41 research outputs found

    Ultra-high temperature concentrated solar thermal energy

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    Given the extremely high surface temperature of the Sun (~5778 K), solar radiation has the theoretical potential, in accordance with the second law of thermodynamics, to heat a receiver on Earth up to ultra-high temperatures (specified in this thesis as >1300 K). However, there is a gap between theory and practice, as contemporary solar thermal energy systems are still limited to temperatures below 900 K due to material and mechanical limitations. Running solar thermal energy at ultra-high temperatures promises greater energy conversion efficiencies for power plants by upgrading their basic cycles to include more advanced power cycles. Furthermore, the provision of solar thermal energy at ultra-high temperatures can unlock a wide range of energy-intensive industrial applications, including hydrogen and cement production, which can contribute to decarbonising sectors which are difficult to electrify. This thesis proposes a novel concept of an ultra-high temperature solar cavity receiver based on an optically exposed liquid metal heat transfer fluid, which flows down a corrugated back plate. The concept is investigated using a quasi-steady-state analytical energy model, in addition to a radiation-coupled Computational Fluid Dynamics (CFD) solution. The developed analysis methods are tailored to the proposed class of receivers, nonetheless, they can be generalised for broad solar receiver analysis or for analysing similar problems involving volumetric radiation absorption in other thermal applications. The concept is shown implementable at its absorptive cavity configuration with an overall (optical and thermal) receiver efficiency exceeding 70%. The proposed concept is a step towards narrowing the technological mismatch, in terms of temperature and scale, between state-of-the-art thermal energy storage and concentrated solar thermal at ultra-high temperatures. A characterisation of prospective ultra-high temperature receivers is presented, which involved a review of state-of-the-art solar thermal technologies with the purpose of identifying the existing challenges to operating at ultra-high temperatures. Based on this characterisation, the proposed receiver is designed to address the literature concerns. The proposed receiver concept involved novel engineering features, including the use of refractory containment materials and a transparent ceramic window to seal the aperture. Therefore, the conceptual investigation attempted to address possible concerns that might be introduced by the new features. Finally, the proposed receiver is demonstrated in a concentrated solar power plant application to emphasise, using quantitative terms, the benefits of operating the receiver at ultra-high temperatures for large-scale applications

    IoT Applications Computing

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    The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

    Data-Driven Research On Engineering Design Thinking And Behaviors In Computer-Aided Systems Design: Analysis, Modeling, And Prediction

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    Research on design thinking and design decision-making is vital for discovering and utilizing beneficial design patterns, strategies, and heuristics of human designers in solving engineering design problems. It is also essential for the development of new algorithms embedded with human intelligence and can facilitate human-computer interactions. However, modeling design thinking is challenging because it takes place in the designer’s mind, which is intricate, implicit, and tacit. For an in-depth understanding of design thinking, fine-grained design behavioral data are important because they are the critical link in studying the relationship between design thinking, design decisions, design actions, and design performance. Therefore, the research in my dissertation aims to develop a new research platform and new research approaches to enable fine-grained data-driven methodology that helps foundation ally understand the designers’ thinking and decision-making strategies in engineering design. To achieve this goal, my research has focused on modeling, analysis, and prediction of design thinking and designers’ sequential decision-making behaviors. In the modeling work, different design behaviors, including design action preferences, one step sequential decision behavior, contextual behavior, long short-term memory behavior, and reflective thinking behavior, are characterized and computationally modeled using statis tical and machine learning techniques. For example, to model designers’ sequential decision making, a novel approach is developed by integrating the Function-Behavior-Structure (FBS) design process model into deep learning methods, e.g., the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. In the work on analysis, this dissertation focuses primarily on different clustering analysis techniques. Based on the behaviors modeled, designers showing similar behavioral patterns can be clustered, from which the common design patterns can be identified. Another analysis performed in this dissertation is on the comparative study of different sequential learning techniques, e.g., deep learning models versus Markov chain models, in modeling sequential decision-making behaviors of human designers. This study compares the prediction accuracy of different models and helps us obtain a better understanding of the performance of deep-learning models in modeling sequential design decisions. Finally, in the work related to prediction, this dissertation aims to predict sequential design decisions and actions. We first test the model that integrates the FBS model with various deep-learning models for the prediction and evaluate the performance of the model. Then, to improve the accuracy of the prediction, we develop two approaches that directly and indirectly combine designer-related attributes (static data) and designers’ action sequences (dynamic data) within the deep learning-based framework. The results show that with ap propriate configurations, the deep-learning model with both static data and dynamic data outperforms the models that only rely on the design action sequence. Finally, I developed an artificial design agent using reinforcement learning with a data-driven reward mechanism based on the Markov chain model to mimic human design behavior. The model also helps validate the hypothesis that the design knowledge learned by the agent from one design problem is transferable to new design problems. To support fine-grained design behavioral data collection and validate the proposed approaches, we develop a computer-aided design (CAD)-based research platform in the application context of renewable engineering systems design. Data are collected through three design case studies, i.e., a solarized home design problem, a solarized parking lot design problem, and a design challenge on solarizing the University of Arkansas (UARK) campus. The contribution of this dissertation can be summarized in the following aspects. First, a novel research platform is developed that can collect fine-grained design behavior data in support of design thinking research. Second, new research approaches are developed to characterize design behaviors from multiple dimensions in a latent space of design thinking. We refer to such a latent representation of design thinking as design embedding. Furthermore, using deep learning techniques, several different predictive models are developed that can successfully predict human sequential design decisions with prediction accuracy higher than traditional sequential learning models. Third, by analyzing designers’ one-step sequential design behaviors, common and beneficial design patterns are identified. These patterns are found to exist in many high-performing designers in the three respective design problems studied. Fourth, new knowledge has been obtained on the ability of deep learning-based models versus traditional sequential learning models to predict sequential design decisions of human designers. Finally, a novel research approach is developed that helps test the hypothesis of transferability of design knowledge. In general, this dissertation creates a new avenue for investigating designers’ thinking and decision-making behaviors in systems design context based on the data collected from a CAD environment and tested the capability of various deep-learning algorithms in predicting human sequential design decisions

    Towards a Sustainable Life: Smart and Green Design in Buildings and Community

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    This Special Issue includes contributions about occupants’ sustainable living in buildings and communities, highlighting issues surrounding the sustainable development of our environments and lives by emphasizing smart and green design perspectives. This Special Issue specifically focuses on research and case studies that develop promising methods for the sustainable development of our environment and identify factors critical to the application of a sustainable paradigm for quality of life from a user-oriented perspective. After a rigorous review of the submissions by experts, fourteen articles concerning sustainable living and development are published in this Special Issue, written by authors sharing their expertise and approaches to the concept and application of sustainability in their fields. The fourteen contributions to this special issue can be categorized into four groups, depending on the issues that they address. All the proposed methods, models, and applications in these studies contribute to the current understanding of the adoption of the sustainability paradigm and are likely to inspire further research addressing the challenges of constructing sustainable buildings and communities resulting in a sustainable life for all of society

    Design and optimisation of a heliostat field and a sodium receiver for next-generation CSP plants

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    The heliostat field and receiver subsystems are crucial in a concentrated solar power (CSP) plant, where solar irradiance is converted into thermal power. It is essential to optimise the subsystems at the design stage to maximise the energy yield and to minimise the cost. This thesis presents different modelling methodologies and case studies for the optimal design of the heliostat field and tubular receiver subsystems for next-generation CSP plants. Firstly, heliostat aiming strategy affects the thermo-mechanical performance of the receiver and is a key factor for receiver reliability and interactions between the field and receiver. For the external cylindrical receiver, the modified deviation-based aiming (MDBA) method is proposed as a fast and accurate heliostat aiming strategy based on ray-tracing. The new aiming model enables efficient use of ray-tracing together with receiver thermal and mechanical models to closely match the flux distribution to local values of allowable flux on the receiver. The MDBA method maximises the thermal output while respecting thermal stress limits on the receiver, and is then coupled to a new co-optimisation technique to design the heliostat field and receiver together. In the co-optimisation method, instantaneous optical, thermal and mechanical models are integrated in an annual system-level model to capture the highly transient behaviour of the subsystem, and the design is optimised using a genetic algorithm. Several techniques are implemented to make this complex and computationally expensive problem tractable. The co-optimisation method can be used to maximise the annual solar-to-thermal efficiency or to minimise the levelised cost of energy (LCOE). It is found that the receiver flow configuration, including the flow path pattern and pipe diameter, affects receiver performance. Hence, the proposed integrated design methodology is used to explore the optimal flow configuration for a receiver with an oversized field at both design-point and annual conditions. The results show that the optimal receiver flow configuration achieves a low fraction of heliostat defocusing with a 20% oversized field, although the benefits on the annual energy yield are weakened by capacity limits of other system components. Therefore, a system-level optimisation is implemented with relative sizing of the field, receiver, sodium-salt heat exchanger, storage and power block to achieve the lowest LCOE. An iterative surrogate-based optimisation (SBO) technique is proposed to accelerate the optimisation process. The best achieved LCOE is below 60.0 USD/MWh, within the range targeted by the DoE Gen3 program. A high capacity factor of 83.2% is achieved in the optimal design. A further topic in this PhD thesis is the option of spillage skirts and secondary reflectors for performance enhancement of a cavity receiver

    Development of a control strategy to compensate transient behaviour due to atmospheric disturbances in solar thermal energy generation systems using short-time prediction data

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    La energía solar térmica concentrada (CSP) es una forma prometedora de energía renovable que puede aprovechar la energía del sol y ayudar a sustituir el uso de combustibles fósiles para la generación de electricidad. Sin embargo, enfrenta retos para aumentar su despliegue a nivel mundial. Las torres solares, un tipo de tecnología CSP, se componen principalmente de un campo solar y una torre en la que un receptor funciona como intercambiador de calor para alimentar un bloque de potencia. El campo solar está formado por miles de heliostatos, que son espejos capaces de seguir el sol y proyectar la luz solar concentrada sobre el receptor. Las torres solares con almacenamiento térmico funcionan continuamente, pero están sujetas a perturbaciones causadas por la interacción de la luz solar con la atmósfera. Este comportamiento puede afectar la integridad del receptor. Para determinar la posición de cada helióstato se utilizan complejos métodos de optimización. Sin embargo, estos métodos están sujetos a incertidumbre en los parámetros y no pueden compensar perturbaciones en tiempo real, como las nubes, debido a su costo computacional. Esta tesis aborda esta cuestión como un problema de control, reduciendo el número de variables. En lugar de encontrar el ángulo de elevación y azimutal para miles de helióstatos, se utilizan dos variables dentro de grupos de helióstatos. A continuación, se implementa una estrategia de control por retroalimentación, aprovechando esta reducción dimensional. Además, la metodología desarrollada en esta tesis utiliza información de un sistema de predicción de radiación solar a corto plazo de última generación, dentro de una novedosa estrategia de control adaptativo para el campo solar.DoctoradoDoctor en Ingeniería Mecánic

    Solar Power System Plaing & Design

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    Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

    As Energias Renováveis na Transição Energética : Livro de Comunicações do XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar

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    CIES2020: XVII Congresso Ibérico e XIII Congresso Ibero-Americano de Energia Solar, Lisboa, Portugal: LNEG, 3-5 Novembro, 2020.RESUMO: O CIES2020, reúne sob o lema da “As Energias Renováveis na Transição Energética”, refletindo uma conjuntura de mudança necessária e urgente em todos os sectores das nossas Sociedades, no nosso comportamento no uso da “Energia”, quer em termos individuais, nas famílias nas empresas e sobretudo na mudança de paradigma dos Sistemas Energéticos que impactam a todos os níveis, nas Cidades, nos Edifícios, nos Transportes, e onde o papel das Energias Renováveis assume um papel prioritário e principal, na luta contra as alterações climáticas, a descarbonização energética na defesa do Planeta e da sustentabilidade das futuras gerações. O CIES2020, apresentou-se com 3 tópicos principais: 1) As Energias Renováveis na Tran sição Energética; 2) As Energias Renováveis no Desenvolvimento Sustentável das Comunidades e 3) As Energias Renováveis a Sociedade e a Economia . Tentámos assim abranger todas as áreas tecnológicas das Energias Renováveis, as suas aplicações e utilizações, bem como os novos desafios futuros que estão a acontecer em termos de Inovação Tecnológica e respetivos impactos na Sociedade.info:eu-repo/semantics/publishedVersio

    Settings-Free Hybrid Metaheuristic General Optimization Methods

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    Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.This research and APC was funded by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 co-financed by FEDER funds, and by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, co-financed by FEDER funds
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