Thermal Prediction Method Research Articles

MODELING THERMAL BEHAVIOR IN HIGH-POWER SEMICONDUCTOR DEVICES USING THE MODIFIED OHM’S LAW

This paper addresses the challenge of thermal management in high-power semiconductor devices, where increasing power densities and complex operating environments demand more accurate thermal prediction methods. Traditional approaches often rely on simplified models that do not account for the crucial factor of temperature-dependent resistance variations. This limitation leads to inaccurate device temperature predictions, potentially compromising device reliability. This work proposes a novel approach for thermal management by introducing the first empirical application of a Modified Ohm’s Law. This modified law incorporates an exponential term to account for the non-linear relationship between temperature, current, and resistance. The paper demonstrates through simulations and empirical validation that the Modified Ohm’s Law offers a more accurate representation of thermal behavior compared to the standard version. This translates to more precise predictions of device temperature, especially during periods of rapid temperature changes. The validation process goes beyond simply establishing the Modified Ohm’s Law. It provides valuable insights into the thermal dynamics of the device, allowing for the refinement of simulation parameters used to assess various cooling strategies. These strategies include simulating different heat sink geometries and materials, modifying airflow rates over the device’s surface, and exploring the impact of Thermal Interface Materials (TIMs) between the device and the heat sink. By incorporating these elements, the simulations provide a more comprehensive picture of the device’s thermal behavior under various operating conditions and cooling configurations. Ultimately, this paper not only advances the theoretical understanding of thermal management but also offers practical benefits. Through enabling more accurate thermal predictions, the Modified Ohm’s Law model paves the way for informed decision-making in device design and optimization.

Development of a simplified thermal performance prediction method based on free-running temperatures and building envelope characteristics

Various thermal prediction methods and tools for conceptual building design have been rapidly developed in the past decades. However, most of these methods have some drawbacks to obsess their applications, such as heavily relying on very detailed information of building envelope components, and their thermal conductivity data with complicated computation models or formulas; and difficulties for architects to understand and judge the outcomes of such predictions in a conceptual design. Therefore, this research targets on developing a simplified method to predict the integrated thermal performance of a building with simplified envelope parameters and hourly free-running temperatures in the conceptual design stage. It categorizes the scattered design parameters into three combined thermal characteristic indexes and applies a serials of corresponding quasi-steady calculation methods for building thermal processes to predict hourly free-running temperatures. The interrelationships between these three combined indexes and two overall thermal performance indicators are verified via a sensitivity analysis, and are presented in two charts, which can easily be adopted in a conceptual building design. This method has been validated in an experimental case study. It can help architects to quickly find the thermal design performance of a designed building in the conceptual design stage.

Fast Iron Loss and Thermal Prediction Method for Power Density and Efficiency Improvement in Switched Reluctance Machines

To effectively find an optimal solution in the development process of electrical machines, it is essential to predict machine iron loss and thermal behavior in the predesign stage. Most existing publications only consider thermal behavior by roughly limiting the maximum current density. However, especially for high-speed application, iron loss can also be an essential part in thermal-behavior estimation. This paper proposes a simplified model to efficiently calculate iron loss and thermal behavior in the design procedure of switched reluctance machines. For this purpose, a maximum coenergy loop control method is implemented to facilitate the estimation of flux waveform and machine torque. Three different iron loss calculation methods are compared in terms of accuracy and time. Besides, a simplified lumped parameter thermal network is applied for machine sizing. Furthermore, the impact of number of turns and peak magnetomotive force on the losses and machine sizing is discussed. The efficiency and power density of various machine geometries are comparatively analyzed. Finally, the accuracy of the simplified model is verified by comparison with finite element simulations and experiment results.

Optimal Planning of Dynamic Thermal Management for NANS (N-App N-Screen) Services

Existing multi-screening technologies have been limited to mirroring the current screen of the smartphone onto all the connected external display devices. In contrast, NANS (N-App N-Screen) technology is able to display different applications (N-App) on different multiple display devices (N-Screen) using only a smartphone. For such NANS services, this paper empirically shows that the thermal violation constraint is more critical than the battery life constraint. For preventing the thermal violation, the existing DTM (Dynamic Thermal Management) techniques cannot be used since they consider thermal violations as abnormal, and hence prevent them by severely throttling CPU frequencies resulting in serious QoS degradation. In NANS service scenarios it is normal to operate in high temperature ranges to continue services with acceptable QoS. Targeting such scenarios, we first propose a novel thermal prediction method specially designed for NANS services. Based on the novel thermal prediction method, we then propose a novel DTM technique called, “thermal planning” to provide sustainable NANS services with sufficiently high QoS without thermal violations.

Development of an Algorithm for Predicting the Thermal Distribution by using CT Image and the Specific Absorption Rate

During hyperthermia therapy, cancer cells are heated to a temperature in the range of 40 ~ 45 °C for a defined time period to damage these cells while keeping healthy tissues at safe temperatures. Prior to hyperthermia therapy, the amount of heat energy transferred to the cancer cells must be predicted. Among various non-invasive methods, the thermal prediction method using the specific absorption rate (SAR) is the most widely used method. The existing methods predict the thermal distribution by using a single constant for the mass density in one organ through assignment. However, because the SAR and the bio heat equation (BHE) vary with the mass density, the mass density of each organ must be accurately considered. In this study, the mass density distribution was calculated using the relationship between the Hounsfield unit and the mass density of tissues in preceding research. The SAR distribution was found using a quasi-static approximation to Maxwell’s equation and was used to calculate the potential distribution and the energy distributions for capacitive RF heating. The thermal distribution during exposure to RF waves was determined by solving the BHE with consideration given to the considering contributions of heat conduction and external heating. Compared with reference data for the mass density, our results was within 1%. When the reconstructed temperature distribution was compared to the measured temperature distribution, the difference was within 3%. In this study, the density distribution and the thermal distribution were reconstructed for the agar phantom. Based on these data, we developed an algorithm that could be applied to patients.

Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network

Abstract The solar radiation fluctuation occurs at practically anywhere on the earth. When a solar thermal power generation system is designed for the areas with considerable solar radiation fluctuation, the collector tube exit temperature becomes more difficult to predict and requires significant calculation time. This paper presents a fast and accurate transient thermal prediction method to predict the parabolic trough collector tube exit temperature. In this work, an artificial neural network (ANN) is combined with the principle of superposition. ANN is used to predict the exit temperature rise caused by a single heat flux pulse in the first step of the proposed methodology, while superposition is used to predict the from multiple heat flux pulses in the second step. Limited cases of conjugate heat transfer analytical results by the finite element method (FEM) are used to train the ANN. The one-day exit fluid temperature from 7 a.m. to 6 p.m. is predicted within 1 min of computational time with mean absolute deviation less than 2 K. The exit fluid temperature of the collector tube for one year operation can be predicted in less than 6 h. Because fluid velocity is included in the input parameters, the proposed methodology is especially useful for flow control simulations where a constant exit temperature is targeted. Through this, the optimum performance of collector tube under multiple radiation conditions can be assessed during an early design phase of parabolic solar trough systems. The predicted results can be used for initial system planning, heat balance analysis, and system design. Since the method shows good prediction capability under the fluctuating solar radiation as well as the stable solar radiation, it is applicable to be used for designing the parabolic trough technology at any weather conditions in the world.

TRANSIENT THERMAL PERFORMANCE PREDICTION METHOD FOR PARABOLIC TROUGH SOLAR COLLECTOR UNDER FLUCTUATING SOLAR RADIATION

As the effect of the global warming is becoming noticeable, the importance for environmental sustainability has been raised. Parabolic trough solar thermal collector system, which is one of the solutions to reduce the carbon dioxide emission, is a mature technology for electricity generation. Malaysia is a tropical country with long daytime, which makes suitable for solar thermal applications with parabolic trough solar thermal collectors. However, the high humidity causes the solar radiation to fluctuate. In order to simulate the solar thermal collectors’ performance at an early design stage of solar thermal power generation systems, fast still accurate transient thermal performance prediction methodis required. Although multiple transient thermal simulation methodologies exist, they are not suited especially at an early design stage where quick but reasonably accurate thermal performance prediction is needed because of their long calculation time. In this paper, a transient thermal prediction method is developed to predict exit temperature of parabolic trough collectors under fluctuating solar radiation. The method is governed by simple summation operations and requires much less calculating time than the existing numerical methods. If the radiation heat loss at the parabolic trough collector tube surface is small, the working fluid temperature rise may be approximated as proportional to the receiving heat flux. The fluctuating solar radiation is considered as a series of heat flux pulses applied for a short period of time. The time dependent solar collector exit temperature is approximated by superimposing the exit temperature rise caused by each heat flux pulse. To demonstrate the capabilities of the proposed methodology, the solar collector exit temperature for one-day operation is predicted. The predicted solar collector exit temperature captures the trend of a finite element analysis result well. Still, the largest temperature difference is 38.8K and accuracy is not satisfactory. Currently, the accuracy of the proposed method is being improved. At the same time, its capabilities are being expanded.