• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2276-2283
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• 2018

A 50kW-4000rpm interior permanent magnet synchronous machine (IPMSM) applied to the high-performance electric vehicle (EV) is introduced in this paper. The main work of this paper is that a 2-D T-type lumped-parameter thermal network (LPTN) model is presented for IPMSM temperature rise calculation. Thermal conductance matrix equation is generated based on calculated thermal resistance and loss. Thus the temperature of each node is obtained by solving thermal conductance matrix. Then a 3-D liquid-solid coupling model is built to compare with the 2-D T-type LPTN model. Finally, an experimental platform is established to verify the above-mentioned methods, which obtains the measured efficiency map and current wave at rated load case and overload case. Thermocouple PTC100 is used to measure the temperature of the stator winding and iron core, and the FLUKE infrared-thermal-imager is applied to measure the surface temperature of IPMSM and controller. Test results show that the 2-D T-type LPTN model have a high accuracy to predict each part temperature.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2329-2334
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• 2018

This paper discusses the thermal effect of the number of permanent-magnet (PM) layers in an outer rotor machine. Depending on the number of axial-layer of PM, the operating temperature is compared analytically and experimentally. The electromagnetic analysis is performed using 3-dimensional time varying finite element method to get the heat sources depending on axial-layered PM models. Then thermal analysis is conducted using the lumped-parameter-thermal-network method for each case. Two outer rotor machines, which have the different number of axial-layer of PM, are manufactured and tested to validate the analysis results.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.975-983
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• 2013

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink(R) and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

• Tunnel and Underground Space
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• v.6 no.4
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• pp.335-341
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• 1996

This fundamental study analyzes the relationship between rock thermal properties and psychrometric properties in underground space and has a ultimate goal to develope technologies for predicting major environmental variables. The study is divided into 2 subjects (1) developement of a basic model for predicting temperature and humidity, (2) analysis of the validity of the model through application to a local underground storage space for military supplies. The basic model is built for the network of tunnel-shaped underground spaces. The model takes into account rock thermal properties and changes in moisture content in the air due to condensation/evaporation on the rock surface. Using lumped-parameter analytical method, heat flux from or to the surrounding rock is calculated and then the psychrometric properties(air quantity, pressure, temperature, humidity) are estimated through network simulation. The model can be utilized regardless of the tunnel type. The study site is a local storage space built in rock, mainly granite gneiss and quartz-porphyry. It is a U-shaped tunnel, 593.5m long and 6x6.5m wide. Relative humidity inside has to be strictly controlled under 55% to avoid erosion of a certain types of supplies stored in 6 chambers with the capacity of 300~1.000 ton. The thermal conductivity varies between 2.734 and 2.779W/m$^{\circ}C$ and the thermal diffusivity is in the range of 1.119 and $1.152{\times}10^{-6}\;m^2/s$ the specific heat between 910 and $920\;J/kg^{\circ}C$. Relative errors of the predicted values of dry/wet temperature and relative humidity are 0.8~3.0%, 0~7.5% and 0~7.0%, respectively. Apparent errors associated with the rock surface temperature seems to be partly due to the intrinsic limitations in the infrared thermometer used in this study.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.