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This paper describes a 3D finite element (FE) based method to calculate the proximity losses for magnetic components in power conversion system. The proximity loss is the main concern of copper loss which causes ac losses in the winding. The FE model is built based on the definitions of the geometries, meshes, materials, electric circuits, boundary conditions, load conditions, as well as the characteristics of the wire. The total proximity loss is the sum of the power losses of each element calculated with the power loss density function using the obtained nodal flux densities via finite element analysis (FEA) at the given load condition. Owing to a detailed model with all the geometric parameters and thus the flux leakage and end-winding effects can be considered, this FEA approach can predict the flux density more accurately. In addition, non-sinusoidal current is analyzed to calculate the actual power loss in current distortion condition. Experimental tests have been implemented to validate the method. The approach is capable of calculating the energy consumption in power converter for efficiency approvement and energy management.

The accurate calculation of pressurized water reactor burned fuel neutron source intensity plays a significant role in the subcritical reactivity measurement. The calculation methods of both spontaneous fission source and (α, n) neutron source were researched in this paper. There were multiple sources of alpha particle in reactor core, including 238Pu, 239Pu, 240Pu, 241Am, 242Cm, 244Cm, etc. Among these sources, the dominant source was coming from 242Cm decay. 242Cm approximation method as well as ratio fitting method were proposed for obtaining (α, n) neutron source intensity. In 242Cm approximation method, all alpha particle sources were neglected with the exception of 242Cm. The ORIGEN code was used to analyze the ratio of (α, n) to the spontaneous fission neutron of 242Cm for different enrichment assemblies at different shutdown time. It is found that the ratio of (α, n) to the spontaneous fission neutron of 242Cm is fixed around 0.191. Therefore, the neutron number density of spontaneous fission neutron is multiplied by the factor of 0-191 to obtain (α, n) neutron source intensity due to 242Cm decay. On account of neglecting all (α, n) neutron source intensity with the exception of 242Cm, it is foreseeable that 242Cm approximation method would underestimate (α, n) neutron source intensity. In ratio fitting method, the ratio of (α, n) to the total neutron source intensity was analyzed. It is found that the ratio of (α, n) to the total neutron source intensity is linearly related to the burunp when the burnup is greater than 20 000 MW·d/tU. Therefore, two fitting methods were proposed to obtain the ratio of (α, n) to the total neutron source intensity. The polynomial fitting method was used with burnup less than 20 000 MW·d/tU, on the other hand, the liner fitting method was adopted with burnup greater than 20 000 MW·d/tU. Based on the in-house core design code package, the burned fuel neutron source calculation module was developed. In order to account for reactor core space effect and burnup history effect, the micro-depletion correction method in core design code package was used to obtain accurate number densities of actinide nuclides. The developed module was verified by calculating fuel assembly examples. Comparing with the reference code, the maximum errors of total source intensity is about 5% provided by both 242Cm approximation method and ratio fitting method. With the developed burned fuel neutron source intensity calculation module, both radial and axial distributions of burned fuel neutron source intensity in the reactor core are obtained. These distributions are of significant importance to ex-core detector signal measurements. The work in this paper is an important technology support for subcritical rod worth measurement development.

Given the high saturation, strong nonlinearity, and tight coupling characteristics of the magnetic paths in a switched reluctance motor (SRM), accurately modeling flux-linkage is challenging, thus leading to excessive torque ripple during SRM torque control. In order to enhance the precision of the SRM flux-linkage model, a neural network-based method for the parameter identification of SRM's nonlinear flux-linkage model was proposed in this study. Logistic mapping elements were incorporated into the feedback layer of the diagonal recurrent neural network (DRNN), and chaotic control parameters were designed. Then, the construction of an analytical model of the nonlinear exponential flux-linkage function using the chaotic diagonal recurrent neural network (CDRNN) was established. By utilizing sample data of flux-linkage, current, and angular position, the parameters of this model were estimated, thus achieving a precise nonlinear exponential function model of the flux-linkage. Results show that, the integration of logistic mapping in the CDRNN feedback layer effectively prevents the local minima typically associated with conventional DRNN. The maximum error in the identified flux-linkage is less than 0.01 Wb, and the accuracy reached 95%. Compared with the DRNN method, the CDRNN approach demonstrates significantly reduced errors and higher model precision. This study offers valuable insights for enhancing the performance of SRM torque ripple control. [ABSTRACT FROM AUTHOR]