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Nowadays, brain tumor has become deadly disease. Therefore, early diagnosis can prevent the severity of the next stage. The existing methods are inefficient in predicting the optimal cluster center (CC) in radial basis neural network (RBNN), which leads to inaccuracy. To overcome this issue, we proposed an improved whale optimization algorithm (IWOA) of RBNN to maximize the convergence speed and accuracy. Initially, the MRI input images are fed to preprocessing steps. Then, the image segmentation is carried out by fuzzy-c means (FCM) clustering for identifying the tumor region. These tumor and non-tumor images undergo a feature extraction process utilizing principle component analysis (PCA), mean, entropy, and wavelet transform. The obtained feature vector is fed to the RBNN layer. It needs an optimal CC, which can be considered by using the newly proposed IWOA. RBNN classifies the abnormality of the brain into brain tumor, inflammatory disease, stroke, and degenerative. Its performance is tested on three datasets. Based on the report of the evaluation, the proposed FCM-IWOA-RBNN gives high accuracy. [ABSTRACT FROM AUTHOR]

The defrosting performance of automotive air conditioners plays an important role in driving safety. This paper uses CFD to simulate the internal flow field of the automobile numerically. Simulation results show that the flow distribution is unreasonable. The horizontal grilles are added at the outlets to improve the defrosting performance of the automobile. Air-flow jet angle and the length of the air conditioning outlets (L1, L2) are selected as design variables based on the radial basis neural network to find the optimal combination scheme. The area of the defrosting dead corner has been reduced from 20-5% after optimization, and the frost layer of the front windshield has been completely melted in 25 minutes. The experiment test is conducted to verify the improvement of the defrosting performance of automotive air conditioners. The design methodology can be applied to the development of the air conditioner. [ABSTRACT FROM AUTHOR]

This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance.

The double vane pump is a special type of flow vane centrifugal pump. It adopts a design with less blades, which leads to a disadvantage that the performance of the double vane pump is inferior to that of the multi-blade pump at the same specific velocity. Its stability is 3%-8% lower than of a vane centrifugal pump.Therefore, it is necessary to improve the work efficiency by optimizing the hydraulic design. This article took a double-passage sewage pump model 80QW50-15-4 as the research object. The optimization objective was to design the head and efficiency of the flow point. ANSYS CFX(computational fluid dynamics x) was used to perform numerical simulation to obtain performance data. According to the two-dimensional hydraulic drawing of the initial model pump, the three-dimensional modeling software Pro/Engineer5.0 was used to simulate the water body of the impeller and the volute and to perform mesh division and irrelevance verification. The model pump was subjected to numerical simulation and experiment of clear water medium, and the performance curve was obtained and compared. The error analysis showed that the maximum error of head and efficiency was 3.9% and 1.7%, which meant that the performance prediction model established by this method had high accuracy. Partial initial model impeller structure parameters were selected for performance impact analysis. The Plackett-Burman screening test was used to determine the blade wrap angle, blade outlet angle and impeller outlet width were significant factors affecting head and efficiency of design flow. According to Fang Kaitai's unified design table, training samples of RBF(radial basis function) neural network were arranged, so as to establish important structural parameters and performance prediction models, and generated 5 groups of structural parameters random for neural network testing and error analysis. The head and efficiency performance prediction model trained by radial basis neural network was introduced into the particle swarm optimization algorithm as the fitness evaluation model of particle swarm optimization algorithm. The Pareto optimal solution set of head and efficiency was obtained, and the optimal head and efficiency were selected. In addition, this paper also studied the performance and internal flow field differences of the initial individual, the optimal individual of head and the optimal individual of efficiency when transporting different media. It was known from the performance curve that the performance of individuals was improved when transporting different media. The reason for the performance improvement was revealed by the internal flow field distribution map. In order to verify the practicability of the optimization results, a clear water test was performed on the optimal head and the most efficient individual to obtain a performance curve and compared with the performance curve of the initial individual. Among them, the experimental head of the optimal head at the design flow point increased by 0.96 m than the initial individual, the increase rate reached 5.5%, the efficiency increased by 1.6 percentage point; the efficiency of the best individual increased by 10.11 percentage point, the head decreased slightly but met the design requirements. The test proved that the optimization effect was obvious. This optimization method improves the hydraulic characteristics of impeller and the performance of double vane pump. [ABSTRACT FROM AUTHOR]

High temperature resistance and corrosion resistance make ceramic materials available for heat exchangers operating under high temperature or harsh chemical conditions. And the importance of the effect of flow maldistribution on thermal-hydraulic performance of heat exchangers has been demonstrated by a lot of researches. In this work, the nonuniformity of fluid flow is focused on to improve the performance of a microchannel ceramic heat exchanger. The inlet part of a microchannel ceramic heat exchanger is optimized using surrogate model coupled with genetic algorithm. Specifically, 30 sample points are designed by Latin hypercube sampling method and calculated by computational fluid dynamics. Radial basis neural network is established with the sample data and employed to predict the specific fluid flow distribution within design space as a surrogate model. Specifically, the surrogate model predicts the specific flow distribution instead of a single target value of nonuniformity in previous surrogate model. The results indicate that such a method has a significant advantage over the previous surrogate model. The genetic algorithm is implemented to search for the optimal point. The nonuniformity of fluid flow is reduced by 68.2% and pressure drop is increased by 6.6% by the optimization, which means the uniformity of fluid flow in the heat exchanger is improved significantly with just a little cost of pressure drop. [ABSTRACT FROM AUTHOR]

Abstract: This investigation applied a hybrid method, which combined a trained radial basis network (RBN) and a sequential quadratic programming (SQP) method [R. Fletcher, Practical Methods of Optimizations, vol. 1, Unconstrained Optimization, and vol. 2, Constrained Optimization, John Wiley and Sons Inc., New York, 1981], to determine an optimal parameter setting for photoresist (PR) coating processes of photolithography in wafer manufacture. Nine experimental runs based on an orthogonal array table were utilized to train the RBN and the SQP method was applied to search for an optimal setting. An orthogonal array table provided an economical and systematic arrangement of experiments to map the relationship between controlled parameters and desired outputs. In this study, a mean thickness and non-uniformity of the thickness of the PR were selected as monitored quality targets for the PR coating process. In addition, the PR temperature, the chamber humidity, the spinning rate, and the dispensation rate were four controlled parameters. The PR temperature and the chamber humidity were found to be the most significant factors in the mean thickness and non-uniformity of the thickness for the PR coating process from the analysis of variance (ANOVA). [Copyright &y& Elsevier]

This study analyzes the contour distortions of polypropylene (PP) composite components applied to the interior of automobiles. Combining a trained radial basis network (RBN) [] and a sequential quadratic programming (SQP) method [], an optimal parameter setting of the injection molding process can be determined. The specimens are prepared under different injection molding conditions by varying melting temperatures, injection speeds and injection pressures of three computer-controlled progressive strokes. Minimizing the contour distortions is the objective of this study. Sixteen experimental runs based on a Taguchi orthogonal array table are utilized to train the RBN and the SQP method is applied to search for an optimal solution. In this study, the proposed algorithm yielded a better performance than the design of experiments (DOE) approach. In addition, the analysis of variance (ANOVA) is conducted to identify the significant factors for the contour distortions of the specimens. [ABSTRACT FROM AUTHOR]

This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance. [ABSTRACT FROM AUTHOR]