Hot Spot Temperature Research Articles

Design and modeling of indirectly driven magnetized implosions on the NIF

The use of magnetic fields to improve the performance of hohlraum-driven implosions on the National Ignition Facility (NIF) is discussed. The focus is on magnetically insulated inertial confinement fusion, where the primary field effect is to reduce electron-thermal and alpha-particle loss from the compressed hotspot (magnetic pressure is of secondary importance). We summarize the requirements to achieve this state. The design of recent NIF magnetized hohlraum experiments is presented. These are close to earlier shots in the three-shock, high-adiabat (BigFoot) campaign, subject to the constraints that magnetized NIF targets must be fielded at room-temperature, and use ≲1 MJ of laser energy to avoid the risk of optics damage from stimulated Brillouin scattering. We present results from the original magnetized hohlraum platform, as well as a later variant that gives a higher hotspot temperature. In both platforms, imposed fields (at the capsule center) of up to 28 T increase the fusion yield and hotspot temperature. Integrated radiation-magneto-hydrodynamic modeling with the Lasnex code of these shots is shown, where laser power multipliers and a saturation clamp on cross-beam energy transfer are developed to match the time of peak capsule emission and the P2 Legendre moment of the hotspot x-ray image. The resulting fusion yield and ion temperature agree decently with the measured relative effects of the field, although the absolute simulated yields are higher than the data by 2.0−2.7×. The tuned parameters and yield discrepancy are comparable for experiments with and without an imposed field, indicating the model adequately captures the field effects. Self-generated and imposed fields are added sequentially to simulations of one BigFoot NIF shot to understand how they alter target dynamics.

A Method for Calculating Hot-Spot Temperature of Traction Transformer Based on Multiphysical Model and Combined Neural Network

A Method for Calculating Hot-Spot Temperature of Traction Transformer Based on Multiphysical Model and Combined Neural Network

Coupled EMAG-CFD-Thermal analysis of a novel SST system utilizing forced-liquid cooling with biodegradable dielectric fluid

Solid state transformers (SSTs) are at the forefront of emerging technologies, enhancing distribution and transmission networks. These transformers facilitate the development of highly controllable and robust smart grids, seamlessly integrating renewable energy sources and accommodating connections for both alternating current and direct current components/lines at medium or low voltage levels. This research paper introduces an innovative SST incorporating the inductive power transfer (IPT) technology, currently under development within SSTAR, a Horizon Europe project. This novel device operates at a frequency of 50 kHz and provides with an output power of 50 kW. The IPT system is submerged in an ester-based, biodegradable dielectric fluid, providing effective cooling and insulation characteristics. The primary focus of this research is to highlight the guidelines for the optimal design of the forced liquid cooling system of the SST module. The analysis employs a multi-faceted simulation strategy to calculate the cooling aspects of the transformer, utilizing the commercial software ANSYS. This involves 3D coupled Electromagnetic (EMAG) - Computational Fluid Dynamics (CFD) simulations, as well as stand-alone CFD and thermal FEA simulations, ensuring robust cross-verification of the obtained results. This novel SST exhibits improved thermal performance, achieving a hot-spot temperature of 58 °C, the lowest value found in similar research studies. The findings underscore the efficiency and robustness of the proposed design, marking a significant step towards the realization of active and environmentally-friendly electrical networks.

Experimental and optimization research of the rack thermal environment based on the dynamic server power

Dynamic fluctuations in server power consumption significantly impact the thermal environment within the rack and the efficiency of computer room air conditioners (CRACs), necessitating thorough analysis it for safe and effective thermal management. Drawing on field experiment, this study presents an overview of the range and characteristics of variations in rack power consumption within a data center, revealing that 74.3 % of racks display periodic changes in power consumption. The change cycle has a diurnal pattern, and the dynamic changes in rack power consumption depend on the number of servers, the amount of task processing, and the nature of the customers. Subsequently, we designed the rack thermal environment experimental platform to analyze the response characteristics of the rack thermal environment to server power consumption changes and the variation rules of the thermal environment with various server layouts. The results show that a shift in server power severely affects the rack outlet temperature and is accompanied by a specific delay phenomenon. The near heat source effect, thermal buoyancy, and top heat accumulation primarily affect and form the rack thermal environment. It is recommended to place the server in the middle of the rack and with a 1U or 2U gap or uniform arrangement to create a better thermal environment. Finally, we propose three methods for optimizing the rack's thermal environment. Compared to the use of blind plates, the installation of air inlet deflectors, rear baffles, and top outlets can collectively reduce the hot spot temperature by up to 1.3 °C, 2.1 °C, and 0.4 °C, with the supply heat index (SHI) optimized at 0.184, 0.142, and 0.13. The return temperature index (RTI) was optimized at 88.99 %, 88.76 %, and 92.5 %, respectively.

Constructal design for a composite structure with an “X”-shaped high conductivity channel and a “T”-shaped fin

Based on constructal theory, a novel composite heat dissipation structure model with an “X”-shaped high conductivity channel and an exteriorly connected “T”-shaped fin within a square heat-generation body is developed. The optimal constructs with minimum maximum temperature difference are obtained through multi-variable optimization. The impacts of the ratio of high-to-low thermal conductivity and area proportion of the high conductivity channels on the optimal constructs are investigated. The research indicates that enhancing the conductivity ratio and area proportion can result in a notable reduction in the maximum temperature of square heat-generation body. For instance, when the ratio of high-to-low thermal conductivity is raised from 100 to 600, the quintic minimum dimensionless maximum temperature difference experiences a reduction of 48.08 %. Compared with one-degree-of-freedom, three-degree-of-freedom and five-degree-of-freedom optimizations, two-degree-of-freedom and four-degree-of-freedom optimizations can significantly reduce the dimensionless maximum temperature difference in the square heat-generation body. The minimum dimensionless maximum temperature difference after five-degree-of-freedom optimization is 9.44 % lower than that after one-degree-of-freedom optimization. Furthermore, when comparing the composite heat dissipation structure model made up of “arrow”-shaped high conductivity channel and exteriorly connected “T”-shaped fin, the composite heat dissipation structure model herein exhibits a significant reduction in hot spot temperature.

Shock-induced nanoscale pore collapse and hotspot in cyclotetramethylene tetranitramine (HMX)

Pore collapse induced hotspot formation is a key mechanism for initiating detonation of shocked high explosives. Accurate continuum models that faithfully capture the pore collapse dynamics and resulting hotspot temperatures for multiscale initiation of high explosive are still lacking. Here, an atomistically informed dislocation plasticity model is developed for cyclotetramethylene tetranitramine (HMX), which includes nonlinear thermoelasticity, pressure-dependent and temperature-dependent dislocation motion and homogeneous dislocation nucleation, and new melting criteria. Nanoscale pore collapse simulations based on the proposed model could well reproduce the pore collapse transition from a strength-dominated regime to a hydrodynamic regime observed in molecular dynamics results. During the pore collapse transition, dislocation motion and dislocation generation induced plasticity is decoupled and evaluated. It indicates that the homogeneous dislocation nucleation suppresses the dislocation motion. Through linkage analysis of dislocation physical quantity histories of tracer particles, this work clarifies the interplay between physical dependences and dislocation mechanisms on pore collapse behaviors for the first time. The results reveal that the transition of pore collapse regime emerges from the strong pressure-dependent homogeneous dislocation nucleation. In addition, the pore collapse responses and energy localizations of the proposed model are further compared with different strength models to prove the importance of pressure dependence and homogeneous dislocation nucleation on pore collapse behaviors. Based on the simulations of pore collapse, the potential heat sources are quantified to provide a physical understanding of hotspot formation mechanisms. The present work demonstrates a key step in multiscale modeling of high explosive initiation in that atomistic simulation results are directly used to guide and refine the mesoscale model of energetic crystal used in the continuum.

Thermal Safety Study of Emulsion Explosive Matrix under the Coupled Effects of Environmental Pressure and Bubble Content with Internal Heat Source

Emulsion explosives have become a hot topic in various studies due to their explosive combustion characteristics and detonation performance under different environmental pressures. The thermal safety of an emulsified matrix was studied with ignition energy as the characterization. A minimum ignition energy test experimental system for emulsion matrices was established in this research. The system simulated the occurrence of hot spots inside emulsion matrices using an electric heating wire. The effect of bubbles on the thermal safety of the emulsified matrix was studied by adding expanded perlite additive to the emulsified matrix. This study investigated the variation trend in the minimum ignition energy of the emulsion matrix under the coupled effect of bubbles and ambient pressure using the orthogonal experimental method. The impacts of two factors on the thermal safety of the emulsion matrix were studied at different hot-spot temperatures. Coupled analysis experiments were conducted on emulsion matrices containing 0%, 1.5%, and 3% expanded perlite under pressure environments of 1 atm, 2 atm, and 3 atm. The critical hot-spot temperature of the emulsion matrix significantly decreases with increasing bubble content at 1 atm and 2 atm pressures, as revealed by intuitive analysis and analysis of variance. However, at 3 atm of pressure, the bubble content in the emulsion matrix has no significant effect on its critical hot-spot temperature. The results show that the thermal safety of the emulsified matrix decreases with the increase in the content of expanded perlite and environmental pressure, and the influence of environmental pressure is more significant than that of the bubble content. This paper’s research content serves as a reference for a safe emulsified matrix and as an experimental basis for establishing a production line for developing new equipment.

Hydrothermal performance of a heat sink using Plate-fins: Experimental and numerical investigations

The development of electronic devices is accompanied by the generation of significant heat. A novel plate-fins design with various orientation angles of 30°, 35°, 37.5°, 40° and 45° was studied. The plate-fins design aims to boost the performance by increasing the surface area and redirecting airflow towards the heat sink plate. The experimental results obtained by this work were employed to validate the numerical results for pin-fins heat sink. A three-dimensional computational domain was investigated using ANSYS Fluent 19.1. The air flow was turbulent and modelled using k-ω turbulent flow. The plate-fins with an orientation angle of 37.5ᴼ exhibit the optimum hydrothermal performance among other configurations. Equally, several optimisation parameters including the number of rows, the diameter and thickness of the pins and the width of the plate-fins were investigated. It was observed that the heat sink with two rows of plate-fins outperformed other designs. This configuration resulted in a significant reduction of 2 % in hot spot temperature and a notable reduction of 60.8 % in pressure drop.

Simulation Study on Nondestructive Testing of Winding Hot Spot Temperature Based on Ultrasonic Emission

Simulation Study on Nondestructive Testing of Winding Hot Spot Temperature Based on Ultrasonic Emission

Techno-economic strategy for mitigating Hot-Spot/Partial shading of photovoltaic systems

Photovoltaic systems (PVSs) are the cornerstone of various hybrid renewable energy systems (HRESs). Although the mitigation of hotspots is one of the most crucial challenges for HRESs planners and operators, despite some of the current research trying to find solutions to the hotspot problem, researchers manage to lower the hotspot temperature but without considering commercialization scale perspectives due to design complexity and high-cost consequences. To tackle these challenges, a modified eco-technical strategy for mitigating the hotspot of PVSs is developed in this article. The proposed strategy is simple, economical, and reliable. Wherein additional IGBT is connected in series with the shaded cells in addition to the presence of the prevailing bypass diode (BPD) to distribute the shading effect on the shaded cells. The effectiveness of the modified mitigation strategy is validated during various scenarios of partial shading at solar irradiation change. In addition, the proposed simplified strategy is designed, implemented, and executed using MATLAB/SIMULINKTM. In addition, a practical experiment is conducted in different scenarios and compared with the traditional strategy. The results of the simulation and the practical experiment validate the efficiency and ability of the modified strategy to reduce the power dissipation in the shaded cells by reducing the voltage across the reverse-biased solar cell. The average dissipated power of the shaded cells is reduced by about 74% at low irradiance levels.The thermographic images are produced to validate that the temperature of the shaded cells attached to the proposed simplified strategy under mismatch conditions, has decreased by up to 5 °C compared to the traditional strategy (bypass diode) under the same operational and environmental conditions. The total output power of the modules equipped with the suggested strategy has increased compared to the traditional strategy (BPD) under the same operational and environmental conditions while preserving the reliability of PVSs-based HRESs.

Ignition mechanism and chemical reaction of the micro-damage polymer-bonded explosives under different inertial loading conditions

Understanding the impact-induced ignition properties and energy release behavior of polymer-bonded explosives (PBXs) is critical for the safety of explosive systems. In this study, a new impact test component was designed using a light gas gun to quantify the ignition mechanism and chemical reaction of micro-damaged PBXs under different inertial loading conditions. A constitutive model was developed to describe the mechanical-thermal-chemical response of the PBXs. This model was employed to further investigate the correlation between microcracks, debonding, hot spots, and chemical reactions. The results show that the stress state of the material is not uniformly distributed due to the micro-inhomogeneities and structural defects of PBXs. The shear friction of the microcracks contributes to localized hot spots, thereby inducing ignition. The critical loading condition for ignition is the length of the steel pillar is 32 mm. The damage and hotspot temperatures of the anterior lateral and posterior lateral regions are greater than those of other locations. The ignition response is accentuated with longer steel pillars, resulting in a more violent release of energy.

Inversion Method for Transformer Winding Hot Spot Temperature Based on Gated Recurrent Unit and Self-Attention and Temperature Lag.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.

Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering high-performance and safe EVs.

A gradient-based strategy to construct efficient heat conduction path for arbitrary configurations

The heat conduction optimization problem is a widely investigated abstraction of various heat dissipation problems in industry and electronics cooling. This paper studies the optimization strategy that produces optimized heat conduction path made of high thermal conductivity materials, with the objective of lowering the hot spot temperature or improving temperature uniformity. The automatic differentiation (AD) technique is a general-purpose method to calculate the partial derivatives of any continuous function composed by elementary functions with respect to multiple independent variables. It is introduced to calculate the gradient of the objective function with respect to the thermal conductivity field, so that the optimization procedure can be guided directly towards the minimization of the desired objective function. This paper investigated the configurations of both continuous-value thermal conductivity and discrete-value thermal conductivity. Results indicate that the present optimization strategy is able to handle various types of boundary conditions and objective functions. The present method is compared with the iterative method based on variational principle (Var), showing that the AD method performs better than the Var method in most situations.

Sensitivity analysis of turbine stage aerothermal characteristics and blade cooling performance considering combustor swirl and hot spot

This paper investigates the global sensitivity of high-pressure-turbine (HPT) first stage cascades aerothermal characteristics and blade cooling performance under the influence of combustor swirl and hot spot. The combustor outflow conditions impact on turbine working performance and robustness. This paper is focused on the influence of uncertain combustor swirling flow and hot spot, rather than deterministic working conditions. A simplified combustor outflow simulation method is firstly proposed, and the effects of combustor outflow are quantified by combining chaos polynomial expansion with Sobol’ decomposition method. The results show that different rotating directions of the swirl lead to restricted influences on the averaged aerothermal performance of the turbine stage, but significantly affects its robustness. The positive swirl increases the total pressure loss generated by the lower passage vortex of the stator cascade, and the high uncertainty region of the stator cascade total pressure loss profile is the combined products of the residual swirl and the interaction between the passage vortex and mainstream. The total pressure loss of the rotor cascade due to leakage vortex has low sensitivity to the hot spot and swirl strength, while the total pressure loss generated by the passage vortex is affected by hot spot temperature. Temperature distribution affected by the passage vortex is sensitive to swirl strength. Generally, uncertainty variance of stage aerothermal parameters with positive swirl is about 30% larger than that of the negative case, since the positive swirl has stronger interactions with the blades, leading to more significant uncertainty. However, the averaged heat transfer coefficients with positive swirl are 37% less sensitive, compared to the negative swirl case. The results give better understanding on the influence mechanisms of turbine inflow conditions, emphasize the meaning of investigating uncertain characteristics of the turbine cascades. Investigations of this article provide reference for robust turbine blade cooling design.

Transformer oil electrical–thermal characteristics analysis and evaluation

Transformer oil plays the role of heat dissipation and insulation in power transformers, and its electrical and thermal characteristics are directly related to the stable operation of the transformer. At the same time, it is important to master the electric–thermal characteristics of transformer oil for transformer state evaluation, latent fault prediction, and maintenance planning. Based on this, the gas production rule and electrical characteristics of transformer oil under accelerated aging are studied in this paper. First, the experimental platform for power transformer aging is established to accelerate the aging of power transformers. Second, the accelerated aging transformer oil is taken as the research object, and the power frequency, DC, lightning, and gas chromatography are measured and analyzed. Finally, a transformer oil aging evaluation model is established, which takes into account four factors: carbon and oxygen gas factors in transformers, life loss factors calculated by hotspot temperature, hydrocarbon gas factors dissolved in transformer oil, and oil quality factors, and conducts aging evaluation. The conclusion shows that the T parameter proposed in this paper can effectively evaluate the quality of transformer oil. When T is less than or equal to 0.6, there is no aging of the oil. When T is greater than 0.6 and less than 1, the transformer oil has been aging, but it is not manifested. Those are parameters whose chromatography and breakdown voltage are not far beyond the IEEE standard. When T = 1, the aging of oil with significant changes in gas content and breakdown voltage can be observed. When T is greater than 1, the oil has undergone serious aging.

Optimization of dielectric oils for immersion liquid cooling of server rack

Abstract In this research, two-phase immersion cooling is utilized to cool a server rack of 8 kW that contains 42 servers. Three-dimensional numerical simulation of server rack model is analyzed by ANSYS 18.1, Computational Fluid Dynamics (CFD). Comparative analysis between two types of dielectric engineered oils is performed, FC-72 and a new oil technology SF-33. The simulation shows a great decrease in temperature ranges for model using SF-33 oil with 34% decrease of the minimum temperature where the most surfaces of servers are at temperatures between 37 °C to 48 °C. Using SF-33 oil makes the hot spot temperature is higher than that for FC-72 by about 3°C. Power consumption for cooling with SF-33 oil is higher due to higher amount of vapor.

Calculation and research of hotspot temperature analysis of converter transformer

Calculation and research of hotspot temperature analysis of converter transformer

Different Thermal Models for Calculating Transformer Hot-Spot Temperature and Comparing with Measurement Data

Different Thermal Models for Calculating Transformer Hot-Spot Temperature and Comparing with Measurement Data

Machine learning-based thermal performance study of microchannel heat sink under non-uniform heat load conditions

The parallel microchannel heat sink stands as a pivotal solution in managing high heat flux electronics due to its efficient heat transfer characteristics and ease of manufacturing. While numerous studies have explored the thermal performance and flow characteristics of microchannel heat sinks, most have focused on uniform heat loads or relied heavily on numerical methods. This study presents an experimental system tailored to generate data for analyzing the thermal performance of microchannel heat sinks under various conditions. Leveraging this dataset, four distinct machine learning models Artificial Neural Network (ANN), XGBoost, LightGBM, and K-nearest neighbor (KNN) were trained using 22 input features, totalling 560 data points categorised into geometry parameters, heating patterns, and boundary conditions details. The models were tasked with predicting six response variables: the average base temperature of the heat sink, temperature change (ΔT), hotspot temperature, heat transfer coefficient (h), Nusselt number (Nu), and thermal resistance (Rth). Among the four machine learning models, XGBoost exhibited a good predictive accuracy of an average R2 value of 0.98 and MAE values of 2.1 across all responses. Furthermore, the study delved into the impact of varying input features on prediction accuracy, revealing a consistent enhancement in accuracy with the inclusion of more features across all models.