We propose a new atmospheric model based on first-principles for the simulation of clouds. Our approach is able to simulate the realistic formation of various cloud types, such as cumulus, stratus, stratocumulus, their temporal evolution, and transitions between cloud types. Moreover, we are able to model strongly rotating thunderstorms known as supercells. Our method allows us to simulate cloud formations of up to about 20 km 20 km at interactive rates. For the intuitive exploration, we identified a light-weight parameter set to interactively explore cloud formations. We demonstrate that our model can be coupled with data from real-time weather services to simulate cloud formations in the now. Moreover, we present a novel approach for the simulation of wildfires. Our model is able to realistically capture the combustion process of trees, heat transfer with the environment and fire propagation between trees. We demonstrate that our approach is capable of realistically simulating the propagation of fire through entire ecosystems with varying vegetation occupancy. We integrated our atmospheric model which allows us to simulated clouds emerging from the evaporation of water from burning trees leading to complex so called flammagenitus patterns which are usually observed over wildfires. Our system runs at interactive rates which enables the exploration of wildfires in different environments.

Accelerating physics-based simulations has been an evergreen topic across different scientific communities. This dissertation is devoted to this subject addressing bottlenecks in state-of-the-art approaches to the simulation of fluids of large-scale scenes, viscous threads, magnetic fluids, and the simulation of fibers and thin structures. The contributions within the thesis are rooted in mathematical modeling and numerical simulation as well as in machine learning. The first part deals with the simulation of incompressible flow in a multigrid fashion. For the variational viscous equation, geometric multigrid is inefficient. An Unsmoothed Aggregation Algebraic Multigrid method is devised with a multi-color Gauss-Seidel smoother, which consistently solves this equation in a few iterations for various material parameters. This framework is 2.0 to 14.6 times faster compared to the state-of-the-art adaptive octree solver in commercial software for the large-scale simulation of both non-viscous and viscous flow. In the second part, a new physical model is devised to accelerate the macroscopic simulation of magnetic fluids. Previous work is based on the classical Smoothed-Particle Hydrodynamics (SPH) method and a Kelvin force model. Unfortunately, this model results in a force pointing outwards causing significant levitation problems limiting the application of more advanced SPH frameworks such as Divergence-Free SPH (DFSPH) or Implicit Incompressible SPH (IISPH). This shortcoming has been addressed with this new current loop magnetic force model resulting in more stable and fast simulations of magnetic fluids using DFSPH and IISPH. Following a different trajectory, the third part of this thesis aims for the acceleration of iterative solvers widely used to accurately simulate physical systems. We speedup the simulation for rod dynamics with Graph Networks by predicting the initial guesses to reduce the number of iterations for the constraint projection part of a Position-based Dynamics solver. Compared to existing methods, this approach guarantees long-term stability and therefore leads to more accurate solutions.

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Yes / Physics-based skin deformation methods can greatly improve the realism of character animation, but require non-trivial training, intensive manual intervention, and heavy numerical calculations. Due to these limitations, it is generally time-consuming to implement them, and difficult to achieve a high runtime efficiency. In order to tackle the above limitations caused by numerical calculations of physics-based skin deformation, we propose a simple and efficient analytical approach for physics-based skin deformations. Specifically, we (1) employ Fourier series to convert 3D mesh models into continuous parametric representations through a conversion algorithm, which largely reduces data size and computing time but still keeps high realism, (2) introduce a partial differential equation (PDE)-based skin deformation model and successfully obtain the first analytical solution to physics-based skin deformations which overcomes the limitations of numerical calculations. Our approach is easy to use, highly efficient, and capable to create physically realistic skin deformations. / This research is supported by the PDE-GIR project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (No.778035), the National Natural Science Foundation of China (Grant No.51475394), and Innovate UK (Knowledge Transfer Partnerships KTP.010860). Shaojun Bian is also supported by Chinese Scholar Council. Xiaogang Jin is supported by the Key Research and Development Program of Zhejiang Province (No.2018C01090) and the National Natural Science Foundation of China (No.61732015).

Complex helicopter models that include effects typically ignored in control models, such as an analytical formulation for fuselage aerodynamics, blade lead-lagging and flexibility, and tail rotor aerodynamics, are derived. The landing gear, horizontal tailplane, a fully articulated main rotor, main rotor downwash, and blade flapping are also modeled. The modeling process is motivated by the desire to build control oriented, physics based models that directly result in ordinary differential equations (ODE) models which are sufficiently rich in dynamics information. A physics based model simplification procedure, which is called new ordering scheme, is developed to reduce the number of terms in these large nonlinear ODE models, while retaining the same number of governing equations of motion. The resulting equations are trimmed and linearized around several flight conditions (i.e. straight level flight, level banked turn, and helical turn) using Maple and Matlab. The resulting trims and model modes are validated against available literature data. The linearized models are first used for the design of variance constrained controllers with inequality constraints on outputs or inputs, output variance constrained controllers (OVC) and input variance constrained controllers (IVC), respectively. The linearized helicopter models are also used for the design of online controllers which exploit the constrained model predictive control (MPC) theory. The ability of MPC to track highly constrained, heterogeneous discontinuous trajectories is examined. The performance and robustness of all these controllers (e.g. OVC, IVC, MPC) are thoroughly investigated with respect to several modeling uncertainties. Specifically, for robustness studies, variations in the flight conditions and helicopter inertial properties, as well as blade flexibility effects, are considered. Furthermore, the effectiveness of adaptive switching between controllers for the management of sensor failure during helicopter operations is studied using variance constrained controllers. Finally, the simultaneous design of the helicopter and control system is examined using simultaneous perturbation stochastic approximation in order to save active control energy. / Ph. D.

This dissertation aims to contribute towards the understanding and modeling of tribological phenomena of contact, friction, and wear in viscoelastic materials with application in rubber compounds. Tribiological properties of rubber compounds are important for many applications such as tires, shoe heels and soles, wiper blades, artificial joints, O-ring seals, and so on. In all these applications, the objective is to maximize the friction coefficient to avoid slipping and reduce the wear rate to improve the life expectancy and performance of the products. The first topic in this study focuses on a novel multiscale contact theory proposed by Persson and explains the advantages of this theory over other classical contact theories. The shortcomings of this theory are also investigated, and three methods are proposed to improve Persson's original contact model by correcting the approximation of deformation in the contact area. The first method is based on the original Greenwood and Williamson (GW) contact theory, which neglects the effect of elastic coupling between asperities. The second method is based on an improved version of GW theory, which considers the elastic coupling effect of asperities in an approximate way. The third method is based on the distribution of local peaks of asperities, which is particularly suitable to determine the fraction of a skewed height profile involved in tribological processes. This method can be implemented within the framework of other proposed methods. Since the height profiles of rough surfaces studied in this dissertation are approximately normally distributed, the second correction method is applied to the original contact model to calculate the real contact area and friction coefficient. The second topic addresses the theoretical model of hysteresis friction in viscoelastic materials. The multiscale temperature rise of the rubber surface due to hysteresis friction is also modeled and the effect of flash temperature on the real contact area and friction coefficient is studied. Since the hysteresis friction is not the only mechanism involved in the rubber friction, a semi-empirical model is added to the hysteresis model to include the contribution of adhesion and other processes on the real contact area. Based on the improved multiscale contact theory, a pressure-dependent friction model is also developed for viscoelastic materials, which is in good agreement with experimental results. The third topic deals with the theory of stationary crack propagation in viscoelastic materials and the effect of crack tip flash temperature on the instability of crack propagation observed in some experimental results in the literature. Initially, a theoretical model is developed to calculate the tearing energy vs crack tip velocity in a Kelvin-Voigt rubber model. Besides, two coupled iterative algorithms are developed to calculate the temperature field around the crack tip in addition to the tearing energy as a function of crack tip velocity. In this model, the effect of crack tip flash temperature on the tearing energy is considered to update the relation between tearing energy vs crack tip velocity, which also affects the flash temperature. A theoretical model is also developed to calculate the contribution of the hysteresis effect to the tearing energy vs crack tip velocity using the dynamic modulus master curve of a rubber compound. Then, the low-frequency fatigue test results are compared with the theoretical predictions and used in the framework of powdery rubber wear theory to calculate the stationary rubber wear rate due to fatigue crack propagation. Moreover, a sliding friction and wear test set-up, with both indoor and outdoor testing capability, is developed to validate the theoretical models. The experimental results confirm that the theoretical model can successfully predict the friction coefficient when there is no trace of thermochemical degradation on the rubber surface. Investigating the wear mechanism of rubber samples on three different surfaces reveals that the contribution of fatigue wear rate is less important than other wear mechanisms such as abrasive wear due to sharp asperities or thermochemical degradation due to a significant rise of temperature on the contact area. Finally, the correlation between friction coefficient and wear rate on different surfaces is studied, and it is found that the relation between friction and wear rate strongly depends on the dominant wear mechanism, which is determined by the surface characteristics, sliding velocity, normal load, and contact flash temperature. / PHD / The objective of this dissertation is to understand and develop models for contact, friction, and wear in rubber-like materials. Friction and wear of rubber-like materials are important in many applications such as tires, shoe heels and soles, wiper blades, artificial joints, O-ring seals, and so on. In all these applications, it is desired to maximize the friction to avoid slipping and reduce the mass loss due to abrasion to improve the life expectancy of the products. The first topic in this dissertation focuses on a novel multiscale contact theory proposed by Persson and different approaches proposed in this work to improve this theory. Then, the real contact area is calculated using an improved version of the contact model. The second topic addresses the theoretical model of rubber friction due to hysteresis energy dissipation and the effect of frictional heating on the real contact area. Since the hysteresis friction is not the only mechanism involved in the rubber friction, a semi-empirical model is also used to include the contribution of adhesion and other processes on the real contact area. Based on the improved contact theory, a pressure-dependent friction model is also developed for rubber-like materials, which is in good agreement with the experimental results. The third topic deals with the theory of stationary crack propagation in rubberlike materials and the effect of crack tip temperature rise on the instability of crack propagation observed in some experimental results in the literature. The low-frequency fatigue test results are compared with the theoretical predictions, and the results are used in the framework of powdery rubber wear theory to calculate the rubber wear rate due to slow crack propagation. A sliding friction and wear test set-up is also developed to validate the theoretical models. The theoretical model of the friction coefficient is successfully validated by experimental results. Investigating the rubber wear on different surfaces reveals that the contribution of fatigue wear rate is less important than the other wear mechanisms. The correlation between friction coefficient and wear rate on different surfaces reveals that relation between friction and wear rate strongly depends on the dominant wear mechanism, which is determined by the surface characteristics, sliding velocity, normal load, and temperature rise on the contact surface.

Changes in climatic regimes and land use have led to increases in wildfire activities around the world. Wildfires are now happening more frequently, at higher altitudes, and higher severities. Adverse impacts of wildfires can last years after the fire has been contained through post-fire geohazards, such as shallow landslides. Post-wildfire shallow landslides are often mobilized by rainfall and due to fire-induced changes in soil and land cover properties and near-surface processes. This study aims to develop a physics-based framework to evaluate the stability of burned hillslopes against rainfall-triggered shallow landslides. A coupled hydromechanical infiltration model is developed by employing a closed-form solution of the Richards equation. The model is integrated into an infinite slope stability analysis to capture the effect of temporal changes in the pressure head profile of an unsaturated vegetated slope on its stability. The proposed model considers the antecedent condition of soil and vegetation cover, the time-varying nature of rainfall intensity, and wildfire-induced changes in soil properties, root reinforcement, transpiration rate, and canopy interception. The efficacy of the proposed framework is illustrated through modeling a case study in the Las Lomas watershed in California, USA. The watershed was a part of a larger area that was burned in the San Gabriel Complex Fire (consisting of two separate fires, the Fish Fire and the Reservoir Fire) in 2016. Three years later, during a heavy rainstorm in January 2019, the affected area, including the Las Lomas watershed, experienced widespread landslides. The proposed framework is then integrated into a geographic information system (GIS) to generate a susceptibility map of post-wildfire rainfall-triggered shallow landslides. The applicability of the proposed framework at a regional scale is tested for the entire area affected by the San Gabriel Complex Fire to model the observed shallow landslides within the boundaries of the Fish Fire and the Reservoir Fire. The findings of this study can be used to warn the community of post-wildfire shallow landslides activities.

The spatial variation of wave climate plays a crucial role in erosion, sediment transport, and the design of management actions in coastal areas. Low energy wave systems occur frequently and over a wide range of geographical areas. There is a lack of studies assessing wave model performance in low-energy environments at a regional scale. Therefore, this research aims to model a low energy wave system using a high-resolution dataset. The specific objectives of this study involves 1) using cluster analysis and extensive field measurements to understand the spatial behavior of ocean waves, 2) develop a physics based model of wind-driven waves using high-resolution measurements, and 3) compare machine learning and physics-based models in simulating wave climates. The findings of this study indicate that clustering can effectively assess the spatial variation of the wave climate in a low energy system, with depth identified as the most important influencing factor. Additionally, the physics-based model showed varying performance across different locations within the study area, accurately simulating wave climates in some locations but not in others. Finally, the machine learning model demonstrated overall acceptable performance and accuracy in simulating wave climates and revealed better agreement with observed data in estimating central tendency compared to the physics-based model. The physics-based model performed more favorably for dispersion metrics. These findings contribute to our understanding of coastal dynamics. By providing insights into the spatial behavior of wave climates in low energy systems and comparing the performance of physics-based model and machine learning model, this research contributes to the development of effective coastal management strategies and enhances our understanding of coastal processes.

Claymation is an animation technique developed around a century ago and featured inseveral video games. However, traditional claymation production is not most accessible togame developers due to its specialized skill requirements. This research aims to make theclaymation visual style more accessible by digitally emulating its characteristics usingmethods widely used in game development. Our approach involved developing and iteratingdigital techniques to replicate the surface texture of clay models and the jittery animationquality in a 3D physics-based game, the emulated results of which were evaluated throughinternal and external playtesting. The research team concluded that a combination ofdisplacement maps, normal maps, and color adjustments could be used to emulate realisticclay surface textures. Meanwhile, 3D animation and physics-based animation approacheswere explored to recreate the jittery animation look. The team found that, within 3Danimation, strategic use of still frames and manual motion variance addition to each in-motionpose are effective jitter emulation methods, with the latter being more effective. With physics-based animation, adding a randomized value to an object’s physically simulated motionsimulates motion jitters. The research compiled the emulation process into steps, serving asemulation guidelines for future projects.

<p></p><p></p><p>Peatlands are the most efficient natural carbon sink on the planet. They are the most carbon-intensive storages than any other vegetation types. However, recent studies indicate that global peatlands can potentially release 6% of the global soil carbon into the atmosphere when they are drained or deforested. They cover only about 3% of the total global land area, but sequester over 30% of the Earth’s soil organic carbon. Peatlands in northern mid-to-high latitudes (45°-90°N) occupy ~90% of the global peatland area and account for ~80% of the total global peat organic carbon stock. Those peatlands are mainly located in Canada, Russia, and the USA. Peatlands in tropical regions cover ~10% of the global peatlands area and store 15-19% of the global peat organic carbon. They are mainly distributed in Southeast Asia and South and Central America. The temperature at the global scale has been rising since the middle of the last century and has accelerated during the last 40 years and the warming will continue in this century. The large storage of soil organic carbon within the peatlands can significantly respond to the changing climate by varying the roles between their carbon sink (from atmosphere to soil) and source (from soil to atmosphere) activities. This dissertation focuses on quantifying the soil organic carbon dynamics in North America and South America using mechanistically-based biogeochemistry models. </p><p></p><p>Peatlands in Alaska occupy 40 million hectares and account for ~10% of the total peatland area in northern mid-to-high latitudes. The regional soil organic carbon dynamics and its response to climate are still with large uncertainty. Most of the studies on peatlands to date are based on short-term site-level observation. This dissertation first used an integrated modeling framework that coupled the dynamics of hydrology, soil thermal regime, and ecosystem carbon and nitrogen to quantify the long-term peat carbon accumulation in Alaska during the Holocene. Modeled hydrology, soil thermal regime, carbon pools and fluxes and methane emissions were evaluated using long-term observation data at several peatland sites in Minnesota, Alaska, and Canada. The model was then applied for a 10,000-year (15 ka to 5 ka; 1 ka = 1000 cal yr before present) simulation at four peatland sites. The model simulations matched the observed carbon accumulation rates at fen sites during the Holocene (R^2= 0.88, 0.87, 0.38 and -0.05 for four sites respectively using comparisons in 500-year bins from 15 ka to 5 ka). The simulated (2.04 m) and observed peat depths (on average 1.98 m) also compared well (R^2 = 0.91). The early Holocene carbon accumulation rates, especially during the Holocene thermal maximum (HTM) (35.9 g 〖C m〗^(-2) yr^(-1)), were estimated up to 6-times higher than the rest of the Holocene (6.5 g 〖C m〗^(-2) yr^(-1)). It suggested that high summer temperature and the lengthened growing season resulted from the elevated insolation seasonality, along with wetter-than-before conditions might be major factors causing the rapid carbon accumulation in Alaska during the HTM. The sensitivity tests indicated that, apart from climate, initial water-table depth and vegetation canopy were major drivers to the estimated peat carbon accumulation. </p><p></p><p>To further quantify the regional long-term soil organic carbon accumulation rates and the current carbon stocks in Alaska, the second part of my research focused on quantifying the soil organic carbon accumulation in multiple Alaskan terrestrial ecosystems over the last 15,000 years for both peatland and non-peatland ecosystems. Comparable with the previous estimates of 25-70 Pg carbon (C) in peatlands and 13-22 Pg C in non-peatland soils within 1-m depth in Alaska using peat core data, our model estimated a total SOC of 36-63 Pg C at present, including 27-48 Pg C in peatland soils and 9-15 Pg C in non-peatland soils. Current living vegetation stored 2.5-3.7 Pg C in Alaska with 0.3-0.6 Pg C in peatlands and 2.2-3.1 Pg C in non-peatlands. The simulated average rate of peat soil C accumulation was 2.3 Tg C yr^(-1) with a peak value of 5.1 Tg C yr^(-1) during the Holocene Thermal Maximum (HTM) in the early Holocene, four folds higher than the average rate of 1.4 Tg C yr^(-1) over the rest of the Holocene. The accumulation slowed down, or even ceased, during the neo-glacial climate cooling after the mid-Holocene, but increased again in the 20th century. The model-estimated peat depths ranged from 1.1 to 2.7 m, similar to the field-based estimate of 2.29 m for the region. The changes in vegetation and their distributions were the main factors to determine the spatial variations of SOC accumulation during different time periods. Warmer summer temperature and stronger radiation seasonality, along with higher precipitation in the HTM and the 20th century might have resulted in the extensive peatland expansion and carbon accumulation. </p><p>Most studies on the role of tropical peatlands have focused on Indonesian peatlands. Few have focused on the Amazon basin, where peatlands remain intact and have been a long-term carbon sink. To address the problem, my third study quantified the carbon accumulation for peatland and non-peatland ecosystems in the Pastaza-Marañon foreland basin (PMFB), the most extensive peatland complex in the Amazon basin from 12,000 years before present to 2100 AD. Model simulations indicated that warming accelerated peat carbon loss while increasing precipitation accelerated peat carbon accumulation at millennial time scales. The uncertain parameters and spatial variation of climate were significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, the warming effect on increasing peat carbon loss might overwhelm the wetter effect on increasing peat carbon accumulation. Peat soil carbon accumulation rate in the PMFB slowed down to 7.9 (4.3~12.2) g C m^(-2) yr^(-1) from the current rate of 16.1 (9.1~23.7) g C m^(-2) yr^(-1) and the region might turn into a carbon source to the atmosphere at -53.3 (-66.8~-41.2) g C m^(-2) yr^(-1) (negative indicates source), depending on the level of warming. Peatland ecosystems showed a higher vulnerability than non-peatland ecosystems as indicated by the ratio of their soil carbon density changes (change of soil carbon/existing soil carbon stock) ranging from 3.9 to 5.8). This was primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with non-peatland ecosystems under future climate conditions. Peatland and non-peatland soils in the PMFB might lose up to 0.4 (0.32~0.52) Pg C by 2100 AD with the largest loss from palm swamp. The carbon-dense Amazonian peatland might switch from a current carbon sink into a source in the 21st century.</p><p>Peatlands are important sources and sinks for greenhouse gases (carbon dioxide and methane). Their carbon (C) balance between soil and atmosphere remains unquantified due to the large data gaps and uncertainties in regional peat carbon estimation. My final study was to quantify the C accumulation rates and C stocks within North America peatlands over the last 12,000 years. I find that 85-174 Pg C have been accumulated in North American peatlands over these years including 0.37-0.76 Pg C in subtropical peatlands in this region. During the 10- 8 ka period, the warmer and wetter conditions might have played an important role in stimulating peat C accumulation by enhancing plant photosynthesis. The enhanced peat decomposition due to warming through the Holocene slows down carbon accumulation in the region.</p><div><br></div><p><br></p>