This thesis considers one-dimensional moving mesh (MM) methods coupled with semi-Lagrangian (SL) discretisations of partial differential equations (PDEs) for meteorological applications. We analyse a semi-Lagrangian numerical solution to the viscous Burgers’ equation when using linear interpolation. This gives expressions for the phase and shape errors of travelling wave solutions which decay slowly with increasing spatial and temporal resolution. These results are verified numerically and demonstrate qualitative agreement for high order interpolants. The semi-Lagrangian discretisation is coupled with a 1D moving mesh, resulting in a moving mesh semi-Lagrangian (MMSL) method. This is compared against two moving mesh Eulerian methods, a two-step remeshing approach, solved with the theta-method, and a coupled moving mesh PDE approach, which is solved using the MATLAB solver ODE45. At each time step of the SL method, the mesh is updated using a curvature based monitor function in order to reduce the interpolation error, and hence numerical viscosity. This MMSL method exhibits good stability properties, and captures the shape and speed of the travelling wave well. A meteorologically based 1D vertical column model is described with its SL solution procedure. Some potential benefits of adaptivity are demonstrated, with static meshes adapted to initial conditions. A moisture species is introduced into the model, although the effects are limited.

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We investigate the impact of cash reserves upon the optimal behaviour of a modelled firm that has uncertain future revenues. To achieve this, we build up a corporate financing model of a firm from a Real Options foundation, with the option to close as a core business decision maintained throughout. We model the firm by employing an optimal stochastic control mathematical approach, which is based upon a partial differential equations perspective. In so doing, we are able to assess the incremental impacts upon the optimal operation of the cash constrained firm, by sequentially including: an optimal dividend distribution; optimal equity financing; and optimal debt financing (conducted in a novel equilibrium setting between firm and creditor). We present efficient numerical schemes to solve these models, which are generally built from the Projected Successive Over Relaxation (PSOR) method, and the Semi-Lagrangian approach. Using these numerical tools, and our gained economic insights, we then allow the firm the option to also expand the operation, so they may also take advantage of favourable economic conditions.

We study stockpiling problems under uncertain economic and physical factors, and investigate the valuation and optimisation of storage systems where the availability and spot price of the underlying are both subject to stochasticity. Following a Real Options valuation approach, we first study financial derivatives linked to Asian options. A comprehensive set of boundary conditions is compiled, and an alternative (and novel) similarity reduction for fixed-strike Asian options is derived. Hybrid semi-Lagrangian methods for numerically solving the related partial differential equations (PDEs) are implemented, and we assess the accuracy of the valuations thus obtained with respect to results from classical finite-difference valuation methods and with respect to high precision calculations for valuing Asian options with spectral expansion theory techniques. Next we derive a PDE model for valuing the storage of electricity from a wind farm, with an attached back-up battery, that operates by trading electricity in a volatile market in order to meet a contracted fixed rate of energy generation; this system comprises two diffusive-type (stochastic) variables, namely the energy production and the electricity spot price, and two time-like (deterministic) variables, specifically the battery state and time itself. An efficient and novel semi-Lagrangian alternating-direction implicit (SLADI) methodology for numerically solving advection-diffusion problems is developed: here a semi-Lagrangian approach for hyperbolic problems of advection is combined with an alternating-direction implicit method for parabolic problems involving diffusion. Efficiency is obtained by solving (just) tridiagonal systems of equations at every time step. The results are compared to more standard semi-Lagrangian Crank-Nicolson (SLCN) and semi-Lagrangian fully implicit (SLFI) methods. Once he have established our PDE model for a storage-upgraded wind farm, a system that depends heavily on the highly stochastic nature of wind and the volatile market where electricity is sold, we derive a Hamilton-Jacobi-Bellman (HJB) equation for optimally controlling charging and discharging rates of the battery in time, and we assess a series of operation regimes. The solution of the related PDE models is approached numerically using our SLADI methodology to efficiently treat this mixed advection and diffusion problem in four dimensions. Extensive numerical experimentation confirms our SLADI methodology to be robust and yields highly accurate solutions and efficient computations, we also explore effects from correlation between stochastic electricity generation and random prices of electricity as well as effects from a seasonal electricity spot price. Ultimately, the objective of approximating optimal storage policies for a system under uncertain economic and physical factors is accomplished. Finally we examine the steady-state solution of a stochastic storage problem under uncertain electricity market prices and fixed demand. We use a HJB formulation for optimally controlling charging and discharging rates of the storage device with respect to the electricity spot price. A projected successive over-relaxation coupled with the semi-Lagrangian method is implemented, and we explore the use of boundary-fitted coordinates techniques.

Current climate models have a limited ability to increase spatial resolution because numerical stability requires the time step to decrease. I describe initial experiments with two independent but complementary strategies for attacking this "time barrier". First I describe computational experiments exploring the performance improvements from overlapping computation and communication on hybrid parallel computers. My test case is explicit time integration of linear advection with constant uniform velocity in a three-dimensional periodic domain. I present results for Fortran implementations using various combinations of MPI, OpenMP, and CUDA, with and without overlap of computation and communication. Second I describe a semi-Lagrangian method for tracer transport that is stable for arbitrary Courant numbers, along with a parallel implementation discretized on the cubed sphere. It shows optimal accuracy at Courant numbers of 10-20, more than an order of magnitude higher than explicit methods. Finally I describe the development and stability analyses of the time integrators and advection methods I used for my experiments. I develop explicit single-step methods with stability up to Courant numbers of one in each dimension, hybrid explicit-implict methods with stability for arbitrary Courant numbers, and interpolation operators that enable the arbitrary stability of semi-Lagrangian methods.

This thesis presents an automatic animation system for jellyfish that is based on a physical simulation of the organism and its surrounding fluid. Our goal is to explore the unusual style of locomotion, namely jet propulsion, which is utilized by jellyfish. The organism achieves this propulsion by contracting its body, expelling water, and propelling itself forward. The organism then expands again to refill itself with water for a subsequent stroke. We endeavor to model the thrust achieved by the jellyfish, and also the evolution of the organism's geometric configuration. <p> We restrict our discussion of locomotion to fully grown adult jellyfish, and we restrict our study of locomotion to the resonant gait, which is the organism's most active mode of locomotion, and is characterized by a regular contraction rate that is near one of the creature's resonant frequencies. We also consider only species that are axially symmetric, and thus are able to reduce the dimensionality of our model. We can approximate the full 3D geometry of a jellyfish by simulating a 2D slice of the organism. This model reduction yields plausible results at a lower computational cost. From the 2D simulation, we extrapolate to a full 3D model. To prevent our extrapolated model from being artificially smooth, we give the final shape more variation by adding noise to the 3D geometry. This noise is inspired by empirical data of real jellyfish, and also by work with continuous noise functions from the graphics community. <p> Our 2D simulations are done numerically with ideas from the field of computational fluid dynamics. Specifically, we simulate the elastic volume of the jellyfish with a spring-mass system, and we simulate the surrounding fluid using the semi-Lagrangian method. To couple the particle-based elastic representation with the grid-based fluid representation, we use the immersed boundary method. We find this combination of methods to be a very efficient means of simulating the 2D slice with a minimal compromise in physical accuracy.

This thesis presents an automatic animation system for jellyfish that is based on a physical simulation of the organism and its surrounding fluid. Our goal is to explore the unusual style of locomotion, namely jet propulsion, which is utilized by jellyfish. The organism achieves this propulsion by contracting its body, expelling water, and propelling itself forward. The organism then expands again to refill itself with water for a subsequent stroke. We endeavor to model the thrust achieved by the jellyfish, and also the evolution of the organism's geometric configuration. <p> We restrict our discussion of locomotion to fully grown adult jellyfish, and we restrict our study of locomotion to the resonant gait, which is the organism's most active mode of locomotion, and is characterized by a regular contraction rate that is near one of the creature's resonant frequencies. We also consider only species that are axially symmetric, and thus are able to reduce the dimensionality of our model. We can approximate the full 3D geometry of a jellyfish by simulating a 2D slice of the organism. This model reduction yields plausible results at a lower computational cost. From the 2D simulation, we extrapolate to a full 3D model. To prevent our extrapolated model from being artificially smooth, we give the final shape more variation by adding noise to the 3D geometry. This noise is inspired by empirical data of real jellyfish, and also by work with continuous noise functions from the graphics community. <p> Our 2D simulations are done numerically with ideas from the field of computational fluid dynamics. Specifically, we simulate the elastic volume of the jellyfish with a spring-mass system, and we simulate the surrounding fluid using the semi-Lagrangian method. To couple the particle-based elastic representation with the grid-based fluid representation, we use the immersed boundary method. We find this combination of methods to be a very efficient means of simulating the 2D slice with a minimal compromise in physical accuracy.

Cette thèse présente l'étude et le développement de méthodes numériques pour la résolution d'équations de transport, en particulier d'une méthode de remapping bidimensionnel dont un avantage important par rapport aux algorithmes existants est la propriété de conservation de la masse. De nombreux cas-tests permettront de comparer ces approches entre elles ainsi qu'à des méthodes de référence. On s'intéressera en particulier aux équations dites de Vlasov-Poisson et du Centre-Guide, qui apparaissent très classiquement dans le cadre de la physique des plasmas. / This PhD thesis presents the study and development of numerical methods for the resolution of transport equations, in particular a bidimensional remapping method whose main advantage over existing algorithms is the property of mass conservation. Numerous test cases are presented in order to compare these approaches with regard to the others and with reference methods. Focus is made on the so-called Vlasov-Poisson and Center-Guide equations, that appear very classically in the domain of plasma physics.

Cette thèse s’intéresse à la modélisation magnétohydrodynamique des écoulements de fluides conducteurs d’électricité multi-échelles en mettant l’emphase sur deux applications particulières de la physique solaire: la modélisation des mécanismes des variations de l’irradiance via la simulation de la dynamo globale et la reconnexion magnétique. Les variations de l’irradiance sur les périodes des jours, des mois et du cycle solaire de 11 ans sont très bien expliquées par le passage des régions actives à la surface du Soleil. Cependant, l’origine ultime des variations se déroulant sur les périodes décadales et multi-décadales demeure un sujet controversé. En particulier, une certaine école de pensée affirme qu’une partie de ces variations à long-terme doit provenir d’une modulation de la structure thermodynamique globale de l’étoile, et que les seuls effets de surface sont incapables d’expliquer la totalité des fluctuations. Nous présentons une simulation globale de la convection solaire produisant un cycle magnétique similaire en plusieurs aspects à celui du Soleil, dans laquelle le flux thermique convectif varie en phase avec l’ ́energie magnétique. La corrélation positive entre le flux convectif et l’énergie magnétique supporte donc l’idée qu’une modulation de la structure thermodynamique puisse contribuer aux variations à long-terme de l’irradiance. Nous analysons cette simulation dans le but d’identifier le mécanisme physique responsable de la corrélation en question et pour prédire de potentiels effets observationnels résultant de la modulation structurelle. La reconnexion magnétique est au coeur du mécanisme de plusieurs phénomènes de la physique solaire dont les éruptions et les éjections de masse, et pourrait expliquer les températures extrêmes caractérisant la couronne. Une correction aux trajectoires du schéma semi-Lagrangien classique est présentée, qui est basée sur la solution à une équation aux dérivées partielles nonlinéaire du second ordre: l’équation de Monge-Ampère. Celle-ci prévient l’intersection des trajectoires et assure la stabilité numérique des simulations de reconnexion magnétique pour un cas de magnéto-fluide relaxant vers un état d’équilibre. / This thesis concentrates on magnetohydrodynamical modeling of multiscale conducting fluids with emphasis on two particular applications of solar physics: the modeling of solar irradiance mechanisms via the numerical simulation of the global dynamo and of magnetic reconnection. Irradiance variations on the time scales of days, months, and of the 11 yr solar cycle are very well described by changes in the surface coverage by active regions. However, the ultimate origin of the long-term decadal and multi-decadal variations is still a matter of debate. In particular, one school of thought argues that a global modulation of the solar thermodynamic structure by magnetic activity is required to account for part of the long-term variations, in addition to pure surface effects. We hereby present a global simulation of solar convection producing solar-like magnetic cycles, in which the convective heat flux varies in phase with magnetic energy. We analyze the simulation to uncover the physical mechanism causing the positive correlation and to predict potential observational signatures resulting from the flux modulation. Magnetic reconnection is central to many solar physics phenomena including flares and coronal mass ejections, and could also provide an explanation for the extreme temperatures (T ∼ 106K) that charaterize the coronna. A trajectory correction to the classical semi-Lagrangian scheme is presented, which is based on the solution to a second-order nonlinear partial differential equation: the Monge-Amp`ere equation. Using the correction prevents the intersection of fluid trajectories and assures the physical realizability of magnetic reconnection simulations for the case of a magneto- fluid relaxing toward an equilibrium state.

During the work of this research project we were interested in mathematical techniques that give us an insight to the following questions: How do we understand the trading decisions made by a manager of a hedge fund and what influences these decisions? In what way does an illiquid market affect these decisions and the performance of the fund? And how does the payment scheme affect the investor's decisions? Based on existing work on hedge fund management, we start with a fund that can be modelled with one risky investment and one riskless investment. Next, subject to the hedge fund special reward scheme we maximise the expected utility of wealth of the manager, by controlling the percentage invested in the risky investment, namely the portfolio. We use stochastic control techniques to derive a partial differential equation (PDE) and numerically obtain its corresponding viscosity solution, which provides a weak notion of solutions to these PDEs. This is then taken to a liquidity constrained scenario, to compare the behaviour of the two scenarios. Using the same approach as before we notice that due to the liquidity restriction we cannot use a simple model to combine the risky and riskless investments as a total amount, and hence the PDE is one order higher than before. We then model an investor who is investing in the hedge fund subject to the manager's optimal portfolio decisions, with similar mathematical tools as before. Comparisons between the investor's expected utility of wealth and the utility of having the money invested in the risk-free investment suggests that, in some cases, the investor is paying more to the manager than the return he is receiving for having invested in the hedge fund, compared to a risk-free investment. For that reason we propose a strategic game where the manager's action is to allocate the money between the two assets and the investor's action is to add money to the fund when he expects profit. The result is that the investor profits from the option to reinvest in the fund, although in some extreme cases the actions of the manager make the investor receive a negative value for having the option.