Refrigeration System: Capacity Modulation Methods

Energy conservation and reduction of the global warming effect become one of the most important subjects in the world. Since refrigeration system energy consumption is steadily increasing in overall energy consumption, this system is under research. Refrigeration systems are full of energy conservation that is having minimum energy consumption while satisfying the user’s needs. Refrigeration system applications where the load may vary over a wide range, due to lighting, product loading, ambient weather variations, or other factors during operation, can be optimized by capacity modulation. There are many ways to achieve capacity modulation. This paper presents literature review of various capacity modulation methods which reduce the energy consumption of the refrigeration system and decrease CO2 emission indirectly. In this paper, on/off control, digital scroll compressor, cylinder unloading, hot gas bypass, slide valve, multiple compressor, and variable speed capacity control methods are presented. In addition, electrical control techniques for the refrigeration capacity modulation applications are summarized

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

Propulsion Control Technology Development Needs to Address NASA Aeronautics Research Mission Goals for Thrusts 3a and 4

The Commercial Aero-Propulsion Control Working Group (CAPCWG), consisting of propulsion control technology leads from The Boeing Company, GE Aviation, Honeywell, Pratt & Whitney, Rolls-Royce, and NASA (National Aeronautics and Space Administration) Glenn Research Center, has been working together over the past year to identify propulsion control technology areas of common interest that we believe are critical to achieving the challenging NASA Aeronautics Research goals for Thrust 3a: Ultra-Efficient Commercial Vehicles - Subsonic Transports, and Thrust 4: Transition to Alternative Propulsion and Energy. This paper describes the various propulsion control technology development areas identified by CAPCWG as most critical for NASA to invest in. For Thrust 3a these are: i) Integrated On-Board Model Based Engine Control and Health Management; ii) Flexible and Modular Networked Control Hardware and Software Architecture; iii) Intelligent Air/Fuel Control for Low Emissions Combustion; and iv) Active Clearance Control. For Thrust 4a, the focus is on Hybrid Electric Propulsion (HEP) for single aisle commercial aircraft. The specific technology development areas include: i) Integrated Power and Propulsion System Dynamic Modeling for Control; ii) Control Architectures for HEP; iii) HEP Control Verification and Validation; and iv) Engine/Airplane Control Integration. For each of the technology areas, the discussion includes: problem to be solved and how it relates to NASA goals, and the challenges to be addressed in reducing risk

Automotive firmware extraction and analysis techniques

An intricate network of embedded devices, called Electronic Control Units (ECUs), is responsible for the functionality of a modern vehicle. Every module processes a myriad of information and forwards it on to other nodes on the network, typically an automotive bus such as the Controller Area Network (CAN). Analysing embedded device software, and automotive in particular, brings many challenges. The analyst must, especially in the notoriously secretive automotive industry, first lift the ECU firmware from the hardware, which typically prevents unauthorised access. In this thesis, we address this problem in two ways: - We detail and bypass the access control mechanism used in diagnostic protocols in ECU firmware. Using existing diagnostic functionality, we present a generic technique to download code to RAM and execute it, without requiring physical access to the ECU. We propose a generic firmware readout framework on top of this, which only requires access to the CAN bus. - We analyse various embedded bootloaders and combine dynamic analysis with low-level hardware fault attacks, resulting in several fault-injection attacks which bypass on-chip readout protection. We then apply these firmware extraction techniques to acquire immobiliser firmware by two different manufacturers, from which we reverse engineer the DST80 cipher and present it in full detail here. Furthermore, we point out flaws in the key generation procedure, also recovered from the ECU firmware, leading to a full key recovery based on publicly readable transponder pages

A novel framework for enhancing marine dual fuel engines environmental and safety performance via digital twins

The Internet of Things (IoT) advent and digitalisation has enabled the effective application of the digital twins (DT) in various industries, including shipping, with expected benefits on the systems safety, efficiency and environmental footprint. The present research study establishes a novel framework that aims to optimise the marine DF engines performance-emissions trade-offs and enhance their safety, whilst delineating the involved interactions and their effect on the performance and safety. The framework employs a DT, which integrates a thermodynamic engine model along with control function and safety systems modelling. The DT was developed in GT-ISE© environment. Both the gas and diesel operating modes are investigated under steady state and transient conditions. The engine layout is modified to include Exhaust Gas Recirculation (EGR) and Air Bypass (ABP) systems for ensuring compliance with ‘Tier III’ emissions requirements. The optimal DF engine settings as well as the EGR/ABP systems settings for optimal engine efficiency and reduced emissions are identified in both gas and diesel modes, by employing a combination of optimisation techniques including multi-objective genetic algorithms (MOGA) and Design of Experiments (DoE) parametric runs. This study addresses safety by developing an intelligent engine monitoring and advanced faults/failure diagnostics systems, which evaluates the sensors measurements uncertainty. A Failure Mode Effects and Analysis (FMEA) is employed to identify the engine safety critical components, which are used to specify operating scenarios for detailed investigation with the developed DT. The integrated DT is further expanded, by establishing a Faulty Operation Simulator (FOS) to simulate the FMEA scenarios and assess the engine safety implications. Furthermore, an Engine Diagnostics System (EDS) is developed, which offers intelligent engine monitoring, advanced diagnostics and profound corrective actions. This is accomplished by developing and employing a Data-Driven (DD) model based on Neural Networks (NN), along with logic controls, all incorporated in the EDS. Lastly, the manufacturer’s and proposed engine control systems are combined to form an innovative Unified Digital System (UDS), which is also included in the DT. The analysis of marine (DF) engines with the use of an innovative DT, as presented herein, is paving the way towards smart shipping.The Internet of Things (IoT) advent and digitalisation has enabled the effective application of the digital twins (DT) in various industries, including shipping, with expected benefits on the systems safety, efficiency and environmental footprint. The present research study establishes a novel framework that aims to optimise the marine DF engines performance-emissions trade-offs and enhance their safety, whilst delineating the involved interactions and their effect on the performance and safety. The framework employs a DT, which integrates a thermodynamic engine model along with control function and safety systems modelling. The DT was developed in GT-ISE© environment. Both the gas and diesel operating modes are investigated under steady state and transient conditions. The engine layout is modified to include Exhaust Gas Recirculation (EGR) and Air Bypass (ABP) systems for ensuring compliance with ‘Tier III’ emissions requirements. The optimal DF engine settings as well as the EGR/ABP systems settings for optimal engine efficiency and reduced emissions are identified in both gas and diesel modes, by employing a combination of optimisation techniques including multi-objective genetic algorithms (MOGA) and Design of Experiments (DoE) parametric runs. This study addresses safety by developing an intelligent engine monitoring and advanced faults/failure diagnostics systems, which evaluates the sensors measurements uncertainty. A Failure Mode Effects and Analysis (FMEA) is employed to identify the engine safety critical components, which are used to specify operating scenarios for detailed investigation with the developed DT. The integrated DT is further expanded, by establishing a Faulty Operation Simulator (FOS) to simulate the FMEA scenarios and assess the engine safety implications. Furthermore, an Engine Diagnostics System (EDS) is developed, which offers intelligent engine monitoring, advanced diagnostics and profound corrective actions. This is accomplished by developing and employing a Data-Driven (DD) model based on Neural Networks (NN), along with logic controls, all incorporated in the EDS. Lastly, the manufacturer’s and proposed engine control systems are combined to form an innovative Unified Digital System (UDS), which is also included in the DT. The analysis of marine (DF) engines with the use of an innovative DT, as presented herein, is paving the way towards smart shipping

Real-time fault identification for developmental turbine engine testing

Hundreds of individual sensors produce an enormous amount of data during developmental turbine engine testing. The challenge is to ensure the validity of the data and to identify data and engine anomalies in a timely manner. An automated data validation, engine condition monitoring, and fault identification process that emulates typical engineering techniques has been developed for developmental engine testing.An automated data validation and fault identification approach employing enginecycle-matching principles is described. Engine cycle-matching is automated by using an adaptive nonlinear component-level computer model capable of simulating both steady state and transient engine operation. Automated steady-state, transient, and real-time model calibration processes are also described. The model enables automation of traditional data validation, engine condition monitoring, and fault identification procedures. A distributed parallel computing approach enables the entire process to operate in real-time.The result is a capability to detect data and engine anomalies in real-time during developmental engine testing. The approach is shown to be successful in detecting and identifying sensor anomalies as they occur and distinguishing these anomalies from variations in component and overall engine aerothermodynamic performance. The component-level model-based engine performance and fault identification technique of the present research is capable of: identifying measurement errors on the order of 0.5 percent (e.g., sensor bias, drift,level shift, noise, or poor response) in facility fuel flow, airflow, and thrust measurements; identifying measurement errors in engine aerothermodynamic measurements (rotorspeeds, gas path pressures and temperatures); identifying measurement errors in engine control sensors (e.g., leaking/biased pressure sensor, slowly responding pressure measurement) and variable geometry rigging (e.g., misset guide vanes or nozzle area) that would invalidate a test or series of tests; identifying abrupt faults (e.g., faults due to domestic object damage, foreign object damage, and control anomalies); identifying slow faults (e.g., component or overall engine degradation, and sensor drift). Specifically, the technique is capable of identifying small changes in compressor (or fan) performance on the order of 0.5 percent; and being easily extended to diagnose secondary failure modes and to verify any modeling assumptions that may arise for developmental engine tests (e.g., increase in turbine flow capacity, inaccurate measurement of facility bleed flows, horsepower extraction, etc.).The component-level model-based engine performance and fault identification method developed in the present work brings together features which individually and collectively advance the state-of-the-art. These features are separated into three categories: advancements to effectively quantify off-nominal behavior, advancements to provide a fault detection capability that is practical from the viewpoint of the analysis,implementation, tuning, and design, and advancements to provide a real-time fault detection capability that is reliable and efficient

New Techniques for On-line Testing and Fault Mitigation in GPUs

L'abstract è presente nell'allegato / the abstract is in the attachmen

Performance analysis and dynamics of innovative SOFC hybrid systems based on turbocharger-derived machinery

La crescente consapevolezza su temi quali il cambiamento climatico e l\u2019inquinamento atmosferico ha portato a politiche nazionali ed internazionali mirate allo sviluppo di sistemi energetici innovativi e sostenibili. Tra di essi, le fuel cell sono uno dei pi\uf9 promettenti, essendo caratterizzate da alte efficienze e basse emissioni. In particolare, i sistemi ibridi basati sull\u2019integrazione di fuel cell ad alta temperatura con dispositivi derivati da turbocompressori hanno attirato l\u2019attenzione del mondo accademico e dell\u2019industria negli ultimi decenni. Tuttavia, la complessit\ue0, la fragilit\ue0 e l\u2019alto costo di questi impianti ha rallentato il loro sviluppo, e solo poche grandi aziende sono state in grado di realizzare prototipi completi. Le difficolt\ue0 tecniche affrontate dalla comunit\ue0 scientifica hanno messo in luce l\u2019importanza delle simulazioni per progettare, testare, controllare e analizzare i sistemi ibridi a fuel cell. Sulla base di tale esperienza, questa tesi mira ad espandere la attuale conoscenza sui sistemi ibridi a fuel cell a ossidi solidi, ponendo una particolare attenzione su un innovativo sistema turbocompresso di piccola taglia, alimentato con biogas e recentemente introdotto all\u2019interno del progetto europeo Bio-HyPP. Lo scopo principale della tesi \ue8 determinare se questo tipo di sistema possa essere una valida alternativa ai sistemi basati su microturbine a gas, analizzando il suo comportamento in relazione a diversi scenari, sia stazionari, sia transitori. Per fare ci\uf2, \ue8 necessario definire i vincoli operativi del sistema e sviluppare un sistema di controllo in grado di rispettarli, ottimizzando al tempo stesso le prestazioni dell\u2019impianto. Inoltre, l\u2019affidabilit\ue0 dei sistemi ibridi pu\uf2 essere migliorata grazie all\u2019implementazione di strumenti diagnostici e di procedure per prevenire il pompaggio del compressore. La parte finale della tesi \ue8 mirata allo studio di tali strumenti, al loro sviluppo e alla loro integrazione con il sistema di controllo. Tutte le attivit\ue0 presentate in questa tesi sono state svolte facendo affidamento su strumenti di simulazione. Ci\uf2 \ue8 stato possibile grazie alla collaborazione tra il Laboratorio di Matematica Applicata, Simulazione e Modellistica Matematica e il Thermochemical Power Group dell\u2019Universit\ue0 degli Studi di Genova. Dopo aver presentato il layout del sistema a fuel cell con turbocompressore, un dettagliato modello stazionario dell\u2019impianto sviluppato in Matlab\uae-Simulink\uae \ue8 stato utilizzato per progettare una strategia, basata sul controllo di valvole installate sull\u2019impianto, in grado di rispettare tutti i suoi vincoli operativi. Successivamente, \ue8 stata svolta un\u2019analisi di prestazioni in off-design, considerando allo stesso tempo diverse condizioni di carico di potenza e di temperatura ambiente. Tale analisi \ue8 stata utilizzata per confermare l\u2019efficacia della strategia di controllo proposta, e per valutare le capacit\ue0 del sistema con turbocompressore. Successivamente \ue8 stato creato un modello dinamico utilizzando lo strumento TRANSEO, in modo da studiare il comportamento del sistema durante i transitori. Avendo adottato una strategia di controllo basata sulla valvola di cold bypass, \ue8 stata analizzata la risposta del sistema ad una sua apertura a gradino, al fine di progettare un sistema di controllo efficace e reattivo, in grado di mantenere la massima temperatura di cella costante e, allo stesso tempo, di rispettare i vincoli del sistema. Sono stati progettati quattro diversi controllori, che successivamente sono stati testati su due diversi scenari di variazione di carico e confrontati sulla base di vari parametri operativi. La parte finale della tesi ha riguardato lo sviluppo di innovativi strumenti che possano aumentare l\u2019affidabilit\ue0 dei sistemi ibridi a fuel cell a ossidi solidi, in particolare tecniche di prevenzione del pompaggio e sistemi di diagnostica basati su reti Bayesiane. Un modello semplificato del sistema con turbocompressore \ue8 stato sviluppato in TRANSEO e sono state testate diverse tecniche di prevenzione del pompaggio: condizionamento del flusso d\u2019aria, iniezione di acqua, ricircolo e bleed, installazione di un eiettore all\u2019imbocco del compressore. Le soluzioni pi\uf9 efficaci sono state integrate con il controllore del sistema ibrido e sono state testate durante un transitorio per evitare che il punto operativo del compressore si avvicinasse al pompaggio. Infine, grazie ad una collaborazione tra l\u2019Universit\ue0 degli Studi di Genova e la M\ue4lardalens H\uf6gskola di V\ue4ster\ue5s, in Svezia, sono state sviluppate delle reti Bayesiane per la diagnostica di sistemi ibridi a fuel cell a ossidi solidi con microturbina a gas. Questa attivit\ue0 \ue8 stata svolta simulando il sistema su Matlab\uae-Simulink\uae e creando le reti Bayesiane su Hugin Expert. Due sistemi di diagnostica, uno per la microturbina e uno per la fuel cell, sono stati sviluppati e testati in condizioni stazionarie. Il secondo \ue8 stato anche testato in condizioni dinamiche e integrato con il sistema di controllo per prevenire l\u2019usura della cella. In conclusione, questa tesi ha messo in luce il grande potenziale dei sistemi ibridi SOFC-turbocompressore, mostrando la loro alta efficienza in un ampio intervallo di condizioni operative in termini di carico elettrico e temperatura ambiente. La tesi ha anche dimostrato che \ue8 possibile garantire il corretto funzionamento di questi sistemi durante diversi scenari transitori, implementando controllori a cascata progettati per agire sulla valvola di bypass freddo per controllare la massima temperatura della cella. Per quanto riguarda la possibilit\ue0 di migliorare l\u2019affidabilit\ue0 di tali sistemi, le tecniche basate sul ricircolo del compressore sono risultate essere le pi\uf9 efficaci per allontanare il sistema da una condizione di pompaggio. I risultati delle simulazioni mostrano come la loro integrazione con strumenti di monitoraggio possa prevenire diverse situazioni di pericolo. La parte finale della tesi ha mostrato come il deterioramento dei sistemi ibridi a SOFC possa essere limitato grazie a reti Bayesiane, che sono state utilizzate per diagnosticare accuratamente le condizioni di un sistema SOFC-microturbina a gas, ma potrebbero ugualmente essere applicate su impianti con turbocompressore.The growing awareness on climate change and pollution has brought to national and international policies aimed at promoting the development of innovative and environmentally sustainable energy systems. Among these systems, fuel cells are one of the most promising technologies, characterized by high energy conversion efficiencies and low emissions. In particular, hybrid systems based on the integration of a high temperature fuel cell with turbocharger-derived machinery have drawn the interest of academia and industry over the past decades. However, the complexity, fragility and high cost of these plants have slowed down their development, and only a few big companies were able to build complete prototypes. The technological challenges faced by the scientific community have highlighted the importance of simulations to design, test, control and analyse fuel cell hybrid systems. Based on this experience, this thesis wants to expand the current knowledge on solid oxide fuel cell hybrid systems, with a particular focus on an innovative small-scale biofueled turbocharged layout, which was introduced recently within the Bio-HyPP European project. The main goal of this thesis is to determine if this kind of system can be a viable alternative to micro gas turbine-based systems, analysing its steady-state and transient behaviour in various operating conditions. To do this, it is necessary to define the system operative constraints, and to develop a control system capable of ensuring their compliance, while optimizing the plant performance. The possibility of increasing the reliability of solid oxide fuel cell hybrid systems is finally investigated, considering the implementation of surge prevention techniques and diagnostic tools. All these activities strongly relying on simulation tools. This was possible thanks to the collaboration between the Laboratory of Applied Mathematics, Simulation and Mathematical Modelling with the Thermochemical Power Group of the University of Genoa. After introducing the layout of the turbocharged fuel cell system, a detailed steady-state model of the plant is developed in Matlab\uae-Simulink\uae and used to design a strategy, based on the control of valves installed on the plant, able to comply with its many operative constraints. Then, an off-design performance analysis of the system is performed, considering simultaneously various conditions of power load and ambient temperature. This analysis is used to confirm the effectiveness of the proposed control strategy and to assess the capabilities of the turbocharged system. A dynamic model is created using the TRANSEO tool to study the transient behaviour of the system. Having adopted a control strategy based on the cold bypass valve, the response of the system to a valve opening step change is analysed in order to design an effective and responsive control system, able to keep the fuel cell maximum temperature constant while complying with the system constraints. Four different controllers are designed, tested on two different load variation scenarios and compared on the basis of many parameters. The final part of the thesis regards the development of innovative tools aimed at improving the reliability of solid oxide fuel cell hybrid system, in particular surge prevention techniques and Bayesian belief network-based diagnosis systems. A simplified dynamic model of the turbocharged SOFC system is developed in TRANSEO, and various surge prevention techniques are tested on it: intake air conditioning, water spray at compressor inlet, air bleed and recirculation, and installation of an ejector at the compressor intake. The most effective procedures are integrated with the controller of the hybrid system and tested during a transient scenario to prevent the compressor operative point from approaching a surge condition. Bayesian belief networks aimed at diagnosing the status of SOFC hybrid systems are developed thanks to a collaboration between the University of Genoa and the M\ue4lardalens H\uf6gskola of V\ue4ster\ue5s, Sweden. A micro gas turbine \u2013 solid oxide fuel cell system is considered for this study, but the methodology could be easily extended to turbocharged plants. The activity is carried out simulating the system on Matlab\uae-Simulink\uae and designing the Bayesian networks on Hugin Expert. Two different diagnosis systems, one for the turbomachinery and one for the fuel cell stack, are developed and tested on stationary conditions. The second one is also tested during transients and integrated with the control system to prevent degradation of the fuel cells. In conclusion, this thesis highlighted the great potential of turbocharged SOFC hybrid systems, showing high energy conversion efficiencies in a wide operative range in terms of load and ambient conditions. It also showed that the proper operation of the system is possible during various transient scenarios, implementing cascade controllers designed to act on a cold bypass valve to control the SOFC maximum temperature. Regarding the possibility of improving the reliability of these systems, surge prevention techniques based on compressor recirculation appeared as the most effective ones. Simulation results suggest that their integration with a surge precursors detection tool could avoid the occurrence of many potentially dangerous scenarios. The final part of this thesis showed that the durability of SOFC hybrid systems could be further improved thanks to Bayesian belief networks, which were proved to effectively diagnose the status of SOFC-MGT systems but could be applied to turbocharged plants as well

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs). Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM) motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG). Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized