Definition and verification of a set of reusable reference architectures for hybrid vehicle development

Current concerns regarding climate change and energy security have resulted in an increasing demand for low carbon vehicles, including: more efficient internal combustion engine vehicles, alternative fuel vehicles, electric vehicles and hybrid vehicles. Unlike traditional internal combustion engine vehicles and electric vehicles, hybrid vehicles contain a minimum of two energy storage systems. These are required to deliver power through a complex powertrain which must combine these power flows electrically or mechanically (or both), before torque can be delivered to the wheel. Three distinct types of hybrid vehicles exist, series hybrids, parallel hybrids and compound hybrids. Each type of hybrid presents a unique engineering challenge. Also, within each hybrid type there exists a wide range of configurations of components, in size and type. The emergence of this new family of hybrid vehicles has necessitated a new component to vehicle development, the Vehicle Supervisory Controller (VSC). The VSC must determine and deliver driver torque demand, dividing the delivery of that demand from the multiple energy storage systems as a function of efficiencies and capacities. This control component is not commonly a standalone entity in traditional internal combustion vehicles and therefore presents an opportunity to apply a systems engineering approach to hybrid vehicle systems and VSC control system development. A key non-­‐functional requirement in systems engineering is reusability. A common method for maximising system reusability is a Reference Architecture (RA). This is an abstraction of the minimum set of shared system features (structure, functions, interactions and behaviour) that can be applied to a number of similar but distinct system deployments. It is argued that the employment of RAs in hybrid vehicle development would reduce VSC development time and cost. This Thesis expands this research to determine if one RA is extendable to all hybrid vehicle types and combines the scientific method with the scenario testing method to verify the reusability of RAs by demonstration. A set of hypotheses are posed: Can one RA represent all hybrid types? If not, can a minimum number of RAs be defined which represents all hybrid types? These hypotheses are tested by a set of scenarios. The RA is used as a template for a vehicle deployment (a scenario), which is then tested numerically, thereby verifying that the RA is valid for this type of vehicle. This Thesis determines that two RAs are required to represent the three hybrid vehicle types. One RA is needed for series hybrids, and the second RA covers parallel and compound hybrids. This is done at a level of abstraction which is high enough to avoid system specific features but low enough to incorporate detailed control functionality. One series hybrid is deployed using the series RA into simulation, hardware and onto a vehicle for testing. This verifies that the series RA is valid for this type of vehicle. The parallel RA is used to develop two sub-­‐types of parallel hybrids and one compound hybrid. This research has been conducted with industrial partners who value, and are employing, the findings of this research in their hybrid vehicle development programs

A principle based system architecture framework applied for defining, modeling & designing next generation smart grid systems

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 81).A strong and growing desire exists, throughout society, to consume electricity from clean and renewable energy sources, such as solar, wind, biomass, geothermal, and others. Due to the intermittent and variable nature of electricity from these sources, our current electricity grid is incapable of collecting, transmitting, and distributing this energy effectively. The "Smart Grid" is a term which has come to represent this 'next generation' grid, capable of delivering, not only environmental benefits, but also key economic, reliability and energy security benefits as well. Due to the high complexity of the electricity grid, a principle based System Architecture framework is presented as a tool for analyzing, defining, and outlining potential pathways for infrastructure transformation. Through applying this framework to the Smart Grid, beneficiaries and stakeholders are identified, upstream and downstream influences on design are analyzed, and a succinct outline of benefits and functions is produced. The first phase of grid transformation is establishing a robust communications and measurement network. This network will enable customer participation and increase energy efficiency through smart metering, real time pricing, and demand response programs. As penetration of renewables increases, the high variability and uncontrollability of additional energy sources will cause significant operation and control challenges. To mitigate this variability reserve margins will be adjusted and grid scale energy storage (such as compressed air, flow batteries, and plugin hybrid electric vehicles or PHEV's) will begin to be introduced. Achieving over 15% renewable energy penetration marks the second phase of transformation. The third phase is enabling mass adoption, whereby over 40% of our energy will come from renewable sources. This level of penetration will only be achieved through fast supply and demand balancing controls and large scale storage. Robust modeling must be developed to test various portfolio configurations.by Gregory Sachs.S.M.in Engineering and Managemen

Architecting complex systems for robustness

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2007.Vita.Includes bibliographical references (p. 121-128).Robust design methodologies are frequently utilized by organizations to develop robust and reliable complex systems. The intent of robust design is to create systems that are insensitive to variations from production, the environment, and time and use. While this process is effective, it can also be very time consuming and resource intensive for an engineering team. In addition, most robust design activity takes place at the detailed design phase, when the majority of the product life cycle cost has already been committed. Addressing robustness and the "ilities" at the architecture level may be more effective because it is the earliest and highest leverage point in the product development process. Furthermore, some system architectures are inherently more robust than others. In this thesis, a framework based on principles is proposed to architect complex systems for type I and II robustness. The principles are obtained by tracing the architectural evolution of the jet engine, which is an extremely complex system that has evolved to high reliability. This framework complements existing robust design methods, while simultaneously incorporating the robustness focus earlier in the product development process.by Jason C. Slagle.S.M

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

-ilities Tradespace and Affordability Project – Phase 3

One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering and associated management practices – “SE and Management Transformation (SEMT).” The Grand Challenge goal for SEMT is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise- oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Digitalisaation hyödyt höyryturbiinien käyttöomaisuuden hallinnassa

Steam turbines are considered long-lived and require little attention during normal operation. Cost optimizations due to infrequent demand for turbine expertise, together with retiring workforce, have resulted in increasing shortage of know-how. Digitalization could substitute unavailable turbine resources, but the projects and investment have been challenging to initiate and incentivize. The objective of this thesis was to map the benefits of digitalized steam turbine asset management, what kind of challenges digitalization could mitigate and how the implementation could be facilitated. The research confirmed that turbine operating companies lack the domain know-how and resources required for some current systems and demands. Prolonging of overhauls and deficiencies in asset management, such as insufficient documenting and data utilization, were observed to be other main challenges. Increased downtime and unoptimized practices and systems reduce efficiency, usability, reliability and availability. Advanced diagnostics in condition monitoring systems could increase availability and reliability by enabling optimized condition-based maintenance and facilitate shorter overhauls by reducing unforeseen findings. Solutions and service that allow faster fact-finding in anomalies would increase availability as well. Asset management systems with more connectivity, centralization, user-friendliness and AI would reduce downtime by enhancing planning, documenting and spare part management. Such systems could also increase usability and the overall efficiency of operations and maintenance. Main hindrances for digitalization are the imbalance between costs and perceived added value, and insufficient focus on the usability of asset management systems. Development of advanced solutions in current business models is disincentivized. Long-term contracts could enable the implementation of best practices, reduce risks and incentivize higher quality of services. Partnership business models facilitate mutual benefits better than short-term and stand-alone services.Höyryturbiinit ovat yleensä pitkäikäisiä ja vaativat vain vähän huomiota normaalin käynnin aikana. Huollon ja turbiiniosaamisen harvan tarpeen takia höyryturbiinien käyttö- ja hallintakustannuksista on jatkuvasti säästetty. Tämä, yhdessä eläköityvän työvoiman kanssa, on johtanut krooniseen tietotaidon puutteeseen höyryturbiinilaitoksilla. Digitalisaatiolla voisi korvata puuttuvia resursseja, mutta projektien ja investointien kanssa on ollut ongelmia. Tämän diplomityön tarkoituksena oli kartoittaa höyryturbiinien digitaalisen käyttöomaisuuden hallinnan hyötyjä, millaisiin haasteisiin se voisi vastata, ja mitä digitalisaation hyötyjen menestyksekäs implementointi vaatisi. Tutkimus varmisti, että loppukäyttäjillä on puutetta turbiiniosaamisesta ja -resursseista, joita tämänhetkiset systeemit ja tarpeet vaatisivat. Muita suuria haasteita olivat huoltojen pitkittymiset ja puutteet käyttöomaisuuden hallinnassa, kuten riittämätön dokumentointi ja mitatun datan hyödyntäminen. Pitkittyvät huollot ja optimoimattomat toiminnot kasvattavat epäkäytettävyysaikaa ja pienentävät tehokkuutta, käytön helppoutta ja luotettavuutta. Kehittyneen turbiinidiagnostiikan hyödyntäminen kunnonvalvonnassa voisi kasvattaa käytettävyyttä ja luotettavuutta mahdollistamalla turbiinin todelliseen huoltotarpeeseen perustuvan huollon. Ennakoivalla analytiikalla voitaisiin vähentää odottamattomia löydöksiä, jotka ovat yksi yleisin syy huoltojen pitkittymiseen. Käytettävyyttä lisäisivät myös ratkaisut ja palvelut, joilla nopeutettaisiin ongelmanratkaisua häiriötilanteissa. Käyttö- ja kunnossapitojärjestelmien parempi liitettävyys, keskitettävyys ja käyttäjäystävällisyys sekä tekoälyn hyödyntäminen tehostaisivat käyttöomaisuuden, kuten varaosien, hallintaa ja helpottaisivat suunnittelua ja dokumentointia. Merkittävimpiä esteitä turbiinien käyttöomaisuuden hallinnan digitalisaatiolle ovat kustannusten ja hahmotetun lisäarvon epätasapaino sekä riittämätön huomio käyttöomaisuuden hallinnan optimointiin. Edistyksellisten digitaalisten ratkaisujen kehittämiselle ja loppuasiakkaalle tarjoamiseen ei ole riittävästi kannustimia. Pitkäaikaiset ylläpitosopimukset voisivat mahdollistaa parhaiden käytäntöjen implementoinnin, vähentää liiketoimien riskiä ja tehdä korkeimmankin laadun palveluista ja ratkaisuista kannattavampia. Pitkäaikaiseen kumppanuuteen perustuvat liiketoimintamallit fasilitoivat osapuolten yhteisiä etuja paremmin, kuin lyhytaikaiset erillissopimukset yksittäisille ratkaisuille ja palveluille

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems