Thermal Prediction Model Research Articles

Study of Convective Heat Transfer in Grinding Applied to Tool Carbide

Abstract The grinding temperature is of great importance for the quality and integrity of machined cemented carbide tool. Tool edge surfaces may be damaged by softening or being stressed, hardened, burned, or cracked. Former research on grinding temperature prediction often made assumptions to simplify heat convection due to the grinding fluid. However, these simplifying assumptions can sometimes undermine the mathematical relationships between grinding conditions and surface temperature, particularly in low-temperature grinding where fluid convection is most important. This paper is an attempt to provide an improved comprehensive thermal model for the prediction of contact temperatures and for monitoring and control of thermal damage. Based on previous thermal model research, this paper tackles a key element of the thermal model for temperature prediction. It proposes a convective heat transfer model based on the classic theory of turbulent flow passing a plate. Theoretical predictions from the thermal model of turbulent flow developed in this paper are compared with experimental values. Predictions are further compared with values from a previously published laminar flow model. And it is shown that the new model leads to a significant reduction in predicted temperatures. The results suggest that the thermal model for the turbulent flow provides a reasonable estimate of predicted temperature values within the region of the fluid boiling temperature. The estimates appear to be an improvement compared with the laminar flow thermal model. The turbulent flow thermal model is considered to improve estimates of background contact temperatures in grinding cemented carbide.

Bayesian updates for indoor thermal comfort models

Achieving thermal comfort through sustainable indoor design is an increasing concern. Thermal comfort modelling is crucial for achieving building energy saving. This study reviews and categorizes major developments and trends in the field of thermal comfort research in recent years. Discrepancies between actual and predicted results of thermal sensation and thermal satisfaction suggests a performance gap in Fanger's model. Based on the current research gaps identified, a practical solution is proposed to improve the reliability of thermal comfort predictions. Two Bayesian updating protocols, namely individual updating and global updating, are put forward and the use of Bayesian approach to systemically update current thermal comfort beliefs with openly available field data is demonstrated. Besides being a practical tool for modelling thermal comfort using the best information available (i.e. existing models and field survey data), the proposed Bayesian updating provides an achievable solution to the present challenges in establishing a reliable thermal comfort prediction model.

Proton Beam Production of Curie Scale Ac-225 at 100 MeV and Below

The DOE National Isotope Program for radionuclide production operates two intermediate energy, high intensity accelerator-based production facilities: The 100 MeV Isotope Production Facility (IPF) at Los Alamos National Laboratory and the 200 MeV Brookhaven Linac Isotope Producer (BLIP). They are two of only eight intermediate energy production facilities worldwide and often operate with record breaking beam intensities on target. These two facilities will be used for year-round Ci-scale batch production of Ac-225 via proton bombardment of thorium targets. High power targetry for this purpose is being developed as part of the US DOE Tri-Lab production development effort. Production cross sections measured in recent years dictate that Ci-scale production at a 100 MeV facility such as IPF requires much higher beam currents than at higher energy facilities such as BLIP. As a result, targets used for production at these lower energies must withstand power levels that are higher by a factor that approaches an order of magnitude. While existing target designs have been used to successfully produce up to ∼200 mCi at end-of-bombardment, improvements are needed to achieve Ci-scale production. As part of the US DOE Tri-Lab effort, ultra-high power thorium targets are being developed to withstand 100 MeV proton beam currents up to 450 μA. Based on thermal model predictions, a major emphasis is placed on the enhancement of thermal contact between the thorium target and its containment. Several enhancement approaches and techniques are being explored with the goal to down select and integrate the most appropriate technique into a procedure for the routine fabrication of IPF targets. This presentation will provide a status update on the ongoing targetry R&D effort to achieve Ci-scale production. In the context of recent upgrades at IPF, updated Ac-225 production projections will also be presented based on expected target thermal performance at ultra-high beam currents and yields obtained from recent pilot Ac-225 production campaigns.

Analysis of the microstructural evolution of silicon nitride irradiated with swift Xe ions

The evolution of 220 MeV Xe ion induced radiation damage in polycrystalline Si3N4 is studied, within the fluence range 5 × 1011– 2 × 1014 cm−2, using transmission electron microscopy techniques. These irradiation conditions allow for the study of track morphology in both crystalline and in radiation-amorphized Si3N4. The average track size in the polycrystalline samples is 1.9 ± 0.4 nm and 3.1 ± 0.5 nm in the radiation-amorphized samples. The larger track sizes in the radiation-amorphized material is in agreement with predictions of the inelastic thermal spike model and the role of thermal conductivity in latent track formation.

Thermal conductivity of sands treated with microbially induced calcite precipitation (MICP) and model prediction

Thermal resistance between underground energy geo-structures and surrounding soils limits heat exchange efficiency particularly when soils are at low saturation degrees or nearly dry conditions. Microbially induced calcite precipitation (MICP) technique has great potential to enhance soil thermal properties thus increasing the possibility to expand geothermal applications to arid environments. This study investigates the thermal conductivity (k) of MICP treated sands in drying process by using a newly developed dual probe heat pulse (DPHP) sensor. The results show that thermal conductivity of sands was significantly improved after MICP treatment, and k value increased as the number of treatment cycles increased. The improvement in k is attributed to the MICP induced CaCO3 crystals functioning as “thermal bridges” among sand grains, which provides more highly effective heat transfer path and increases surface contact area in heat exchange process. Based on the normalized thermal conductivity (kr) concept and the experimental data obtained in this study, a modified thermal conductivity predictive model was proposed for MICP treated sands. The model prediction matched the experimental results very well, and the predicted values of k agreed with the measured values with only 10% deviation in the entire saturation range.

A Coupled Tank-in-Series Electrochemical Engineering and Thermal Model for Lithium Sulfur Batteries

Lithium sulfur (LiS) batteries are a promising option for next-generation energy storage due to their high energy density. Applications where weight is especially important like electric flight and electric vehicles have driven the study of this chemistry because lithium ion batteries are approaching their practical limits. For these applications, the demand for packing more energy into a battery can lead to higher resistive batteries. For lithium sulfur batteries in particular, the resistance is seen to increase with higher capacities per cell (1). As the required energy and subsequently battery packs increase, the importance of heat dissipation for safety and performance also grows, which requires an accurate thermal model for design and control. Physics-based models enable a look into the internal states of the battery, which correspond to physical phenomena that can contribute to decreased performance under certain conditions. The mathematical model for lithium sulfur batteries developed by Kumaresan et al (2) was the first one-dimensional physics-based model for lithium sulfur batteries. This discharge model was developed with porous electrode theory and considers transport, kinetics, thermodynamics, and morphological changes. The LiS chemistry includes a reduction of solid sulfur to different order polysulfides and finally a precipitation of Li2S, which leads to complexity due to multiple charge carriers and dynamic electrode morphology. The corresponding physics-based model results in a numerically stiff set of equations with variables evolving across many orders of magnitude. To alleviate the computational footprint and still maintain a high degree of accuracy, we have previously developed a mass-conserving lumped model through volume-averaging, called the Lithium Sulfur Tank-in-Series Model. This model, with average quantities in each region, eliminates the spatial dependence for increased computational efficiency and fewer model parameters that expedites model use in estimation, control, and optimization. In this work, the LiS tank-in-series model is coupled with a thermal model (3). The thermal model includes heat generation due to internal resistance and entropic contributions, and Stroe et al. used the thermal model predictions with a simple empirical model. Coupling the thermal model with a more accurate predictive model allows further insight into battery performance. The model predictions are compared with experimental data from 19.5 Ah pouch cells. Acknowledgments The authors are thankful for financial support from the Battery500 Consortium, BAE Systems, and the Joint Center for Aerospace Technology Innovation. References J. Offer and M. Wild, Lithium Sulfur Batteries, p. 149, John Wiley & Sons, New Jersey (2019).Kumaresan, Y. Mikhaylik, and R. E. White, J. Electrochem. Soc., 155, A576 (2008).I. Stroe, V. Knap, M. Swierczynski, and E. Schaltz, ECS Trans., 77, 467–476 (2017).

Real-time thermal deformation compensation method for active phased array antenna panels

The electromagnetic (EM) performance of active phased array antennas (APAAs) is significantly influenced by thermal deformation of the antenna panels caused by thermal power consumption and ambient temperature change. This paper proposes a method to reduce the influence of thermal deformation on the EM performance of small APAAs via real-time compensation. In the proposed method, a thermal deformation prediction model is first constructed based on analysis of experimental data. The model is then used to accurately predict thermal deformation based on the measured values of several key temperature sensors in real time, and the EM performance is compensated by adjusting the array phase difference controlled by a phased shifter according to the predicted results. The results of experiments conducted indicate that the thermal deformation model has high prediction accuracy, and the decrease in EM performance caused by antenna panel thermal deformation is effectively reduced by the proposed method. Thus, the proposed method has positive implications for improving the overall performance of APAAs.

Comparative thermal comfort study in educational buildings in autumn and winter seasons

Maintaining thermal comfort throughout an entire year is complicated because conditions vary greatly throughout the seasons. The aim of this article was to understand the influence of seasonal characteristics on the thermal comfort in classrooms and to propose a method to select suitable adaptive coefficients for different conditions. Field investigations were conducted in an educational building in Tianjin during autumn and winter. In the study, multiple environmental parameters were investigated, including air temperature, black globe temperature, relative humidity, air velocity, and the vertical temperature difference (VTD). The respondents’ thermal sensations were collected via questionnaires. The respondents’ thermal sensation and the influence of seasonal characteristics on thermal sensation were analyzed. The results showed that the thermal neutral temperature varied in different seasons, and the deviations between the mean thermal sensation vote (MTSV) and the predicted mean vote (PMV) showed obvious discrepancies throughout the different seasons. Furthermore, the adaptive coefficient αs for the adaptive thermal comfort prediction model (APMV) were 0.25 and 0.57 for autumn and winter, respectively. Based on the aforementioned result, a discriminant function (DFA) using the seasonal factors to select the suitable α was obtained.

Effect of microstructure and initial cell conditions on thermal inactivation kinetics and sublethal injury of Listeria monocytogenes in fish-based food model systems

The development of more accurate predictive models that describe the microbial kinetics of mild thermal treatments of foods requires knowledge concerning the influence of food microstructure and initial cell conditions on foodborne pathogens’ inactivation kinetics. The effect of food microstructure and initial cell conditions on thermal inactivation kinetics and sublethal injury (SI) of Listeria monocytogenes was investigated at 59, 64 and 69°C. Fish-based food model systems with different microstructures, possessing minimal compositional and physicochemical variations, were used. L. monocytogenes growth morphology had no significant influence on thermal inactivation kinetics. A gelled matrix resulted in a lower specific inactivation rate kmax and a higher residual cell population Nres, while the presence of fat droplets resulted in a higherkmaxand did not influenceNres. SI was higher in viscous than in gelled systems and more prominent for cells that were grown inside the matrix. Hence, predictive thermal inactivation models could benefit from the inclusion of factors related to the nature of the food matrix and fat properties. Starting inactivation from cells that were grown inside the matrix, resulted in lower (i.e., fail-safe)kmaxvalues and more uncertainty onNres as compared to starting from cells grown at optimal conditions.

Key point selection in large-scale FBG temperature sensors for thermal error modeling of heavy-duty CNC machine tools

Thermal error is one of the main factors that influence the machining accuracy of computer numerical control (CNC) machine tools. It is usually reduced by thermal error compensation. Temperature field monitoring and key temperature measurement point (TMP) selection are the bases of thermal error modeling and compensation for CNC machine tools. Compared with small- and medium-sized CNC machine tools, heavy-duty CNC machine tools require the use of more temperature sensors to measure their temperature comprehensively because of their larger size and more complex heat sources. However, the presence of many TMPs counteracts the movement of CNC machine tools due to sensor cables, and too many temperature variables may adversely influence thermal error modeling. Novel temperature sensors based on fiber Bragg grating (FBG) are developed in this study. A total of 128 FBG temperature sensors that are connected in series through a thin optical fiber are mounted on a heavy-duty CNC machine tool to monitor its temperature field. Key TMPs are selected using these large-scale FBG temperature sensors by using the density-based spatial clustering of applications with noise algorithm to reduce the calculation workload and avoid problems in the coupling of TMPs for thermal error modeling. Back propagation neural network thermal error prediction models are established to verify the performance of the proposed TMP selection method. Results show that the number of TMPs is reduced from 128 to 5, and the developed model demonstrates good prediction effects and strong robustness under different working conditions of the heavy-duty CNC machine tool.

A thermal environmental model for indoor air temperature prediction and energy consumption in pig building

Indoor thermal environment is a critical factor for animal health and production in confined livestock facilities. In order to improve indoor thermal environment control and save energy, a novel dynamic thermal exchange model was developed using the energy balance equation (EBE) and 87 days of data collected in three different seasons in a pig building to simulate the heat transfer and energy consumption in the building. To evaluate the performances of the EBE model, a comparison was made using adaptive neuro fuzzy inferring system (ANFIS) for indoor air temperature prediction. Also, the EBE model was evaluated comparing its outputs of indoor temperature with the dataset of six days, under three different ventilation modes (Min-vent, Low-vent, and High-vent) that represent for the cold, warm and hot weather, obtained through a monitoring period in pig buildings during the production. The results showed that, under three different ventilations modes, the maximum errors between the EBE model simulated and measured data were 1.5 °C compared with 2.6 °C of the ANFIS model; and the averaged coefficients of determination R2 were 0.945 and 0.743, respectively, for the EBE and ANFIS models. Compared with the present ventilation operation, there was 358.301 kW h power saved with the EBE model in a pig room during the whole research period of 87 days. Therefore, this research has several practical applications: the model can be used in developing strategies of indoor thermal environmental control, it can also increase the knowledge about energy consumption in the livestock house.

GA-BP in Thermal Fatigue Failure Prediction of Microelectronic Chips

A thermal fatigue life prediction model of microelectronic chips based on thermal fatigue tests and solder/substrate interfacial singularity analysis from finite element method (FEM) analysis is established in this paper. To save the calculation of interfacial singular parameters of new chips for life prediction, and improve the accuracy of prediction results in actual applications, a hybrid genetic algorithm–artificial neural network (GA–ANN) strategy is utilized. The proposed algorithm combines the local searching ability of the gradient-based back propagation (BP) strategy with the global searching ability of a genetic algorithm. A series of combinations of the dimensions and thermal mechanical properties of the solder and the corresponding singularity parameters at the failure interface are used to train the proposed GA-BP network. The results of the network, together with the established life prediction model, are used to predict the thermal fatigue lives of new chips. The comparison between the network results and thermal fatigue lives recorded in experiments shows that the GA-BP strategy is a successful prediction technique.

Thermal simulation and phase modeling of bulk metallic glass in the powder bed fusion process

One of the major challenges with the powder bed fusion process (PBF) and formation of bulk metallic glass (BMG) is the development of process parameters for a stable process and a defect-free component. The focus of this study is to predict formation of a crystalline phase in the glass forming alloy AMZ4 during PBF. The approach combines a thermal finite element model for prediction of the temperature field and a phase model for prediction of crystallization and devitrification. The challenge to simulate the complexity of the heat source has been addressed by utilizing temporal reduction in a layer-by-layer fashion by a simplified heat source model. The heat source model considers the laser power, penetration depth and hatch spacing and is represented by a volumetric heat density equation in one dimension. The phase model is developed and calibrated to DSC measurements at varying heating rates. It can predict the formation of crystalline phase during the non-isothermal process. Results indicate that a critical location for devitrification is located a few layers beneath the top surface. The peak is four layers down where the crystalline volume fraction reaches 4.8% when 50 layers are built.

A Review about Thermal Comfort in Aircraft

Thermal comfort is an important factor which affects both work efficiency and life quality. On the basis of satisfying the normal life of the crew and reliable work of equipment, the thermal comfort is increasingly pursued through the design of the environmental control system of modern craft. Thus, a comprehensive survey of the thermal comfort in the cockpit is carried out. First of all, factors affecting the thermal comfort in aircraft cabin are summarized, including low relative humidity, mean radiant temperature, colored light, human metabolic rate and gender, among which the first three factors are environmental factors and the other two are human factors. Although noise is not a factor affecting thermal comfort, it is an important factor in the overall satisfaction of the aircraft cabin environment. Then the thermal comfort prediction models are introduced, including thermal comfort models suitable for steady state uniform environment and thermal comfort models suitable for transient non-uniform environment. Then the limitations of the typical thermal comfort models applied to aircraft are discussed. Since the concept of thermal adaptation has been gradually accepted in recent years, many field studies on thermal adaptation have been carried out. Therefore, the adaptive thermal comfort models are summarized and analyzed systematically in this paper. At present, mixing ventilation (MV) system is widely used in most commercial aircraft. However, the air quality under the MV system is very poor, and contaminants cannot be effectively eliminated. So a noticeable shift is the design of ventilation system for cabin drawing lessons from the surface buildings. Currently, the most interesting question is that whether the traditional mixing ventilation (MV) system in an aircraft can be replaced by or combined with displacement ventilation (DV) system without decreasing thermal comfort. A reduction of energy consumption is a valuable gain. Additionally, various seat personalized ventilation systems have also been proposed which could effectively reduce the risk of infectious diseases. At present, optimal design of airflow in aircraft cabin is the most commonly used method to enhance thermal comfort and save energy. The optimal design of the aircraft cabin colored lighting system, however, is also worth trying.

Proton Beam Production of Curie Scale Ac-225 at 100 MeV and Below

The DOE National Isotope Program for radionuclide production operates two intermediate energy, high intensity accelerator-based production facilities: The 100 MeV Isotope Production Facility (IPF) at Los Alamos National Laboratory and the 200 MeV Brookhaven Linac Isotope Producer (BLIP). They are two of only eight intermediate energy production facilities worldwide and often operate with record breaking beam intensities on target. These two facilities will be used for year-round Ci-scale batch production of Ac-225 via proton bombardment of thorium targets. High power targetry for this purpose is being developed as part of the US DOE Tri-Lab production development effort. Production cross sections measured in recent years dictate that Ci-scale production at a 100 MeV facility such as IPF requires much higher beam currents than at higher energy facilities such as BLIP. As a result, targets used for production at these lower energies must withstand power levels that are higher by a factor that approaches an order of magnitude. While existing target designs have been used to successfully produce up to ∼200 mCi at end-of-bombardment, improvements are needed to achieve Ci-scale production. As part of the US DOE Tri-Lab effort, ultra-high power thorium targets are being developed to withstand 100 MeV proton beam currents up to 450 μA. Based on thermal model predictions, a major emphasis is placed on the enhancement of thermal contact between the thorium target and its containment. Several enhancement approaches and techniques are being explored with the goal to down select and integrate the most appropriate technique into a procedure for the routine fabrication of IPF targets. This presentation will provide a status update on the ongoing targetry R&D effort to achieve Ci-scale production. In the context of recent upgrades at IPF, updated Ac-225 production projections will also be presented based on expected target thermal performance at ultra-high beam currents and yields obtained from recent pilot Ac-225 production campaigns.

Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II

The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15–25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.

Strangeness and nuclei production in nuclear collisions

A critical review of the most recent results obtained on the study of strangeness and (anti–)nuclei production in nuclear collision at ultra–relativistic energies will be presented. Recent measurements of strangeness production at the LHC in high-multiplicity proton-proton (pp) and proton-lead (p–Pb) collisions have shown features that are reminiscent of those observed in lead-lead (Pb–Pb) collisions. These observations warrant a comprehensive measurement of the production of identified particles and are not described satisfactorily by the available Monte Carlo predictions. Another intriguing but not fully understood mechanism, in the high energy physics sector, is the one responsible for the production of light nuclei and anti–nuclei. A detailed description of the available experimental data about (anti–)matter production will be reported and compared with the available models, namely the coalescence approach and the thermal model predictions.

Development of the data-driven thermal satisfaction prediction model as a function of human physiological responses in a built environment

The purpose of this study is to investigate and determine the relationship between occupants’ thermal satisfaction and physiological responses in an office environment, and to estimate their thermal satisfaction level via human physiological signals. This study adapted the heart rate and seven local body skin temperatures as physiological signals, as well as human factors (gender, age, BMI), to determine establish a thermal satisfaction prediction model by combining human factors and physiological signals. The results revealed significant correlations between overall thermal satisfaction levels and local body skin temperatures, as well as heart rates. The heart rates showed a negative correlation with overall thermal satisfaction, and the skin temperature of the forehead, arm, wrist (back and front), chest, and belly also revealed a significant correlation with the thermal satisfaction levels of the study participants. This study also determined the order and priority of local skin temperatures (as well as gender and BMI) by their impact on thermal satisfaction. Considering all human physiological factors and practical application of the results, the local skin temperatures of the forehead, wrist (back), and gender demonstrated 88.52% accuracy for estimating thermal satisfaction, which provided significant validation for practical use of this procedure.

Detection of spindle thermal state accuracy of vertical CNC machine tool

In order to test the thermal accuracy of spindle system of mining vertical CNC machine tools, to enhance the error compensation ability of the system accuracy detection model and improve the machine tool processing accuracy, a test platform for thermal accuracy of spindle system was constructed. Five-point method was used to test the thermal accuracy of spindle system, and thermocouple and infrared thermal imager were used to measure the spindle system. Temperature change data at key points of temperature rise are used to identify temperature sensitive measurement points by grey comprehensive correlation analysis method. A thermal accuracy prediction model based on cooperative quantum particle algorithm is constructed. The prediction effect of the model is evaluated. The experimental results show that the thermal accuracy detection model has high detection accuracy.

Prediction of the solid effective thermal conductivity of fatty acid/carbon material composite phase change materials based on fractal theory

The thermal conductivity of a phase change material (PCM) is an important parameter that affects the heat transfer characteristics of PCM. In this study, the fractal theory is conducted to study the prediction model of the effective thermal conductivity of fatty acid/carbon material composite PCMs in the solid phase. Based on the Sierpinski carpet model of fractal geometry, a series of fractal units with different fractal dimensions are developed for the fatty acid/carbon material composite PCMs. Combined with a self-similar principle and thermal resistance network theory, a general solution for the dimensionless effective thermal conductivity of a typical Sierpinski carpet fractal unit is then derived. Further, dimensionless effective thermal conductivity prediction models of three typical structures of fatty acid/carbon material composite PCMs are obtained by comparing the experimental data. Finally, the thermal conductivity prediction model from this study demonstrated a better reliability than that of the commonly used QP prediction model and Maxwell-Eucken prediction model. This prediction model could be used to predict the effective thermal conductivity of composite PCMs.