Energy-Efficient Algorithms

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms. We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity. These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.Comment: 40 pages, 8 pdf figures, full version of work published in ITCS 201

Superstructure optimisation of a water minimisation network with a embedded multicontaminant electrodialysis model

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2016The water-energy nexus considers the relationship between water and energy resources. Increases in environmental degradation and social pressures in recent years have necessitated the development of manufacturing processes that are conservative with respect to both these resources, while maintaining financial viability. This can be achieved by process integration (PI); a holistic approach to design which emphasises the unity of processes. Within the realm of PI, water network synthesis (WNS) explores avenues for reuse, recycle and regeneration of effluent in order to minimise freshwater consumption and wastewater production. When regeneration is required, membrane-based treatment processes may be employed. These processes are energy intensive and result in a trade-off between water and energy minimisation, thus creating an avenue for optimisation. Previous work in WNS employed a black box approach to represent regenerators in water minimisation problems. However, this misrepresents the cost of regeneration and underestimates the energy requirements of a system. The aim of the research presented in this dissertation is to develop an integrated water regeneration network synthesis model to simultaneously minimise water and energy in a water network. A novel MINLP model for the design of an electrodialysis (ED) unit that is capable of treating a binary mixture of simple salts was developed from first principles. This ED model was embedded into a water network superstructure optimisation model, where the objective was to minimise freshwater and energy consumption, wastewater productions, and associated costs. The model was applied to a pulp and paper case study, considering several scenarios. Global optimisation of the integrated water network and ED design model, with variable contaminant removal ratios, was found to yield the best results. A total of 38% savings in freshwater, 68% reduction in wastewater production and 55% overall cost reduction were observed when compared with the original design. This model also led to a 80% reduction in regeneration (energy) cost.GS201

Robust simulation and optimization methods for natural gas liquefaction processes

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 313-324).Natural gas is one of the world's leading sources of fuel in terms of both global production and consumption. The abundance of reserves that may be developed at relatively low cost, paired with escalating societal and regulatory pressures to harness low carbon fuels, situates natural gas in a position of growing importance to the global energy landscape. However, the nonuniform distribution of readily-developable natural gas sources around the world necessitates the existence of an international gas market that can serve those regions without reasonable access to reserves. International transmission of natural gas via pipeline is generally cost-prohibitive beyond around two thousand miles, and so suppliers instead turn to the production of liquefied natural gas (LNG) to yield a tradable commodity. While the production of LNG is by no means a new technology, it has not occupied a dominant role in the gas trade to date. However, significant growth in LNG exports has been observed within the last few years, and this trend is expected to continue as major new liquefaction operations have and continue to become operational worldwide. Liquefaction of natural gas is an energy-intensive process requiring specialized cryogenic equipment, and is therefore expensive both in terms of operating and capital costs. However, optimization of liquefaction processes is greatly complicated by the inherently complex thermodynamic behavior of process streams that simultaneously change phase and exchange heat at closely-matched cryogenic temperatures. The determination of optimal conditions for a given process will also generally be nontransferable information between LNG plants, as both the specifics of design (e.g. heat exchanger size and configuration) and the operation (e.g. source gas composition) may have significantly variability between sites. Rigorous evaluation of process concepts for new production facilities is also challenging to perform, as economic objectives must be optimized in the presence of constraints involving equipment size and safety precautions even in the initial design phase. The absence of reliable and versatile software to perform such tasks was the impetus for this thesis project. To address these challenging problems, the aim of this thesis was to develop new models, methods and algorithms for robust liquefaction process simulation and optimization, and to synthesize these advances into reliable and versatile software. Recent advances in the sensitivity analysis of nondifferentiable functions provided an advantageous foundation for the development of physically-informed yet compact process models that could be embedded in established simulation and optimization algorithms with strong convergence properties. Within this framework, a nonsmooth model for the core unit operation in all industrially-relevant liquefaction processes, the multi-stream heat exchanger, was first formulated. The initial multistream heat exchanger model was then augmented to detect and handle internal phase transitions, and an extension of a classic vapor-liquid equilibrium model was proposed to account for the potential existence of solutions in single-phase regimes, all through the use of additional nonsmooth equations. While these initial advances enabled the simulation of liquefaction processes under the conditions of simple, idealized thermodynamic models, it became apparent that these methods would be unable to handle calculations involving nonideal thermophysical property models reliably. To this end, robust nonsmooth extensions of the celebrated inside-out algorithms were developed. These algorithms allow for challenging phase equilibrium calculations to be performed successfully even in the absence of knowledge about the phase regime of the solution, as is the case when model parameters are chosen by a simulation or optimization algorithm. However, this still was not enough to equip realistic liquefaction process models with a completely reliable thermodynamics package, and so new nonsmooth algorithms were designed for the reasonable extrapolation of density from an equation of state under conditions where a given phase does not exist. This procedure greatly enhanced the ability of the nonsmooth inside-out algorithms to converge to physical solutions for mixtures at very high temperature and pressure. These models and submodels were then integrated into a flowsheeting framework to perform realistic simulations of natural gas liquefaction processes robustly, efficiently and with extremely high accuracy. A reliable optimization strategy using an interior-point method and the nonsmooth process models was then developed for complex problem formulations that rigorously minimize thermodynamic irreversibilities. This approach significantly outperforms other strategies proposed in the literature or implemented in commercial software in terms of the ease of initialization, convergence rate and quality of solutions found. The performance observed and results obtained suggest that modeling and optimizing such processes using nondifferentiable models and appropriate sensitivity analysis techniques is a promising new approach to these challenging problems. Indeed, while liquefaction processes motivated this thesis, the majority of the methods described herein are applicable in general to processes with complex thermodynamic or heat transfer considerations embedded. It is conceivable that these models and algorithms could therefore inform a new, robust generation of process simulation and optimization software.by Harry Alexander James Watson.Ph. D

Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag

We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential applications in energy storage, electronics packaging, and superconductivity. For the systematic exploration of the large configuration space, we relied on our recently developed MAISE-NET framework that constructs accurate neural network interatomic potentials and utilizes them to accelerate ab initio global structure searches. The scan of over two million candidate phases at a fraction of the typical ab initio calculation cost has uncovered 29 possible intermetallics thermodynamically stable at different temperatures and pressures (1 bar and 20 GPa). Notable predictions of ambient-pressure materials include a simple hP6-NaSn$_2$ phase, fcc-based Pd-rich alloys, tI36-PdSn$_2$ with a new prototype, and several high-temperature Sn-rich ground states in the Na-Sn, Cu-Sn, and Ag-Sn systems. Our modeling work also involved ab initio (re)examination of previously observed M-Sn compounds that helped explain the entropy-driven stabilization of known Cu-Sn phases. The study demonstrates the benefits of guiding structure searches with machine learning potentials and significantly expands the number of predicted thermodynamically stable crystalline intermetallics achieved with this strategy so far

Enhancing materials properties via targeted doping

In this thesis a series of theoretical studies aimed at enhancing the optical and electrical properties of selected oxide and hydride materials via defect incorporation is presented. Large-scale screening for useful defects was performed on two transparent tin-based oxide materials: a natively p-type tin monoxide and an intrinsically n-type tin dioxide. Novel dopant candidates that promise amplified charge-carrier generation if incorporated successfully were uncovered for both compounds. We further showed that some of these dopant elements are able to maintain the optical properties displayed by the bulk phases of the oxides. The two studies revealed the affinity of tin monoxide for both hole and electron free-carriers when doped appropriately, while tin dioxide was shown to be a strictly n-type conductor. The possibility of improving both optical and electronic attributes of tin-oxide materials further was investigated by exploring the interactions between impurity atoms and intrinsic defects of the host. Isovalent silicon doping in tin dioxide was shown to suppress absorption states arising from oxygen deficiencies, thus, presenting a novel path for improving optical properties in transparent conductive oxide materials. In tin monoxide, halogen interstitials were observed to bond with native tin vacancies ionizing them to higher charge states, which result in improved p-type carrier generation. Finally, acceptor doping was also considered in large band gap hydride materials under compression. Defects in ice, H$_{2}$O, and polyethylene, H$_{2}$C$_{n}$, were studied by identifying high-pressure phases that display covalent bonding and can, therefore, be successfully doped. The possibility of such doped phases displaying a superconducting transition was addressed and a transition temperature of 60 K in ice-X and a 35 K in a polymeric high-pressure phase of polyethylene was estimated

Improving the Functional Control of Ferroelectrics using Insights from Atomistic Modelling

Lead zirconate titanate is a ferroelectric material of considerable interest with a wide range of technological applications. It has been the subject of many experimental and theoretical studies yet there are a number of unsolved questions preventing further miniaturisation and optimisation of this and other ferroelectric materials. Exotic ultra-dense domain morphologies, as an example, offer an exciting avenue for the development of novel nanoelectronics. In this work, large scale molecular dynamics is used to construct a strain-temperature phase diagram of the domain morphology of PbTiO3 ultrathin films. By sampling a wide range of strain values over a temperature range up to the Curie temperature, it is found that epitaxial strain induces the formation of a variety of closure- and in-plane domain morphologies. The local strain and ferroelectric-antiferrodistortive coupling at the film surface vary for the strain mediated transition sequence and this could offer a route for experimental observation of the morphologies. Remarkably, a new nanobubble domain morphology is identified that is stable in the high-temperature regime for compressively strained PbTiO3. It is demonstrated that the formation mechanism of the nanobubble domains morphology is related to the wandering of flux closure domain walls, which is characterised using the hypertoroidal moment. Molecular dynamics calculations, supplemented with electrical measurements from collaborators, are used to provide insight into the microscopic switching properties of near-morphotropic PZT. The simulations and experiments exhibit qualitatively similar hysteretic behaviour of the polarisation at different temperatures, showing widening of the Polarisation - Electric field hysteresis loops, and the decrease of the coercive field towards high temperatures. Remarkably, polarisation switching at low temperatures is shown to occur via a polarisation rotation and growth mechanism that is fundamentally different from the high temperature switching, where nucleation is rate limiting. Analysis of B-cation contributions show that nucleation and switching are facilitated by Zr centred unit cells and, by extension, Ti centred unit cells in Zr-rich environments. Ti-rich clusters in morphotropic PZT, at low temperature, are observed to have suppressed ferroelectric displacements which may incorrectly be perceived as ferroelectrically inactive `dead-layers'. Finally, fundamental insight into the microscopic mechanisms of the ageing processes are provided. From simulations of the prototypical ferroelectric material PbTiO3, it is demonstrated that experimentally observed ageing phenomena can be reproduced from intrinsic interactions of defect-dipoles related to dopant-vacancy associates, even in the absence of extrinsic effects. Variation of the dopant concentration is shown to modify the material's hysteretic response, identifying a universal method to reduce loss and tune the electromechanical properties of inexpensive ceramics for efficient technologies

Stretching the Limits in Thermoplastic Forming of Bulk Metallic Glasses

Metallic glasses (MG) suggest that superb mechanical properties can be paired with plastic-like processing. Their high strength and elasticity are often paired with fracture toughness. Their supercooled liquid region gives rise to plastic-like processing and suggests parts and shapes that can otherwise not be obtained for crystalline metals. However, current processing techniques only allow for limited options in terms of geometry, thicknesses uniformity, and shape complexity. In the first part of my thesis, I introduce the form-giving aspect of metallic glass thermoforming, by introducing stretch blow molding, to expand the geometries that can be fabricated with metallic glasses. For this I developed a model, which allows to quantify stretch blow molding and provides insight into its potential use and limitations. We demonstrate that with stretch blow molding overall strains exceeding 2000% are achievable, significantly higher than the previously reported ~150% of blow molding. In the second part of my thesis, I focused on the effect of the processing on metallic glasses properties. This is motivated by the current understanding that most metallic glasses lack sufficient ductility or toughness when fabricated under conditions resulting in bulk glass formation. To address this shortcoming, I used strain rate to excite the liquid while simultaneously cooling it to freeze the excited liquid into a glass with a higher fictive temperature. Microscopically, straining causes the structure to dilate, hence “pulls” the structure energetically up the potential energy landscape. Upon further cooling, the resulting excited liquid freezes into an excited glass that exhibits enhanced ductility. I used Zr44Ti11Cu10Ni10Be25 as an example to pull metallic glasses through this excited liquid cooling method, which can lead to the tripling of bending ductility