<p dir="ltr">Approximately 95% of global steel production relies on continuous casting, there is a need for a practical, cost-effective, and accurate method to guide real-world production. A successful integration of three individual CFD models – spray cooling model, solidification model, and carbon segregation model – was accomplished. To understand the heat transfer behavior on a heated surface, a three-dimensional model was used to simulate the interaction of liquid droplets with a heated surface during the secondary cooling process, employing air-mist nozzles. The real nozzle layout, as employed in a full-scale continuous caster to provide HTC data on slab surface. For solidification model, enthalpy-porosity methods were applied to estimate the metallurgical length and surface temperatures. Carbon transport within the continuous caster was considered, revealing a phenomenon of positive segregation at the center of the slab. Building upon this foundation, further investigations were carried out to assess the implications of nozzle clogging. These effects encompass surface temperature, metallurgical length, and carbon concentration. Commercial software ANSYS Fluent 2021 R2 and Simcenter STAR-CCM+ 2302 are chosen for their exceptional computational performance. MATLAB and Python play key roles in both pre and post processing, including mapping HTC profiles, visualizing shell growth, and extracting temperature and cooling profiles.</p>

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">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">Results from the Flow Boiling and Condensation Experiment (FBCE), which collected the first flow boiling and condensation data in long-duration, steady microgravity through experiments performed onboard the International Space Station (ISS), are presented. Prior to the ISS experiments, a new correlation for flow boiling critical heat flux (CHF) is developed from data obtained in Earth gravity at different orientations and short durations of microgravity onboard parabolic flight. The new correlation accounts for the influence of gravity in the direction of the flow, impacting vapor removal from the channel, and perpendicular to the heated walls, affecting bubble detachment from the walls, on flow boiling CHF. Novel flow boiling experiments in long-duration microgravity were performed with one or two opposite walls heated using the Flow Boiling Module (FBM), which simultaneously captures heat transfer data and high speed images of flow patterns. The unique microgravity CHF results are presented, and parametric trends are correlated to variations in flow patterns. The results are divided into subcooled and saturated inlet conditions and applicable correlations are assessed. The newly proposed correlation outperforms is the best preforming for the entire database, validating its use in microgravity. Visual observations leading up to CHF justify use of the Interfacial Lift-off model, which predicts CHF with good accuracy for all operating conditions. The data obtained onboard the ISS is consolidated with the prelaunch database to develop highly accurate artificial neural networks (ANNs) for flow boiling heat transfer and CHF in microgravity. The ANNs are developed using a systematic approach that enables the prediction of physical trends. Instabilities observed during subcooled flow boiling are further investigated in dedicated experiments performed at an elevated data capture rate of 30 Hz and extended image capture period up to 28 s. Criteria was proposed to demarcate the stable and unstable operating conditions, and a new correlation to predict the onset of flow instability is proposed. Lastly, microgravity flow condensation heat transfer experiments were conducted onboard the ISS, yielding the first flow condensation data in stable microgravity. Trends in the data are discussed and the two-phase mixture Reynolds number is found to be strongly correlated to local heat transfer coefficient. A separated flow model for annular flow is found to accurately predict trends in average heat transfer coefficient, but underpredicts the microgravity database.</p>

<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">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 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>

This research work is focused on a conceptual combination of membrane-based energy recovery ventilator (ERV) and phase change material (PCM) to provide energy savings in building heating, ventilation & air-conditioning (HVAC) systems. An ERV can recover thermal energy and moisture between the outside fresh air (OFA) entering into the building and the exhaust air (EA) leaving from the building thus reducing the energy consumption of the HVAC system for cooling and heating the spaces inside the building. The membranes were stacked parallel to each other forming adjacent channels in a counter-flow arrangement for OFA and EA streams. Heat and moisture is diffused through the membrane core. Flat-plate encapsulated PCM is arranged in OFA duct upstream/downstream of the ERV thereby allowing for further reduction in temperature by virtue of free cooling. Paraffin-based PCMs with a melting point of 24°C and 31°C is used in two different configurations where the PCM is added either before or after the ERV. Computational fluid dynamics (CFD), and heat and mass transfer modeling is employed using COMSOL Multiphysics v5.3 to perform the heat and mass transfer analysis for the membrane-based ERV and flat-plate PCMs. An 8-story office building was considered to perform building energy simulation using eQUEST v3.65 from Department of Energy (DOE). Based on the heat and mass transfer analysis, it is found that the sensible effectiveness (heat recovery) stood in the range of 65%-97% while the latent effectiveness (moisture recovery) stood at 55%-80%. Also, the highest annual energy savings achieved were 72,700 kWh in electricity consumption and 358.45 MBtu in gas consumption.