This master thesis project consisted of three parts that all were performed through CFD simulations with the purpose to develop Scania's methods in the subject of CFD. All parts included simulations on Scania's SC92T70 centrifugal compressor. Part one consisted of performing a mesh study for the purpose of reliability, to investigate the convergence of different parameters by refining the boundary layer. The method used is an inflation option called First layer thickness. Five different meshes were generated where the Richardson extrapolation method was used to examine the parameters between the mesh renements. From the result from the examined parameters, an approximate relative error could be calculated to be less than 0.52 %, and a numerical uncertainty of less than 0.35 %, between Mesh3 and Mesh4. In addition to that, Mesh3 had a simulation time of one hour less than for Mesh4. These results motivated the use of mesh3 to be refined enough for further work in this thesis project. This mesh ended at 37, 915, 257 number of elements. The second part consisted of performing steady state CFD simulations, to examine different parameters in order to find indications of the phenomena surge. Here, experimental data was used as reliance to perform CFD simulations on the compressor. Design points from experimental data was used, that ranged from low mass flow rates where surge arises, to high mass flow rates where another phenomena called choke occur. Except for the design points taken from experimental data, a few extra design points where included at low mass flow rates (in the region of surge). The goal was that the analysis of the different parameters would generate fluctuations on the result for the design points in surge region. Four different rotational speeds on the compressor were examined, 56k, 69k, 87k and 110k revolutions per minute. A total of 140 different parameters were examined, where 10 of these indicated on surge. All of these parameters that indicated on surge where found in regions of vicinity to the compressor wheel, which are the regions subjected to the phenomena.The parameters indicating on surge where mass flow, pressure coefficient, static pressure and temperature. Indications where found at the wheel inlet, ported shroud, and wheel outlet interfaces. The indications were only found for the two lower rotational speeds of the compressor wheel. To capture the behaviour on higher rotational speeds, more design points in the region of surge are needed, or transient simulations. Part three of the thesis project consisted of investigating the methodology of performing a Conjugate Heat Transfer model (CHT) with the CFD code CFX. This part has not been performed by Scania before, so a big part of the problem was to investigate if it actually was achievable. The goal was to use this model to calculate the heat transfer between fluid and solid parts, as well as between the solid parts and the ambient. One question Scania wanted to answer was if the CHT model could generate aerodynamic performance that corresponds to Scania's traditional adiabatic model, as well as to experimental data of the compressor. In this part, both solid and fluid domains were included in the geometryto calculate heat transport, in contrast to the traditional adiabatic model that only uses the fluid domains. Because of that, a big part of the work consisted of defining all interfaces connecting together surfaces between all domains. This is needed to model heat transport between the domains. In the set up part in CFX, the CHT model differed a lot from the traditional adiabatic model in that way that the outer walls was not set up as adiabatic anymore. In the CHT model, instead heat transfer is allowed between the outer walls of the fluids and the solids. From the result simulations, one could see that the CHT model was able to compute the heat transfer between fluids and solids. It also managed to export thermal data such as heat flux and wall heat transfer coefficient to be used for mechanical analysis, which is an important part in Scania's work. From the analysis of aerodynamic performance, a conclusion was drawn that the CHT model was able to compute efficiency and pressure ratio that followed the behaviour ofthe traditional adiabatic model as well as experimental data. However, for lowermass flows, the CHT model started to underpredict which could be explained by the geometrical differences between the CHT and adiabatic model. By analysis of temperature, one could see quantitative differences compared to the traditional adiabatic model. For other parameters (static and total pressure), there were no experimental data to be used for comparison. Because of that, an important part in future work of this CHT method development is to perform more experimental test for CFD data to be compared against. Another important part to compare the models is to have an identical geometry. Without an identical geometry, deviations in result will occur that depends on geometry.

Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: one is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise. / Master of Science / Storm surge flooding, which is the rise in sea level caused by tropical cyclones and other storms, is a devastating threat to coastal regions, and its impact is increasing due to sea-level rise (SLR). This poses a considerable risk to communities living near the coast. Therefore, it is crucial to accurately and quickly predict the potential for storm surge flooding. This study aimed to develop a new way that can rapidly estimate peak storm surges under different sea-level rise scenarios for any random synthetic storms of interest and assess the likelihood of their occurrence. The approach is based on historical tropical cyclone datasets and a machine learning model trained on high-quality simulations provided by the US Army Corps of Engineers (USACE). The study focused on the Chesapeake Bay area of the US and estimated the probabilistic tropical cyclone flood hazard in two locations, an urban site in Virginia and a rural site in Maryland. This new approach has the potential to assist in reducing coastal storm risks in vulnerable communities by providing a quick and reliable assessment of the hazard that takes into account the effects of future sea-level rise.

The magnitude of damage caused to the United States (US) coast due to hurricanes has increased significantly in the last decade. During the period 2004-2005, the US experienced seven of the costliest hurricanes in the country's history (NWS TPC-5, 2007) leading to an estimated loss of ~ $158 billion. The present method for predicting hurricane losses, HAZUS (HAZard US), is solely based on hurricane hazard and damage caused to building envelopes only and not to structural systems (Vickery et al., 2006). This method does not take into account an intermediate step that allows for better damage estimates, which is structural response to the hazards that in turn can be mapped to the damage. The focus of this study was to quantify the uncertainty in response of structures to the hurricane hazards associated with hurricanes from performance based engineering perspective. The study enumerates hazards associated with hurricanes events. The hazards considered can be quantified using a variety of measures, such as wind speed intensities, wave and surge heights. These hazards are quantified in terms of structural loads and are then applied to a structural system. Following that, structural analysis was performed to estimate the response from the structural system for given loads. All the possible responses are measured and they are fitted with suitable probability distribution to estimate the probability of a response. The response measured then can be used to understand the performance of a given structure under the various hurricane loads. Dynamic vs. static analysis was performed and results were compared. This will answer a few questions like, if there is any need to do both static and dynamic analysis and how hurricane loads affect the structural material models. This being an exploratory study, available resources, research, and models were used. For generation of annual or extreme values of hazard, various available wind speed, storm surge, and wave height models were studied and evaluated. The wind field model by Batts et al. (1980) was selected for generation of annual wind speed data. For calculation of maximum storm surge height, the Sea, Lake Overland Surges from Hurricane (SLOSH, Jelesnianski et al., 1992) program was used. Wave data was acquired from a National Oceanic and Atmospheric Administration (NOAA) database. The (extreme or annual) wind speed, surge height, and wave height generated were then fitted by suitable probability distributions to find the realizations of hazards and their probabilities. The distribution properties were calculated, correlations between the data were established, and a joint probability distribution function (PDF) of the parameters (wind speed, wave height, and storm surge) was generated. Once the joint distribution of extreme loads was established, the next step was to measure the dynamic response of the structural system to these hazards. To measure the structural response, a finite element model of three-story concrete frame were constructed. Time histories of wind load were generated from wind net pressure coefficients recorded in a wind tunnel test (Main and Fritz, 2006). Wave load time histories were generated using laboratory basin test (Hawke's et al., 1993) wave height time history data and were converted into wave loads using Bernoulli's equation. Surge height was treated as a hydrostatic load in this analysis. These load time histories were then applied to the finite element model and response was measured. Response of the structural system was measured in terms of the mean and maximum displacements recorded at specific nodes of model. Response was calculated for loads having constant mean wind speed and surge/wave and different time histories. The dominant frequency in the wind load time histories was closer to the natural frequency of the structural model used than the dominant frequency in the wave height time histories. Trends in the response for various combinations of mean wind speed, wave height, and surge heights were analyzed. It was observed that responses are amplified with increase in the mean wind speed. Less response was measured for change in mean surge/wave height as the tributary area for wave forces was less compared to wind force. No increase in dynamic amplification factor was observed for increase in force time histories case.

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Our oceans contain tremendous resource potential in the form of mechanical energy. With the ability to capture and convert the energy carried in surface waves into usable electricity, wave energy converters (WECs) have been a long-held aspiration in ocean renewable energy. One of the most popular wave energy design concepts is the Oscillating Surge Wave Energy Converter (OSWEC). True to their namesake, OSWECs extract energy from the surge force induced by incident waves. In their most basic form, OSWECs are analogous to a bottom-hinged paddle which pitches fore and aft in the direction of wave motion. Most commonly, OSWECs are designed for nearshore use in water depths of less than 20 m where they are mounted to the seafloor at their point of rotation. This work seeks to explore the response and design loads of foundation raised OSWECs for use in deeper waters, unlocking new and greater areas of wave energy resource. A foundation raised OSWEC was designed, built, and tested in a laboratory wave tank. The scale OSWEC was modeled using two methods and compared to data from the experiments. The first of these methods is a highly efficient, analytical approach which derives from the solution to the boundary value problem transformed into elliptical coordinates. Previous validation results demonstrate the analytical model is capable of reproducing results from higher fidelity numerical simulations with computation times on the order of seconds. The second approach combines hydrodynamic coefficients evaluated in WAMIT with the open-source time domain solver WEC-Sim. Two model configurations were observed: the scale OSWEC with no external attachments, and the OSWEC with external torsion springs, as to excite the model at its natural period. The pitch displacement, surge and heave forces, and pitch moment were recorded at the base of the model foundation in response to regular waves with periods ranging from 0.8 s to 2.8 s and heights from 1.5 mm to 14.3 mm. The experimental results show the surge force and pitch moment increase drastically across the observed period range from the addition of external springs. The increase is 20–30 times greater in the most extreme cases. Little to no change in heave forcing was observed between the configurations. The analytical and numerical models capture the natural period of the two configurations well, but the pitch displacement responses of both models fall short of the observations by as much as 60-80% at some periods. Excellent agreement in surge, heave, and pitch loading was obtained between the experimental data and both models. The models were used to simulate a simple power takeoff (PTO) system to approximate the additional PTO torque on the OSWEC. This torque was found to be substantial in magnitude relative to the pitch foundation moment over much of the observed period range.

Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities. / Doctor of Philosophy / Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.

Recent hurricanes have made it clear that housing is the single greatest component of all losses in terms of economic value and buildings damaged. Housing damage resulting from floods has increased in the United States, despite local, state and federal encouragement to mitigate flood hazards and regulate development in flood-prone areas (Atreya, 2013). The two primary causes of these increased costs are: (1) a rise in the occurrence and strength of the extreme weather events, and (2) increased development and value of property in physically vulnerable areas. The overlap of the above two factors resulted in tremendous losses of property in Staten Island and other coastal communities along the Atlantic Coast. Hurricane Sandy was a reminder of how vulnerable such areas could be. After hurricane Sandy, damaged properties experienced higher than usual housing sales and changed property values. This research, seeks to improve the current state of knowledge about housing market following a major disaster through examining single-family housing sales and prices in Staten Island, New York. The housing price recovery rate was much slower for the properties that sustained damage, and the impacts lasted for at least four years after the storm. Researchers studying housing recovery have utilized a variety of indicators like financial characteristics, government policies, social parameters, damage, housing characteristics, etc. to capture the dimensions of recovery. In Sandy's case damage was the major influencing parameter, and it completely changed the housing dynamics of the affected coastal regions. Housing market, in terms of damage, restoration, and recovery, is a fundamental indicator of disaster resilience. Every community is different and so are the effects of disasters on residential markets. This study clearly highlights this point and underscores the importance of using contextual methods and data sets in conducting the research. / Master of Urban and Regional Planning / Recent hurricanes have made it clear that housing is the single greatest component of all losses in terms of economic value and buildings damaged. Housing damage resulting from floods has increased in the United States, despite local, state and federal encouragement to mitigate flood hazards and regulate development in flood-prone areas (Atreya, 2013). The two primary causes of these increased costs are: (1) a rise in the occurrence and strength of the extreme weather events, and (2) increased development and value of property in physically vulnerable areas. The overlap of the above two factors resulted in tremendous losses of property in Staten Island and other coastal communities along the Atlantic Coast. Hurricane Sandy was a reminder of how vulnerable such areas could be. After hurricane Sandy, damaged properties experienced higher than usual housing sales and changed property values. This research, seeks to improve the current state of knowledge about housing market following a major disaster through examining single-family housing sales and prices in Staten Island, New York. The housing price recovery rate was much slower for the properties that sustained damage, and the impacts lasted for at least four years after the storm. Researchers studying housing recovery have utilized a variety of indicators like financial characteristics, government policies, social parameters, damage, housing characteristics, etc. to capture the dimensions of recovery. In Sandy’s case damage was the major influencing parameter, and it completely changed the housing dynamics of the affected coastal regions. Housing market, in terms of damage, restoration, and recovery, is a fundamental indicator of disaster resilience. Every community is different and so are the effects of disasters on residential markets. This study clearly highlights this point and underscores the importance of using contextual methods and datasets in conducting the research.