The behaviour of particles in pulsating and oscillating flows is of practical interest in devices such as pulsation reactors and ultrasonic elevators. In addition to the resulting flow patterns, the influence of the flow on heat and mass transfer is often important. The state of the art in this area is already quite well developed with many different models, theories, and experiments published. However, only small parameter ranges of the behaviour of particles in pulsating and oscillating flows are considered, while an overarching theoretical framework does not yet exist. Therefore, this work presents a three-stage model for the behaviour of solid single particles in oscillating (pulsating) flows. The relative velocity between particle and fluid as well as the flow patterns around the particle, together with the heat and mass transfer at the particle are considered. The model levels build on top of each other, with the introduced ϵ-Re plain as a common connection between the levels. The number of input parameters could be limited to the five most important ones (fluid velocity amplitude, fluid oscillation frequency, fluid temperature, particle diameter, particle density), but these are considered in very large ranges. The relative velocity is largely calculated analytically using various flow resistance approaches. Direct numerical simulations were carried out to qualitatively estimate the flow patterns around the particle. The quantitative determination of a meta correlation for the entire ϵ-Re plane was carried out using 33 data sets from the literature. Conditions in pulsation reactors are particularly emphasized and their influence investigated.:Chapter 1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Chapter 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 3. State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1. Material Treatment in the Pulsation Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2. Particle Motion in an Oscillating Fluid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3. Steady Streaming (Flow Pattern). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4. Heat and Mass Transfer in Oscillating Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5. Heat and Mass Transfer in Pulsating Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.6. Non-continuum Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 4. Basic Assumptions and Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1. Input Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2. Pulsating Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3. Forces on the Particle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.4. Motion of Particles - Stokes Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.5. Harmonic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.6. Dimensionless Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.7. The ϵ-Re Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 5. Motion of the Particle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.1. Drag Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.2. Slip Velocity Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.3. Particle Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.4. Navigation in the ϵ-Re Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.5. Extension of the Stokes Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.6. Additional Effects at Micro Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.7. Analytical Particle Motion - Summary and Conclusion . . . . . . . . . . . . . . . . . . . . 61 Chapter 6. Flow Patterns in the Vicinity of the Particle . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.1. Creeping Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2. Quasi-steady Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3. Steady Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Chapter 7. Heat and Mass Transfer to Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7.1. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7.2. The Quasi-Steady HMT Area of the Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7.3. Models for Oscillating Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.4. Meta Correlation Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7.5. Deviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.6. Quasi-Steady Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.7. Heat and Mass Transfer to Small Particles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.8. Conclusion of Heat and Mass Transfer to Particles . . . . . . . . . . . . . . . . . . . . . . . . . 83 Chapter 8. Summary & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 8.1. Model Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 8.2. Inŕuence of input parameters on the HMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 8.3. The ϵ-Re Plane in the Special Case of the Pulsation Reactor . . . . . . . . . . . . . . 91 8.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Chapter 9. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Appendix A. Derivation and Solution of Particle Motion in the Stokes Model . . . . . i Appendix B. Derivation and Solution of Particle Motion in the Landau & Lifshitz Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Appendix C. Derivation of Deviation between Stokes and Schiller & Naumann . . . . x Appendix D. Parameters and Algorithm of the Direct Numerical Simulation and Flow Pattern Visualisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Appendix E. Conducted Data Preparation for HMT Models . . . . . . . . . . . . . . . . . . . . . . xv / Das Verhalten von Partikeln in pulsierenden und oszillierenden Strömungen findet praktisches Interesse in Apparaten wie Pulsationsreaktoren und Ultraschalllevitatoren. Dabei ist neben den entstehenden Strömungsmustern oft der Einfluss der Strömung auf den Wärme- und Stoffübergang von Bedeutung. Der Stand der Technik in der Literatur in diesem Bereich ist bereits recht weit entwickelt mit vielen verschiedenen Modellen, Theorien und Experimenten. Dabei werden jedoch stets nur kleine Parameterbereiche des Verhaltens von Partikeln in pulsierenden und oszillierenden Strömungen betrachtet, während ein übergreifender theoretischer Rahmen noch nicht existiert. Deshalb wird in dieser Arbeit ein dreistufiges Modell vorgestellt für das Verhalten von festen Einzelpartikeln in oszillierenden (pulsierenden) Fluidströmungen. Sowohl die Relativgeschwindigkeit zwischen Partikel und Fluid als auch die Strömungsmuster um das Partikel und der Wärme- und Stoffübergang am Partikel werden hierbei betrachtet. Die Modellebenen bauen aufeinander auf, wobei die eingeführte ϵ-Re-Ebene die Modellebenen miteinander verbinden. Die Anzahl der Eingangsparameter konnte auf die wichtigsten fünf (Fluidgeschwindigkeitsamplitude, Fluidoszillationsfrequenz, Fluidtemperatur, Partikeldurchmesser, Partikeldichte) begrenzt werden, diese werden jedoch in sehr großen Bereichen betrachtet. Die Relativgeschwindigkeit wird mittels verschiedener Strömungswiderstandsansätze größtenteils analytisch berechnet. Zur qualitativen Abschätzung der Strömungsmuster um das Partikel wurden direkte numerische Simulationen durchgeführt. Die quantitative Bestimmung einer Metakorrelation für die gesamte ϵ-Re-Ebene wurde mittels 33 Datensätze aus der Literatur durchgeführt. Dabei werden Bedingungen in Pulsationsreaktoren besonders herausgestellt und deren Einfluss untersucht.:Chapter 1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Chapter 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 3. State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1. Material Treatment in the Pulsation Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2. Particle Motion in an Oscillating Fluid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3. Steady Streaming (Flow Pattern). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4. Heat and Mass Transfer in Oscillating Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5. Heat and Mass Transfer in Pulsating Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.6. Non-continuum Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 4. Basic Assumptions and Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1. Input Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2. Pulsating Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3. Forces on the Particle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.4. Motion of Particles - Stokes Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.5. Harmonic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.6. Dimensionless Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.7. The ϵ-Re Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 5. Motion of the Particle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.1. Drag Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.2. Slip Velocity Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.3. Particle Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.4. Navigation in the ϵ-Re Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.5. Extension of the Stokes Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.6. Additional Effects at Micro Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.7. Analytical Particle Motion - Summary and Conclusion . . . . . . . . . . . . . . . . . . . . 61 Chapter 6. Flow Patterns in the Vicinity of the Particle . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.1. Creeping Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2. Quasi-steady Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3. Steady Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Chapter 7. Heat and Mass Transfer to Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7.1. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7.2. The Quasi-Steady HMT Area of the Plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7.3. Models for Oscillating Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.4. Meta Correlation Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7.5. Deviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.6. Quasi-Steady Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.7. Heat and Mass Transfer to Small Particles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.8. Conclusion of Heat and Mass Transfer to Particles . . . . . . . . . . . . . . . . . . . . . . . . . 83 Chapter 8. Summary & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 8.1. Model Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 8.2. Inŕuence of input parameters on the HMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 8.3. The ϵ-Re Plane in the Special Case of the Pulsation Reactor . . . . . . . . . . . . . . 91 8.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Chapter 9. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Appendix A. Derivation and Solution of Particle Motion in the Stokes Model . . . . . i Appendix B. Derivation and Solution of Particle Motion in the Landau & Lifshitz Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Appendix C. Derivation of Deviation between Stokes and Schiller & Naumann . . . . x Appendix D. Parameters and Algorithm of the Direct Numerical Simulation and Flow Pattern Visualisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Appendix E. Conducted Data Preparation for HMT Models . . . . . . . . . . . . . . . . . . . . . . xv

<p dir="ltr">On many hypersonic vehicles, gaps are present on the outer surface of the vehicle and the interaction of the hypersonic freestream flow over these gaps can cause significant heat transfer to the vehicle. The project described in this thesis analyzed selected hypersonic gap problems and attempted to offer solutions to combat the heat transfer occurring in the gap. The first section of this thesis is a parametric study to understand the changes to the heat transfer and flow that modifications to the gap geometry can make. The second section is a comparison of the computational model to experimental data. The results of the studies show that adding a simple fillet or chamfer to the downstream step of the gap can reduce the maximum heat flux by over 90%. These results can be used to reduce the heat transfer caused by flow impingement in the gaps of hypersonic vehicles with a simple modification of the geometry and is consistent with the findings of other work in gap heating.</p>

<p dir="ltr">Grid convergence and scaling analyses have not been done rigorously for practical large-eddy simulations (LES). The challenge arises from the fact that there are two grid-related length scales: grid size and LES filter width. It causes the numerical and model errors in LES to be inherently coupled, making the convergence of either error difficult to analyze. This study works to overcome the challenge by developing scaling laws that can be used to guide the convergence analysis of errors in LES. Three different convergence cases are considered, and their respective scaling laws are developed by varying the ratio between grid size and filter width. A pool fire is adopted as a test case for the convergence analysis of LES. Qualitative and quantitative assessments of the LES results are made first to ensure reliable numerical solutions. In the subsequent scaling analysis, it is found that the results are consistent with their respective scaling laws. The results provide strong support to the developed scaling laws. The work is significant as it proposes a rigorous way to guide convergence analysis of LES errors. In a world where LES already has a wide range of applicability and is still becoming more prominent, it is imperative to have a thorough understanding of how it works including its convergence and scaling laws with respect to the change of grid size and filter width.</p>

<p dir="ltr">Increased scrutiny from customers and regulators to design aeroengines that are more efficient and environmentally friendly has pushed the need to investigate new engine architectures, manufacturing techniques, and computational fluid dynamic methods. This has led to the development of the CSTAR Gen. 2.5 centrifugal compressor, which uses an additively manufactured diffusion system and investigates the aerodynamic performance of an axi-centrifugal aeroengine. Additionally, an isolated unsteady diffuser computational model was previously developed that seeks to significantly reduce the computational cost of unsteady CFD in the diffuser.</p><p dir="ltr">The research presented in this paper is part of an ongoing attempt to utilize the capabilities of isolated unsteady diffuser modeling and rapid prototyping enabled through additive manufacturing in CSTAR Gen. 2.5 to develop a design framework that allows for quick computational and experimental analysis of diffusion systems in centrifugal compressors. Specifically, the scope of isolated unsteady diffuser modeling, which was previously only implemented in CSTAR Gen. 1 and at a single loading condition, is expanded by analyzing computational instabilities when applying the methodology to CSTAR Gen. 2.5 and analyzing results from four loading conditions (high loading, design point, low loading, and near choke) along a speedline.</p><p dir="ltr">Computational instabilities in the CSTAR Gen. 2.5 isolated diffuser models were determined to be caused by the decreased vaneless space compared to Gen. 1, which led to less “mixed” flow at the impeller outlet and a stronger diffuser potential field affecting the inlet profile. A boundary profile correction approach was developed which slightly increased very low total pressure near the diffuser shroud and negative radial velocity regions near the shroud and pitchwise locations of the diffuser vane leading edges while minimizing the overall affected area. The correction was successfully validated using 3D flow structure and minimum, average, and maximum total pressure, absolute velocity magnitude, and pressure comparisons at the diffuser inlet between an isolated and full-stage model.</p><p dir="ltr">Prediction capabilities of 3D flow structures and 1D performance parameters by isolated unsteady diffuser models were validated with results from full-stage unsteady models at each loading condition. The analysis indicated consistent performance by the isolated unsteady diffuser model at all loading conditions. An overall agreement in 3D flow structures was found, and trends in the full-stage unsteady models along the speedline were tracked well by the isolated unsteady model. At all loading conditions, there was a consistent over-representation of the separation region along the diffuser vane pressure side in the diffuser passage, overprediction of total pressure magnitude at the core of the flow at the diffuser outlet, and over- or underprediction of total pressure loss and static pressure recovery respectively. The similarity in the results between full-stage and isolated unsteady models, tracking of trends along the speedline, and consistent differences in 3D flow structure predictions and 1D performance parameters validates the isolated unsteady diffuser methodology for use at loading conditions from surge to choke.</p>

<p dir="ltr">This thesis explores novel methods of steel reheating, simulating hydrogen as a cleaner fuel in the combustion furnace and magnetic induction heating as a viable alternative, by utilizing advanced numerical simulations, including Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), to assess their performance and feasibility.</p><p dir="ltr">Hydrogen, known for its potential to significantly reduce carbon dioxide emissions, is examined as a substitute for natural gas. Simulations revealed that hydrogen combustion results in higher flame temperatures and heat fluxes. While the CFD model achieved a high level of accuracy, with a maximum temperature error of 3% and an average deviation of 7% from real-world data, hydrogen fuel caused an increase in heat flux by up to 12% and higher slab surface temperatures. These changes led to steeper thermal gradients and increased stress, with peak stress levels reaching 90% of material limit. This simulation approach provides valuable data on the performance of different furnace fuels, helping to identify optimal fuel blends and configurations that minimize the risk of material failure while enhancing furnace efficiency.</p><p dir="ltr">The impact of scale formation on steel surfaces during reheating was also investigated. A mathematical model based on linear-parabolic equations was integrated into CFD simulations to predict scale growth. This model was validated against experimental data, showing an average error of 6%. The presence of scale led to a reduction in core temperature by up to 31 K and a 7.6% decrease in heat flux, which negatively affected heating efficiency. Scale formation also caused a significant drop in thermal conductivity, impacting heat transfer and slab uniformity. Pre-heating zone contributed minimally to overall scale formation despite its extended duration whereas a majority of scale growth was observed in the heating zone. Applications of this model include improving reheat furnace model efficiency and optimizing furnace operation to minimize scale.</p><p dir="ltr">Magnetic induction heating was also explored as an alternative to combustion-based reheating, assessing its potential benefits and challenges. The simulation results, validated with an average error of approximately 7% compared to literature data. showed uniform temperature distribution, and reduced stress levels with optimal power settings around 80 kW. A 3D transient simulation modeled an adaptive power cycle to minimize thermal stress highlighting the effectiveness of adaptive soaking strategies over continuous soaking in managing thermal stress, improving heating efficiency and material integrity.</p>

<p dir="ltr">The CFD-CAA method combines computational fluid dynamics (CFD) and computational aeroacoustics (CAA) techniques to analyze the interaction between fluid flow and the generation and propagation of sound. CFD is primarily concerned with simulating fluid flow patterns, while CAA focuses on predicting noise generation and its propagation in fluids. The CFD-CAA method provides a powerful tool for understanding and predicting the acoustic behavior of turbulent flows. By combining the strengths of CFD and CAA, this approach provides more precise and comprehensive analyses across various fields, thereby contributing to enhanced designs and noise control strategies.</p><p dir="ltr">Within industrial applications, a primary concern is noise source identification. This process enables engineers to locate and quantify the strength of noise sources within a system, facilitating the implementation of more effective strategies during the design process. A novel methodology, computational statistically optimized near-field acoustic holography (C-SONAH), is proposed to virtually identify aeroacoustic sources. Initially, sound pressure is obtained using the CFD-CAA method, followed by the application of the SONAH algorithm to locate acoustic sources and predict the sound field. C-SONAH offers computational advantages over direct CAA methods for simulating sound produced by systems with rotating elements, as CAA analyzes sources on the moving elements, making sound field calculation computationally expensive. The SONAH procedure converts these rotating sources into a series of equivalent stationary planar or cylindrical waves, reducing the number of sources and the time required to compute the sound field from each source. This methodology was demonstrated by characterizing the aerodynamic noise produced by a bladeless fan. The sound pressure level obtained by C-SONAH method was validated by the data predicted by the direct CFD-CAA method. Acoustic maps were reconstructed at different locations and frequencies, revealing that the C-SONAH method can predict noise sources generated by airflow and rotating components within the fan. Thus, it serves as an effective tool for understanding the aeroacoustic noise generation mechanism and guiding the design optimization of similar products.</p><p dir="ltr">The CFD-CAA method is also a powerful tool for design optimization. Computational simulations are typically less expensive and time-consuming than building and maintaining experimental setups, particularly for large or complex projects. Additionally, simulations reduce the need for multiple physical prototypes, which can shorten the development cycle. CFD-CAA simulations provide detailed flow and acoustic field data, including variables that may be difficult or impossible to measure experimentally, such as pressure distributions, velocity fields, and turbulent structures. In this dissertation, aeroacoustic characteristics and flow field information of vortex whistles were investigated using the CFD-CAA method. The simulation results clearly illustrate the swirling motion created in the vortex whistle cylinder and also demonstrate the linear frequency versus flow rate relationship characteristic of the whistle. The design of the vortex whistle was optimized based on the acoustic response and flow resistance by both simulations and experiments. The results suggest that the whistle with a thin inlet exhibits the best performance at high flow rates, while the whistle with a scale of 0.5 is the most sensitive to low flow rates, making it suitable for pediatric applications.</p><p dir="ltr">In CFD-CAA simulations, the time step typically cannot be too small due to limited computational resources. This constraint results in an aliasing error in spectral analysis. Consequently, an anti-aliasing operation prior to sampling is necessary to eliminate such errors from the acoustic source terms. In the present study, an anti-aliasing filter based on the compact finite difference formulation was designed within a time-domain, compact filter scheme. This filter was directly applied to the Navier-Stokes solver prior to sampling for CAA analysis. A cavity flow case was simulated to validate this mitigation strategy. The results indicate that the artificial spectral peak induced by aliasing error is removed without affecting other signature peaks. The anti-aliasing filter was also applied to more complex cases for predicting the acoustic field of a vortex whistle. The acoustic field around the vortex whistle, with both constant and variable inlet flow rates, was simulated, and the aliasing peak was successfully removed. Although the peak magnitudes decreased slightly due to the filter, the signature frequencies remained unchanged. Thus, the simulation with anti-aliasing operation can predict acoustic features without introducing aliasing errors, even if the time step is not sufficiently small, thereby significantly reducing simulation time.</p><p dir="ltr">In engineering applications, once noise sources are identified, the subsequent concern is noise reduction. An effective strategy for noise reduction involves acoustical absorbing materials to minimize noise emissions from components. Traditionally, experiments in engineering applications have focused on surface treatments to explore noise control techniques. However, the CFD-CAA method commonly assumes smooth and purely reflective wall surfaces. Consequently, there is growing interest in incorporating impedance boundary conditions into the CFD-CAA method. Since impedance boundary conditions are defined in the frequency domain, while CFD-CAA simulations operate in the time domain, direct implementation is not feasible. To address this issue, several methods have been proposed to define time-domain impedance boundary conditions in simulations. In the present study, a wall softness model was implemented in the CFD-CAA method and to examine a vortex whistle featuring an acoustically permeable surface. In simulations, an impedance boundary condition representing the properties of melamine foam was defined over the surface of a cylindrical cavity. The simulation results were validated against experimental data obtained from a vortex whistle with melamine foam. The findings revealed that the impedance of the melamine foam contributed to noise reduction at high frequencies. Additionally, at low airflow rates, the impedance boundary condition enhanced the signal-to-noise ratio for the low-frequency peak, which is advantageous in clinical applications.</p>

<p dir="ltr">The thermal efficiency of gas turbines improves with higher turbine inlet temperatures (TIT) or compressor outlet pressure. Nowadays, gas turbines achieve TITs up to 1600 °C for power generation and 2000 °C for aircraft. These temperatures far exceed the limits where structural integrity can be maintained. For Ni-based superalloys with thermal barrier coatings, that limit is about 1200 °C. Gas turbines can operate at these high temperatures because all parts of the turbine component that contact the hot gases are cooled so that material temperatures never exceed those limits. </p><p dir="ltr">Gas-turbine vanes and blades are cooled by internal and film cooling with the cooling air extracted from the compressor. Since the extracted air could be used to generate power or thrust, the amount of cooling air used must be minimized. Thus, numerous researchers have investigated fluid flow and heat transfer in internal and film cooling to enable effective cooling with less cooling flow. For internal cooling, significant knowledge gaps persist, notably in ducts with varying cross sections. Reviews of existing literature indicate a lack of studies on flow and heat transfer in cooling ducts that account for the taper in the blade geometry from root to tip for both power-generation and aircraft gas turbines.</p><p dir="ltr">This study investigates the flow and heat transfer in ribbed and smooth tapered U-ducts, under conditions relevant to turbine cooling by using computational fluid dynamics (CFD) and a reduced-order model (ROM) developed in this study. The CFD analysis was based on steady Reynolds-Averaged Navier-Stokes (RANS) equations with the Shear Stress Transport (SST) turbulence model. The CFD analysis examined the effects of rotation number (Ro = 0, 0.0219, 0.0336, 0.0731), Reynolds number (Re = 46,000, 100,000, 154,000), and taper angle (α = 0°, 1.41°) under conditions that are relevant to electric-power-generation gas turbines. CFD results obtained showed increasing the taper angle significantly increases both the friction coefficient and the Nusselt number, regardless of rotation. With rotation at Ro = 0.0336 and Re = 100,000, the maximum increase in the average friction coefficient and Nusselt number due to taper was found to be 41.7% and 36.6% respectively. Without rotation at Re = 46,000, those increases were 11.5% and 14.7% respectively. </p><p dir="ltr">The ROM was derived from the integral continuity, momentum, and energy equations for a thermally and calorically perfect gas to provide rapid assessments of radially outward flow in tapered ducts subjected to constant heat flux. The ROM was used to study the effects of taper angle (α = 0°, 1.5°, 3.0°), ratio of mean radius to hydraulic diameter (Rm/Dh = 45, 150), rotation number (Ro = 0, 0.025, 0.25), Reynolds number (Re = 37,000, 154,000), and thermal loadings (q" = 5×104, 105 W/m2) on the mean density, velocity, temperature, and pressure along the duct. The parameters studied are relevant to both electric-power-generation and aircraft gas turbines. Results obtained show density and pressure variations to be most affected by the rotation number, while velocity along the duct is most affected by the duct’s taper angle. Additionally, it was found that if the taper angle is sufficiently large (α = 3°), then the temperature could reduce along the duct despite being heated because the thermal energy is converted to mechanical energy. When compared to a duct without taper, the mass flow rate of the cooling air could be reduced by up to 44% to achieve the same temperature distribution of the cooling flow along the duct.</p><p dir="ltr">The ROM developed was assessed by comparing against grid-converged CFD results for both ribbed and smooth sections of the duct. The validation study showed the maximum relative errors for density, velocity, temperature, and pressure distributions to be 0.6%, 3.3%, 0.4%, and 0.3% for smooth sections, and 3.2%, 5.6%, 0.9%, and 3.0% for ribbed sections, respectively. Thus, the ROM developed has accuracy comparable to CFD based on steady RANS but is order of magnitude more efficient computationally, making it a valuable tool for preliminary design. </p><p><br></p>

<p>Particle suspensions in viscoelastic fluids (e.g., polymeric fluids, liquid crystalline solutions, gels) are ubiquitous in industrial processes and in biology. In such fluids, particles often acquire lift forces that push them to preferential streamlines in the flow domain. This lift force depends greatly on the fluid’s rheology, and plays a vital role in many applications such as particle separations in microfluidic devices, particle rinsing on silicon wafers, and particle resuspension in enhanced oil recovery. Previous studies have provided understanding on how fluid rheology affects the motion of spherical particles in simple viscoelastic fluid flows such as shear flows. However, the combined effect of more complex flow profiles and particle shape is still under-explored. The main contribution of this thesis is to: (a) provide understanding on the migration and rotation dynamics of an arbitrary-shaped particle in complex flows of a viscoelastic fluid, and (b) develop guidelines for designing such suspensions for general applications.</p> <p><br></p> <p>In the first part of the thesis, we develop theories based on the second-order fluid (SOF) constitutive model to provide solutions for the polymeric force and torque on an arbitrary-shaped solid particle under a general quadratic flow field. When the first and second normal stress coefficients satisfy  <strong>Ψ</strong>₁  = −2 <strong>Ψ</strong> ₂ (corotational limit), the fluid viscoelasticity modifies only the fluid pressure and we provide exact solutions to the polymer force and torque on the particle. For a general SOF with  <strong>Ψ</strong> ₁ ≠  −2 <strong>Ψ</strong> ₂, fluid viscoelasticity modifies the shear stresses, and we provide a procedure for numerical solutions. General scaling laws are also identified to quantify the polymeric lift based on different particle shapes and orientation. We find that the particle migration speed is directly proportional to the length the particle spans in the shear gradient direction (L_sg), and that polymeric torques lead to unique orientation behavior under flow.</p> <p><br></p> <p>Secondly, we investigate the migration and rotational behavior of prolate and oblate spheroids in various viscoelastic, pressure-driven flows. In a 2-D slit flow, fluid viscoelasticity causes prolate particles to transition to a log-rolling motion where the particles orient perpendicular to the flow-flow gradient plane. This behavior leads to a slower overall migration speed (i.e., lift) of prolate particles towards the flow centerline compared to spherical particles of the same volume. In a circular tube flow, prolate particles align their long axis along the flow direction due to the extra polymer torque generated by the velocity curvature in all radial directions. Again, this effect causes prolate particles to migrate slower to the flow centerline than spheres of the same volume. For oblate particles, we quantify their long-time orientation and find that they migrate slower than spheres of the same volume, but exhibit larger migration speeds than prolate particles. Lastly, we examine the effect of normal stress ratio ? <strong>α</strong>  = <strong>Ψ</strong> ₂ /<strong>Ψ</strong>₁on the particle motion and find that this parameter only quantitatively impacts the particle migration velocity but has negligible effect on the rotational dynamics. We therefore can utilize the exact solution derived under the corotational limit (?<strong>α</strong> = −1/2) for a quick and reasonable prediction on the particle dynamics.</p> <p><br></p> <p>We next experimentally investigate the migration behavior of spheroidal particles in microfluidic systems and draw comparisons to our theoretical predictions. A dilute suspension of prolate/oblate microparticles in a density-matched 8% aqueous polyvinylpyrrolidone (PVP) solution is used as the model suspension system. Using brightfield microscopy, we qualitatively confirm our theoretical predictions for flow Deborah numbers 0 < De < 0.1 – i.e., that spherical particles show faster migration speed than prolate and oblate particles of the same volume in tube flows.</p> <p><br></p> <p>We finally design a holographic imaging method to capture the 3-D position and orientation of dynamic microparticles in microfluidic flow. We adopt in-line holography setup and propose a straightforward hologram reconstruction method to extract the 3-D position and orientation of a non-spherical particle. The method utilizes image moment to locate the particle and localize the detection region. We detect the particle position in the depth direction by quantifying the image sharpness at different depth position, and uses principal component analysis (PCA) to detect the orientation of the particle. For a semi-transparent particle that produces complex diffraction patterns, a mask based on the image moment information can be utilized during the image sharpness process to better resolve the particle position.</p> <p><br></p> <p>In the last part of this thesis, we conclude our work and discuss the future research perspectives. We also comment on the possible application of current work to various fields of research and industrial processes.</p> <p><br></p>

<p>  </p> <p>Water scarcity is a global problem impacting a majority of the world population. A significant proportion of the global population is deprived of clean drinking water, an impact felt by the rural as well as urban population. Saltwater desalination provides an attractive option to produce clean water. Some technologies to generate potable water include reverse osmosis (RO), multi-stage flash distillation (MSF), vapor compression distillation and multi-effect distillation (MED). Distillation plants such as those in MED often have falling-film evaporators operating at low energy conversion efficiency and hence distillation is performed over multiple stages (or effects). Porous materials can be utilized as evaporators in such plants with the objective of leveraging their superior efficiency. This can potentially decrease the number of effects over which distillation occurs. However, evaporation of high-salinity salt solution eventually results in salt precipitation which can cause fouling and induce structural damages, especially if the precipitates appear within the porous medium. Crystallization-induced structural damages are also of significant concern to building materials and for their role in weathering of historical monuments. It is thus crucial to understand the mechanisms governing salt precipitation in a porous medium.</p> <p>Transport of solute in such a medium is either driven by flow of the solution (advection) or by concentration gradients (diffusion). The dynamics of solute transport is further complicated due to the involvement of a reaction term accounting for any salt precipitation. The relative strengths of these driving forces determine the solute transport behavior during an evaporation-driven process. The wide-scale applications of solute transport and its complicated nature warrant investigation, both experimental and theoretical, of the dependence of solute transport and the subsequent precipitation on the operating conditions and the properties of the porous medium.</p> <p>This dissertation first focuses on developing a novel modeling framework for evaluating the transient behavior of the solute mass fraction profile within the domain of a one-dimensional porous medium, and extending its capability to predict the formation of salt precipitate in the medium.  Experimental investigations are then performed to study the formation of precipitate on sintered porous copper wicks of different particle-size compositions, and developing a mechanistic understanding of the governing principles.</p> <p>A numerical modeling framework is developed to analyze evaporation-driven solute transport. Transient advection-diffusion equations govern the salt mass fraction profile of the solution inside the porous medium. These governing equations are solved to obtain the solute mass fraction profile within the porous medium as well as the effloresced salt crust. Further accounting for precipitation allows a study of the formation and growth of efflorescence and subflorescence. Crystallization experiments are performed by allowing a NaCl solution to evaporate from a porous medium of copper particles and the subflorescence trends predicted by the model are validated. The modeling framework offers a comprehensive tool for predicting the spatio-temporal solute mass fraction profiles and subsequent precipitation in a porous medium.</p> <p>The dependence of efflorescence pattern on the properties of a porous medium is also investigated. Efflorescence patterns are visually observed and characterized on sintered copper particle wicks with spatially unimodal and bimodal compositions of different particle sizes. Efflorescence is found to form earlier and spread readily over a wick made from smaller particles, owing to their lower porosity, while it is limited to certain areas of the surface for wicks composed of the larger particles. A scaling analysis explains the observed efflorescence patterns in the bimodal wicks caused by particle size-induced non-uniform porosity and permeability. The non-uniformity reduces the advective flux in a high-permeability region by diverting flow towards a low-permeability region. This reduction in advective flux manifests as an exclusion distance surrounding a crystallization site where efflorescence is not expected to occur. The dependence of this exclusion distance on the porosity and permeability of the porous medium and the operating conditions is investigated. A large exclusion distance associated with the regions with bigger particles in the bimodal wicks explains preferential efflorescence over the regions with smaller particles. This novel scaling analysis coupled with the introduction of the exclusion distance provides guidelines for designing heterogeneous porous media that can localize efflorescence.</p> <p>Additionally, droplet interactions with microstructured superhydrophobic surfaces as well as soft surfaces were investigated during the course of this dissertation, separate from the above investigations. These investigations involve the interplay of surface energies with electrical or elastic energies and are studied both experimentally and through theoretical models, and therefore are retained as additional chapters in the thesis as being of relevant interest.  Electrowetting experiments are performed on superhydrophobic surfaces with re-entrant structures to study their resilience to the Cassie-to-Wenzel transition. The deformation of soft surfaces caused by forces exerted by microscale droplets is studied and the resulting interaction between multiple droplets is explored. </p>

Cette thèse porte sur le développement d’une approche pratique de modélisation/simulation des systèmes solaires combinés Photovoltaïques/Thermiques PV/T. Il s’agit d’une approche basée sur un modèle d’ordre réduit en représentation d’état (ORRE). En effet, les systèmes solaires thermiques, électriques et combinés intégrés aux bâtiments possèdent des spécificités permettant de s’affranchir des méthodes numériques classiques (mécanique des fluides numérique et thermique numérique). Ces méthodes sont réputées dans le domaine de l’aérodynamique, de l’aéraulique…etc. Par contre, dans le domaine du mix-énergétique tels que celui considéré dans ce mémoire, l’application directe de ce modèle peut conduire à des dépassements des capacités mémoire ou des temps de calcul exorbitants. Une alternative est de développer des méthodes adaptées au problème physique considéré, en traitant l’aspect multi-physique toute en restant dans une taille de données raisonnable et du temps de calcul réduit. La méthodologie de modélisation consiste à réduire les dimensions des équations qui régissent le problème. En se basant sur la symétrie du système, puis en découpant le système en zones de contrôle basées sur une valeur moyenne gouvernée par les nombres adimensionnels de Biot (Bi) et de Fourier (Fo). Les résultats obtenus en fonctionnement dynamique pourront nous fournir des paramètres de sorties, plus particulièrement, les rendements électrique, thermique et la puissance de circulation du fluide caloporteur. L’avantage de l’approche proposée réside dans la simplification du modèle résultant, qui est représenté par un seul système d’équations algébriques en représentation d’état regroupant tous les éléments physiques du système en fonctionnement dynamique (conditions aux limites variables dans le temps). Ce modèle regroupe la variable fondamentale qui est la température, et les deux types de contrôle et de conception. De plus, le modèle d’ORRE est intégrable dans le fonctionnement en temps réel des systèmes PV/T intégrés aux bâtiments (PV/T-Bât) afin d’accompagner leurs régulation et gestion des flux mise en jeu. Le modèle ainsi proposé a fait l’objet d’une validation où les résultats numériques ont été comparés aux résultats expérimentaux. En effet, quatre configurations ont été étudiées et évoquées dans une approche linéaire. Les résultats obtenus montrent une cohérence tolérable entre les résultats expérimentaux, et numériques. Cette cohérence a été évaluée en termes d’incertitude entre les résultats du modèle et le cas étudié expérimentalement. Le cas d’un système non-linéaire a été également abordé. En effet, rares sont les travaux qui ont été publiés mettant en valeur les phénomènes non-linéaires dans les systèmes complexes PV/T-Bât, Ainsi, on a développé avec la même stratégie, des modèles bilinéaires qui modélise le mieux possible le comportement thermique dans les systèmes PV/T-Bât. Une étude d’optimisation du système multi-physique en introduisant une étude paramétrique est menée en terme afin d’étudier la sensibilité des paramètres sur le rendement énergétique. Cependant, les études d’optimisation paramétriques restent limitées et insuffisantes à cause de la résolution mono-objectif du problème d’optimisation, alors que notre système manifeste un comportement combiné et multi-physique de nature contradictoire. Pour ce faire, une optimisation multi-objectifs est introduite avec trois fonctions objectif en employant l’algorithme génétique NSGA-II. L’originalité de notre méthode est d’employer l’algorithme en régime dynamique afin de choisir la conception du système la plus optimale. Les résultats trouvés peuvent contribuer à améliorer la conception des systèmes PV/T-Bât et l’optimisation de leur fonctionnement / This thesis consists to develop a simplified model approach for Photovoltaic / Thermal (PV / T) combined solar system based on state-space reduced order model. The building integrated solar systems are getting high attention in these last decencies, as well as the performance increasing which require high numerical methods to improve the design and reducing the costs. In one hand, the CFD methods are useful tool to predict the energy (mechanical and thermal) of combined PV/T systems, but it requires an expensive computing capacities and exorbitant calculation times, On the other hand, the PV/T systems can generate both the electrical and thermal flows, and requires an easily and performant optimization model. An alternative is to develop methods that are adapted to the physical problem under consideration, treating the multi-physics aspect while remaining in a reasonable data size and reduced computing time. The first part of the current thesis consists to develop a mathematical model which consists of reducing the dimensions of the governed equations. Based on the symmetry of the geometry, the system is subdivided into control areas which governed by the dimensionless Biot (Bi) and Fourier (Fo) numbers. The obtained results in dynamic mode can provide output key parameters, more particularly the electrical and thermal efficiencies and the dissipated hydrodynamic power. The advantage of this approach lies in the simplification of the resulting model, which is represented by a single state-space representation that groups all the physical elements of the system into dynamic mode, i.e. in continuous variation of the boundary condition. This model groups the fundamental variable, which is the temperature, and two type parameters, which are the control parameters and the design parameters. In addition, the reduced order model can be integrated into real-time operation of building-integrated PV / T (BIPV/T) systems in order to support their regulation and management of intervening flows. In order to validate the use of our model, it is necessary to test it for several cases of Building Integrated PV/T systems (BIPV/T). For this, four major configurations were studied and discussed in a linear approach; the found results show a good agreement with experimental works. A second level has been developed as part of our thesis work, which is the non-linearity in combined PV / T and BIPV/T systems; in particular, bilinear models have been developed with the same strategy which best models the thermal behavior in BIPV/T systems. The second issue, related to Multi-physics aspect. Furthermore, in order to evaluate the sensitivity of the parameters, a parametric optimization has been made with dimensionless numbers. However, parametric optimization studies remain limited and insufficient because of the single-objective resolution of the optimization problem, whereas our system manifests a mixed and multi-physics behavior with contradictory nature. To do this, a multi-objective optimization is introduced with three objective functions using the NSGA-II genetic algorithm. The originality of our method is to use the algorithm in dynamic mode in order to choose the design of the optimal system. The found results can contribute to the design of BIPV/T systems and optimize their operation