Optimal brushless DC motor design using genetic algorithms

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.     

一种快速有效鉴定CRISPR/Cas9诱导水稻突变体的方法

突变体的筛选与鉴定是育种工作中的重要环节。该研究基于高光谱成像技术实现了水稻CRISPR/Cas9突变体种子的可视化鉴别。采集了水稻HD野生型和CRISPR/Cas9突变体种子共1 200粒样本的高光谱图像数据,通过Kennard-Stone算法,按照2∶1的比例构建了建模集(800)和预测集(400)。对水稻种子的原始光谱经过WT预处理后,通过2nd derivative提取了24个特征波长,分别基于全谱和特征波长建立径向基函数神经网络(RBFNN),极限学习机(ELM)和K最邻近法(KNN)模型。试验结果表明,无论是基于全谱还是特征波长神经网络模型都取得了良好的识别能力。通过2nd derivative提取的特征波长结合RBFNN模型也取得了较好的鉴别结果,其建模集和预测集分别达到了92.25%和89.50%。基于2nd derivative-RBFNN结合图像处理技术,可以实现水稻CRISPR/Cas9突变体种子的可视化鉴别,实现种子的定位和识别。结果表明应用高光谱成像技术,结合化学计量学方法和图像处理技术对水稻CRISPR/Cas9突变体的鉴别具有可行性,可为水稻育种中大量突变体的快速、准确地筛选和鉴定提供技术手段。     

径向馈入和轴向馈入对感应加速腔性能的影响

 感应加速腔有径向馈入和轴向馈入两种常用的脉冲功率馈入方式。在理论上分析了不同功率馈入方式对感应加速腔输出电压平顶的影响,并对分析结果进行实验论证。实验采用1B2C结构,用相同的脉冲功率源馈入径向腔和轴向腔,测量此两种加速腔的电压波形。测得轴向腔±1％电压平顶时间为61 ns,径向腔±1％电压平顶时间为62 ns,两种腔都可满足±1%电压平顶大于60 ns的要求。此外对不同功率馈入方式导致的横向阻抗的变化进行了数值模拟,分别计算了采用这两种馈入方式的加速腔模型的横向阻抗,发现轴向加速腔的横向阻抗较小。     

4单元矩形径向线螺旋阵列天线的理论分析和数值模拟

 提出了一种便于组合的矩形径向线螺旋阵列天线。介绍了该矩形阵列天线的提出背景以及工作原理,分析了L型电磁组合探针的耦合特性,设计并数值模拟了中心频率为4.0 GHz的4单元矩形径向线螺旋阵列天线。模拟结果表明：该天线易于实现单元天线激励幅度和相位的均匀性及其随频率变化的平坦性。该口径为90 mm×90 mm 的天线在中心频率4.0 GHz 下,方向性系数为12.57 dB,轴向轴比值1.55;在3.60～4.05 GHz的频率范围内方向性系数大于11.6 dB,轴比小于1.55,反射系数小于0.2。     

Modelling of micro-electrochemical machining parameters used for machining of holes on copper plate

The electrochemical machining is a non-conventional machining process based on the electrolysis principle. It is used to machine various features on conducting engineering materials with the required accuracy and precision. So an attempt has been made to machine micro-holes on a 300 μm thick copper plate as an anode and a hollow stainless steel tool electrode of 250 μm diameter as the cathode. The machining operation is performed on the in-house developed micro-ECM experimental setup with controlled machining parameters voltage, concentration, and duty factor varied in three levels. A full factorial experimental plan is used to study the output responses material removal rate (MRR), radial overcut (ROC), circularity, and taper angle (TA). Later an adaptive neuro-fuzzy inference system (ANFIS) model has been developed and shown the effectiveness of the developed model in the paper. The Sugeno fuzzy model has been used in ANFIS to generate the fuzzy rules required for the model. Out of 27 experiments, 22 machining data are used for training the model, and the remaining 5 machining data are used for testing the developed model. The average error observed between the ANFIS predicted values and experimental values of MRR is 12.56%, circularity is 43.09%, ROC is 13%, and TA is 27.53%, respectively.     

Semiclassical resolvent bounds for compactly supported radial potentials

We employ separation of variables to prove weighted resolvent estimates for the semiclassical Schrödinger operator $?h^2\mathrm{\Delta }+V(|x|)?E$ in dimension $n\ge 2$, where $h,E>0$, and $V:[0,\infty )\to \mathbb{R}$ is $L^{\infty }$ and compactly supported. The weighted resolvent norm grows no faster than $\exp ?(C{h}^{?1})$, while an exterior weighted norm grows $～h^{?1}$. We introduce a new method based on the Mellin transform to handle the two-dimensional case.     

Neural networks for predicting air gap membrane distillation performance

The hollow fiber air gap membrane distillation (AGMD) has recently attracted tremendous attention for desalination and wastewater treatment due to its high packing density, low conductive heat loss, and latent heat recovery capability. Utilizing fast and accurate modeling tools to predict MD performance can result in the further development of desalination technologies. However, simple and time-saving prediction models to assess the AGMD performance were not abundant. Herein, AGMD performance, including permeate flux (J) and gained output ratio (GOR) was predicted through multiple linear regression (MLR) model, back propagation neural network (BP ANN) and radial basis function neural network (RBF ANN) under different hot temperatures (T_h), coolant temperatures (T_c), feed flow rates (F), and feed concentration (c). A total of 30 sets of data were used to train the proposed models, the other 10 external validation datasets not used for training the models were applied to validate the prediction accuracy. The results depicted that RBF ANN (SPREAD = 30, N = 30) showed greatest prediction performance (R² = 0.99–1) compared with BP ANN and MLR models (R² = 0.98–0.99; R² = 0.89–0.97). The computing time consumption of RBF ANN was higher than BP ANN. According to the Mean impact value (MIV) analysis, T_h had the strongest effect on J and GOR. Increasing T_h and decreasing c both had positive impacts on J and GOR, but increasing T_c or F resulted in a trade-off influence. A genetic algorithm (GA) was employed to optimize J and GOR simultaneously, the optimum J and GOR could reach 6.00 kg/m²·h and 7.70 respectively. In this study, the three prediction models proved their abilities to predict AGMD performance and further provide guidance in the actual membrane distillation water treatment process.     

A novel radial basis Bayesian regularization deep neural network for the Maxwell nanofluid applied on the Buongiorno model

The aim of this work is to provide the numerical solutions of the fluid model by using the stochastic computing paradigms. The linear/exponential stretching sheets on magneto-rotating flow based on the Maxwell nanofluid have been provided using the Buongiorno model with the impacts of uneven heat source/sink, varying thermal conductivity and reactive species. The solutions of this transformed ordinary differential exponential stretching sheet model have been presented using a novel ‘radial basis’ (RB) activation function together with the Bayesian regularization deep neural network (BRDNN), i.e., RB-BRDNN. The deep neural network is presented into two hidden layers, while thirteen and twenty-five numbers of neurons have been used in the first and second layer. A reference dataset is proposed using the Runge-Kutta scheme for the model. The correctness of the stochastic RB-BRDNN procedure is examined through the comparison of proposed and database results, whereas minimal absolute error values provide the accuracy of the scheme. The reliability and competence of the computing RB-BRDNN solver is authenticated using the state transitions, correlation, regression, and error histograms.     

三维声波测井探测特性分析与处理技术应用

三维声波测井技术是近年来测井领域的重要进展,由于其集成了井孔轴向、径向和周向的三维信息探测能力,因而在各种复杂油气藏勘探开发中具有广阔的应用前景.从井孔声波探测的基本原理入手,对三维声波测井在井孔径向和周向的探测特性进行了研究.并且在传统资料处理方法的基础上,研发了适合三维声波测井的各向异性快速反演技术、径向速度层析反...