Tesis por compendio / [ES] El objetivo principal de esta tesis es establecer una metodología para predecir el consumo de combustible y las emisiones de un motor de encendido por compresión en condiciones transitorias. Además, su objetivo es explorar cómo las diferentes configuraciones del motor y los factores ambientales impactan el comportamiento del motor utilizando un enfoque de modelado 0D/1D. Además, el estudio pretende extender esta metodología a los motores duales, analizando específicamente las características de combustión de metano-diésel e hidrógeno-diésel. Para lograrlo, la herramienta de modelado 0D/1D se ajustó y validó meticulosamente utilizando un motor diésel de cuatro cilindros. Esta alineación entre la simulación y datos experimentales se centró especialmente en factores cruciales como la presión, la liberación de calor, las temperaturas en los fluidos del motor y el par. Se realizó un análisis exhaustivo del Balance Energético Global (GEB) utilizando VEMOD (Virtual Engine Model). Este análisis proporcionó información detallada sobre el consumo del motor y su reacción en diversas condiciones de funcionamiento, particularmente durante el Ciclo de ensayo mundial armonizado de vehículos ligeros (WLTC). La comparación de términos energéticos entre diferentes condiciones ambientales y de motor destacó aspectos como la fricción, la transferencia de calor y la acumulación de calor. Además, el análisis GEB permitió explorar cómo se distribuía la energía con diferentes temperaturas y altitudes ambientes. El estudio también evaluó las emisiones de NOx, revelando patrones influenciados por factores como las tasas de recirculación de gases de escape (EGR) y la temperatura de admisión. En el ámbito de los motores de combustible dual, se elaboró y validó un modelo de combustión utilizando la herramienta de simulación 0D/1D. La atención inicial se centró en la combustión de metano-Diesel, validada con datos experimentales. Posteriormente, el alcance de este modelo se amplió para simular la combustión de hidrógeno-Diesel. Esta tesis ha introducido con éxito una metodología que utiliza VEMOD para predecir el consumo y las emisiones del motor en distintos escenarios. El análisis exhaustivo arrojó luz sobre cómo funcionan los mecanismos de distribución de energía y cómo diferentes factores influyen en el comportamiento del motor. La aplicación de esta metodología a motores de encendido por compresión ha demostrado su versatilidad y capacidad de predicción, lo que la convierte en una herramienta valiosa para investigar escenarios futuros, también con combustiones duales. / [CA] L'objectiu principal d'aquesta tesi és establir una metodologia per predir el consum de combustible i les emissions d'un motor d'encesa per compressió en condicions transitòries. A més, pretén explorar com diferents configuracions de motors i factors ambientals afecten el comportament del motor mitjançant un enfocament de modelització 0D/1D. A més, l'estudi s'esforça a estendre aquesta metodologia als motors de doble combustible (duals), analitzant específicament les característiques de combustió de metà-dièsel i hidrogendièsel. Per aconseguir-ho, l'eina de modelització 0D/1D es va ajustar minuciosament i es va validar mitjançant un motor dièsel de quatre cilindres. Aquesta alineació entre dades de simulació i món real es va centrar especialment en factors crucials com la pressió, l'alliberament de calor, les temperatures dels fluids del motor i el parell. Es va realitzar una anàlisi completa del Balanç Global d'Energia (GEB) mitjançant VEMOD (Virtual Engine Model). Aquesta anàlisi va proporcionar una visió profunda sobre el consum del motor i la seua reacció en diverses condicions de funcionament, especialment durant el Cicle mundial d'assaig de vehicles lleugers harmonitzats (WLTC). La comparació de termes energètics entre diferents condicions ambientals i del motor van posar de manifest aspectes com la fricció, la transferència de calor i l'acumulació de calor. A més, l'anàlisi GEB va explorar com es va distribuir l'energia amb diferents temperatures i altituds ambientals. L'estudi també va valorar les emissions de NOx, revelant patrons influenciats per factors com la recirculació de gasos d'escapament (EGR) i la temperatura d'admissió. En l'àmbit dels motors duals, es va elaborar i validar un model de combustió mitjançant l'eina de simulació 0D/1D. El focus inicial es va centrar en la combustió metà-Diesel, validada amb dades experimentals. Posteriorment, l'abast d'aquest model es va ampliar per simular la combustió hidrogen-Diesel. Aquesta tesi ha introduït amb èxit una metodologia que utilitza VEMOD per predir el consum i les emissions del motor en diferents escenaris. L'anàlisi completa va donar llum a com funcionen els mecanismes de distribució d'energia i com diferents factors influeixen en el comportament del motor. L'aplicació d'aquesta metodologia als motors d'encesa per compressió va demostrar la seva versatilitat i capacitats de predicció, convertint-la en una valuosa eina per investigar els futurs escenaris, fins i tot amb combustions duals. / [EN] The main aim of this thesis is to establish a methodology for predicting fuel consumption and emissions of a compression ignition engine in transient conditions. Additionally, it aims to explore how different engine setups and environmental factors impact the engine's performance using a 0D/1D modelling approach. Moreover, the study strives to extend this methodology to dual fuel engines, specifically analysing methane-Diesel and hydrogen- Diesel combustion characteristics. The 0D/1D modelling tool was meticulously fine-tuned and validated using a four-cylinder Diesel engine to achieve this. This alignment between simulation and experimental data focused on crucial factors such as pressure, heat release, engine fluid temperatures and torque. A comprehensive Global Energy Balance (GEB) analysis was conducted using VEMOD (Virtual Engine Model). This analysis provided insights into the engine consumption and performance under diverse operating conditions, particularly during the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The comparison of energy terms across different engine and boundary conditions highlighted aspects such as friction, heat rejection, and heat accumulation. Additionally, the GEB analysis allowed exploration of how energy was split across varying ambient temperatures and altitudes. The study also assessed NOx emissions, revealing patterns influenced by factors such as Exhaust Gas Recirculation (EGR) rates and intake temperature. A combustion model was developed and validated using the 0D/1D simulation tool in the scope of dual fuel engines. The initial focus was on methane-Diesel combustion, validated against experimental data. Subsequently, this model scope was expanded to simulate hydrogen-Diesel combustion. This thesis has successfully introduced a methodology based on VEMOD to predict engine consumption and emissions across varying scenarios. The comprehensive analysis illuminated how energy distribution mechanisms operate and how factors influence engine performances. The application of this methodology to compression ignition engines demonstrated its versatility and prediction capabilities, making it a valuable tool for investigating future combustion scenarios, including dual fuel operation. / This research has been partially funded by the European Union’s Horizon 2020 Framework Programme for research, technological development and demonstration under grant agreement 723976 (“DiePeR”) and by the Spanish government under the grant agreement TRA2017-89894-R (”MECOEM”) and I was supported by FPI grant with reference PRE2018-084411. / Artham, S. (2023). Contribution to the simulation of new standard testing cycles by means of a 0D/1D tool [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/201238 / Compendio

В этом исследовании изучаются беспрецедентные нарушения, вызванные пандемией COVID-19 в глобальных моделях потребления энергии. Используя ретроспективный подход, различные методологии, включая классическое машинное обучение и алгоритмы классификации временных рядов, используются для анализа энергетических данных, охватывающих период пандемии и после нее. Набор данных охватывает различные источники энергии, что позволяет изучать исторические тенденции как до, так и после COVID-19. Также изучаются региональные различия в моделях потребления энергии в ключевых регионах, таких как ОЭСР, БРИКС, СНГ и Ближний Восток. Конкретное исследование случая, сосредоточенное на Нью-Йорке, углубляется в тенденции потребления энергии в городе и влияние правил COVID-19. Представляя модель рекуррентной нейронной сети (RNN) для прогнозирования потребления энергии, исследование подчеркивает потенциал передовых методов моделирования в понимании и прогнозировании динамики потребления энергии. Применение модели RNN к данным о потреблении энергии в Нью-Йорке позволяет сравнивать прогнозируемые и фактические данные за 2020 год. Результаты подчеркивают значительные сдвиги в тенденциях глобального потребления энергии, раскрывая глубокое влияние пандемии на спрос на энергию и ее использование. Обсуждаются последствия этих сдвигов, подчеркивая необходимость адаптации энергетической политики и инфраструктуры к меняющемуся глобальному ландшафту. Рекомендации по будущим направлениям исследований предоставляются для улучшения понимания динамического взаимодействия между внешними потрясениями, такими как пандемии, и динамикой глобального потребления энергии. / This research investigates the unprecedented disruptions caused by the COVID-19 pandemic on global energy consumption patterns. Employing a retrospective approach, diverse methodologies including classic machine learning and time series classification algorithms are utilized to analyze energy data spanning the pandemic period and beyond. The dataset encompasses various energy sources, enabling examination of historical trends both pre and post-COVID-19. Regional disparities in energy consumption patterns across key regions like OECD, BRICS, CIS, and the Middle East are also explored. A specific case study focusing on New York delves into the city's energy consumption trends and the impact of COVID-19 regulations. Introducing a Recurrent Neural Network (RNN) model for energy consumption prediction, the study highlights the potential of advanced modeling techniques in understanding and forecasting energy usage dynamics. Application of the RNN model to New York's energy consumption data allows comparison between predicted and actual 2020 data. The findings underscore significant shifts in global energy consumption trends, revealing the pandemic's profound impact on energy demand and utilization. Implications of these shifts are discussed, emphasizing the necessity of adapting energy policies and infrastructure to the evolving global landscape. Recommendations for future research directions are provided to enhance comprehension of the dynamic interplay between external shocks, such as pandemics, and global energy consumption dynamics.

Many people do not like thinking about the future. If they do, over 50% of Canadians think “our way of life” (p. 7) will end within 100 years and over 80% of Canadians think “we need to change our worldview and way of life if we are to create a better future for the world” (Randle & Eckersley, 2015, p. 9). There is a good reason for this. Alarms have sounded over global urgent complex problems with potential for catastrophic consequences such as the development of artificial intelligence, climate change, mass extinction, nuclear war and pandemics (Marien & Halal, 2011). Society is also increasingly fragmenting as imminent crises build on lack of understanding, the sense of incapacity to act, fear, distrust, blame and a lack of hope. This struggle for humanity’s survival is complicated by the turbulent global environment in which institutions continue to follow path-dependent trajectories set forth in a different time and context. Governments at various levels face a problem of “fit” between current structures and processes, that have not progressed sufficiently to meet changing needs of a global society mired in complexity and governance challenges. However, hope exists. Incremental progress on many fronts and a massive amount of efforts and resources are being engaged worldwide. There are emerging fields, lenses and tools that can potentially alleviate complex problems and address this emergency. The purpose of this dissertation is to understand and assess dialogue-based foresight practices being applied towards complex problems in Canada to provide insights into how these practices can assist society to alleviate global urgent complex problems and their impacts, within this backdrop of looming crises. Foresight, alternatively known as future studies or scenario-building, is a forward-looking practice recognized and used globally with over 100 research organizations focused on foresight, widespread usage by firms and over 18 countries involved in foresight activities (Berze, 2014b). Overall literature findings suggest foresight is widely and at least incrementally effective with a number of impacts in various areas (Calof, Miller, & Jackson, 2012; March, Therond, & Leenhardt, 2012; Meissner, Gokhberg, & Sokolov, 2013) but the extent of this effectiveness, the mechanisms involved, and the specific foresight benefits per type of project needs further research and evidence. For instance, limited literature exists on whether foresight can transform complex situations and if so, under what conditions. Thus, opportunities exist for assessing and increasing foresight’s impact. This dissertation is a contextualized, systematic empirical study that taps into transdisciplinary literature and practice, case studies of how foresight has been used to address specific types of complex problems in Canada, as well as surveys and interviews with foresight experts and participants. This dissertation uses a foresight community scan and a comparative case study approach to provide practical and theoretical benefits to foresight and complex problem area stakeholders. The research focuses on studying the broad interactions of foresight and identifying the impacts of dialogue-based foresight projects on people and the outcomes of complex problems. The dissertation concludes that dialogue-based foresight is a valuable and unique practice for ameliorating complex problems and their consequences. Insights are offered towards dialogue-based foresight’s potential contributions within the context of other efforts directed at humanity’s struggle for survival and global complex problems. These insights can then foster the further development and application of dialogue-based foresight on a global scale to alleviate complex problems and their effects. The dissertation outlines recommendations on key next steps to realize these potential contributions. / Graduate