9,162 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

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    Data Tiling for Sparse Computation

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    Many real-world data contain internal relationships. Efficient analysis of these relationship data is crucial for important problems including genome alignment, network vulnerability analysis, ranking web pages, among others. Such relationship data is frequently sparse and analysis on it is called sparse computation. We demonstrate that the important technique of data tiling is more powerful than previously known by broadening its application space. We focus on three important sparse computation areas: graph analysis, linear algebra, and bioinformatics. We demonstrate data tiling's power by addressing key issues and providing significant improvements---to both runtime and solution quality---in each area. For graph analysis, we focus on fast data tiling techniques that can produce well-structured tiles and demonstrate theoretical hardness results. These tiles are suitable for graph problems as they reduce data movement and ultimately improve end-to-end runtime performance. For linear algebra, we introduce a new cache-aware tiling technique and apply it to the key kernel of sparse matrix by sparse matrix multiplication. This technique tiles the second input matrix and then uses a small, summary matrix to guide access to the tiles during computation. Our approach results in the fastest known implementation across three distinct CPU architectures. In bioinformatics, we develop a tiling based de novo genome assembly pipeline. We start with reads and develop either a graph or hypergraph that captures internal relationships between reads. This is then tiled to minimize connections while maintaining balance. We then treat each resulting tile independently as the input to an existing, shared-memory assembler. Our pipeline improves existing state-of-the-art de novo genome assemblers and brings both runtime and quality improvements to them on both real-world and simulated datasets.Ph.D

    Reliability and Security of Compute-In-Memory Based Deep Neural Network Accelerators

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    Compute-In-Memory (CIM) is a promising solution for accelerating DNNs at edge devices, utilizing mixed-signal computations. However, it requires more cross-layer designs from algorithm levels to hardware implementations as it behaves differently from the pure digital system. On one side, the mixed-signal computations of CIM face unignorable variations, which could hamper the software performance. On the other side, there are potential software/hardware security vulnerabilities with CIM accelerators. This research aims to solve the reliability and security issues in CIM design for accelerating Deep Neural Network (DNN) algorithms as they prevent the real-life use of the CIM-based accelerators. Some non-ideal effects in CIM accelerators are explored, which could cause reliability issues, and solved by the software-hardware co-design methods. In addition, different security vulnerabilities for SRAM-based CIM and eNVM-based CIM inference engines are defined, and corresponding countermeasures are proposed.Ph.D

    Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

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    In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    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

    Mars delivery service - development of the electro-mechanical systems of the Sample Fetch Rover for the Mars Sample Return Campaign

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    This thesis describes the development of the Sample Fetch Rover (SFR), studied for Mars Sample Return (MSR), an international campaign carried out in cooperation between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The focus of this document is the design of the electro-mechanical systems of the rover. After placing this work into the general context of robotic planetary exploration and summarising the state of the art for what concerns Mars rovers, the architecture of the Mars Sample Return Campaign is presented. A complete overview of the current SFR architecture is provided, touching upon all the main subsystems of the spacecraft. For each area, it is discussed what are the design drivers, the chosen solutions and whether they use heritage technology (in particular from the ExoMars Rover) or new developments. This research focuses on two topics of particular interest, due to their relevance for the mission and the novelty of their design: locomotion and sample acquisition, which are discussed in depth. The early SFR locomotion concepts are summarised, covering the initial trade-offs and discarded designs for higher traverse performance. Once a consolidated architecture was reached, the locomotion subsystem was developed further, defining the details of the suspension, actuators, deployment mechanisms and wheels. This technology is presented here in detail, including some key analysis and test results that support the design and demonstrate how it responds to the mission requirements. Another major electro-mechanical system developed as part of this work is the one dedicated to sample tube acquisition. The concept of operations of this machinery was defined to be robust against the unknown conditions that characterise the mission. The design process led to a highly automated robotic system which is described here in its main components: vision system, robotic arm and tube storage
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