Design and optimisation of the limaçon rotary compressor

The limaçon positive displacement machine is characterised by its internal geometry and unique mechanical motion; both based on a mathematical curve known as the limaçon of Pascal. The limaçon technology offers many advantages, such as compact size and double‐acting functionality, and its great potential for fluid processing applications has been proven by a number of patents and innovative designs in engines, expanders, and pumps. However, no commercial application of the limaçon technology in the field of positive displacement compressors has been reported in the literature. This could be attributed to the fact that the potential of the limaçon technology for gas compression has not been established as yet. The process of establishing potential is necessary before funds and resources are dedicated to investing in prototyping and testing. This process entails a considerable amount of modelling, coding and analysis as one must ensure the embodiment is geometrically capable of delivering suction and compression strokes, ports can be arranged to support the workings of these strokes, a number of measurable parameters can be identified as impacting compressor performance and it is possible to calculate a set of parameters which optimise this performance. To achieve this objective, a comprehensive mathematical model of a limaçon machine, implemented as a compressor,was first developed. The model, which is multi‐physical in nature, spans such domains as kinematics, fluid dynamics, characteristics of the port flow, internal leakage due to seal vibration, dynamics of the discharge valve, and thermodynamics. Subsequently, the simulation of the model has been performed to numerically study the operational characteristics of the limaçon compressor and to investigate the effect of various parameters on the compressor performance. It was found that the increase in the operating speed and pressure ratio would lead to negative effects on machine performance, especially on volumetric efficiency. Additionally, the results of simulations indicated that the level of fluid over‐compression is influenced by the characteristics of the discharge valve. To ensure the suitability of limaçon technology for use in positive displacement compressors, a study was undertaken to determine whether such an embodiment lent itself to optimisation efforts. For this purpose, the thorough mathematical model which has been developed to simulate compressor workings was then used for optimisation purposes whereby a Bayesian optimisation procedure was applied. The optimisation procedure was conducted in a two‐stage fashion where the first stage optimises the machine dimensions to meet volumetric requirements specified by the designer; and the second stage focuses on revealing the optimum combination of port geometries that improves machine performance. A numerical illustration was presented to prove the validity of the presented approach, and the results show that considerable improvements in the isentropic and volumetric efficiencies can be attained. Moreover, the optimised design was tested under different operating speeds and pressure ratios to investigate its robustness. It was found that the optimised design can exhibit relatively stable performance when the working conditions vary within a small bandwidth around that used in the optimisation procedure. The limaçon technology has three embodiments, namely the limaçon‐to‐limaçon (L2L), the limaçon‐to‐circular, and the circolimaçon. The circolimaçon embodiment features using circular arcs, rather than limaçon curves, to develop profiles for the rotor and housing. This embodiment simplifies the manufacturing process and reduces the production cost associated with producing a limaçon technology. A feasibility study of the circolimaçon embodiment was conducted by comparing its performance with that of the L2L type device. The machine dimensions and port geometries obtained from the optimisation procedure were used in the comparative study. A nonlinear three‐degree of freedom model was presented to describe the dynamic behaviour of the apex seal during the machine operation. Additionally, the leakage through the seal‐housing gap was formulated by considering the inertia and viscous effects of the flow. The results from the case study suggest that the circolimaçon embodiment exhibits comparable performance to the L2L‐type machine, despite having more significant seal vibrations. Moreover, it was also discovered that the circolimaçon compressor with a small capacity undergoes a lower level of seal dynamics, indicating better machine reliability.Doctor of Philosoph

An integrated dimensionality reduction and surrogate optimization approach for plant‐wide chemical process operation

From Wiley via Jisc Publications RouterHistory: received 2020-12-14, rev-recd 2021-06-01, accepted 2021-06-15, pub-electronic 2021-07-02Article version: VoRPublication status: PublishedAbstract: With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data‐driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large‐scale industrial chemical systems

Marco de trabajo termodinámico integrado para la absorción de refrigerantes fluorados en líquidos iónicos

El sector de la refrigeración y aire acondicionado tiene un elevado impacto ambiental ocasionado por las emisiones indirectas asociadas al consumo de energía de los equipos de refrigeración, así como a las emisiones directas de los gases refrigerantes de efecto invernadero. Esta tesis está consagrada al desarrollo de un marco de trabajo que combina estudios experimentales, modelado matemático y herramientas computacionales novedosas destinado a la selección de líquidos iónicos que proporcionen las características termodinámicas más adecuadas para su uso como disolventes de hidrofluorocarbonos e hidrofluoroolefinas en dos tipos de aplicaciones: i) sistemas de refrigeración por absorción con eficiencia energética mejorada, y ii) destilaciones extractivas para separar mezclas de refrigerantes obtenidas a partir de dispositivos al final de su vida útil, y recuperar los gases con bajo potencial de calentamiento atmosférico para su reutilización. La presente tesis doctoral contribuye a la evolución del sector de la refrigeración hacia la economía circular y propone las herramientas necesarias para el desarrollo de procesos que faciliten esta transición y la mitigación de los efectos del cambio climático.The refrigeration and air conditioning sector has an elevated environmental impact resulting from the indirect emissions derived of its energy consumption, and from the direct emissions of GWP hydrofluorocarbons from equipment at its end of life. This thesis develops an integrated framework that combines experimental studies, mathematical modeling, and novel computational tools for the selection of ionic liquids with the adequate thermodynamic properties for their use as solvents of hydrofluorocarbons and hydrofluoroolefins in two applications: i) absorption refrigeration systems that increase the efficiency of the refrigeration devices, and ii) extractive distillations aimed to separate azeotropic and close-boiling-point mixtures of fluorinated gases, with the goal of recovering low-GWP refrigerants from end-of-life equipment. This doctoral thesis contributes to the evolution of the refrigeration sector towards the circular economy and proposes the necessary tools for the development of processes that facilitate this transition, as well as the mitigation of the effects of climate change.Además, la investigación de esta tesis ha sido parcialmente financiada por el Fondo Europeo de Desarrollo Regional en el marco del programa Interreg-Sudoe a través del proyecto KET4F-Gas-SOE2/P1/P0823 “KET4F-GAS: Reducción del impacto ambiental de los gases fluorados en el espacio SUDOE mediante tecnologías facilitadoras esenciales” y por el Ministerio de Ciencia e Innovación a través de la Agencia Estatal de Investigación (MCIN/AEI/10.1039/501100011033) en el marco del proyecto PID2019-105827RB-I00 “Funcionalización de membranas como elemento clave en el desarrollo de procesos avanzados de separación”, correspondiente a la convocatoria de 2019 «Proyectos de I+D+i», en el marco del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i Orientada a los Retos de la Sociedad, del Pla Estatal de Investigación Científica y Técnica y de Innovación 2017-2020

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

Abstract: With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data‐driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large‐scale industrial chemical systems

Quantitative analysis of positive-displacement compressor models tested in extrapolation scenarios

Testing and evaluation of select semi-empirical compressor models is carried out to quantify performance in modulation (variable speed), extrapolation, and additionally, variable superheat scenarios. Three representative literature models and an artificial neural network (ANN) model are benchmarked against the industry standard AHRI model. A methodology quantifying model performance, compared against experimental data, in said scenarios is presented. Data used is of high-fidelity taken from either a hot-gas bypass load stand or compressor calorimeter. Scroll, screw, reciprocating, and spool compressor technologies were collected with R410A, R1234ze(E), R134a, and R32 refrigerants totaling 434 experimental points. Data is divided into training, extrapolation, variable speed, and variable superheat data splits to examine model performance. Mean Absolute Percentage Error (MAPE) is computed for mass flow rate and power after training models with training data and evaluating them against the other data splits. Two literature models are true semi-empirical formulations while the other, the ANN, and AHRI model are more empirical in nature. Neither semi-empirical model predicted all compressors. When the compressor type is predicted, the semi-empirical models yield MAPE’s less than 8%, 5%, and 4% for mass flow rate and power prediction in extrapolation, modulation, and variable superheat scenarios, respectively. The exception is the Popovic and Shapiro model performing at 21% MAPE in variable superheat power prediction for the spool compressor with R1234ze(E). The ANN showed highest errors of 9.3%, 12%, and 17% in extrapolation, modulation, and variable superheat scenarios, respectively. All models outperformed the AHRI model by several orders of magnitude in these scenarios

NEXT GENERATION HEAT PUMP SYSTEM EVALUATION METHODOLOGIES

Energy consumption of heat pump (HP) systems plays a significant role in the global residential building energy sector. The conventional HP system evaluation method focused on the energy efficiency during a given time scale (e.g., hourly, seasonally, or annually). Nevertheless, these evaluation methods or test metrics are unable to fully reflect the thermodynamic characteristics of the system (e.g., the start-up process). In addition, previous researchers typically conducted HP field tests no longer than one year period. Only limited studies conducted the system performance tests over multiple years. Furthermore, the climate is changing faster than previously predicted beyond the irreversible and catastrophic tipping point. HP systems are the main contributor to global warming due to the increased demands but also can be a part of the solution by replacing fossil fuel burning heating systems. A holistic evaluation of the HP system’s global warming impact during its life cycle needs to account for the direct greenhouse gas (GHG) emissions from the refrigerant leakage, indirect GHG emissions from the power consumption and embodied equipment emissions. This dissertation leverages machine learning, deep learning, data digging, and Life Cycle Climate Performance (LCCP) approaches to develop next generation HP system evaluation methodologies with three thrusts: 1) field test data analysis, 2) data-driven modeling, and 3) enhanced life cycle climate performance (En-LCCP) analysis. This study made following observations: First, time-average performance metrics can save time in extensive data calculation, while quasi-steady-state performance metrics can elucidate some details of the studied system. Second, deep-learning-based algorithms have higher accuracy than conventional modeling approaches and can be used to analyze the system's dynamic performance. However, the complicated structure of the networks, numerous parameters needing optimization, and longer training time are the main challenges for these methods. Third, this dissertation improved current environmental impact evaluation method considering ambient conditions variation, local grid source structure, and next-generation low-GWP refrigerants, which led the LCCP results closer to reality and provided alternative methods for determining LCCP input parameters with limited-data cases. Future work could be studying the uncertainty within the deep learning networks and finding a general process for modeling settings. People may also develop a multi-objective optimization model for HP system design while considering both the LCCP and cost

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included