed identification and control system effectively achieves the desired outcome.

Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding

The process parameters directly affect the quality and performance of laser welding (LW) by shaping the molten pool appearance. However, with the increase in the dimensions of process parameters and the number of samples, the relationship between process parameters and molten pool often exhibits high levels of uncertainty and nonlinearity. Machine learning (ML) techniques have been used for complex LW systems modeling, but they may be limited and unable to fully capture the complex relationships when faced with complex systems. Additionally, traditional ML techniques may lack interpretability within the model, making it difficult to understand the input features and their impact on the output. To address these issues, a method that combines an interpretable multi-task neural network (MTNN) and particle swarm optimization (PSO) is proposed. Firstly, an LW experimental platform is constructed, and the orthogonal design is conducted for data collection. In the data preparation stage, the curriculum learning strategy is employed to reorganize the raw experimental data. Subsequently, the MTNN is constructed to automatically learn feature representations among input process parameters, capture nonlinear relationships between data, and share weights across multiple related tasks to improve efficiency and performance. Furthermore, the PSO is designed to determine the constraints and objective functions. In addition, the shapley additive explanation (SHAP) method is utilized to calculate the importance of process parameters and enhance the interpretability. Finally, the optimization results are validated, and the results demonstrate that the optimized results of the proposed method have achieved satisfactory performance compared to the traditional ML methods.

Towards accuracy improvement in solution of inverse kinematic problem in redundant robot: A comparative analysis

AbstractThe redundant manipulators have more DOFs (degree of freedoms) than it requires to perform specified task. The inverse kinematic (IK) of such robots are complex and high nonlinear with multiple solutions and singularities. As such, modern Artificial Intelligence (AI) techniques have been used to address these problems. This study proposed two AI techniques based on Neural Network Genetic Algorithm (NNGA) and Particle Swarm Optimization (PSO) algorithm to solve the inverse kinematics (IK) problem of 3DOF redundant robot arm. Firstly, the forward kinematics for 3 DOF redundant manipulator has been established. Secondly, the proposed schemes based on NNGA and PSO algorithm have been presented and discussed for solving the inverse kinematics of the suggested robot. Thirdly, numerical simulations have been implemented to verify the effectiveness of the proposed methods. Three scenarios based on triangle, circular, and sine-wave trajectories have been used to evaluate the performances of the proposed techniques in terms of accuracy measure. A comparison study in performance has been conducted and the simulated results showed that the PSO algorithm gives 7% improvement compared to NNGA technique for triangle trajectory, while 2% improvement has been achieved by the PSO algorithm for circular and sine-wave trajectories.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Temperature compensation model for non-dispersive infrared CO2 gas sensor based on WOA-BP algorithm

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor.

Study on winter wheat leaf area index inversion employing the PSO-NN-PROSAIL model

ABSTRACT Leaf area index (LAI) assessment methods relying on physical and empirical models are considered to be the most commonly used method at present, but their estimation efficiency and accuracy are deficient. Although the hybrid model of these two methods can address these issues, a poor coupling mechanism can easily occur. Given that, a PROSAIL model coupling particle swarm optimization (PSO) neural network (NN) algorithm (PSO-NN-PROSAIL model) was introduced to invert the winter wheat LAI (WWLAI) at five distinct growth stages. The Xiangfu District in the east of Kaifeng City, Henan Province was served as the study region. Based on the measured WWLAI data at varying stages and GF-1 WFV satellite images, the initial analysis focused on assessing the PROSAIL model’s sensitivity in simulating vegetation canopy reflectance. It then calculated six vegetation index models according to the wavelength reflectance of GF-1 WFV and analysed their correlation with LAI to select the input parameters that could be used in the model. Normalized differential vegetation Index (NDVI) and ratio vegetation Index (RVI) as well as vegetation canopy reflectance were employed as input parameters to invert the WWLAI by adopting the PSO-NN-PROSAIL model. The experimental results showed the following: (1) In the vegetation index model, the determination coefficient (R2) of NDVI and RVI was greater than 0.68, implying that NDVI and RVI might serve as input factors for the proposed model in this paper; (2) LAI and chlorophyll a + b content (Cab) were most sensitive to the PROSAIL model in near-infrared and visible light bands; and (3) the PSO-NN-PROSAIL model possessed better LAI inversion accuracy. In summary, the model proposed in this paper provided a technical reference for rapid and accurate remote sensing monitoring of WWLAI.

Incident angle optimization with multiple conflicting objectives based on BP-MOPSO in oblique laser interferometry

The oblique incident phase-shifting interferometric measurement method is an effective technique for assessing gear tooth form deviation. In the measurement process, the tooth flank incident angle is a crucial factor determining the quality of interferograms. Addressing the challenge in traditional optical path design, where the selection of the tooth flank incident angle encounters a trade-off among quality features such as measurable tooth area (MTA), interference fringe density (IFD) and interferogram compression ratio (ICR), this paper proposes an optimized method for the tooth flank incident angle. Firstly, a comprehensive evaluation model is established to calculate and analyze the MTA, IFD and ICR of the helical gear. Secondly, by integrating BP neural network and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, a tooth flank incident angle optimization method is devised to simultaneously optimize the measurement light incident angle γ and the rotation angle of the gear axis β. Finally, simulation analyses and physical experiments are employed to demonstrate the feasibility of the proposed method, compared to before optimization, the pseudocorrelation (PSD) value has increased by an average of 34%, and the residual points have decreased by an average of 80%, confirming its capability to improve quality interferogram and measurement accuracy.

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

AbstractIn gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.

Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world’s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company’s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public’s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

RETRACTED ARTICLE: Network security threat detection technology based on EPSO-BP algorithm

With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.

Neural network-based modeling of solid oxide fuel cells for marine applications

As the solid oxide fuel cell (SOFC) experimental test is still quite cost-effective and time-consuming, there is a growing need for developing effective simulation tools to reduce the time and cost of the marine SOFC performance test and optimization. The present paper is aimed to study the modeling and simulation of marine solid oxide fuel cells by artificial intelligence method. A neural network based on a particle swarm optimization algorithm is used to establish a marine solid oxide fuel cell model for voltage/current characteristic analysis. The model is also compared with BP neural network and Hopfield neural network methods. The simulation results compared with experimental data show that the effectivity of the particle swarm optimization neural network algorithm is best, which can accurately predict the voltage/current characteristic curves of a SOFC under different fuel flow-air volume ratios. The model study can provide support for SOFC performance characteristics analyses and has significant potential in SOFC optimization applications.

Optimization of the double-slot blown airfoil with jet at the leading and trailing edges of the flap

An active lift augmentation technique combining trailing edge blowing and blown flap is explored in this study. A double-slot blown flap airfoil is designed based on NACA23015. By optimizing the configuration parameters via the neural network surrogate model and particle swarm optimization algorithm, the lift coefficient at the 8° angle of attack reaches ∼5.14, which is approximately a 24% increasement relative to the baseline single-slot blown flap airfoil. In the configuration optimization, a longer flap facilitates lower pressure on the upper airfoil surface. Additionally, the aerodynamic flap effect caused by the double-slot jet flow intensifies the lift peaks and the pressure decreases over the airfoil. Ultimately, redistributing the jet momentum to the two slots achieves a highly efficient blown flap airfoil design.

Multi-objective optimization design of aerodynamic and infrared characteristics of multi-stream serpentine nozzle

A multi-objective optimization design of multi-stream serpentine nozzle was carried out to improve the aerodynamic performance and reduce the infrared radiation signal. A numerical simulation was carried out to investigate the effects of nozzle pressure ratio (NPR), Aspect ratio (AR) and Ratio of length and diameter (L/D). Computational fluid dynamics (CFD) simulation was employed to study the aerodynamic performance of the multi-stream serpentine nozzle, and the narrow-band method with discrete transfer method were used to calculate its infrared radiation intensity distribution characteristics. A 3-factor, 21-level orthogonal table was designed based on the orthogonal experimental method, and multi-stream serpentine nozzle with different geometrical parameter and operating conditions were designed to obtain the aerodynamic performance and infrared radiation intensity data of each experimental sample point. Radial Basis Function (RBF) neural network and Multi-Objective Particle Swarm Optimization algorithm were used to optimize the geometry and operating conditions with better aerodynamic performance and weaker infrared signal. The result shows that the Pareto solutions obtained by the optimization have good accuracy compared with the numerical simulation results, with an aerodynamic performance error of 0.02% and an infrared radiation intensity error of 0.24%.

Enhancing Thermo-Acoustic Waste Heat Recovery through Machine Learning: A Comparative Analysis of Artificial Neural Network–Particle Swarm Optimization, Adaptive Neuro Fuzzy Inference System, and Artificial Neural Network Models

Waste heat recovery stands out as a promising technique for tackling both energy shortages and environmental pollution. Currently, this valuable resource, generated through processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a traveling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS, and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy (R2=0.9959), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. In doing so, it is possible to obtain an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision.

Prediction of Transformer Oil Temperature Based on an Improved PSO Neural Network Algorithm

Abstract: In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved PSO neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain extent. Background: The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters. Objective: This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements. Methods: The paper utilizes an oil temperature prediction model based on an improved PSO neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm. Results: This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm. Conclusion: This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.

Evaluation of mine water quality based on the PCA–PSO–BP model

Abstract To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry.