<p>The main contribution of this thesis is on the study of optimization methods in aft hull design. The optimization methods are inverse geometry design methods to find an aft hull with the flow velocities we specify. The analytic foundation for the flow is given by Stratford in [31], and gives a prescribed velocity distribution on the aft body. With the parameter β we have adjusted this flow to have a certain margin to separation along the pressure recovery region.</p><p>This principle and optimization method are successfully applied to design of ships with pram-type aft hull. The 2D optimized profiles corresponds to centerline buttock, and 3D hull sections are extended from this centerline buttock with a bilge radius. </p><p>Stratfords original pressure distribution for pressure recovery region were meant for Reynolds numbers up to 107. We have extended Stratfords formula to yield for ship full scale Reynolds numbers to 109. </p><p>Different optimization methods were programmed and tested. The best routine for our optimization of aft hull with Stratford flow, was when the offset y-value were the optimization parameter to be changed. When we tried to optimize a complete 2D profile with a given pressure distribution, it worked best to use the variables in a B-spline as the optimization parameter.</p><p>Extensive windtunnel tests and towing tank tests are carried out. The tests verified the hydrodynamic properties of the hulls.</p><p>Towing tests indicates that the optimized hull lines have lower total resistance than conventional ships with the same main dimensions. Both the frictional, viscous pressure resistance and wave making resistance are significantly lower. Further we can increase cargo capacity with the same power consumption, and achieve a more favourable distribution of the displacement in the aft hull.</p><p>This study has shown us that the slant angle for the bottom of the aft hull should not excess 15º with horizontal plane due to danger of separation over the bilge, and longitudinal vortices forming. </p>

The main contribution of this thesis is on the study of optimization methods in aft hull design. The optimization methods are inverse geometry design methods to find an aft hull with the flow velocities we specify. The analytic foundation for the flow is given by Stratford in [31], and gives a prescribed velocity distribution on the aft body. With the parameter β we have adjusted this flow to have a certain margin to separation along the pressure recovery region. This principle and optimization method are successfully applied to design of ships with pram-type aft hull. The 2D optimized profiles corresponds to centerline buttock, and 3D hull sections are extended from this centerline buttock with a bilge radius. Stratfords original pressure distribution for pressure recovery region were meant for Reynolds numbers up to 107. We have extended Stratfords formula to yield for ship full scale Reynolds numbers to 109. Different optimization methods were programmed and tested. The best routine for our optimization of aft hull with Stratford flow, was when the offset y-value were the optimization parameter to be changed. When we tried to optimize a complete 2D profile with a given pressure distribution, it worked best to use the variables in a B-spline as the optimization parameter. Extensive windtunnel tests and towing tank tests are carried out. The tests verified the hydrodynamic properties of the hulls. Towing tests indicates that the optimized hull lines have lower total resistance than conventional ships with the same main dimensions. Both the frictional, viscous pressure resistance and wave making resistance are significantly lower. Further we can increase cargo capacity with the same power consumption, and achieve a more favourable distribution of the displacement in the aft hull. This study has shown us that the slant angle for the bottom of the aft hull should not excess 15º with horizontal plane due to danger of separation over the bilge, and longitudinal vortices forming.

A finite element solver has been developed for performing analysis and sensitivity analysis with Poisson's equation. An application of Poisson's equation in fluid dynamics is that of poential flow, in which case Posson's equaiton reduces to Laplace's equation. The stiffness matrix and sensitivity of the stiffness matrix are evaluated by direct integrations, as opposed to numerical integration. This allows less computational effort and minimizes the sources of computational errors. The capability of evaluating sensitivity derivatives has been added in orde to perform design sensitivity analysis of non-lifting airfoils. The discrete-direct approach to sensitivity analysis is utilized in the current work. The potential flow equations and the sensitivity equations are computed by using a preconditionaed conjugate gradient method. This method greatly reduces the time required to perfomr analysis, and the subsequent design optimization. Airfoil shape is updated at each design iteratioan by using a Bezier-Berstein surface parameterization. The unstrucured grid is adapted considering the mesh as a system of inteconnected springs. Numerical solutions from the flow solver are compared with analytical results obtained for a Joukowsky airfoil. Sensitivity derivaatives are validated using carefully determined central finite difference values. The developed software is then used to perform inverse design of a NACA 0012 and a multi-element airfoil.

In this thesis, we study the problem of what microscopic thermodynamic driving forces can stabilize target macroscopic structures. First, we demonstrate that inverse statistical mechanical optimization can be used to rationally design inter-particle interactions that display target open structures as ground states over a wide range of thermodynamic conditions. We focus on designing simple interactions (e.g., isotropic, convex-repulsive) that drive the spontaneous assembly of material constituents to low-coordinated ground states of diamond and simple cubic lattices. This is significant because these types of phases are typically accessible given more complex systems (e.g., particles with orientation-dependent attractive interactions) and for a narrow range of conditions. We subject the optimal interactions to stringent stability tests and also observe assembly of the target structures from disordered fluid states. We then use extensive free energy based Monte Carlo simulation techniques to construct the equilibrium phase diagrams for the model materials with interactions designed to feature diamond and simple cubic ground states, i.e., at zero temperatures. We find that both model materials, despite the largely featureless interaction form, display rich polymorphic phase behavior featuring not only thermally stable target ground state structures, but also a variety of other crystalline (e.g., hexagonal and body-centered cubic) phases. Next, we investigate whether isotropic interactions designed to stabilize given two-dimensional (2D) lattices (e.g., honeycomb or square) will favor their analogous three-dimensional (3D) structures (e.g., diamond or simple cubic), and vice versa. We find a remarkable transferability of isotropic potentials designed to stabilize analogous morphologies in 2D and 3D, irrespective of the exact interaction form, and we discuss the basis of this cross-dimensional behavior. Our results suggest that computationally inexpensive 2D material optimizations can assist in isolating rare isotropic interactions that drive the assembly of materials into 3D open lattice structures. / text

Airfoil design is one of the most important aspects of aircraft design. Slight changes in airfoil geometry can lead to significant changes in a wide variety of aircraft performance metrics. Inverse design methods offer an efficient alternative to standard direct methods. The key to this design problem is to derive a direct relationship between changes in airfoil geometry and changes in pressure or velocity distributions. This relationship is then used to modify an initial airfoil and its pressure distribution to match a target pressure distribution, which is specified by design parameters. At this point, the engineer now has a final airfoil based off of the design requirements. This paper attempts to provide a quick and easy inverse design method for a wide variety of supersonic scenarios. This is accomplished by using the class-shape transformation technique to parameterize airfoils during an iterative process. The robustness of the method is demonstrated through several distinct design cases including supersonic airfoils, unique geometries, and a Sears-Haack body.

No description available.

The development of modern Machine Learning (ML) frameworks trained on large datasets established a rapid increase in the performance of cognitive computing algorithms for a wide range of applications. However, due to the processing capacity restrictions of electronics, scaling up the existing state-of-the-art is currently meeting a bottleneck. Recently, flat-optics arose as a promising alternative to conventional electronics due to intrinsic parallelism, tuneability, and high-speed of optical computations. Finding scalable, highly effective designs that can tolerate fabrication defects brought on by nanoscale manufacturing processes and the demanding design specifications of the end task is one of the main hurdles of flat optics. In this study, we address this problem by introducing an end-to-end optimization methodology that is robust to fabrication intolerance and performance losses due to material absorption and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN). For these structures, we provide theoretical guarantees for universal approximation, an ability to approximate arbitrary defined material's transfer function. We validate this framework's capability by creating several optical components achieving near unity efficiencies for vectorial light processing with broadband spectral responses and pre-defined wavefront characteristics. In addition, leveraging the high-dimensional capabilities of that system, we present a novel concept of spectral-informed imaging, which does not require the use of spectral analyzers or complex mechanical filters but uses an artificial-intelligence engineered, "hardware" flat-optics surface that processes spectral encoding at the speed of light inside silicon (Si) metasurface.

<p> This thesis contributes significantly to the advancement of the response sensitivity analysis with time-domain electromagnetic (EM) solvers. The proposed self-adjoint sensitivity approaches achieve unprecedented computational efficiency. The response Jacobians are computed as a simple post-process of the field solution and the approaches can be applied with any commercial time-domain solver. The proposed sensitivity solvers are a breakthrough in the sensitivity analysis of high-frequency structures since they can be implemented as standalone software or plugin for EM simulators. The goal is to aid the solution of microwave design and inverse problems.</p> <p> The sensitivity information is crucial in engineering problems such as gradient-based optimization, yield and tolerance analyses. However, due to the lack of robust algorithms, commercial EM simulators provide only specific engineering responses not their sensitivities (or derivatives with respect to certain system parameters). The sensitivities are typically obtained by response-level finite difference (FD) approximations or parameter sweeps. For each design parameter of interest, at least one additional full-wave analysis is performed. Such approaches can easily become prohibitively slow when the number of design parameters is large.</p> <p> However, no extra system analysis is needed with the self-adjoint sensitivity analysis methods. Both the responses and their Jacobian are obtained through a single system analysis. In this thesis, two self-adjoint sensitivity solvers are introduced. They are based on a self-adjoint formulation which eliminates the need to perform adjoint system analysis. The first sensitivity solver is based on a self-adjoint formula which operates on the time waveforms of the field solution. Three different approaches associated with this sensitivity solver have been presented. The first approach adopts the staggered grid of the finite-difference time-domain (FDTD) simulation. We refer it as the original self-adjoint approach. The second approach is the efficient coarse-grid approach. It uses a coarse independent FD grid whose step size can be many times larger than that of the FDTD simulation. The third approach is the accurate central-node approach. It uses a central-node grid whose field components are collocated in the center of the traditional Yee cell.</p> <p> The second self-adjoint sensitivity solver is based on a spectral sensitivity formula which operates on the spectral components of the E-field instead of its time waveforms. This is a memory efficient wideband sensitivity solver. It overcomes the drawback associated with our first sensitivity solver whose memory requirements may become excessive when the number of the perturbation grid points is very large. The spectral approach reduces the memory requirements roughly from Gigabytes to Megabytes. The focus of this approach is on microwave imaging applications where our first sensitivity solver is inapplicable due to the excessive memory requirements. The proposed sensitivity solver is also well suited for microwave design problems.</p> <p> The proposed self-adjoint sensitivity solvers in this thesis are verified by numerous examples. They are milestones in sensitivity analysis because they have finally made EM simulation-based optimization feasible.</p> / Thesis / Doctor of Philosophy (PhD)

Nanophotonics is the study and technological application of the interaction of electromagnetic waves (light) and matter at the nanometer scale. The field's extensive research focuses on generating, detecting, and controlling light using nanoscale features such as nanoparticles, waveguides, resonators, nanoantennas, and more. Exploration in the field is highly dependent on computational methods, which simulate how light will interact with matter in specific situations. However, as nanophotonics advances, so must the computational techniques. In this thesis, I present my work in various numerical studies in nanophotonics, sorted into three categories; plasmonics, inverse design, and deep learning. In plasmonics, I have developed methods for solving advanced material models (including nonlinearities) for small metallic and epsilon-near-zero features and validated them with other theoretical and experimental results. For inverse design, I introduce new methods for designing optical pulse shapes and metalenses for focusing high-harmonic generation. Finally, I used deep learning to model plasmonic colour generation from structured metal surfaces and to predict plasmonic nanoparticle multipolar responses.

A new receiver concept utilizing impinging jet cooling technology has been developed for a small scale solar dish-Brayton system. In a typical impinging receiver design, the jet nozzles are distributed evenly around the cylindrical absorber wall above the solar peak flux region for managing the temperature at an acceptable level. The absorbed solar irradiation is partially lost to the ambient by radiation and natural convection heat transfer, the major part is conducted through the wall and taken away by the impingement jets to drive a gas turbine. Since the thermal power requirement of a 5 kWe Compower® micro gas turbine (MGT) perfectly matches with the power collected by the EuroDish when the design Direct Normal Irradiance (DNI) input is 800 W/m2, the boundary conditions for the impinging receiver design in this work are based on the combination of the Compower®MGT and the EuroDish system. In order to quickly find feasible receiver geometries and impinging jet nozzle arrangements for achieving acceptable temperature level and temperature distributions on the absorber cavity wall, a novel inverse design method (IDM) has been developed based on a combination of a ray-tracing model and a heat transfer analytical model. In this design method, a heat transfer model of the absorber wall is used for analyzing the main heat transfer process between the cavity wall outer surface, the inner surface and the working fluid. A ray-tracing model is utilized for obtaining the solar radiative boundary conditions for the heat transfer model. Furthermore, the minimum stagnation heat transfer coefficient, the jet pitch and the maximum pressure drop governing equations are used for narrowing down the possible nozzle arrangements. Finally, the curves for the required total heat transfer coefficient distribution are obtained and compared with different selected impinging arrangements on the working fluid side, and candidate design configurations are obtained. Furthermore, a numerical conjugate heat transfer model combined with a ray-tracing model was developed validating the inverse design method and for studying the thermal performance of an impinging receiver in detail. With the help of the modified inverse design method and the numerical conjugate heat transfer model, two impinging receivers based on sintered α-SiC (SSiC) and stainless steel 253 MA material have been successfully designed. The detailed analyses show that for the 253 MA impinging receiver, the average air temperature at the outlet and the thermal efficiency can reach 1071.5 K and 82.7% at a DNI level of 800 W/m2 matching the system requirements well. Furthermore, the local temperature differences on the absorber can be reduced to 130 K and 149 K for two different DNI levels, which is a significant reduction and improvement compared with earlier published cavity receiver designs. The inverse design method has also been verified to be an efficient way in reducing the calculation costs during the design procedure. For the validation and demonstration of the receiver designs, a unique experimental facility was designed and constructed. The facility is a novel high flux solar simulator utilizing for the first time Fresnel lenses to concentrate the light of 12 commercial high power Xenon-arc lamps. Finally, a prototype of a 253 MA based impinging was experimentally studied with the help of the 84 kWe Fresnel lens based high flux solar simulator in KTH. / <p>QC 20151123</p> / Optimised Microturbine Solar Power System , OMSOP