ENGINEERING-ORIENTED BENCHMARKING AND APPLICATION-BASED MAGNETIC MATERIAL MODELING IN TRANSFORMER RESEARCH

The paper highlights the engineering-oriented benchmarking and application-based magnetic material modeling, as two important events in transformer research, reviews the newly extended progress in TEAM (Testing Electromagnetic Analysis Methods) Problem 21 Family, and presents the related benchmarking results

ENGINEERING-ORIENTED BENCHMARKING AND APPLICATION-BASED MAGNETIC MATERIAL MODELING IN TRANSFORMER RESEARCH

The paper highlights the engineering-oriented benchmarking and application-based magnetic material modeling, as two important events in transformer research, reviews the newly extended progress in TEAM (Testing Electromagnetic Analysis Methods) Problem 21 Family, and presents the related benchmarking results

A neural network model of magnetic hysteresis for computational magnetics

A neural network to implement a hysteresis model for a magnetic material within a finite element program is described. It is shown that such a system can match the results produced by a Preisach model but the time overhead can be considerably reduced thus making feasible the solution of large problems involving hysteretic materials

Modeling Magnetic Materials using Artificial Neural Networks

The accurate and effective modeling of magnetic materials is critical to the prediction of the performance of electromagnetic devices. The paper discusses the use of artificial neural networks as a uniform method for modeling the behavior of magnetic materials both isotropic and anisotropic, and with and without hysteresis

Modeling of magnetic properties of GO electrical steel based on Epstein combination and loss data weighted processing

The extended modeling of the magnetic properties of GO (Grain Oriented) electrical steel is presented in this paper which is based on a set of standard and scaled-down Epstein frames and a proposed two-level weighted processing of Epstein data, including the mean magnetic path length, specific magnetization loss and exciting power. The effects of excitation frequency, strip angle and ambient temperature on the results obtained from the Epstein frames are investigated. It is shown that using the proposed Epstein combination and the two-level weighted processing method is an efficient way of building a model for determining magnetic losses more realistically, hence, improving the value of Epstein strip measurement data

Resilient cooling strategies – A critical review and qualitative assessment

The global effects of climate change will increase the frequency and intensity of extreme events such as heatwaves and power outages, which have consequences for buildings and their cooling systems. Buildings and their cooling systems should be designed and operated to be resilient under such events to protect occupants from potentially dangerous indoor thermal conditions. This study performed a critical review on the state-of-the-art of cooling strategies, with special attention to their performance under heatwaves and power outages. We proposed a definition of resilient cooling and described four criteria for resilience—absorptive capacity, adaptive capacity, restorative capacity, and recovery speed —and used them to qualitatively evaluate the resilience of each strategy. The literature review and qualitative analyses show that to attain resilient cooling, the four resilience criteria should be considered in the design phase of a building or during the planning of retrofits. The building and relevant cooling system characteristics should be considered simultaneously to withstand extreme events. A combination of strategies with different resilience capacities, such as a passive envelope strategy coupled with a low-energy space-cooling solution, may be needed to obtain resilient cooling. Finally, a further direction for a quantitative assessment approach has been pointed out

Generalized Material Models for Coupled Magnetic Analysis

The solution of coupled magnetic and thermal systems is important for the design of many electromagnetic devices. To achieve this, it is important to have an effective material model. This paper proposes a general material model based on a neural network which can take into account the temperature dependence of the magnetization curve

Dual-energy CT texture analysis with machine learning for the evaluation and characterization of cervical lymphadenopathy

Purpose: To determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or normal lymph nodes. Materials and methods: A retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC. Results: In the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively. Conclusion: Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy