327 research outputs found
Topology Optimization of Silicon Anode Structures for Lithium-Ion Battery Applications
This thesis presents a topology optimization methodology for the systematic design of optimal multifunctional silicon anode structures in lithium-ion batteries. In order to develop next generation high performance lithium-ion batteries, key design challenges relating to the silicon anode structure must be addressed, namely the lithiation-induced mechanical degradation and the low intrinsic electrical conductivity of silicon. As such, this work considers two design objectives of minimum compliance under design dependent volume expansion, and maximum electrical conduction through the structure, both of which are subject to a constraint on material volume. Density-based topology optimization methods are employed in conjunction with regularization techniques, a continuation scheme, and mathematical programming methods. The objectives are first considered individually, during which the iteration history, mesh independence, and influence of prescribed volume fraction and minimum length scale are investigated. The methodology is subsequently extended to a bi-objective formulation to simultaneously address both the compliance and conduction design criteria. A weighting method is used to derive the Pareto fronts, which demonstrate a clear trade-off between the competing design objectives. Furthermore, a systematic parameter study is undertaken to determine the influence of the prescribed volume fraction and minimum length scale on the optimal combined topologies. The developments presented in this work provide a foundation for the informed design and development of silicon anode structures for high performance lithium-ion batteries
Nonparametric Bayesian Inference on Bivariate Extremes
The tail of a bivariate distribution function in the domain of attraction of
a bivariate extreme-value distribution may be approximated by the one of its
extreme-value attractor. The extreme-value attractor has margins that belong to
a three-parameter family and a dependence structure which is characterised by a
spectral measure, that is a probability measure on the unit interval with mean
equal to one half. As an alternative to parametric modelling of the spectral
measure, we propose an infinite-dimensional model which is at the same time
manageable and still dense within the class of spectral measures. Inference is
done in a Bayesian framework, using the censored-likelihood approach. In
particular, we construct a prior distribution on the class of spectral measures
and develop a trans-dimensional Markov chain Monte Carlo algorithm for
numerical computations. The method provides a bivariate predictive density
which can be used for predicting the extreme outcomes of the bivariate
distribution. In a practical perspective, this is useful for computing rare
event probabilities and extreme conditional quantiles. The methodology is
validated by simulations and applied to a data-set of Danish fire insurance
claims.Comment: The paper has been withdrawn by the author due to a major revisio
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
Design optimization of rotating bodies
The presented work focuses on design optimization problems for a general class of rotating bodies with different kinds of support. The rotation of rigid bodies causes vibrations which can lead to undesired noise and to a damage of the rotor. Therefore, the target of the optimization is to change the design of the rotor such that certain resonance frequencies are avoided in the operating speed range and the amplitude in the resonance case is reduced. Based on a suitable physical model, which includes the important effects of rotary inertia and gyroscopic moments, the equation of motion for the continuous rotor is obtained. The solution of this equation leads to a generalized eigenvalue problem. The resulting natural frequencies and eigenmodes are target values of our optimization. The corresponding operators are non-symmetric due to the presence of the gyroscopic terms. Using suitable boundary conditions the compactness of the operator can be shown which is used to prove the solvability of the eigenvalue problem. The existence of solutions of the optimization problem follows. For the numerical solution of the problem a discretization, based on a variational formulation, is introduced. We prove that the solutions of the discretized optimization problem converge towards the solution of the continuous optimization problem if the discretization parameter tends to zero. The discretized optimization problem is numerically solved by an iterative optimization process and the application of different algorithms of the class of sequential convex programming. A mode tracking procedure to follow the modes of interest is considered. Moreover, ideas are presented, how a nonempty set of solutions can be achieved by multiobjective optimization approaches. Computational results for two different turbocharger models are shown which are supported either by linear spring and damper or nonlinear fluid-film bearings. A significant reduction of mass of the rotor and of the amplitudes of the target modes is achieved in the considered cases. Further improvements are obtained by changes in the bearing configuration. All in all, the design optimization process for the rotating bodies leads to a reduction of noise and fatigue of material and an increase of efficiency
The optimisation of brass instruments to include wall vibration effects
This thesis focuses on the design optimisation of a brass instrument. The bore profile of such an instrument is known to be the primary influence on the sound of the instrument as it directly controls the shape of the air-column contained within the instruments' walls. It has long been claimed, however, that other factors, such as the wall material and wall vibrations, are also significant, although to a lesser degree. In recent years, it has been proven that wall vibrations do indeed have an audible effect on the sound (Moore et al 2005, Kausel et al 2007, Nachtmann et al 2007, Kausel, Zietlow and Moore 2010). This effect corresponds to a relative increase in the power of upper harmonics of the sound spectrum when vibrations are greatest, and relative increase in the power of the lower harmonics, in particular the fundamental, when vibrations are at their least. The result is a timbral difference where a greater relative power in the upper harmonics results in a 'brighter' sound, and where the opposite results in a 'darker' sound. Studies have also found that the degree of the wall vibration is increased when the resonant frequencies of the air-column and those of the instruments' structure align. It is this principle that this work is based on.
The primary objective of this work was to devise a suitable approach for incorporating the wall vibration effect into an optimisation method to investigate the optimum designs for two scenarios: maximum wall vibration and minimum wall vibration. It was also of interest to investigate if there were any design characteristics for each scenario.
Two analysis methods were investigated for their suitability, namely free and forced vibration using finite element analysis (FEA). Different approaches to defining the design variables were explored and the suitability of different optimisation algorithms was investigated. The free vibration approach was found to be inadequate for this application due to the inherent omission of valuable magnitude information. The forced vibration approach was found to be more successful, although it was not possible to align a resonance with each frequency of interest
Multiobjective Design Optimization using Nash Games
International audienceIn the area of pure numerical simulation of multidisciplinary coupled systems, the computational cost to evaluate a configuration may be very high. A fortiori, in multi- disciplinary optimization, one is led to evaluate a number of different configurations to iterate on the design parameters. This observation motivates the search for the most in- novative and computationally efficient approaches in all the sectors of the computational chain : at the level of the solvers (using a hierarchy of physical models), the meshes and geometrical parameterizations for shape, or shape deformation, the implementation (on a sequential or parallel architecture; grid computing), and the optimizers (deterministic or semi-stochastic, or hybrid; synchronous, or asynchronous). In the present approach, we concentrate on situations typically involving a small number of disciplines assumed to be strongly antagonistic, and a relatively moderate number of related objective functions. However, our objective functions are functionals, that is, PDE-constrained, and thus costly to evaluate. The aerodynamic and structural optimization of an aircraft configuration is a prototype of such a context, when these disciplines have been reduced to a few major objectives. This is the case when, implicitly, many subsystems are taken into account by local optimizations. Our developments are focused on the question of approximating the Pareto set in cases of strongly-conflicting disciplines. For this purpose, a general computational technique is proposed, guided by a form of sensitivity analysis, with the additional objective to be more economical than standard evolutionary approaches
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Simulation of sea-state sequences
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the most important inputs given to the networks, but the starting day of the simulated period was also necessary. However, the code replaced the day with the corresponding time and the season. The networks were trained by a simulated annealing algorithm and the outputs of the two sets of networks were used for calculating the parameters of the probability density function (pdf) of the proposed hepta-parameter distribution. After the calculation of the seven parameters of the pdf from the network outputs, the Hs and of the future sea-state is predicted by generating random numbers from the corresponding pdf.
In another part of the thesis, vertical piles have been studied with the goal of identifying the range of sea-states suitable for the safe pile driving operation. Pile configuration including the non-linear foundation and the gap between the pile and the pile sleeve shims were modeled using the finite elements analysis facilities within ABAQUS. Dynamic analyses of the system for a sea-state characterized by Hs and and modeled as a combination of several wave components were performed. A table of safe and unsafe sea-states was generated by repeating the analysis for various sea-states. If the prediction for a particular sea-state is repeated N times of which n times prove to be safe, then it could be said that the predicted sea-state is safe with the probability of 100(n/N).
The last part of the thesis deals with the Hs return values. The return value is a widely used measure of wave extremes having an important role in determining the design wave used in the design of maritime structures. In this part, Hs return value was calculated demonstrating another application of the above simulation of future 3-hourly Hs’s. The maxima method for calculating return values was applied in such a way that avoids the conventional need for unrealistic assumptions. The significant wave height return value has also been calculated using the convolution concept from a model presented by Anderson et al. (2001)
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