In this research, two comprehensive numerical models were developed using ABAQUS/CAE Finite Element (FE) software: 1) geothermal model, and 2) coupled thermo-hydro-mechanical model. In the first model, a purely heat transfer analysis was performed to reproduce the conditions at the site and investigate the subsurface thermal regime beneath the road embankment. The existence of a frozen section (frost bulb) underneath the embankment and its size and location were investigated by the model. The second model concentrated on the mechanical behaviour of the road embankment. Temperature-dependent thermal and mechanical properties were used for all the materials. Model parameters were calibrated using the results of the triaxial and oedometer tests which have been conducted by previous researchers. A fully-coupled and a sequentially-coupled analysis were conducted. The results of the two analyses were compared to each other and to the field measurements. / February 2016

Shape Memory Alloy is an amazing material, which can “remember” and return to its original shape when heated due to its temperature dependent phase transformation. Shape Memory Alloy wire has significant potential for application in the automobile industry due to its high ratio of energy / weight and silent actuation. However, a dependable method to measure the operating temperature of SMA wire and a reliable heat transfer model to characterize the dynamics of the SMA wire limit its widespread use in the automobile industry. This thesis presents a detailed description of the work performed to develop a reliable method for determining surface temperature of current carrying SMA wires and the development of a heat transfer correlation for natural convection cooling of heated SMA wires. The major findings of the research are as follows: When a spot welded thermocouple measures the temperature of a current carrying SMA wire, there is a “spurious voltage” ΔV added to the thermo electro-motive force (EMF) of the thermocouple as a result of a voltage drop across the two points of contact that the thermocouple wires make with the SMA wire. This leads to an erroneous temperature reading that can be higher or lower than the actual temperature depending on the direction of current flow. When the carrying current is reversed in direction, the “spurious voltage” becomes –ΔV allowing a correct temperature reading to be obtained by averaging the readings based on opposed current flow. A two-step spot welding procedure for attaching thermocouples to SMA wire can eliminate the influence of the “spurious voltage” in the temperature reading. By spot welding the thermocouple wires onto the SMA wire one by one, the thermocouple lead offset is eliminated and the thermocouple provides an accurate point source reading. Infrared thermal imaging can be a good supplement in the experiment to monitor errors in temperature readings from thermocouples. Due to the curvature of the SMA wire, the temperatures of the locations on the SMA wire that are the closest to the infrared camera represent the temperature of the SMA wire. So a line analysis across the SMA wire on the software “ThermaCAM” is required to determine the temperature of the SMA wire by infrared thermal imaging and the highest temperature on the line is the temperature of the SMA wire. A new natural convective heat transfer correlation comprising the inclination angle φ is developed based on experimental results, which can be used to predict the temperature of a SMA wire given its diameter and inclination angle. The comparisons show that the new correlation agrees with existing correlations in a vertical orientation and for small Rayleigh numbers (0.001 < RaD < 0.05) in the horizontal orientation. The correlation developed in this work for horizontal orientation tends to overestimate values of Nusselt numbers as predicted in other correlations when the Rayleigh number is high (0.05 < RaD < 0.6). It is speculated that this overestimation can be attributed to a temperature distortion associated with thermocouple measurement at or near ambient pressure conditions.

The overarching goal of this work is to address the design of next-generation, high torque density electrical machines through numerical optimization using an integrated thermal-electromagnetic design tool that accounts for advanced cooling technology. A parametric thermal model of electric machines was constructed and implemented using a finite difference approach incorporating an automated, self segmenting mesh generation. A novel advanced cooling technology is proposed to improve thermal transport in the machine by removing heat directly from the windings via heat exchangers located between the winding bundles. Direct winding heat exchange (DWHX) requires high convective transport and low pressure loss. The heat transfer to pressure drop tradeoff was addressed by developing empirically derived Nusselt number and friction factor correlations for micro-hydrofoil enhanced meso-channels. The parametric thermal model, advanced cooling technique, Nusselt number and friction factor correlations were combined with a parametric electromagnetic model for electric machines. The integrated thermal-electromagnetic model was then used in conjunction with particle swarm optimization to determine optimal conceptual designs. The Nusselt number correlation achieves an R² value of 0.99 with 95% of the data falling within ± 2.5% similarly the friction factor correlation achieves an R² value of 0.92 with 95% of the data falling within ± 10.2%. The integrated thermal-electromagnetic design tool, incorporating DWHX, generated an optimized 20 kW permanent magnet electric machine design achieving a torque density of 23.2 N-m/L based on total system volume.

Dans le cadre du développement du véhicule électrique hybride, les machines électriques pour la traction sont l’objet d’un effort toujours plus important de recherche et de développement. En particulier, les contraintes d’encombrement allouées à ces machines sont toujours plus sévères et la recherche se porte vers des structures de machines compactes. C’est dans ce contexte que nos travaux se sont portés sur l’étude et le dimensionnement de machine à flux axial pour une application hybridation douce (Mild Hybrid) d’alterno-démarreur monté sur vilebrequin de puissance 50 kW et de couple 205 Nm en régime transitoire. Un état de l’art des machines à flux axial est présenté. Une analyse des configurations de bobinage avec la méthode de l’étoile des encoches est détaillée. Un début d’analyse de la machine à commutation de flux est proposé. La méthodologie de dimensionnement est étayée. Elle repose sur des études de sensibilité, un dimensionnement paramétrique, mais aussi une optimisation de la machine. Les modèles utilisés sont de type éléments finis et surface de réponse par plan d’expériences. Enfin, une étude thermique de la machine est effectuée et des pistes sont données pour l’amélioration de l’échange thermique par refroidissement diphasique. / In the context of development of the hybrid electric vehicle, electric machines for traction are under extensive investigation. In particular, volume constraints are more and more severe and research is carried out towards compact structures. This work is focused on the study and the design of axial flux machine for a mild-hybrid application of an integrated starter generator mounted on the crankshaft. Its ratings in transient mode are 50 kW and 205 Nm. A literature review of axial flux machines is presented. A analysis of winding configurations with star of slots method is detailed. A beginning of analysis of switching-flux machine is proposed. The methodology of design is detailed. It is based on sensitivity analysis, parametric design and optimization of the machine. Utilized models are finite element model and response surface by design of experiments. Finally, a thermal study of the machine is carried out and some ideas are given to improve the thermal exchange by diphase cooling.

This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. HVAC on/off behaviors are simulated by using Mathematical Program with Complementary Constraints (MPCCs) and solved in near real-time with a nonlinear solver. Removing HVAC on/off as a discrete variable decreases potential solutions and consequently reduces solve time and increases the probability of reaching a more optimal solution. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day compared to a home with regular temperature control. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. Homes using this home energy optimization will contribute less to the grid peak load and therefore, improve grid stability and reduce the amplitude of load following cycles for utilities. This case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real-time, and mixed empirical / first principles machine learning methods to guide the model structure.

In diesem Beitrag wird ein thermisches Modell beschrieben, mit dem die Temperaturverteilung einer elektrischen Maschine während des dynamischen Betriebes berechnet werden kann. Die Maschine kann so ohne Überschreiten von Temperaturgrenzen weiter ausgenutzt werden. / In this paper a thermal model for dynamic operations is proposed to calculate the transient temperature distribution in the electrical machine. With the help of the model it is possible to increase availability during overload operation. Temperature limit overrun can be avoided.

No description available.

Localized heating leads to generation of thermal Hotspots that affect performance and reliability of an Integrated Circuit(IC). Functional workloads determine the locations and temperature of hotspots on a die. Programs are classified into phases based on program execution profile. During a phase, spatial power dissipation pattern of an application remains unchanged. In this thesis, we present a systematic approach for developing a synthetic workload from a functional workload to create worst case temperature of a target hotspot in 2D and 3D IC. These synthetic workload are designed to create thermal stress patterns, which would help in characterizing the thermal characteristics of micro architecture to worst case temperature transient which is an important problem in Industry. Our approach is based on the observation that, worst case temperature at a particular location in 2 D IC is determined not only by the current activity in that region, but also by the past activities in the surrounding regions. Therefore, if the surrounding areas were “pre-heated” with a different workload, then the target region may become hotter due to slower rate of lateral heat dissipation Similarly in case of 3D IC, the workload applied to each of the dies in 3D IC keeps on changing continuously, thus the hotspot could be found in any of the stacked layers. Thus the creation of localized hotspot at a particular location in a stacked 3D IC layer depends not only on the present activity at that location but also on the previous activity in the surrounding region and also on the activity of layers below it. Accordingly, (i) we develop a wavelet-based canonical spatio-temporal heat dissipation model for program traces, and use (ii) a novel Integer Linear Programming (ILP) formulation to rearrange program phases to generate target worst case hotspot temperature in 2D and 3D IC. We apply this formulation to target another well-known problem of (iii) maximizing temperature between a pair of co-ordinates in an IC. Experimental results show that by taking the spatio-temporal effect into account and with dynamic phase change behavior, we could raise temperature of a hotspot higher than what is possible otherwise. ICs are often tested at worst-case system operating conditions to assure that, all ICs shipped will function properly in the end system. Thus hotspot temperature maximization is an important in design verification and testing.

No description available.

In order to make gas chromatography (GC) more widely accessible, considerable effort has been made in developing miniaturized GC systems. Thermal gradient gas chromatograpy (TGGC), one of the heating methods used in GC, has recieved attention over the years due to it's ability to enhance analyte focusing. The present work seeks to develop high performance miniaturized GC systems by combining miniaturized GC technology with thermal gradient control methods, creating miniaturized thermal gradient gas chromatography (µTGGC) systems. To aid in this development a thermal control system was developed and shown to successfully control various µTGGC systems. DAQ functionality was also included which allowed for the recording of temperature and power data for use in modeling applications. Thermal models of the various µTGGC systems were developed and validated against the recorded experiemental data. Thermal models were also used to aid in decisions required for the development of new µTGGC system designs. The results from the thermal models were then used to calibrate and validate a stochastic GC transport model. This transport model was then used to evaluate the effect of thermal gradient shape on GC separation performance.