922 resultados para energy efficiency, performance assessment, retrofit
Resumo:
Stirling engines with parabolic dish for thermal to electric conversion of solar energy is one of the most promising solutions of renewable energy technologies in order to reduce the dependency from fossil fuels in electricity generation. This paper addresses the modelling and simulation of a solar powered Stirling engine system with parabolic dish and electric generator aiming to determine its energy production and efficiency. The model includes the solar radiation concentration system, the heat transfer in the ther- mal receiver, the thermal cycle and the mechanical and electric energy conversion. The thermodynamic and energy transfer processes in the engine are modelled in detail, including all the main processes occur- ring in the compression, expansion and regenerator spaces. Starting from a particular configuration, an optimization of the concentration factor is also carried out and the results for both the transient and steady state regimes are presented. It was found that using a directly illuminated thermal receiver with- out cavity the engine efficiency is close to 23.8% corresponding to a global efficiency of 10.4%. The com- ponents to be optimized are identified in order to increase the global efficiency of the system and the trade-off between system complexity and efficiency is discussed.
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Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.
Resumo:
Nello sport di alto livello l’uso della tecnologia ha raggiunto un ruolo di notevole importanza per l’analisi e la valutazione della prestazione. Negli ultimi anni sono emerse nuove tecnologie e sono migliorate quelle pre-esistenti (i.e. accelerometri, giroscopi e software per l’analisi video) in termini di campionamento, acquisizione dati, dimensione dei sensori che ha permesso la loro “indossabilità” e l’inserimento degli stessi all’interno degli attrezzi sportivi. La tecnologia è sempre stata al servizio degli atleti come strumento di supporto per raggiungere l’apice dei risultati sportivi. Per questo motivo la valutazione funzionale dell’atleta associata all’uso di tecnologie si pone lo scopo di valutare i miglioramenti degli atleti misurando la condizione fisica e/o la competenza tecnica di una determinata disciplina sportiva. L’obiettivo di questa tesi è studiare l’utilizzo delle applicazioni tecnologiche e individuare nuovi metodi di valutazione della performance in alcuni sport acquatici. La prima parte (capitoli 1-5), si concentra sulla tecnologia prototipale chiamata E-kayak e le varie applicazioni nel kayak di velocità. In questi lavori è stata verificata l’attendibilità dei dati forniti dal sistema E-kayak con i sistemi presenti in letteratura. Inoltre, sono stati indagati nuovi parametri utili a comprendere il modello di prestazione del paddler. La seconda parte (capitolo 6), si riferisce all’analisi cinematica della spinta verticale del pallanuotista, attraverso l’utilizzo della video analisi 2D, per l’individuazione delle relazioni Forza-velocità e Potenza-velocità direttamente in acqua. Questo studio pilota, potrà fornire indicazioni utili al monitoraggio e condizionamento di forza e potenza da svolgere direttamente in acqua. Infine la terza parte (capitoli 7-8), si focalizza sull’individuazione della sequenza di Fibonacci (sequenza divina) nel nuoto a stile libero e a farfalla. I risultati di questi studi suggeriscono che il ritmo di nuotata tenuto durante le medie/lunghe distanze gioca un ruolo chiave. Inoltre, il livello di autosomiglianza (self-similarity) aumenta con la tecnica del nuoto.
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Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.
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Over the past years, ray tracing (RT) models popularity has been increasing. From the nineties, RT has been used for field prediction in environment such as indoor and urban environments. Nevertheless, with the advent of new technologies, the channel model has become decidedly more dynamic and to perform RT simulations at each discrete time instant become computationally expensive. In this thesis, a new dynamic ray tracing (DRT) approach is presented in which from a single ray tracing simulation at an initial time t0, through analytical formulas we are able to track the motion of the interaction points. The benefits that this approach bring are that Doppler frequencies and channel prediction can be derived at every time instant, without recurring to multiple RT runs and therefore shortening the computation time. DRT performance was studied on two case studies and the results shows the accuracy and the computational gain that derives from this approach. Another issue that has been addressed in this thesis is the licensed band exhaustion of some frequency bands. To deal with this problem, a novel unselfish spectrum leasing scheme in cognitive radio networks (CRNs) is proposed that offers an energy-efficient solution minimizing the environmental impact of the network. In addition, a network management architecture is introduced and resource allocation is proposed as a constrained sum energy efficiency maximization problem. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.
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Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.
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Energy transition is the response of humankind to the concerning effects of fossil fuels depletion, climate change and energy insecurity, and calls for a deep penetration of renewable energy sources (RESs) in power systems and industrial processes. Despite the high potentials, low impacts and long-term availability, RESs present some limits which need to be overcome, such as the strong variability and difficult predictability, which result in scarce reliability and difficult applicability in steady-state processes. Some technological solutions relate to energy storage systems, equipment electrification and hybrid systems deployment, thus accomplishing distributed generation even in remote sites as offshore. However, all of these actions cannot disregard sustainability, which represents a founding principle for any project, bringing together economics, reliability and environmental protection. To entail sustainability in RESs-based innovative projects, previous knowledge and tools are often not tailored or miss the novel objectives. This research proposes three methodological approaches, bridging the gaps. The first contribute adapts literature-based indicators of inherent safety and energy efficiency to capture the specificities of novel process plants and hybrid systems. Minor case studies dealing with novel P2X processes exemplify the application of these novel indicators. The second method guides the conceptual design of hybrid systems for the valorisation of a RES in a site, by considering the sustainability performances of alternative design options. Its application is demonstrated through the comparison of two offshore sites where wave energy can be valorised. Finally, “OHRES”, a comprehensive tool for the sustainable optimisation of hybrid renewable energy systems is proposed. “OHRES” hinges on the exploitation of multiple RESs, by converting ex-post sustainability indicators into discrimination markers screening a large number of possible system configurations, according to the location features. Five case studies demonstrate “OHRES” versatility in the sustainable valorisation of multiple RESs.
Resumo:
Continuum parallel robots (CPRs) are manipulators employing multiple flexible beams arranged in parallel and connected to a rigid end-effector. CPRs promise higher payload and accuracy than serial CRs while keeping great flexibility. As the risk of injury during accidental contacts between a human and a CPR should be reduced, CPRs may be used in large-scale collaborative tasks or assisted robotic surgery. There exist various CPR designs, but the prototype conception is rarely based on performance considerations, and the CPRs realization in mainly based on intuitions or rigid-link parallel manipulators architectures. This thesis focuses on the performance analysis of CPRs, and the tools needed for such evaluation, such as workspace computation algorithms. In particular, workspace computation strategies for CPRs are essential for the performance assessment, since the CPRs workspace may be used as a performance index or it can serve for optimal-design tools. Two new workspace computation algorithms are proposed in this manuscript, the former focusing on the workspace volume computation and the certification of its numerical results, while the latter aims at computing the workspace boundary only. Due to the elastic nature of CPRs, a key performance indicator for these robots is the stability of their equilibrium configurations. This thesis proposes the experimental validation of the equilibrium stability assessment on a real prototype, demonstrating limitations of some commonly used assumptions. Additionally, a performance index measuring the distance to instability is originally proposed in this manuscript. Differently from the majority of the existing approaches, the clear advantage of the proposed index is a sound physical meaning; accordingly, the index can be used for a more straightforward performance quantification, and to derive robot specifications.
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Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.
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Linear cascade testing serves a fundamental role in the research, development, and design of turbomachines as it is a simple yet very effective way to compute the performance of a generic blade geometry. These kinds of experiments are usually carried out in specialized wind tunnel facilities. This thesis deals with the numerical characterization and subsequent partial redesign of the S-1/C Continuous High Speed Wind Tunnel of the Von Karman Institute for Fluid Dynamics. The current facility is powered by a 13-stage axial compressor that is not powerful enough to balance the energy loss experienced when testing low turning airfoils. In order to address this issue a performance assessment of the wind tunnel was performed under several flow regimes via numerical simulations. After that, a redesign proposal aimed at reducing the pressure loss was investigated. This consists of a linear cascade of turning blades to be placed downstream of the test section and designed specifically for the type of linear cascade being tested. An automatic design procedure was created taking as input parameters those measured at the outlet of the cascade. The parametrization method employed Bézier curves to produce an airfoil geometry that could be imported into a CAD software so that a cascade could be designed. The proposal was simulated via CFD analysis and proved to be effective in reducing pressure losses up to 41%. The same tool developed in this thesis could be adopted to design similar apparatuses and could also be optimized and specialized for the design of turbomachines components.
Resumo:
The High Energy Rapid Modular Ensemble of Satellites (HERMES) is a new mission concept involving the development of a constellation of six CubeSats in low Earth orbit with new miniaturized instruments that host a hybrid Silicon Drift Detector/GAGG:Ce based system for X-ray and γ-ray detection, aiming to monitor high-energy cosmic transients, such as Gamma Ray Bursts and the electromagnetic counterparts of gravitational wave events. The HERMES constellation will also operate together with the Australian-Italian SpIRIT mission, which will house a HERMES-like detector. The HERMES pathfinder mini-constellation, consisting of six satellites plus SpIRIT, is likely to be launched in 2023. The HERMES detectors are based on the heritage of the Italian ReDSoX collaboration, with joint design and production by INFN-Trieste and Fondazione Bruno Kessler, and the involvement of several Italian research institutes and universities. An application-specific, low-noise, low-power integrated circuit (ASIC) called LYRA was conceived and designed for the HERMES readout electronics. My thesis project focuses on the ground calibrations of the first HERMES and SpIRIT flight detectors, with a performance assessment and characterization of the detectors. The first part of this work addresses measurements and experimental tests on laboratory prototypes of the HERMES detectors and their front-end electronics, while the second part is based on the design of the experimental setup for flight detector calibrations and related functional tests for data acquisition, as well as the development of the calibration software. In more detail, the calibration parameters (such as the gain of each detector channel) are determined using measurements with radioactive sources, performed at different operating temperatures between -20°C and +20°C by placing the detector in a suitable climate chamber. The final part of the thesis involves the analysis of the calibration data and a discussion of the results.
Resumo:
Este trabalho foi realizado com o objetivo de avaliar os efeitos do uso de leucena e levedura em dietas para bovinos sobre o metabolismo ruminal, incluindo o pH e as produções de ácido graxos voláteis (AGV), amônia e gás metano. Quatro bovinos machos com 800 kg e fistulados no rúmen foram mantidos em quadrado latino 4 × 4, em arranjo fatorial 2 × 2, composto de dois níveis de leucena (20 e 50% MS) e feno de capim coast-cross na presença ou ausência de levedura. Não houve influência das dietas nos valores médios de pH (média 6,82) e nas concentrações de amônia no rúmen, que variaram de 18 a 21 mg/100 mL. Houve interação entre níveis de leucena e levedura na concentração total de AGV. As dietas não diferiram quanto à concentração de ácido acético, mas os animais alimentados com a dieta com 50% de leucena e contendo levedura apresentaram maiores concentrações médias de ácido propiônico (média 19,14 mM). A emissão de metano reduziu em12,3% em relação à mesma dieta sem levedura e em 17,2% quando os animais foram alimentados com 20% de leucena com levedura. Verificou-se efeito associativo de leucena, quando fornecida em alto nível na dieta (50% MS), e levedura na redução da emissão de metano e na melhoria no padrão de fermentação no rúmen, o que pode reduzir as perdas de energia e melhorar eficiência energética do animal.
Resumo:
A recente crise financeira global traz consigo efeitos como a redução da atividade econômica e, consequentemente, do consumo de energia. Essa pode ser uma importante oportunidade para reorganizar o sistema energético em bases mais sólidas e sustentáveis: a eficiência, a maior participação das fontes renováveis e a descentralização da produção de energia. O Brasil e outros países em desenvolvimento podem aproveitar a experiência dos países desenvolvidos em eficiência energética, complementando com um programa vigoroso em energias renováveis, particularmente as "modernas" (eólica, solar, biomassa e pequenas hidrelétricas). Entretanto, preocupa o cenário inercial nacional, baseado num aumento da participação das fontes fósseis de energia na matriz, na priorização dos recursos à exploração de petróleo e gás natural e na manutenção de padrões insustentáveis de produção e consumo.
Resumo:
The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases. (C) 2011 Elsevier Ltd. All rights reserved.
Resumo:
Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.