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For high torque-density permanent magnet synchronous in-wheel motor, service life and electromagnetic performance are related directly to winding temperature. The purpose of this paper is to investigate the equivalent stator slot model to calculate the temperature of winding accurately.

This paper analyzes the the law of heat flux transfer in slot, which points the main influence factors of equivalent stator slot model. Thermal network model is used to investigate the drawbacks of conventional equivalent model. Based on the law of heat transfer in stator slot, a new layered winding model is put forward. According to winding type and property of impregnations, detailed method and equivalent principle of the new model are presented. The accuracy of this new method has been verified experimentally.

An accurate equivalent stator slot model should be built according to the low of heat transfer. According to theory analysis, the drawbacks of conventional equivalent stator slot model are pointed: it cannot reflect the temperature gradient of winding; the maximum and the average temperature of winding are much higher than actual value. For the new layered model, equivalent principle is related to winding type and property of impregnations, which makes the new model widely used.

This paper presents a new layered model, and shows detailed method, which is more meaningful for designers. The new layered model takes winding type and property of impregnations into account, which makes the new model widely used. It is verified experimentally that layered model is applicable to not only steady-state temperature field but also transient temperature field.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

Elevated temperature material properties are essential in predicting structural member's behavior in high-temperature exposures such as fire. Even though experimental methodologies are available to determine these properties, advanced equipment with high costs is required to perform those tests. Therefore, performing those experiments frequently is not feasible, and the development of numerical techniques is beneficial. A numerical technique is proposed in this study to determine the temperature-dependent thermal properties of the material using the fire test results based on the Artificial Neural Network (ANN)-based Finite Element (FE) model.

An ANN-based FE model was developed in the Matlab program to determine the elevated temperature thermal diffusivity, thermal conductivity and the product of specific heat and density of a material. The temperature distribution obtained from fire tests is fed to the ANN-based FE model and material properties are predicted to match the temperature distribution.

Elevated temperature thermal properties of normal-weight concrete (NWC), gypsum plasterboard and lightweight concrete were predicted using the developed model, and good agreement was observed with the actual material properties measured experimentally. The developed method could be utilized to determine any materials' elevated temperature material properties numerically with the adequate temperature distribution data obtained during a fire or heat transfer test.

Temperature-dependent material properties are important in predicting the behavior of structural elements exposed to fire. This research study developed a numerical technique utilizing ANN theories to determine elevated temperature thermal diffusivity, thermal conductivity and product of specific heat and density. Experimental methods are available to evaluate the material properties at high temperatures. However, these testing equipment are expensive and sophisticated; therefore, these equipment are not popular in laboratories causing a lack of high-temperature material properties for novel materials. However conducting a fire test to evaluate fire performance of any novel material is the common practice in the industry. ANN-based FE model developed in this study could utilize those fire testing results of the structural member (temperature distribution of the member throughout the fire tests) to predict the material's thermal properties.

The purpose of this paper is to compare the ability of popular temperature models, namely, the models given by Alaton et al., by Benth and Benth, by Campbell and Diebold and by Brody et al., to forecast the prices of heating/cooling degree days (HDD/CDD) futures for New York, Atlanta, and Chicago.

To verify the forecasting power of various temperature models, a statistical backtesting approach is utilised. The backtesting sample consists of the market data of daily settlement futures prices for New York, Atlanta, and Chicago. Settlement prices are separated into two groups, namely, “in‐period” and “out‐of‐period”.

The findings show that the models of Alaton et al. and Benth and Benth forecast the futures prices more accurately. The difference in the forecasting performance of models between “in‐period” and “out‐of‐period” valuation can be attributed to the meteorological temperature forecasts during the contract measurement periods.

In future studies, it may be useful to utilize the historical data for meteorological forecasts to assess the forecasting power of the new hybrid model considered.

Out‐of‐period backtesting helps reduce the effect of any meteorological forecast on the formation of futures prices. It is observed that the performance of models for out‐of‐period improves consistently. This indicates that the effects of available weather forecasts should be incorporated into the considered models.

To the best of the author's knowledge this is the first study to compare some of the popular temperature models in forecasting HDD/CDD futures. Furthermore, a new temperature modelling approach is proposed for incorporating available temperature forecasts into the considered dynamic models.

A methodology for producing an elevated-temperature tension stiffening model is presented.

The energy-based stress–strain model of plain concrete developed by Bažant and Oh (1983) was extended to the elevated-temperature domain by developing an analytical formulation for the temperature-dependence of the fracture energy G_f. Then, an elevated-temperature tension stiffening model was developed based on the modification of the proposed elevated-temperature tension softening model.

The proposed tension stiffening model can be used to predict the response of composite floor slabs exposed to fire with great accuracy, provided that the global parameters TS and K_res are adequately calibrated against global structural response data.

In a finite element analysis of reinforced concrete, a tension stiffening model is required as input for concrete to account for actions such as bond slip and tension stiffening. However, an elevated-temperature tension stiffening model does not exist in the research literature. An approach for developing an elevated-temperature tension stiffening model is presented.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

This paper aims to study the mass loss of three-dimensional (3D) printed materials at high temperatures. A preconcentration and analysis technique, static headspace gas chromatography-mass spectrometry (SHS-GC-MS), is demonstrated for the analysis of volatile compounds liberated from fused deposition modeling (FDM) and stereolithography (SLA) 3D printed models under elevated temperatures.

A total of seven commercial 3D printing materials were tested using the SHS-GC-MS approach. The printed model mass and mass loss were examined as a function of FDM printing parameters including printcore temperature, model size and printing speed, and the use of SLA postprocessing procedures. A high temperature resin was used to demonstrate that thermal degradation products can be identified when the model is incubated under high temperatures.

At higher printing temperatures and larger model sizes, the initial printed model mass increased and showed more significant mass loss after thermal incubation for FDM models. For models produced by SLA, the implementation of a postprocessing procedure reduced the mass loss at elevated temperatures. All FDM models showed severe structural deformation when exposed to high temperatures, while SLA models remained structurally intact. Mass spectra and chromatographic retention times acquired from the high temperature resin facilitated identification of eight compounds (monomers, crosslinkers and several photoinitiators) liberated from the resin.

The study exploits the high sensitivity of SHS-GC-MS to identify thermal degradation products emitted from 3D printed models under elevated temperatures. The results will aid in choosing appropriate filament/resin materials and printing mechanisms for applications that require elevated temperatures.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.

Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs.

Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement.

The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load.

The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.

Top-seat angle connection is known as one of the usual uncomplicated beam-to-column joints used in steel structures. This article investigates the fire performance of welded top-seat angle connections.

A finite element (FE) model, including nonlinear contact interactions, high-temperature properties of steel, and material and geometric nonlinearities was created for accomplishing the fire performance analysis. The FE model was verified by comparing its simulation results with test data. Using the verified model, 24 steel-framed top-seat angle connection assemblies are modeled. Parametric studies were performed employing the verified FE model to study the influence of critical factors on the performance of steel beams and their welded angle joints.

The results obtained from the parametric studies illustrate that decreasing the gap size and the top angle size and increasing the top angles thickness affect fire behavior of top-seat angle joints and decrease the beam deflection by about 16% at temperatures beyond 570 °C. Also, the fire-resistance rating of the beam with seat angle stiffener increases about 15%, compared to those with and without the web stiffener. The failure of the beam happens when the deflections become more than span/30 at temperatures beyond 576 °C. Results also show that load type, load ratio and axial stiffness levels significantly control the fire performance of the beam with top-seat angle connections in semi-rigid steel frames.

Development of design methodologies for these joints and connected beam in fire conditions is delayed by current building codes due to the lack of adequate understanding of fire behavior of steel beams with welded top-seat angle connections.