The development and use of thermal models is an integral part of the design process in existing buildings due for refurbishment. Energy predictions for existing buildings are often based on models which assume thermal property values of the building construction elements. However, once built, the actual thermal properties may differ significantly from their estimated values. Possible reasons include thermal bridging, material distortion and moisture content, sub-standard construction on-site and unavailability of construction details. The uncertainties can be reduced if the modelling process can also make use of operational measurements, such as the fuel use and internal temperatures, which have been recorded in the building during operation. To make use of operational data, performance-based models can be used. Performance-based models rely on measured data for the development of the model s architecture and for informing the estimation of the model parameters that would otherwise be based on the modeller s assumptions of the building s characteristics. One solution to the challenge of using performance-based models for existing buildings is to use the Lumped Parameter modelling approach. The Lumped Parameter modelling technique is often used for performance-based modelling of existing buildings due to the moderate knowledge of the building s physical properties required and the limited operational data needed for model training. This thesis investigates the potential of performance-based modelling techniques for existing UK domestic buildings, based on the Lumped Parameter thermal modelling technique, and the use of measured operational data to inform the model structure and parameters. Operational data have been collected in 20 homes as part of the REFIT project, an EPSRC-funded research project on Smart Meters and Smart Homes (REFIT, 2016). This thesis explores 11 houses from the REFIT dataset and, in particular, the temperature, gas and electricity measurements from the participating households, and develops whole-house and sub-system performance-based models using the Lumped Parameter technique. The suitability of simple performance-based Lumped Parameter models in representing typical UK domestic buildings using mainstream operational data such as temperatures and gas consumption measurements is explored. This thesis concludes on the adequacy of the operational data as measured. High correlations (>0.9) between whole-house average indoor temperatures and individual room air temperature measurements prove the use of averages adequate for representing the main rooms of the houses, whereas individual representation of the house s main rooms in use in the same model can prove challenging. A similar result is observed for whole-house radiator representation and the individual radiators. The relationships between the operational data is explored to inform the model structure and to identify collinearity and multi collinearity in the measurements. In terms of whole-house modelling, when using constraints for the parameter values during the model calibration to the measured data the resulting model parameters can be realistic and a good agreement to the measured data can be achieved (on average an RMSE of 1.03 for air temperature). The most significant parameters affecting the mean value of internal air temperatures are the external envelope resistance Re, the non-inertia elements (e.g. windows and doors) resistance, the window area for solar gains, boiler efficiency and the infiltration rate. The indoor air and internal element heat capacitance had the greatest impact on the swing in the internal air temperature (a 75% decrease in the capacitance value resulted in a 190.70% increase in the standard deviation value on average across the 11 houses). The building envelope heat capacitance and the envelope node positioning were the two parameters with the least impact on the model goodness of fit (a 75% decrease in capacitance and a value of 0.9 in envelope node positioning resulted in a 2.57% and 6.68% increase respectively in the RMSE on average across all 11 houses). Finally, the heating system representation using the Lumped Parameter model showed that the whole-house gas consumption data at the meter level, consisting of gas used for space heating as well as other purposes, is inadequate to drive the heating system model. A temperature threshold (e.g. of 1oC) indicating model overprediction can be used to remove the time-stamps of mixed use gas consumption from the model calibration. The heating system model can then be used to quantify gas consumption for space heating and non-space heating uses. In the 11 houses under study, 82.96% of the total gas consumption served for space heating, with 17.04% serving for other non-space heating purposes.

Wafer handling robotics are critical in semiconductor manufacturing to enable tight control of temperature, humidity, and particle contamination during processing. Closed-loop dynamic modeling during the robot design process ensures designs meet throughput and stability specifications prior to prototype hardware purchase. Dynamic models are also used in model-based control to improve performance. This thesis describes the generation and mathematical verification of a dynamic model for a three degrees-of-freedom wafer handling mechanism with one linear and two rotary axes. The dynamic plant model is integrated with motion and motor controller models, and the closed-loop performance is compared with experimental data. Models with rigid and flexible connections are compared, and the flexible connection models are shown to overall agree better with a measured step response. The simulation time increase from the addition of flexible connections can be minimized by modeling only the component stiffnesses that impact the closed-loop mechanism response. A method for selecting which elements to include based on controller bandwidth is presented and shown to significantly improve simulation times with minimal impact on model predictive performance.

Thermal modelling is of great importance in all electric machines but especially in permanent magnet synchronous machines (PMSMs). The thermally fragile permanent magnets (PMs) can more easily be demagnetized at high temperatures. When high speed machines are considered, heat extraction surfaces are small due to the higher energy density. This thesis focuses on the thermal modelling of a high speed slotless PMSM using analytical techniques. From literature it is clear that analytical distributed models have not reached its full potential in thermal modelling of electric machines. Thermal experiments on high speed electric machine, including rotor PM temperature measurements are not commonly found in literature. The thermal behaviour of each component of the machine is influenced by the overall temperature distribution. The widely used lumped parameter (LP) cylindrical component model derived by Mellor et al. is used to derive a LP model of the entire machine. A two dimensional (2-D) analytical distributed model is derived for the rotor PM using the separation of variables method. Three of the boundaries are assumed to be of the convection type and the fourth of constant heat flow type. Different convection coefficients are assumed to exist in the radial and axial directions. The distributed model is verified using COMSOL R and good correlation is shown. The distributed model is used to determine the temperature distribution in the PM and the convection heat flow in the axial direction. Loss calculation is an integral part of thermal modelling. Temperature changes in an electric machine is due to the interaction between the heat generation (losses) and heat removal. The losses found in a high speed slotless PMSM are investigated. A 2-D analytical magnetic model is used to determine the stator lamination loss as well as the stator winding eddy current loss. A simple LP model is derived for the rotor eddy current loss. Due to the relatively large resistivity of the shielding cylinder and PM material, the rotor eddy current loss is a significant part of the total machine loss. The tangential current width is determined empirically in this thesis but a 3-D distributed model which includes end space effects and skin depth could also be used. A large part of thermal modelling is empirically based. The convection and interface resistances are determined through a set of experiments in this thesis. The measured and calculated convection coefficients correlated well for both forced and natural convection cooling. A large temperature increase found during the no-load test can be attributed to large bearing loss, possibly due to axial loading. The LP model is modified to include the phenomena found during the experiments. The thermal model is used to predict the temperatures of a high speed PMSM at rated load and speed. Although the PM is not heated above the Curie temperature, demagnetization is still possible. According to the model, the machine will not be able to operate at full load and speed for extensive periods due to mechanical stress limits being exceeded. The temperature distribution of the PM could not be verified since the temperatures in the air gap and end space could not be measured. It is expected that axial heat flow will be larger than what is currently predicted by the distributed model. A sensitivity analysis was used to investigate the influence of the thermal resistances and losses on the machine temperatures. Methods for reducing the rotor eddy current loss and interface resistances are also discussed. The first contribution of this thesis is the 2-D analytical distributed model for the PM of a high speed PMSM. Hot spots and 2-D heat flow can be analysed using this model. Combining the LP and 2-D analytical distributed models is another contribution. This combines the simplicity and fast solution times of the LP model with the 2-D thermal distribution of the analytical distributed model. The systematic experimental investigation of the thermal behaviour of a high speed PMSM is a further contribution. / Thesis (Ph.D. (Electrical Engineering))--North-West University, Potchefstroom Campus, 2011.

Thermal modelling is of great importance in all electric machines but especially in permanent magnet synchronous machines (PMSMs). The thermally fragile permanent magnets (PMs) can more easily be demagnetized at high temperatures. When high speed machines are considered, heat extraction surfaces are small due to the higher energy density. This thesis focuses on the thermal modelling of a high speed slotless PMSM using analytical techniques. From literature it is clear that analytical distributed models have not reached its full potential in thermal modelling of electric machines. Thermal experiments on high speed electric machine, including rotor PM temperature measurements are not commonly found in literature. The thermal behaviour of each component of the machine is influenced by the overall temperature distribution. The widely used lumped parameter (LP) cylindrical component model derived by Mellor et al. is used to derive a LP model of the entire machine. A two dimensional (2-D) analytical distributed model is derived for the rotor PM using the separation of variables method. Three of the boundaries are assumed to be of the convection type and the fourth of constant heat flow type. Different convection coefficients are assumed to exist in the radial and axial directions. The distributed model is verified using COMSOL R and good correlation is shown. The distributed model is used to determine the temperature distribution in the PM and the convection heat flow in the axial direction. Loss calculation is an integral part of thermal modelling. Temperature changes in an electric machine is due to the interaction between the heat generation (losses) and heat removal. The losses found in a high speed slotless PMSM are investigated. A 2-D analytical magnetic model is used to determine the stator lamination loss as well as the stator winding eddy current loss. A simple LP model is derived for the rotor eddy current loss. Due to the relatively large resistivity of the shielding cylinder and PM material, the rotor eddy current loss is a significant part of the total machine loss. The tangential current width is determined empirically in this thesis but a 3-D distributed model which includes end space effects and skin depth could also be used. A large part of thermal modelling is empirically based. The convection and interface resistances are determined through a set of experiments in this thesis. The measured and calculated convection coefficients correlated well for both forced and natural convection cooling. A large temperature increase found during the no-load test can be attributed to large bearing loss, possibly due to axial loading. The LP model is modified to include the phenomena found during the experiments. The thermal model is used to predict the temperatures of a high speed PMSM at rated load and speed. Although the PM is not heated above the Curie temperature, demagnetization is still possible. According to the model, the machine will not be able to operate at full load and speed for extensive periods due to mechanical stress limits being exceeded. The temperature distribution of the PM could not be verified since the temperatures in the air gap and end space could not be measured. It is expected that axial heat flow will be larger than what is currently predicted by the distributed model. A sensitivity analysis was used to investigate the influence of the thermal resistances and losses on the machine temperatures. Methods for reducing the rotor eddy current loss and interface resistances are also discussed. The first contribution of this thesis is the 2-D analytical distributed model for the PM of a high speed PMSM. Hot spots and 2-D heat flow can be analysed using this model. Combining the LP and 2-D analytical distributed models is another contribution. This combines the simplicity and fast solution times of the LP model with the 2-D thermal distribution of the analytical distributed model. The systematic experimental investigation of the thermal behaviour of a high speed PMSM is a further contribution. / Thesis (Ph.D. (Electrical Engineering))--North-West University, Potchefstroom Campus, 2011.

ALVES, C. A. A study concerning homeostasis and population development of collagen fibers. 2017. 88 f. Tese (Doutorado em Física) – Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2017. / Submitted by Pós-Graduação em Física (posgrad@fisica.ufc.br) on 2017-11-21T16:35:18Z No. of bitstreams: 1 2017_tese_caalves.pdf: 8939939 bytes, checksum: 5cbf75fd845e26cdee776ee15fc2cfbf (MD5) / Approved for entry into archive by Giordana Silva (giordana.nascimento@gmail.com) on 2017-11-22T18:55:25Z (GMT) No. of bitstreams: 1 2017_tese_caalves.pdf: 8939939 bytes, checksum: 5cbf75fd845e26cdee776ee15fc2cfbf (MD5) / Made available in DSpace on 2017-11-22T18:55:25Z (GMT). No. of bitstreams: 1 2017_tese_caalves.pdf: 8939939 bytes, checksum: 5cbf75fd845e26cdee776ee15fc2cfbf (MD5) Previous issue date: 2017 / Collagen is a generic name for the group of the most common proteins in mammals. It confers mechanical stability, strength and toughness to the tissues, in a large number of species. In this work we investigate two properties of collagen that explain in part the choice by natural selection of this substance as an essential building material. In the first study the property under investigation is the homeostasis of a single fiber, i.e., the maintenance of its elastic properties under the action of collagen monomers that contribute to its stiffening and enzymes that digest it. The model used for this purpose is a onedimensional chain of linearly elastic springs in series coupled with layers of sites. Particles representing monomers and enzymes can diffuse along these layers and interact with the springs according to specified rules. The predicted lognormal distribution for the local stiffness is compared to experimental data from electronic microscopy images and a good concordance is found. The second part of this work deals with the distribution of sizes among multiple collagen fibers, which is found to be bimodal, hypothetically because it leads to a compromise between stiffness and toughness of the bundle of fibers. We propose a mechanism for the evolution of the fiber population which includes growth, fusion and birth of fibers and write a Population Balance Equation for that. By performing a parameter estimation over a set of Monte Carlo simulations, we determine the parameters that best fit the available data. / Collagen is a generic name for the group of the most common proteins in mammals. It confers mechanical stability, strength and toughness to the tissues, in a large number of species. In this work we investigate two properties of collagen that explain in part the choice by natural selection of this substance as an essential building material. In the first study the property under investigation is the homeostasis of a single fiber, i.e., the maintenance of its elastic properties under the action of collagen monomers that contribute to its stiffening and enzymes that digest it. The model used for this purpose is a onedimensional chain of linearly elastic springs in series coupled with layers of sites. Particles representing monomers and enzymes can diffuse along these layers and interact with the springs according to specified rules. The predicted lognormal distribution for the local stiffness is compared to experimental data from electronic microscopy images and a good concordance is found. The second part of this work deals with the distribution of sizes among multiple collagen fibers, which is found to be bimodal, hypothetically because it leads to a compromise between stiffness and toughness of the bundle of fibers. We propose a mechanism for the evolution of the fiber population which includes growth, fusion and birth of fibers and write a Population Balance Equation for that. By performing a parameter estimation over a set of Monte Carlo simulations, we determine the parameters that best fit the available data.

Indiana University-Purdue University Indianapolis (IUPUI) / While computed tomographic angiography (CTA) has emerged as a powerful noninvasive option that allows for direct visualization of arterial stenosis(AS), it cant assess the hemodynamic abnormality caused by an AS. Alternatively, trans-stenotic pressure gradient (TSPG) and fractional flow reserve (FFR) are well-validated hemodynamic indices to assess the ischemic severity of an AS. However, they have significant restriction in practice due to invasiveness and high cost. To fill the gap, a new computational modality, called InVascular has been developed for non-invasive quantification TSPG and/or FFR based on patient's CTA, aiming to quantify the hemodynamic abnormality of the stenosis and help to assess the therapeutic/surgical benefits of treatment for the patient. Such a new capability gives rise to a potential of computation aided diagnostics and therapeutics in a patient-specific environment for ASs, which is expected to contribute to precision planning for cardiovascular disease treatment. InVascular integrates a computational modeling of diseases arteries based on CTA and Doppler ultrasonography data, with cutting-edge Graphic Processing Unit (GPU) parallel-computing technology. Revolutionary fast computing speed enables noninvasive quantification of TSPG and/or FFR for an AS within a clinic permissible time frame. In this work, we focus on the implementation of inlet and outlet boundary condition (BC) based on physiological image date and and 3-element Windkessel model as well as lumped parameter network in volumetric lattice Boltzmann method. The application study in real human coronary and renal arterial system demonstrates the reliability of the in vivo pressure quantification through the comparisons of pressure waves between noninvasive computational and invasive measurement. In addition, parametrization of worsening renal arterial stenosis (RAS) and coronary arterial stenosis (CAS) characterized by volumetric lumen reduction (S) enables establishing the correlation between TSPG/FFR and S, from which the ischemic severity of the AS (mild, moderate, or severe) can be identified. In this study, we quantify TSPG and/or FFR for five patient cases with visualized stenosis in coronary and renal arteries and compare the non-invasive computational results with invasive measurement through catheterization. The ischemic severity of each AS is predicted. The results of this study demonstrate the reliability and clinical applicability of InVascular.

Cardiovascular disease, including coronary artery disease and aortic valve stenosis, impacts tens of millions of people annually and carries a massive global economic burden. Advances in medical imaging, hardware and software are leading to an increased interest in the field of cardiovascular computational modelling to help combat the devastating impact of cardiovascular disease. Lumped parameter modelling (a branch of computational modelling) holds the potential of aiding in the early diagnosis of these diseases, assisting clinicians in determining personalized and optimal treatments and offering a unique in-silico setting to study cardiac and circulatory diseases due to its rapid computation time, ease of automation and relative simplicity. In this thesis, cardiovascular lumped parameter modelling is presented in detail and a patient-specific framework capable of simulating blood flow waveforms and hemodynamic data in the heart and coronary arteries was developed. The framework used only non-invasive clinical data and images (Computed Tomography images, echocardiography data and cuff blood pressure) as inputs. The novel model was then applied to 19 patients with aortic stenosis who underwent transcatheter aortic valve replacement. The diastolic coronary flow waveforms in the left anterior descending artery, left circumflex artery and right coronary artery were validated against a previously developed patient-specific 3D fluid-structure interaction model for all 19 subjects (pre and post intervention). There were strong qualitative and quantitative agreements between the two models. After the procedure, aortic valve area and net pressure gradient across the aortic valve improved for almost all the subjects. As for the hemodynamic data, according to the model, there was substantial variability in terms of the increase or decrease post intervention. On average, left ventricle workload and maximum left ventricle pressure decreased by 4.5% and 13.0% while cardiac output, mean arterial pressure and resting heart rate increased by 9.9%, 6.9% and 1.9% respectively. There were also subject specific changes in coronary blood flow (37% had increased flow in all three coronary arteries, 32% had decreased flow in all coronary arteries, and 31% had both increased and decreased flow in different coronary arteries). All in all, a proof-of-concept cardiac and coronary lumped parameter framework was developed, validated, and applied in this thesis. / Thesis / Master of Applied Science (MASc) / The heart is a vital part of the cardiovascular system, which helps deliver and regulate blood flow through the entire human body. The coronary arteries are a crucial part of this system since they deliver blood directly to heart muscles. For numerous reasons, the cardiovascular system can become diseased over time and require clinical treatment. Coronary artery disease and aortic valve stenosis are among the most prevalent cardiovascular diseases globally. While medical imaging on its own is a crucial part of the disease management and treatment process, advanced computational models can further enhance the process and provide clinics with data and predictions they might otherwise miss. In this thesis, a patient specific computational framework capable of simulating blood flow waveforms and cardiovascular data in the heart and coronary arteries using only non-invasive clinical data and images was developed and validated. The novel model was applied to a series of patients with aortic stenosis who underwent heart valve replacement with the aim of studying the impact on coronary blood flow and global cardiovascular metrics.

A mathematical model of blood flow in infants with hypoplastic left heart syndrome (HLHS) was improved by adding cerebral autoregulation. This is the process by which blood vessels constrict or dilate to keep blood flow steady in certain organs during pressure changes. The original lumped parameter model transformed the fluid flow into an electrical circuit. Its behavior is described using a system of thirty-three coupled differential equations that are solved numerically using a fourth-order Runge-Kutta method implemented in MATLAB. A literature review that includes a discussion of autoregulation mechanisms and approaches to modeling them is followed by a description of the model created for this paper. The model is based on the baroreceptor or neurogenic theory of autoregulation. According to this theory, nerves in certain places within the cardiovascular system detect changes in blood pressure. The brain then compensates by sending a signal to blood vessels to constrict or dilate. The model of the control system responded fairly well to a pressure drop with a steady state error of about two percent. Running the model with or without the control system activated had little effect on other parameters, notably cardiac output. A more complete model of blood flow control would include autonomic regulation. This would vary more parameters than local autoregulation, including heart rate and contractility. This is suggested as a topic of further research.

The present research work has been carried out to develop a unified knowledge-based hybrid methodology combining regression-based, lumped parameter and finite element analyses that can be implemented in the initial phase of vehicle design resulting in a superior side crash performance. As a first step, a regression-based model (RBM) is developed between the injury parameter Thoracic Trauma Index (TTI) of the rear SID and characteristic side impact dynamic response variables such as rear door velocity (final) and intrusion supplementing an existing RBM for front TTI prediction. In order to derive the rear TTI RBM, existing public domain vehicle crash test data provided by NHTSA has been used. A computer-based tool with a Graphical User Interface (GUI) has been developed for obtaining possible solution sets of response variables satisfying the regression relations for both front and rear TTI. As a next step in the formulation of the present hybrid methodology for vehicle side impact safety design, a new Lumped Parameter Model (LPM) representing NHTSA side impact is developed. The LPM developed consists of body sub-systems like B-pillar, front door, rear door and rocker (i.e. sill) on the struck side of the vehicle, MDB, and “rest of the vehicle” as lumped masses along with representative nonlinear springs between them. It has been envisaged that for the initial conceptual design to progress, the targets of dynamic response variables obtained from RBM should yield a set of spring characteristics broadly defining the required vehicle side structure. However, this is an inverse problem of dynamics which would require an inordinate amount of time to be solved iteratively. Hence a knowledge-based approach is adopted here to link the two sets of variables i.e., the dynamic response parameters (such as average door and B-pillar velocities, door intrusion, etc.) and the stiffness and strength characteristics of the springs present in LPM. In effect, this mapping is accomplished with the help of an artificial neural network (ANN) algorithm (referred to as ANN_RBM_LPM in the current work). To generate the required knowledge database for ANN_RBM_LPM, one thousand cases of LPM chosen with the help of the Latin Hypercube technique are run with varying spring characteristics. The goal of finding the desired design solutions describing vehicle geometry in an efficient manner is accomplished with the help of a second ANN algorithm which links sets of dynamic spring characteristics with sets of sectional properties of doors, B-pillar and rocker (referred as ANN_LPM_FEM in the current work). The implementation of this approach requires creation of a knowledge database containing paired sets of spring characteristics and sectional details just mentioned. The effectiveness of the hybrid methodology comprising both ANN_RBM_LPM and ANN_LPM_FEM is finally illustrated by improving the side impact performance of a Honda Accord finite element model. Thus, the unique knowledge-based hybrid approach developed here can be deployed in real world vehicle safety design for both new and existing vehicles leading to enormous saving of time and costly design iterations.

This paper presents a numerical model for the evaluation of the actual torque in Gerotor units. The model consists of two major modules: the pre-processor module and the HYGESim module. The preprocessor module consists of the geometric and the mechanical module. The geometric pre-processor module considers the CAD geometry of Gerotor with tolerances as input and it provides as output the geometric features needed to evaluate the rotor loading and the flow features. The mechanical preprocessor module evaluates the forces of interaction at the contact points between the rotors. The flow displaced by the unit is evaluated using a lumped parameter model whereas the lubricating gaps are evaluated by solving the Reynolds Equation. The main novel aspects consist of the evaluation of the frictional losses at various interfaces. An Elasto-Hydrodynamic Lubrication (EHL) approach is used to evaluate the frictional losses at the contact points between the rotors. Tests on a prototype Gerotor unit are performed for the model validation, particularly as pertains to the features of the shaft torque. Additionally, the paper comments on the distribution of the different torque loss contributions associated with the operation of the unit taken as reference.