Carnot Cycle and Heat Engine Fundamentals and Applications II

This second Special Issue connects both the fundamental and application aspects of thermomechanical machines and processes. Among them, engines have the largest place (Diesel, Lenoir, Brayton, Stirling), even if their environmental aspects are questionable for the future. Mechanical and chemical processes as well as quantum processes that could be important in the near future are considered from a thermodynamical point of view as well as for applications and their relevance to quantum thermodynamics. New insights are reported regarding more classical approaches: Finite Time Thermodynamics F.T.T.; Finite Speed thermodynamics F.S.T.; Finite Dimensions Optimal Thermodynamics F.D.O.T. The evolution of the research resulting from this second Special Issue ranges from basic cycles to complex systems and the development of various new branches of thermodynamics

Design optimization of a three-stage transmission using advanced optimization techniques

Gear transmission systems are very important machine elements and their failure can lead to losses or damage of other mechanical components that comprise a machine or device. Since gears are applied in numerous mechanical devices, there is need to design and subsequently optimize them for intended use. In the present work, two objectives, viz., volume and center distance, are minimized for a rotary tiller to achieve a compact design. Two methods were applied: (1) analytical method, (2) a concatenation of the bounded objective function method and teaching–learning-based optimization techniques, thereby improving the result by 44% for the former and 55% for the latter. Using a geometric model and previous literature, the optimal results obtained were validated with 0.01 variation. The influence of design variables on the objective functions was also evaluated using variation studies reflecting on a ranking according to objective. Bending stress variation of 12.4% was less than contact stress at 51% for a defined stress range

Otimização numérica e análise económicana conceção de uma micro-cogeração com motor Stirling e concentrador solar

Tese de doutoramento do Programa Doutoral em Engenharia Industrial e de SistemasThe micro-CHP systems are a promising technology for improving the energy efficiency of small energy conversion units, located near the end user. The combined heat and power production allows the optimal use of the primary energy sources and significant reductions in carbon emissions. Its use, still incipient, has a great potential for applications in the residential sector. This study aims to develop a methodology for the thermal-economic optimization of micro cogeneration units using Stirling cycle engine as prime mover and concentrated solar energy as the heat source. A detailed thermodynamic study was carried out to define the model for the physical characterization of the Stirling engine. The study of the physical model includes three types of analysis: ideal isothermal, ideal adiabatic and non-ideal adiabatic analyses. The latter includes limitations in the heat transfer processes and losses due pumping effects. These analyses were performed through numerical simulations by eveloping a code in MatLab® programming language, based on the model developed by Urieli and Berchowitz. The mathematical modelling was modified, improved and adapted to adjust the configuration of the Stirling engine for cogeneration applications. Subsequently to its implementation, several sensitivity analyses on the operational and geometric parameters were conducted in order to understand which of them have the highest relevance in the Stirling engine performance. The definition of these criteria is crucial in the choice of the decision variables for the thermal-economic optimization model. After characterizing the physical model, a purchase cost equation representative of each system component was defined: a cost equation for each one of the heat exchangers (i.e. heater, regenerator and cooler) and a cost equation representative of the engine bulk. Each cost equation is based on physical parameters, taking into account the sizing of the system. Through data collected from market available Stirling systems, the most appropriate cost coefficients were defined and the cost equations were validated. fter the validation of both physical and economic models, the thermal-economic optimization was formulated. The maximization of the annual worth from the system operation was defined as the objective function, subjected to a set of nonlinear thermodynamic and economic constraints in order to give significance to the numerical results. The model was formulated considering a cost/benefit approach, where the terms of the objective function represent a balance between costs and revenues. The decision variables correspond to geometric and operational parameters with the highest relevance in the system operation. The Pattern Search algorithm was implemented to achieve the numerical solution, using different search methods, i.e., the Nelder-Mead and genetic algorithm method. The optimization model was effective in the determination of the optimal solution and a positive annual worth was obtained for the defined input simulation conditions. The thermal-economic model yielded the combination of decision variables that defines the best configuration for maximum economic benefit. Through the economic assessment of the best solution obtained for the micro-CHP system, and considering the costs for installing a solar concentrator collector, it can be that such a system is economically attractive, with a payback period of approximately 9 years.Os sistemas de micro-cogeração são uma tecnologia muito promissora para a melhoria da eficiência energética das pequenas unidades de conversão de energia localizadas junto ao utilizador final. A produção combinada de calor e eletricidade permite a otimização da utilização das fontes de energia primária e significativas reduções nas emissões de carbono. A sua utilização, ainda incipiente, possui um grande potencial para as aplicações no sector residencial. Este estudo visa o desenvolvimento de uma metodologia de otimização termo-económica dedicada ao desenvolvimento de unidades de micro-cogeração usando como tecnologia os motores de ciclo Stirling e a energia solar como fonte de calor. O trabalho iniciou-se com um estudo detalhado sobre o modelo termodinâmico para a caracterização física do motor Stirling. O estudo do modelo físico incluiu três tipos de análises: a análise ideal isotérmica, a análise ideal adiabática e a análise não ideal onde foram incluídas as limitações na transferência de calor e as perdas devido a efeitos de bombagem. O estudo das diferentes análises foi efetuado através de simulações numéricas com o desenvolvimento de um código de programação em linguagem MatLab®, tendo como base o modelo desenvolvido por Urieli e Berchowitz. Este foi modificado, melhorado e adaptado no sentido de a adequar à configuração do motor Stirling para aplicações em cogeração. Após a sua implementação, foram efetuadas várias análises de sensibilidade a parâmetros, quer operacionais quer geométricos, de modo a compreender quais os critérios mais influentes na performance do motor Stirling. A identificação destes critérios foi fundamental para a definição das variáveis de decisão a usar no modelo de otimização termo-económica. Após a caracterização do modelo físico, procedeu-se à definição das equações dos custos de investimento para cada um dos componentes do sistema. Assim, foram definidas quatro equações de custo: uma equação para cada um dos permutadores de calor (i.e. permutador de aquecimento, arrefecimento e regenerador) e uma equação representativa do corpo do motor. Cada uma das equações de custo foi definida com base em parâmetros físicos, tendo em consideração o dimensionamento do sistema. Através de dados recolhidos de sistemas Stirling já comercializados, foram definidos os coeficientes de custo mais adequados e procedeu-se à sua validação. Foi desenvolvido e implementado o modelo de otimização termo-económica. A maximização do lucro anual decorrente da operação do sistema foi definida como a função objetivo, estando sujeita a um conjunto de restrições não lineares de natureza termodinâmica e económica, com vista a dar significância aos resultados numéricos. O modelo foi formulado numa abordagem custo/benefício em que os termos da função-objetivo representam um balanço entre custos e receitas. O conjunto de variáveis de decisão que correspondem às variáveis geométricas e operacionais de maior relevância no sistema. Foi usado o algoritmo Pattern Search do Matlab® com uso de diferentes métodos de procura, isto é, o Nelder-Mead e os algoritmos genéticos, na resolução numérica do modelo. O modelo de otimização mostrou-se eficaz na determinação da solução ótima, tendo sido obtido lucros na operação do sistema para as condições de simulação definidas, assim como, a combinação ótima para as variáveis de decisão que definem a melhor configuração para o máximo benefício económico. Através da avaliação económica de aquisição deste sistema de micro-cogeração, e considerando os custos de instalação de um concentrador solar, verificou tratar-te de um projeto economicamente atrativo, com retorno de investimento de aproximadamente 9 anos.Fundação para a Ciência e Tecnologia for the PhD grant SFRH/BD/62287/2009. I also have to acknowledge CT2M and CGIT research centres of the School of Engineering at University of Minho for supporting my work

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

Development and Prediction of Sustainable Strategies for Integrating Solar Energy with Desalination

Water desalination is a reliable process for supplying freshwater water, but geography, cost (operating/capital), and environmental impacts pose significant challenges for widespread adoption. Today, nearly all (~ 99%) desalination plants rely on fossil fuels as the primary energy source to produce heat or electricity to drive the desalination process. If this trend continues, carbon emissions from fossil fuel-powered desalination plants could increase to 400 million tons of CO2 per year by 2050. Additionally, the projected waste brine produced at current desalination plants by 2050 may approach 240 km3 per year. Solar desalination technologies (thermal and electric) could provide a sustainable path toward achieving high volume (million gallons per day) renewable driven desalted water while achieving minimal or zero liquid discharge (MLD and ZLD). This is of growing interest in an effort to minimize waste (carbon and brine). Yet, efficient integration between solar capture and desalination remains a critical challenge. Furthermore, the high-energy intensity required to reach saturation in MLD/ZLD processes is a critical obstacle. The aim of this PhD dissertation is to propose a framework that integrates thermodynamics, geographical information systems, and machine learning for developing and predicting sustainable strategies to integrate solar energy with desalination, in an effort to contribute to the development of a sustainable industry with reduced CO2 emissions and brine rejection. Using computational models for large-scale systems and data analysis, this research program aims to evaluate the current state of desalination worldwide and the potential of solar thermal hybrid desalination systems for producing freshwater with low brine rejection. The use of geographic information systems benefits the analysis process allowing to integrate geospatial data. The use of machine learning benefits the processing of high amounts of data and system optimization, decreasing the computational time and resource consumption.Ph.D

Geometrical Optimisation of Receivers for Concentrating Solar Thermal Systems

In concentrated solar thermal technologies, the receiver converts concentrated solar radiation into high-temperature heat. Solar receivers are commonly simulated with a stochastic integration method: Monte-Carlo ray-tracing. The optimisation of the geometry of receivers is challenging when using existing optimisation methods for two reasons: each receiver evaluation using Monte-Carlo ray-tracing requires significant computational effort and the outcome of a simulation involves uncertainty. A series of novel optimisation techniques are proposed to enable gradient-free, stochastic and multi-objective optimisation adapted to such problems. These techniques address the computational load difficulty and the challenge of conducting stochastic optimisation based on uncertain evaluations by introducing the concepts of “Progressive Monte-Carlo Evaluation (PMCE)”, “Intermediate Ray Emission Source (IRES)” and adaptive view-factor calculation. A new “Multi-Objective and Evolutionary PMCE Optimisation (MOEPMCE-O)” method is then built around PMCE to enable multi-objective geometrical optimisation of receivers. PMCE is shown to be able to reduce the computational time of a random search optimisation by more than 90% and is used in the geometrical design of a new receiver for the Australian National University SG4 dish concentrator that achieved 97.1% (±2.2%) of thermal efficiency during on-sun testing. MOE-PMCE-O is applied to a multi-objective tower receiver problem where liquid sodium is used as the receiver heat-carrier in a surround configuration heliostat field. A series of useful geometrical concepts emerge from the results, with geometrical features able to maintain high efficiency while keeping acceptable incident peak flux values with a moderate receiver total mass. Finally, a more fundamental look at the impact of the interaction of concentrating optics on the exergy of radiation available at the receiver location highlights the major role played by concentrator surface slope error in lowering the exergy in concentrated solar thermal systems and quantifies the exergy loss associated with non-ideal match between flux and surface temperature in receivers

Nuclear Propulsion Technical Interchange Meeting, volume 2

The purpose of the meeting was to review the work performed in fiscal year 1992 in the areas of nuclear thermal and nuclear electric propulsion technology development. These proceedings are an accumulation of the presentations provided at the meeting along with annotations provided by authors. The proceedings cover system concepts, technology development, and system modeling for nuclear thermal propulsion (NTP) and nuclear electric propulsion (NEP). The test facilities required for the development of the nuclear propulsion systems are also discussed

Multi-objective optimization of a metal hydride reactor coupled with phase change materials for fast hydrogen sorption time

Recently, the utilization of phase change materials (PCM) for the heat storage/recovery of the metal hydride's reaction heat has received increasing attention. However, the poor heat management process makes hydrogen sorption very slow during heat recycling. In this work, the H2 charging/discharging performance of a metal hydride tank (MHT) filled with LaNi5 and equipped with a paraffin-based (RT35) PCM finned jacket as a passive heat management medium is numerically investigated. Using a two-dimensional mathematical model validated with our in-house experiments, the effects of design parameters such as PCM thermophysical properties and the fin size on hydrogen charging/discharging times of the MHT are investigated systematically. The results showed that the PCM's melting point and apparent heat capacity have a conflicting impact on the hydrogen sorption times, i.e., the low melting point and high specific heat capacity reduce the H2 charging tim

Finite-Time Thermodynamics

The theory around the concept of finite time describes how processes of any nature can be optimized in situations when their rate is required to be non-negligible, i.e., they must come to completion in a finite time. What the theory makes explicit is “the cost of haste”. Intuitively, it is quite obvious that you drive your car differently if you want to reach your destination as quickly as possible as opposed to the case when you are running out of gas. Finite-time thermodynamics quantifies such opposing requirements and may provide the optimal control to achieve the best compromise. The theory was initially developed for heat engines (steam, Otto, Stirling, a.o.) and for refrigerators, but it has by now evolved into essentially all areas of dynamic systems from the most abstract ones to the most practical ones. The present collection shows some fascinating current examples

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems