4,904 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Challenge and Research Trends of Solar Concentrators

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    Primary and secondary solar concentrators are of vital importance for advanced solar energy and solar laser researches. Some of the most recent developments in primary and secondary solar concentrators were firstly presented. A novel three-dimensional elliptical-shaped Fresnel lens analytical model was put forward to maximize the solar concentration ratio of Fresnel-lens-based solar concentrators. By combining a Fresnel lens with a modified parabolic mirror, significant improvement in solar laser efficiency was numerically calculated. A fixed fiber light guide system using concave outlet concentrators was proposed. The absence of a solar tracking structure highlights this research. By shaping a luminescent solar concentrators in the form of an elliptic array, its emission losses was drastically reduced. Simple conical secondary concentrator was effective for thermal applications. New progresses in solar-pumped lasers by NOVA University of Lisbon were presented. By adopting a rectangular fused silica light guide, 40 W maximum solar laser power was emitted from a single Ce:Nd:YAG rod. An aspheric fused silica secondary concentrator and a small diameter Ce:Nd:YAG rod were essential for attaining 4.5 % record solar-to-laser power conversion efficiency. A novel solar concentrator design for the efficient production of doughnut-shaped and top-hat solar laser beams were also reported. More importantly, a novel solar concentrator approach for the emission of 5 kW-class TEM00 mode solar laser beams from one megawatt solar furnace was put forward at the end of this book, revealing promising future for solar-pumped lasers

    Exploration of using phase change material for thermal management of Electric Vehicle Battery

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    Battery thermal management (BTM) has been considered as one of the most important components in battery management system (BMS), as the thermal performance could heavily influence the safety and the performance of the battery and thus effect on the electric vehicle (EV) or hybrid electric vehicle (HEV). Some widely explored BTM systems includes forced air-cooling, direct/indirect fluid-cooling, and heat pipe (HP) systems. The forced air-cooling suffers from the low heat exchange efficiency between the air and the battery wall, while the direct/indirect fluid-cooling method takes the disadvantage of the extensive overall volume with the requirement of some other accessories such as the pump. Besides, HP systems have the limitation as it needs complex system layouts and thus high system weight. Phase change material (PCM) BTM system has attracted increasing interest as the latent heat could be normally greater than the sensible heat (cooling) those in traditional cooling systems. Moreover, PCM BTM system could work independely without supportive energy or extra accessories, which enables itselt to be employed flexibly in the EV and HEV. However, PCM cooling method faces the issue of leakage, which limits its application in the EV battery packs. The form-stable PCM (FSPCM) and micro-encapsulated PCM (MPCM) slurry then comes to the solution. The conventional PCM with the potential to leak in the battery pack could be embeded in the supoorting matrix to form the FSPCM, or micro-encapsulated in the shell to form the MPCM. Both methods provides surroundings to hold the phase transition process inside and prohibited liquid-phase PCM from flowing into the outter. As the research which employed FSPCM or MPCM slurry methodologies in BTM systems was very limited, in this PhD project, FSPCM and MPCM slurry were utilized in BTM systems to evaluate their perofmance in BTM. It was expected to improve the existed BTM technologies, and thus enhance the battery safety and performance for EV, HEV or even further related applications. The methodologies accessible for BTM used in EV or HEV were reviewed in Chapter. 2. The development histories and the features were introduced towards various measures, with the emphasis on PCM BTM, especially FSPCM and MPCM. The scientific gap was therefore pointed out. A numerical model of the FSPCM BTM was established in Chapter. 3, using MATLAB. The BTM performance was compared in four scenarios, and the optimal one was demonstrated to be the coupled FSPCM and air-cooling BTM system. With higher EG mass fraction, the increase rate of Tcor was observed to be reduced. When the EG mass fraction was 4.6 wt%, Tcor at tC was 317.7 K, 5.2 K lower than 322.9 K (without any EG additives). The thickness of FSPCM should be carefully selected according to the heat generated by the target battery pakage. Comprehensively considering the capital cost of the FSPCM BTM or the size of that system, 0.06 could be the competitive candidate among all. The experimental investigation of BTM system performance has been conducted in Chapter. 4. Either MPCM slurry or water worked as the coolant to accompanish the BTM system. Their performance were separately discussed towards different working conditions (battery pack charging rates, and C-rates). The optimal working condition for MPCM slurry integrated BTM system was when the charge rate was 2C, as the group using C-rate of 2C spend the longest time of MPCM slurry in the melting range. In thiscondition ∆Tcor was 26.87 °C Modelling work of BTM system has also been done in Chapter. 5 utilizing ANSYS. The experimented BTM system was modelled with the cooling fluid alternatively switched between MPCM slurry and water, while the cooling module basement made by aluminium or FSPCM discussed in Chapter. 3. High MPCM mass ratio has limited effect on BTM system performance using Al-basement, even though it can slightly enhance the battery cooling performance using FSPCM-basement, in the confined simulation conditions. FSPCM-basement was more promising for a lower battery temperature, compared to Al-basement. The heat transfer between the battery and the coolant was enhanced by the application of FSPCM-basement. The Tcor reduction by substituting aluminium with FSPCM is 0.94%, 0.25%, and 0.01%, respectively, with C-rate ranging from 10C to 3C. In a conclusion, the BTM integrated with FSPCM and PCM showed the potential to be a high-performance BTM for battery packages used in EV/HEV

    Deep learning-powered vision-based energy management system for next-gen built environment

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    Heating, ventilation and air-conditioning (HVAC) systems provide thermally comfortable spaces for occupants, and their consumption is strongly related to how occupants utilise the building. The over- or under-utilisation of spaces and the increased adoption of flexible working hours lead to unnecessary energy usage in buildings with HVAC systems operated using static or fixed schedules during unoccupied periods. Demand-driven methods can enable HVAC systems to adapt and make timely responses to dynamic changes in occupancy. Approaches central to the implementation of a demand-driven approach are accurate in providing real-time information on occupancy, including the count, localisation and activity levels. While conventional occupancy sensors exist and can provide information on the number and location of occupants, their ability to detect and recognise occupancy activities is limited. This includes the operation of windows and appliances, which can impact the building’s performance. Artificial intelligence (AI) has recently become a critical tool in enhancing the energy performance of buildings and occupant satisfaction and health. Recent studies have shown the capabilities of AI methods, such as computer vision and deep learning in detecting and recognising human activities. The recent emergence of deep learning algorithms has propelled computer vision applications and performance. While several studies used deep learning and computer vision to recognise human motion or activity, there is limited work on integrating these methods with building energy systems. Such methods can be used to obtain accurate and real-time information about the occupants for assisting in the operation of HVAC systems. In this research, a demand-driven deep learning framework was proposed to detect and recognise occupancy behaviour for optimising the operation of building HVAC systems. The computer vision-based deep learning algorithm, convolutional neural network (CNN), was selected to develop the vision-based detector to recognise common occupancy activities such as sitting, standing, walking and opening and closing windows. A dataset consisting of images of occupants in buildings performing different activities was formed to perform the training the model. The trained model was deployed to an AI-powered camera to perform real-time detection within selected case study building spaces, which include university tutorial rooms and offices. Two main types of detectors were developed to show the capabilities of the proposed approach; this includes the occupancy activity detector and the window opening detector. Both detectors were based on the Faster R-CNN with Inception V2 model, which was trained and tested using the same approach. In addition, the influence of different parameters on the performance, such as the training data size, labelling method, and how real-time detection was conducted in different indoor spaces was evaluated. The results have shown that a single response 'people detector’ can accurately understand the number of people within a detected space. The ‘occupancy activity detector’ could provide data towards the prediction of the internal heat emissions of buildings. Furthermore, window detectors were formed to recognise the times when windows are opened, providing insights into the potential ventilation heat losses through this type of ventilation strategy employed in buildings. The information generated by the detector is then outputted as profiles, which are called Deep Learning Influence Profiles (DLIP). Building energy simulation (BES) was used to assess the potential impact of the use of detection and recognition methods on building performance, such as ventilation heat loss and energy demands. The generated DLIPs were inputted into the BES tool. Comparisons with static or scheduled occupancy profiles, currently used in conventional HVAC systems and building energy modelling were made. The results showed that the over- or under-estimation of the occupancy heat gains could lead to inaccurate heating and cooling energy predictions. The deep learning detection method showed that the occupancy heat gains could be represented more accurately compared to static office occupancy profiles. A difference of up to 55% was observed between occupancy DLIP and static heat gain profile. Similarly, the window detection method enabled accurate recognition of the opening and closing of windows and the prediction of ventilation heat losses. BES was conducted for various scenario-based cases that represented typical and/or extreme situations that would occur within selected case study buildings. Results showed that the detection methods could be useful for modulating heating and cooling systems to minimise building energy losses while providing adequate indoor air quality and thermal conditions. Based on the developed individual detectors, combined detectors were formed and also assessed during experimental tests and analysis using BES. The vision-based technique’s integration with the building control system was discussed. A heat gain prediction and optimisation strategy were proposed along with a hybrid controller that optimises energy use and thermal comfort. This should be further developed in future works and assessed in real building installations. This work also discussed the limitations and practical challenges of implementing the proposed technology. Initial results of survey-based questionnaires highlighted the importance of informing occupants about the framework approach and how DLIPs were formed. In all, preference is towards a less intrusive and effective approach that could meet the needs of optimising building energy loads for the next-gen built environment

    Fluidized bed plants for heat and power production in future energy systems

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    Fluidized bed (FB) plants are used for heat and power production in several energy systems around the world, with particular importance in systems using large shares of renewable solid fuel, e.g., biomass. These FB plants are traditionally operated for base-load electricity production or for heat production, and thus characterized by relatively small and slow load changes. In parallel, as the transition towards energy systems with net-zero emissions increases the share of variable renewable energy (VRE) sources, the need for implementing variation management strategies at various timescales arises – giving heat and power plants the possibility to adapt their operations to accommodate the inherent variability of VRE sources. Following this, FB technology is envisioned for a wide range of novel applications expected to play significant roles in the decarbonization of energy systems, such as thermochemical energy storage and carbon capture and storage. In this context, research efforts are needed to investigate the technical and economic features of FB plants in energy systems with high levels of VRE.The aim of this thesis is to elucidate the capabilities of FB plants for heat and power production in net-zero emissions energy systems. For this purpose, two main pathways are explored: i) transient operation as fuel-fed plants, and ii) the potential conversion into decarbonized plants, i.e., into VRE-fed layouts providing dispatchable outputs.For fuel-fed FB plants, a dynamic model of biomass-fired FB plants has been developed, considering the two types of FB boilers (BFB and CFB) and including validation against steady-state and transient operational data collected from two commercial plants. As a novelty of this work the model describes both the gas (in-furnace) and water-steam sides such that the interactions between the two can be assessed. The results of the simulations show that i) the characteristic times for the gas side are shorter in BFB furnaces than in CFBs, albeit these times are for both furnace types not longer than those for the water-steam side; ii) the computed timescales for the dynamics of FB plants fall well within those required for offering complementing services to the grid; and iii) the use of control and operational strategies for the water-steam side can confer capabilities superior to fuel-feeding control in terms of avoiding undesirable unburnt emissions and providing temporary overload operation. The retrofit of fuel-fed FB plants into poly-generation facilities cogenerating a combustible biogenic gas is also assessed, revealing that partial combustion of this gas can be used to provide faster inherent dynamics than the original configuration.For VRE-fed FB layouts, techno-economic process modeling has been carried out for large-scale deployment of solar- and electricity-charging processes based on three different chemical systems: i) carbonation/calcination (calcium); ii) thermally reduced redox (cobalt oxides); and iii) chemically reduced redox (iron oxides). One attractive aspect of these layouts is the possibility to build part of them by retrofitting current fuel-fed FB plants. While the technical assessment for solar applications indicates that cobalt-based layouts offer the highest levels of efficiency and dispatchability, calcium-based processes present better economics owing to the use of inexpensive calcium material. The results also show that electricity-charged layouts such as iron looping can play an important role in the system providing variation management strategies to the grid while avoiding costly H2 storage. Further, the economic performances of VRE-fed FB layouts are benefitted by the generation of additional services and products (e.g., carbon capture and on-demand production of H2), and by scenarios with high volatility of the electricity prices

    Futures of shipbuilding in the 22nd century : Explorative industry foresight research of the long-range futures for commercial ship-building, using elements of OpenAI.

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    The shipbuilding industry has historically shaped global trade, logistics, research, and cultural globalization. It was instrumental in exploring and colonizing new continents, thereby significantly shaping our society. Today, it's essential to consider the industry's current transformations and speculate on what shipbuilding might look like in the 22nd century. This study is dedicated to exploring the possible futures of shipbuilding over a long-range time horizon of 70 -100 years. This thesis applied futures research methods to data collected using OpenAI tools and explored possible transformative pathways within the industry. The research offers potential future scenarios and delineates change pathways from external pressures and internal shifts within the shipbuilding system. Additionally, the study highlights the possible applications and implications of utilizing OpenAI technology in a research context. The analysis of shipbuilding incorporates the Multi-Level Perspective (MLP) concept, viewing the industry as a system involving ten groups of key actors. This structure guided the data collection process for the input of the research. The primary research process adheres to traditional futures research methods, which include horizon scanning, systems thinking, scenario building, and causal layered analysis (CLA). Furthermore, the methodology was expanded to incorporate AI-assisted techniques. This includes using AI technology for automated data collection and a separate pathway using ChatGPT-4 for computer-generated scenarios and CLA narratives development. The outcomes from both methodologies are compared, and additional literature research about the applicability and implications of using AI in futures studies. The research has identified critical external drivers of change, originating from fields such as technology, energy, and social development, as well as internal drivers, including biotechnology and diversifying floating structures. The external drivers could influence the future direction of shipbuilding, while the internal factors represent potential changes originating from within the industry. The constructed scenarios are designed to stimulate discussion and provide context for future developmental trajectories of shipbuilding

    Refractory High-entropy Alloys for Advanced Nuclear Applications

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    High-entropy alloys (HEAs), and in particular, refractory HEAs (RHEAs), can offer remarkable thermomechanical properties, corrosion tolerance, and irradiation tolerance, making them candidates for structural materials in extreme environments such as advanced nuclear applications. Despite their apparent potential, the current understanding of the irradiation tolerance of RHEAs and their suitability for such nuclear applications is limited. This dissertation provides an in-depth study into the irradiation response of these alloys, and the mechanisms behind their radiation resistance, and further explores the potential of RHEAs in nuclear applications through material design and optimisation. The first part of this research focusses on the TiZrNbHfTa RHEA – primarily in its nanocrystalline (NC) state – irradiated under conditions representative of advanced nuclear applications. The alloy demonstrated excellent microstructural stability and retained essential mechanical properties post-irradiation, with significantly less hardening compared to traditional alloys irradiated under like conditions. The research then explores the unique mechanisms intrinsic to HEAs, such as high configurational entropy, severe lattice distortion, and sluggish diffusion, clarifying how such mechanisms may improve the irradiation tolerance of RHEAs. However, the research also highlights the sensitivity of RHEAs to phase constitution, with the introduction of additional gregation affecting the alloys’ response to irradiation. Furthermore, the competitive viability of HEAs against existing nuclear structural materials is explored, focussing on potential applications where HEAs could provide superior performance. To foster the development of such alloys, nuclear-relevant property calculations and a framework for alloy design is proposed. A novel RHEA, Ti55Zr30Ta6V5Cr2Fe2 (Ti55), was synthesised and tested, designed with an aim to minimise neutron capture cross section, transmutation losses, and gamma activity. The novel alloy exhibited promising mechanical properties which provide motivation for further material development. This doctoral dissertation significantly contributes to the field of RHEAs for advanced nuclear applications. It offers insights into their behaviours under irradiation and provides guidelines for their design and optimisation. With continued exploration and refinement, the vast potential of RHEAs can be harnessed, potentially addressing materials challenges in the field of nuclear materials

    Graduate Catalog of Studies, 2022-2023

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    Thermodynamic Assessment and Optimisation of Supercritical and Transcritical Power Cycles Operating on CO2 Mixtures by Means of Artificial Neural Networks

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    Feb 21, 2022 to Feb 24, 2022, San Antonio, TX, United StatesClosed supercritical and transcritical power cycles operating on Carbon Dioxide have proven to be a promising technology for power generation and, as such, they are being researched by numerous international projects today. Despite the advantageous features of these cycles enabling very high efficiencies in intermediate temperature applications, the major shortcoming of the technology is a strong dependence on ambient temperature; in order to perform compression near the CO2 critical point (31ºC), low ambient temperatures are needed. This is particularly challenging in Concentrated Solar Power applications, typically found in hot, semi-arid locations. To overcome this limitation, the SCARABEUS project explores the idea of blending raw carbon dioxide with small amounts of certain dopants in order to shift the critical temperature of the resulting working fluid to higher values, hence enabling gaseous compression near the critical point or even liquid compression regardless of a high ambient temperature. Different dopants have been studied within the project so far (i.e. C6F6, TiCl4 and SO2) but the final selection will have to account for trade-offs between thermodynamic performance, economic metrics and system reliability. Bearing all this in mind, the present paper deals with the development of a non-physics-based model using Artificial Neural Networks (ANN), developed using Matlab’s Deep Learning Toolbox, to enable SCARABEUS system optimisation without running the detailed – and extremely time consuming – thermal models, developed with Thermoflex and Matlab software. In the first part of the paper, the candidate dopants and cycle layouts are presented and discussed, and a thorough description of the ANN training methodology is provided, along with all the main assumptions and hypothesis made. In the second part of the manuscript, results confirms that the ANN is a reliable tool capable of successfully reproducing the detailed Thermoflex model, estimating the cycle thermal efficiency with a Root Mean Square Error lower than 0.2 percentage points. Furthermore, the great advantage of using the Artificial Neural Network proposed is demonstrated by the huge reduction in the computational time needed, up to 99% lower than the one consumed by the detailed model. Finally, the high flexibility and versatility of the ANN is shown, applying this tool in different scenarios and estimating different cycle thermal efficiency for a great variety of boundary conditions.Unión Europea H2020-81498
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