Prediction Of Thermal Performance Research Articles

Performance Analysis of Water Heat Pipe for Application of the Passive Cooling System in Nuclear Power Plants

Incorporating heat pipes into passive cooling systems in nuclear reactors offers the benefits of passive operation without external power, a simple design, and high thermal capacity. Accurate thermal performance prediction of the heat pipe is crucial for ensuring safe reactor design and operation. Prior studies on nuclear reactor systems utilizing heat pipes have focused on thermosyphons, which operate by gravity. However, to expand the range of heat pipe applications in reactor systems, experimental investigations of large-scale heat pipes driven by capillary pumping force are required. In this study, a water heat pipe with a 25.4-mm diameter and 4-m length was manufactured to provide thermal experimental results under extreme conditions, such as system rollover or loss-of-cooling accidents. A three-dimensional (3D) printing technique was used to fabricate the high-performance lattice capillary wick structure by combining cubic and diamond lattice structures. The 3D printed wick structure showed 21 to 165 times higher capillarity and enhanced surface properties compared to the screen mesh wick structure. Compared to wickless thermosyphons, the 3D printed wick heat pipe exhibited higher thermal conductivity, stable operation in both vertical and horizontal orientations, and faster startup under extreme conditions.

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

AbstractA major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

Investigating thermal transport across the AlN/diamond interface via the machine learning potential

Aluminum nitride (AlN) is an ultrawide-bandgap semiconductor with excellent potential for high-power applications. However, the heat dissipation issue remains a huge challenge for the use of AlN in high-power devices. A promising solution for this problem is to integrate AlN with the diamond heat sink. Therefore, interfacial thermal transport has become a significant bottleneck in thermal management. In this work, one neuroevolution potential is developed based on neural networks, which significantly improves the accuracy of predicting thermal properties compared to traditional potentials and address the issue of inaccurate thermal performance prediction of AlN/diamond heterostructures using traditional potentials. Molecular dynamics simulations based on NEP is adopted to investigate the crystal-orientation-dependent and interfacial-atom-dependent thermal boundary resistance of the AlN/diamond heterostructures after the interfacial bonding. Compared to bonding with Al and C atoms, the TBR decreases by approximately 50 % after bonding with N and C atoms at the interface. Especially for the AlN (0001)-N-diamond (100) heterostructure, the TBR is 0.95 m2·K·GW−1, very close to the theoretical limit of 0.8 m2·K·GW−1 through the diffuse mismatch model (DMM) theory. Finally, the insightful optimization strategies are proposed in this work which could pave the way for better thermal design and management of AlN/diamond heterostructures.

Deep learning-based inversion framework by assimilating hydrogeological and geophysical data for an enhanced geothermal system characterization and thermal performance prediction

Enhanced geothermal systems (EGS), which are developed by creating artificial fractures to enhance the permeability of deep reservoir, show their its advantage in power generation. However, due to the limited wells in deep depth, characterizing fractured geothermal reservoirs with sparse data is difficult and poses challenges for long-term thermal performance prediction. Interpreting observation data through inversion to characterize the fracture aperture and predict EGS long-term thermal performance is a hopeful scheme. Thus, a joint inversion framework, convolutional variational autoencoder-ensemble smoother with multiple data assimilation (CVAE-ESMDA), is proposed with the following aspects: (i) CVAE is trained to parameterize the high-dimensional Non-Gaussian aperture field, (ii) ESMDA is applied to integrate hydrogeological and geophysical data for aperture characterization. Based on the inversion results, the long-term performance is predicted. The normalized root mean square errors (NRMSEs) of inversion results decrease from 27.66 % to 24.52 % after multi-type data are integrated for CVAE-ESMDA. Furthermore, the NRMSEs of long-term thermal prediction of two production wells also decrease to only 2.3 % and 8.2 %. To further exhibit the advantages of CVAE-ESMDA, another joint inversion framework, principal component analysis (PCA)-ESMDA, is also compared. However, PCA is limited in addressing Non-Gaussian aperture field and its long-term thermal prediction performance is unsatisfactory.

One-dimensional model of manifold microchannels for embedded cooling: Prediction of thermal performance and flow non-uniformity

A one-dimensional model has been developed to accurately predict the thermal performance and flow non-uniformity of the manifold microchannels (MMC) for embedded liquid cooling. The model consists of one-dimensional governing equations derived from the integral relations of momentum and energy over appropriately-defined two separate control volumes. To validate the model, a series of 3-D numerical simulation is conducted over the wide ranges of the Reynolds number (Rem,in) at the manifold inlet from 560 to 3190, the dimensionless hydraulic flow length (x+) from 0.012 to 0.123, and the dimensionless thermal flow length (x∗) from 0.002 to 0.023. It is shown that the model provides accurate predictions of the thermal performance and flow non-uniformity (CV) of MMC heat sinks within the root mean square percentage error (RMSPE) of 6% and 26% for 50 data points, respectively. The significant improvement of the prediction accuracy is made over the earlier models with an error reduction of 82%. Finally, a design guideline for the uniform flow distribution is suggested for the first time based on a newly proposed explicit correlation for predicting the flow non-uniformity: the dynamic pressure at the manifold inlet should be kept smaller than the pressure drop across the microchannels.

Experimental study and neural network-based prediction of thermal performance of applying baffles and nanofluid in the double-pipe heat exchangers

Experimental study and neural network-based prediction of thermal performance of applying baffles and nanofluid in the double-pipe heat exchangers

Development of Thermal Performance Prediction for Large Planar Military Antenna with Multi-Cooling Channels

Large planar military antenna boasts a range of electrical components, including TRA(Transmit-Receive Assembly), signal processors, etc. which engage in computations and calculations. These processes generate a significant amount of heat, leading to unforeseen consequences for the equipment. To mitigate these adverse effects, it's imperative to implement a cooling system that can effectively reduce heat-related issues. Given the antenna's intricate nature and the multitude of components it houses, a two-step estimation process is necessary. The first step involves a comprehensive model calculation to determine the total flow characteristics, while the second step entails a thermal analysis of individual TRA set. In this study, we depicted an antenna set using simplified 3D models of its components, considering their material and thermal properties. The sequential analysis process facilitated the calculation of branched flow rates, providing insights into the individual TRA. This approach also allowed us to design a cooling system for the TRA set, assessing its thermal stability in high-temperature environments. To ensure the optimal performance of TRA, breaking down the analysis into stages based on the cooling system's structure can assist operators in predicting numerical results more effectively.

Numerical Simulation of Heat Pipe Thermal Performance for Aerospace Cooling System Applications

The design of integrated and highly efficient solutions for thermal management is a key capability for different aerospace products, ranging from civil aircraft using hydrogen on board to miniaturized satellites. In particular, this paper discloses a novel numerical tool for the design and thermal performance assessment of heat pipes. To achieve this goal, a numerical Ansys Parametric Design Language code is set up to verify the effective subtractive heat flux guaranteed by the selected heat pipe arrangement. The methodology and related tool show their ability to provide good thermal performance estimates for different heat pipe designs and operating conditions. Specifically, the paper reports two very different test cases: (1) solid metal heat pipes to cool down the crotch leading-edge area of the air intake of a Mach 8 civil passenger aircraft, and (2) a copper-water heat pipe to cool down a Printed Circuit Board of a generic small LEO satellite. The successful application of the methodology and numerical code confirms the achievement of the ambitious goal of developing in-house tools to support heat pipe thermal performance prediction for the entire aerospace domain.

Early Prediction of Thermal Performance for the Electric Drive Assembly Based on Mechanism and Data-driven Modeling

Early Prediction of Thermal Performance for the Electric Drive Assembly Based on Mechanism and Data-driven Modeling

Hysteresis model predictions of thermal performance of hempcrete-based walls with phase change materials

Phase Change Materials (PCMs) can improve the energy performance of buildings by storing and releasing large amounts of thermal energy with low or no temperature variation. However, they require optimal integration to be fully effective. While numerical analysis can assist the successful integration of PCMs into building envelopes, only a few tools can simulate the hysteresis effect, and all of them introduce simplifications that might hinder prediction accuracy, especially in case of interrupted phase change. This work addresses these limitations by proposing a novel algorithm for realistic hysteresis modelling by introducing a new path for changing the liquid fraction during the partial phase change process to enable the phase change curve transition without assuming that the liquid fraction is constant. The new model was validated against experimental results and compared to three widely used PCM hysteresis methods: Track, Switch, and Scale. This study is also the first to compare the effect of several hysteresis models applied to two different PCM integration methods into hempcrete wall assemblies based on microencapsulation and macroencapsulation techniques. The results of the different hysteresis models vary significantly depending on the PCM integration technique, the observed design parameter, the schedule heating setpoints, and the PCM percentage in the wall. When applied to a hempcrete wall with a PCM panel, the models showed a significantly higher sensitivity in the three performance parameters (heating energy consumption, liquid fraction, and inner temperature) than when applied to a hempcrete wall infused with the microencapsulated PCM.

Measurement and prediction of thermal performance of automotive transmission radial lip seals

The paper reports on the use of a developed novel and specific test-rig for the assessment of performance of radial lip seals of automotive transmissions, with particular emphasis on determination of thermal performance. The test-rig can accommodate original components from a donor vehicle, thus accurately represents vehicular conditions. The specific aim is to experimentally determine the effect of shaft surface roughness parameters on the generated contact temperature. Excessive exposure to high temperatures can accelerate degradation and aging of elastomeric seals, resulting in increased wear. Corundum and CBN (Cubic Boron Nitride) ground shafts are used to ascertain the effect of surface roughness parameters in the shaft seal conjunction. An analytical thermal model is also developed in combination with temperature measurements to determine the exact temperature in the lip seal-shaft contact. It is shown that depending on the type of shaft surface grinding process some potential correlation exists between roughness parameters and generated contact temperatures. It is also shown that the contact temperature can be several degrees Celsius higher than those measured in the proximity of the seal contact.

Prediction of latent heat storage transient thermal performance for integrated solar combined cycle using machine learning techniques

The objective of this work is to develop a prediction methodology for latent heat storage thermal performance to reduce computational efforts. Thermal storage can effectively compensate for the limitations of solar energy by extending operation time and leveling fluctuations. Although latent heat storage is advantageous compared to sensible heat storage for its large energy density and stable output temperature, its thermal performance has strong nonlinearity. With the conventional numerical simulations, it takes large computational time to obtain the one year or longer thermal performance of the latent heat storage. In this study, the exit steam enthalpy of latent heat storage for an integrated solar combined cycle (ISCC) is predicted using machine learning techniques. As latent heat storage is used to store solar thermal energy, the inlet steam properties such as enthalpy, pressure, and flow rate are continuously altered. Heat charged-discharged history in the regression model inputs significantly improves the prediction accuracy. The regression models are trained with transient heat transfer analysis results based on meteorological data of Bawean Island, Indonesia. Seven regression algorithms (Gaussian process regression (GPR), kernel approximation, ensemble of trees, support vector machines (SVM), artificial neural networks (ANN), linear regression, and regression trees) are compared and GPR is selected for its root mean square error (RMSE) of 16.9 kJ/kg or less. The RMSE values are equivalent to Bawean results even when the GPR is applied to two other locations: Kupang and Cikarang. Meanwhile, an ensemble classifier, which is selected from nine classification algorithms (Ensemble classifiers, ANN, SVM, decision trees, nearest neighbor classifiers, naive bayes classifiers, kernel approximation classifiers, discriminant analysis, and logistic regression classifiers), is used to determine when to terminate the latent heat storage operation. Owing to its short calculation time, the developed methodology is advantageous at the early design stages of latent heat storage, especially when multiple locations need to be compared.

Heat Mitigation Benefits of Urban Trees: A Review of Mechanisms, Modeling, Validation and Simulation

Modeling, validating, and simulating are three essential parts in investigating the heat mitigation benefits of urban trees (BUT). Therefore, 81 relevant studies from the last ten years are reviewed, analyzed, and summarized in this study. Three main ways for urban trees to adjust the environment are summarized, including shade creation and radiation modification, cooling effects of transpiration, and airflow blocking and modification effects. Research works are analyzed with regard to four categories: (1) heat and moisture exchange mechanisms and their mathematical modeling; (2) verification of modeling predictions based on measurements; (3) thermal performance simulation and prediction; and (4) environmental assessment and human thermal comfort analyses. Future research opportunities are discussed: (1) conduct real-time and in-depth measurements to analyze the mechanisms of heat and moisture transfer of trees in different areas; (2) develop tree radiation attenuation, airflow resistance, and transpiration models to accurately describe heat and moisture transfer processes in the urban environment; and (3) establish a three-dimensional numerical simulation method that can accurately simulate the urban thermal environment with trees. This review provides researchers with an overview and potential research opportunities on the thermal effects of urban trees.

Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R2) and variance coefficient (COV) of 0.99 and 1.65, respectively.

Mechanical Ventilation Heat Recovery Modelling for AccuRate Home—A Benchmark Tool for Whole House Energy Rating in Australia

To manage energy-efficient indoor air quality, mechanical ventilation with a heat recovery system provides an effective measure to remove extra moisture and air contaminants, especially in bathrooms. Previous studies reveal that heat recovery technology can reduce energy consumption, and its calculation needs detailed information on the thermal performance of exhaust air. However, there are few studies on the thermal performance of bathroom exhaust air during and after showers. This study proposed a detailed thermal performance prediction model for bathroom exhaust air based on the coupled heat and mass transfer theory. The proposed model was implemented into the AccuRate Home engine to estimate the thermal performance of residential buildings with heat recovery systems. The time variation of the water film temperature and thickness on the bathroom floor can be estimated by the proposed model, which is helpful in determining whether the water has completely evaporated. Simulation results show that changing the airflow rate in the bathroom has little effect on drying the wet floor without additional heating. The additional air heater installed in the bathroom can improve floor water evaporation efficiency by 24.7% under an airflow rate of 507.6 m3/h. It also demonstrates that heat recovery can significantly decrease the building energy demand with the fresh air load increasing and contribute about 0.6 stars improvement for the houses in Hobart (heating-dominated region). It may be reduced by around 3.3 MJ/(m2·year) for the houses in other regions. With this study, guidelines for optimizing the control strategy of the dehumidification process are put forward.

Prediction of aerodynamic and thermal performances for gas foil journal bearing with an axial cooling throughflow by machine learning method

Prediction of aerodynamic and thermal performances for gas foil journal bearing with an axial cooling throughflow by machine learning method

Thermal and electrical performance prediction of an FSPV system: a case study in the Douro/Portugal climatic conditions

The use of traditional renewable energies such as hydro, wind and solar has been the solution to the world's energy demands in order to make the final energy consumption more sustainable. In the last decades new technical solutions are beginning to emerge like the Floating Solar Photovoltaic systems, which are normally deployed in lakes, and dams. Since it is known that the percentage increase in energy efficiency depends on the location, microclimatic conditions, this work uses the knowledge obtained from data weather stations in Portugal and tries to assess the possibility of installing a system FSPV in the climatic conditions of the Douro/Portugal wine region as a function of the thermal and electrical performance

Comparative study of machine learning methods integrated with different optimisation algorithms for prediction of thermal performance and emissions in a pellet stove

ABSTRACT The parameters of thermal power, excess air, and supply air distance at burner pot are critical to obtain low emissions and high combustion efficiency in a pellet stove. The main aim of this study is to predict the performance and gaseous emissions in a pellet stove with four different supply airflow distances of 15, 30, 60, and 75 mm above the burner pot base at different excess air ratios and thermal power inputs. For this purpose, different models, namely, support vector regression, k-nearest neighbors, and deep neural network, are developed to predict the performance and emissions of the pellet stove. The thermal power, excess air ratio, and supply air distance were used as input variables to predict the flue gas temperature, efficiency, carbon monoxide (CO), nitrogen oxides (NOx), and carbon dioxide (CO2) emissions. The hyperparameters of the designed models are tuned using several optimization approaches: grid search, Bayesian optimization, particle swarm, and genetic algorithms. Experiments are conducted to validate the performances of the models with optimized hyperparameters using statistical metrics such as coefficient of determination, mean-squared error, mean absolute error, and mean absolute percentage error. Analysis results indicate that the deep neural network performs the best with the highest correlation coefficient and lowest error metrics in the prediction of output parameters. The coefficient of determination (R2) values of the DNN model were obtained as 0.8875, 0.8464, 0.934, 0.8692, and 0.9992 in the training phase 0.909, 0.8760, 0.9164, 0.9086, and 0.9991 in the testing phase for the efficiency, Tg, CO, NOx, and CO2 parameters, respectively. The results of this study can serve as a guide to help researchers, engineers, and facility owners or operators predict the gaseous emissions levels and to get an idea of the impact of operating conditions on performance and emissions behaviors.

Theoretical Strategy for Interface Design and Thermal Performance Prediction in Diamond/Aluminum Composite Based on Scattering-Mediated Acoustic Mismatch Model.

Inserting modification layers at the diamond/Al interface is an effective technique in improving the interfacial thermal conductance (ITC) of the composite. However, few study reports the effect of interfacial structure on the thermal conductivity (TC) of diamond/Al composites at room temperature. Herein, the scattering-mediated acoustic mismatch model, suitable for evaluating the ITC at room temperature, is utilized to predict the TC performance of the diamond/Al composite. According to the practical microstructure of the composites, the reaction products at diamond/Al interface on the TC performance are concerned. Results indicate that the TC of the diamond/Al composite is dominantly affected by the thickness, the Debye temperature and the TC of the interfacial phase, meeting with multiple documented results. This work provides a method to assess the interfacial structure on the TC performance of metal matrix composite at room temperature.

Multifluid computational fluid dynamics simulation for sawdust gasification inside an industrial scale fluidized bed gasifier

Scarcity of fossil fuels, and to fulfill the energy demand of the increasing population, researchers need to shift their attention to the alternative of fossil fuels. It would be impractical and time-consuming to conduct extensive experiments on Fluidized Bed Gasifiers (FBG) of varying fuel types, sizes, geometries and operating conditions. The objective of the current research work is focused on numerical simulation of hydrodynamics behavior and sawdust gasification in an industrial scale FBG using the Eulerian-Eulerian multifluid model. For the accurate prediction of thermal and hydraulic performance (bed voidage, suspension density, pressure drop, heat transfer, and combustion kinetics), one should incorporate the correct parameters in the Computational Fluid Dynamics (CFD) simulation of a fluidized bed gasifier. It includes the multiphase gasification modeling using the Eulerian-Eulerian approach with an user defined heterogeneous reaction kinetics. The adopted CFD methodology is validated with experimental data and extended for the prediction of gasification characteristics of sawdust by incorporating eight heterogeneous (moisture release, volatile cracking, tar cracking, tar oxidation, char combustion, CO₂ gasification, steam gasification, methanation reaction) and five homogeneous oxidation of CO, CH₄, H₂, forward and backward water gas shift (WGS) reactions. In the result section, composition of gasification products is analyzed, along with the hydrodynamics of sawdust and sand phase, heat transfer between the gas, sand and sawdust, reaction rates of different homogeneous and heterogeneous reactions are being analyzed along the height of the domain.