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- Title
- Multi-objective evolutionary optimization in greenhouse control for improved crop yield and energy tradeoffs
- Creator
- Llera Ortiz, Jose R.
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
-
The worldwide increase in demand for fresh fruits and vegetables has led to a search for strategies to manage greenhouses in ways that not only meet this demand, but that are also economically viable and environmentally sustainable. A well-established approach for managing greenhouse microclimate is through the automatic control of its mechanical systems such as heaters, ventilators, and shade screens. Such a system is a form of closed-loop control, but only with respect to the greenhouse...
Show moreThe worldwide increase in demand for fresh fruits and vegetables has led to a search for strategies to manage greenhouses in ways that not only meet this demand, but that are also economically viable and environmentally sustainable. A well-established approach for managing greenhouse microclimate is through the automatic control of its mechanical systems such as heaters, ventilators, and shade screens. Such a system is a form of closed-loop control, but only with respect to the greenhouse microclimate, rather than the crop being grown. In practice, conventional greenhouse control is criticized for this focus on climate control instead of crop production, as well as the complexity of managing these systems due to an excessive number of user settings [1]. A more comprehensive form of closed-loop optimal control in greenhouses has been proposed to provide a better degree of control by adjusting the greenhouse climate in response to the growth of the crop being cultivated, but it is still dependent on the external climate around the greenhouse and can lack acceptable alternatives due to the nonlinear nature of the interactions between environmental conditions and plant growth. Unfortunately, monitoring of the real-time response of the crop is not viable for this type of closed-loop control - what can be used instead is a rather sophisticated state model of crop production so that the microclimate conditions can be controlled in order to optimize their effects on the predicted seasonal crop production. Further, this model and the greenhouse microclimate model into which it is integrated must be executable in a short enough timeframe to allow running it thousands of times to optimize the performance of the controller for a given greenhouse structure and location. Having developed such a model, we propose using a form of evolutionary multi-objective optimization to discover a suite of user-selectable control strategies that balance crop productivity with the financial costs of greenhouse climate control. Each of the Pareto-optimal controllers discovered by this approach defines a range of conditions to be maintained via specified control actions, depending upon the crop state and external environmental conditions. Due to the large number of candidates present as the output, the decision-making process will be aided by considering common user preferences as well as algorithmically examining the robustness of solutions in the final Pareto-optimal frontier.
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- Title
- DISSERTATION : NOVEL PARALLEL ALGORITHMS AND PERFORMANCE OPTIMIZATION TECHNIQUES FOR THE MULTI-LEVEL FAST MULTIPOLE ALGORITHM
- Creator
- Lingg, Michael
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
-
Since Sir Issac Newton determined that characterizing orbits of celestial objects required considering the gravitational interactions among all bodies in the system, the N-Body problem has been a very important tool in physics simulations. Expanding on the early use of the classical N-Body problem for gravitational simulations, the method has proven invaluable in fluid dynamics, molecular simulations and data analytics. The extension of the classical N-Body problem to solve the Helmholtz...
Show moreSince Sir Issac Newton determined that characterizing orbits of celestial objects required considering the gravitational interactions among all bodies in the system, the N-Body problem has been a very important tool in physics simulations. Expanding on the early use of the classical N-Body problem for gravitational simulations, the method has proven invaluable in fluid dynamics, molecular simulations and data analytics. The extension of the classical N-Body problem to solve the Helmholtz equation for groups of particles with oscillatory interactions has allowed for simulations that assist in antenna design, radar cross section prediction, reduction of engine noise, and medical devices that utilize sound waves, to name a sample of possible applications. While N-Body simulations are extremely valuable, the computational cost of directly evaluating interactions among all pairs grows quadratically with the number of particles, rendering large scale simulations infeasible even on the most powerful supercomputers. The Fast Multipole Method (FMM) and the broader class of tree algorithms that it belongs to have significantly reduced the computational complexity of N-body simulations, while providing controllable accuracy guarantees. While FMM provided a significant boost, N-body problems tackled by scientists and engineers continue to grow larger in size, necessitating the development of efficient parallel algorithms and implementations to run on supercomputers. The Laplace variant of FMM, which is used to treat the classical N-body problem, has been extensively researched and optimized to the extent that Laplace FMM codes can scale to tens of thousands of processors for simulations involving over trillion particles. In contrast, the Multi-Level Fast Multipole Algorithm (MLFMA), which is aimed for the Helmholtz kernel variant of FMM, lags significantly behind in efficiency and scaling. The added complexity of an oscillatory potential results in much more intricate data dependency patterns and load balancing requirements among parallel processes, making algorithms and optimizations developed for Laplace FMM mostly ineffective for MLFMA. In this thesis, we propose novel parallel algorithms and performance optimization techniques to improve the performance of MLFMA on modern computer architectures. Proposed algorithms and performance optimizations range from efficient leveraging of the memory hierarchy on multi-core processors to an investigation of the benefits of the emerging concept of task parallelism for MLFMA, and to significant reductions of communication overheads and load imbalances in large scale computations. Parallel algorithms for distributed memory parallel MLFMA are also accompanied by detailed complexity analyses and performance models. We describe efficient implementations of all proposed algorithms and optimization techniques, and analyze their impact in detail. In particular, we show that our work yields significant speedups and much improved scalability compared to existing methods for MLFMA in large geometries designed to test the range of the problem space, as well as in real world problems.
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