no / The complexity of powertrain calibration has increased significantly with the development and introduction of new technologies to improve fuel economy and performance while meeting increasingly stringent emissions legislation with given time and cost constraints. This paper presents research to improve the model-based engine calibration optimization using an integrated sequential Design of Experiments (DoE) strategy for engine mapping experiments. This DoE strategy is based on a coherent framework for a model building - model validation sequence underpinned by Optimal Latin Hypercube (OLH) space filling DoEs. The paper describes the algorithm development and implementation for generating the OLH space filling DoEs based on a Permutation Genetic Algorithm (PermGA), subsequently modified to support optimal infill strategies for the model building - model validation sequence and to deal with constrained non-orthogonal variables space. The development, implementation and validation of the proposed strategy is discussed in conjunction with a case study of a GDI engine steady state mapping, focused on the development of an optimal calibration for CO₂ and particulate number (Pn) emissions. The proposed DoE framework applied to the GDI engine mapping task combines a screening space filling DoE with a flexible sequence of model building - model validation mapping DoEs, all based on optimal DoE test plan augmentation using space filling criteria. The case study results show that the sequential DoE strategy offers a flexible way of carrying out the engine mapping experiments, maximizing the information gained and ensuring that a satisfactory quality model is achieved.

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Flood cooling is primarily used to cool and lubricate the cutting tool and workpiece interface during a machining process. But the adverse health effects caused by the use of flood coolants are drawing manufacturers' attention to develop methods for controlling occupational exposure to cutting fluids. Microlubrication serves as an alternative to flood cooling by reducing the volume of cutting fluid used in the machining process. Microlubrication minimizes the exposure of metal working fluids to the machining operators leading to an economical, safer and healthy workplace environment. In this dissertation, a vegetable based lubricant is used to conduct mist, microstructure and wear analyses during end milling AISI 1018 steel using microlubrication. A two-flute solid carbide cutting tool was used with varying cutting speed and feed rate levels with a constant depth of cut. A full factorial experiment with Multivariate Analysis of Variance (MANOVA) was conducted and regression models were generated along with parameter optimization for the flank wear, aerosol mass concentration and the aerosol particle size. MANOVA indicated that the speed and feed variables main effects are significant, but the interaction of (speed*feed) was not significant at 95% confidence level. The model was able to predict 69.44%, 68.06% and 42.90% of the variation in the data for both the flank wear side 1 and 2 and aerosol mass concentration, respectively. An adequate signal-to-noise precision ratio more than 4 was obtained for the models, indicating adequate signal to use the model as a predictor for both the flank wear sides and aerosol mass concentration. The highest average mass concentration of 8.32 mg/m3 was realized using cutting speed of 80 Surface feet per minute (SFM) and a feed rate of 0.003 Inches per tooth (IPT). The lowest average mass concentration of 5.91 mg/m3 was realized using treatment 120 SFM and 0.005 IPT. The cutting performance under microlubrication is five times better in terms of tool life and two times better in terms of materials removal volume under low cutting speed and feed rate combination as compared to high cutting speed and feed rate combination. Abrasion was the dominant wear mechanism for all the cutting tools under consideration. Other than abrasion, sliding adhesive wear of the workpiece materials was also observed. The scanning electron microscope investigation of the used cutting tools revealed micro-fatigue cracks, welded micro-chips and unusual built-up edges on the cutting tools flank and rake side. Higher tool life was observed in the lowest cutting speed and feed rate combination. Transmission electron microscopy analysis at failure for the treatment 120 SFM and 0.005 IPT helped to quantify the dislocation densities. Electron backscatter diffraction (EBSD) identified 4 to 8 µm grain size growth on the machined surface due to residual stresses that are the driving force for the grain boundaries motion to reduce its overall energy resulting in the slight grain growth. EBSD also showed that (001) textured ferrite grains before machining exhibited randomly orientated grains after machining. The study shows that with a proper selection of the cutting parameters, it is possible to obtain higher tool life in end milling under microlubrication. But more scientific studies are needed to lower the mass concentration of the aerosol particles, below the recommended value of 5 mg/m3 established by Occupational Safety and Health Administration (OSHA).

Vegetation has been identified to play a significant role in river environments by providing a wide range of ecosystem services. For this reason, the use of plants has become relevant in river restoration projects. However, the presence of plants in channel beds increases the flow resistance and, thus, the water levels during flood conditions. Additionally, river vegetation, whether instream or riparian, influences the morphological evolution of rivers. Observations show that instream vegetation has a strong impact on bedload transport. Yet, there is a scarcity of sediment transport predictors that directly account for the effects of plants, and existing methods, based on re-calculation of roughness coefficients, may present some inconsistencies. Therefore, an approach that extends Einstein’s (1950) parameters to include the effects of vegetation geometry and spatial density on sediment transport is herein proposed. The new formulations of the dimensionless transport parameter Φ and the flow intensity parameter Ψ were derived for their implementation in existing bedload predictors of the form Φ = (Ψ). The applicability of this new approach considers the presence of submerged and emergent vegetation, but reduces to the original Einstein’s model if vegetation is absent. The research methodology was carried out in four phases. First, a comprehensive literature review for the identification of, mainly, the different effects of vegetation on river morphodynamics, the state-of-the-art knowledge on the flow-sediment-vegetation interactions, and the current approaches to bedload estimation in channels with vegetated beds. Second, the derivation of the extended Einstein’s parameters, starting from a momentum balance for a control volume of a generic channel with instream submerged vegetation (as proposed by Petryk and Bosmajian, 1975). Third, an extensive experimental program carried out on a tilting flume with a mobile bed and with plants being represented by series of aluminum cylinders. Different scenarios of vegetation spatial density were tested while measurements of bedload rate, water level, bed level and flow velocity were periodically performed in order to assess conditions of stationarity and morphodynamic equilibrium. Last, a deep analysis of experimental results allowed for the calibration of the new approach, whereas external datasets from the literature were used to assess its performance in a wide variety of conditions. A study based on four statistical measures showed that the extended Einstein’s parameters are significantly more suitable for bedload rate estimation when compared to the original ones, since predicted and measured values have, on average, the same order of magnitude. Additionally, the new approach outperformed the widely-adopted method of Baptist (2005), which consists of the re-calculation of bed roughness in vegetated settings. Finally, the experimental observations suggest that the submergence ratio and the stem spatial density are the most important traits of river plants to display influence on bedload transport, channel bed stability, and bed form dimensions and patterns. A better understanding of these traits might lead to better prediction capabilities of river evolution. / La vegetazione svolge un ruolo fondamentale negli ambienti fluviali, poiché fornisce un ampio spettro di servizi ecosistemici; per questo essa è una componente rilevante dei progetti di riqualificazione fluviale. Tuttavia, la presenza di piante in alveo aumenta la resistenza al moto e di conseguenza anche il tirante idrico durante gli eventi di piena. Inoltre, la copertura vegetale in alveo e nelle zone riparie influenza l'evoluzione morfologica dei corsi d'acqua. Nonostante le evidenze sperimentali mostrino che la vegetazione in alveo ha un forte impatto sul trasporto dei sedimenti, sono poche le formule di trasporto che tengono conto in modo esplicito dell'effetto della vegetazione e i metodi esistenti, basati sulla determinazione di un coefficiente di scabrezza, possono dare luogo a incongruenze. Per questa ragione, in questa tesi si propone un approccio che estende la formulazione di Einstein (1950) e include l'effetto della geometria e della densità spaziale della vegetazione sul trasporto solido. Sono state derivate nuove espressioni per il parametro di trasporto adimensionale Φ e il parametro di intensità del trasporto Ψ, che possono essere introdotte in modelli di trasporto esistenti del tipo Φ = f(Ψ). Questo nuovo approccio consente di considerare l'effetto della presenza di vegetazione sommersa ed emergente e si riduce al modello originale di Einstein in assenza di vegetazione. L'attività di ricerca si è svolta in quattro fasi. Nella prima fase si è svolta un'analisi approfondita della letteratura mirata soprattutto a identificare gli effetti della vegetazione sulla morfodinamica fluviale, definire lo stato dell'arte relativo alle interazioni fra flusso liquido, sedimenti e vegetazione, ed analizzare gli approcci esistenti per la stima del trasporto di fondo in alvei vegetati. Nella seconda fase si sono derivati i parametri della formulazione di Einstein estesa a partire dal bilancio di quantità di moto per un volume di controllo di un canale generico con vegetazione sommersa (come proposto da Petryk e Bosmajian, 1975). Nella terza fase è stato condotto un esteso set di esperimenti, utilizzando un modello fisico costituito da una canaletta di laboratorio a pendenza variabile e fondo mobile, in cui le piante sono state simulate tramite cilindri in alluminio. Sono stati riprodotti diversi scenari di densità spaziale della vegetazione e sono stati misurati periodicamente la portata solida, la quota della superficie libera e del fondo e la velocità della corrente per valutare le condizioni di stazionarietà ed equilibrio morfodinamico. Infine, il nuovo approccio è stato calibrato sulla base di un'analisi approfondita dei risultati sperimentali e quindi applicato a set di dati di letteratura per valutarne l'accuratezza in un ampio intervallo di condizioni. Un'analisi statistica basata su quattro indicatori ha mostrato che i parametri della formulazione di Einstein estesa producono stime di trasporto solido sensibilmente più accurate rispetto ai parametri originali, in quanto i valori calcolati sono, in generale, dello stesso ordine di grandezza dei valori misurati. Inoltre, il nuovo approccio dà risultati migliori rispetto al metodo di Baptist (2005), ampiamente adottato, che consiste nel ricalcolo della scabrezza per gli alvei vegetati. Infine, le osservazioni sperimentali suggeriscono che il rapporto di sommergenza e la densità spaziale delle piante sono i parametri che influenzano in modo più significativo il trasporto solido, la stabilità del fondo dell'alveo, la scala delle forme di fondo e la loro organizzazione spaziale. Una conoscenza più approfondita di questi aspetti può contribuire a una maggiore capacità di prevedere l'evoluzione dei corsi d'acqua. / Se ha identificado a la vegetación como un actor importante en ambientes fluviales al proporcionar una amplia gama de servicios ecosistémicos. Por esta razón, el uso de plantas se ha vuelto cada vez más relevante en proyectos de restauración de ríos. Sin embargo, su presencia en lechos fluviales impacta la resistencia al flujo, aumentando los niveles del agua en condiciones de inundación. Además, este tipo de vegetación, ya sea que esté en el lecho o en las márgenes, influye en la evolución morfológica de los ríos. Diversas observaciones han mostrado que la vegetación fluvial tiene un fuerte impacto en las tasas de transporte sólido de fondo. A pesar de ello, hay una escasez de métodos confiables para la estimación de este tipo de sedimentos que tome en consideración el efecto de las plantas y, aquéllos que existen, los cuales se basan en la corrección del coeficiente de rugosidad del canal, suelen presentar resultados inconsistentes. Por tanto, se propone aquí un método que extiende las definiciones fundamentales de Einstein (1950) en modo que se incluyan los efectos de la geometría y la densidad espacial de las plantas sobre el transporte sólido. Las nuevas ecuaciones del parámtero de transporte, Φ, y el parámetro de movilidad, Ψ, fueron obtenidas para su implementación en métodos predictores de transporte de fondo de la forma Φ = (Ψ). La aplicabilidad de este nuevo enfoque considera la posibilidad de vegtación fluvial tanto emergente como sumergida, y se reduce a las ecuaciones originales de Einstein si ésta fuera inexistente. La metodología de investigación se llevó a cabo en cuatro fases. Primero, una revisión exhaustiva de la literatura para la identificación, principalmente, de los diferentes efectos de la vegetación en la morfodinámica de ríos, los avances más recientes en el conocimiento sobre las interacciones flujo-sedimento-vegetación, y los métodos actualmente existentes para la estimación del transporte sólido de fondo en canales naturales vegetados. En segundo lugar, la obtención de los parámetros de Einstein extendidos a partir de un balance de momentum para el volumen de control de un canal genérico con vegetación sumergida (según lo propuesto por Petryk y Bosmajian, 1975). En tercer lugar, un extenso programa experimental realizado en un canal de fondo móvil y pendiente variable, con las plantas siendo representadas por series de cilindros metálicos. Se probaron diferentes escenarios de densidad espacial de vegetación, mientras que periódicamente se realizaron mediciones transporte sólido, niveles del agua, topografía del fondo y velocidad del flujo con el objeto de evaluar las condiciones de flujo uniforme y equilibrio morfodinámico. Por último, un análisis profundo de los resultados experimentales permitió la calibración del nuevo método, mientras que se utilizaron datos externos disponibles en la literatura para evaluar su desempeño bajo diversas condiciones. Un estudio basado en cuatro medidas estadísticas mostró que los parámetros extendidos de Einstein son mucho más adecuados para la estimación del transporte de fondo en comparación con los originales, ya que los valores estimados y los medidos muestran, en promedio, el mismo orden de magnitud. Además, el nuevo método superó al propuesto por Baptist (2005), ampliamente utilizado, el cual consiste en la corrección de la rugosidad del canal en presencia de vegetación. Finalmente, las observaciones experimentales sugieren que la sumergencia de las plantas y la densidad espacial de los tallos son las variables más influyentes en el transporte sedimentos de fondo, la estabilidad del lecho, y las dimensiones y patrones de la forma de fondo. Una mejor comprensión de estas variables puede significar una mejor capacidad para predecir la evolución de un río.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24966号 / 医博第5020号 / 新制||医||1069(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 江木 盛時, 教授 浅野 雅秀, 教授 湊谷 謙司 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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As the world becomes increasingly interconnected, modernized, and populated, the demand for energy across the globe is growing at an unprecedented rate. This growth in energy demand has an undeniable impact on increasingly pressing social issues including, climate change, energy security, energy economy, atmospheric chemistry, and air quality. Finding a way to address these issues on a rapid timescale is more important than ever. A common thread running through all of these challenges is that they can be partially or fully addressed with the development of new chemical energy conversion technologies which, in turn, rely on a comprehensive understanding of gas phase kinetics. Examples of promising technologies include renewable fuels (i.e. methanol and hydrogen) and/or reliable, efficient, and clean engines that can accommodate renewable fuels. The development of such technology would enable the use of renewable fuels, thereby reducing emissions and cutting down on harmful byproducts released into the atmosphere. Computational simulations have become a powerful approach for developing and advancing energy technology in a safe, efficient, and effective manner. These computational approaches model reacting flows and are generally known as computational fluid dynamics (CFD). However, in order for these CFD simulations to work effectively and make meaningful predictions, the sub-models used to describe the underlying chemistry (gas phase kinetics) must be accurate; information about underlying chemistry is provided to computational simulations via a chemical kinetic model/mechanism, which describes the chemical reactions that drive the fuel oxidation within the system being simulated. Regarding combustion specifically, the reliability of predictive simulations depends on the availability of accurate data and models not only for chemical kinetics, but also thermochemistry and transport. Further complicating the problem, combustion and chemical kinetics provide a unique challenge in regard to obtaining accurate predictive models; underlying chemical kinetics mechanisms may require unprecedented accuracy to obtain truly predictive combustion modeling. For example, it has been shown in computational simulations that uncertainties in any of several kinetic parameters can yield uncertainties large enough in the physical system being modeled to cause system failure, thereby reducing the effectiveness of computational design approaches that could accelerate technology development. Hence, a strong need exists to develop a method that significantly reduces uncertainties in chemical kinetics parameters to meet the accuracy demands of advanced computational design tools. To this end, it is useful to draw on inspiration from existing methods in the field of combustion and chemical kinetics as well as tangential fields; the most compelling inspiration can be found in the field of thermochemistry in the form of the Active Thermochemical Tables (ATcT). This work presents a novel, analogous approach for chemical kinetics called MultiScale Informatics, or MSI for short. The MSI approach identifies optimized values and quantified uncertainties for a set of molecular parameters (within theoretical kinetics calculations), rate parameters, and physical model parameters (within simulations of experimental observables) as informed by data from various sources and scales. The overarching objectives of this work are to demonstrate how the MSI approach can be used to determine physically meaningful optimized kinetics parameters and quantified uncertainties, unravel webs of interconnected rate constants in complex reaction systems, resolve discrepancies among data sets, and touch on key elements of MSI’s implementation. To demonstrate how these objectives are met, the MSI approach is used to explore the kinetics of three reaction sub-systems. The studies of these sub-systems will demonstrate some key elements of this approach including: the importance of raw data for quantifying the information content of experimental data, the utility of theoretical kinetics calculations for constraining experimental interpretations and providing an independent data source, and the subtleties of target data selection for avoiding unphysical parameter adjustments to match data affected by structural uncertainties. For the first sub-system explored (CH₃ + HO₂), the MSI approach is applied to carefully selected (mostly raw) experimental data and yields an opposite temperature dependence for the channel-specific CH3 + HO2 rate constants as compared to a previous rate-parameter optimization. While both optimization studies use the same theoretical calculations to constrain model parameters, only the present optimization, which incorporates theory directly into the model structure, yields results that are consistent with theoretical calculations. For the second sub-system explored (HO₂ + HO₂), the MSI approach is applied to carefully selected experimental data, leveraging the hydrogen reaction system from the first study with the addition of high level theory calculations for the reaction of HO₂ + HO₂. Recent high-level theoretical calculations predict a mild temperature dependence for HO₂ + HO₂, which is inconsistent with state-of-the-art experimental determinations that upheld the stronger temperature dependence observed in early experiments. Via MSI analysis of the theoretical and experimental data, alternative interpretations of the raw experimental data that uses HO₂ + HO₂ rate constants nearly identical to theoretical predictions are identified – implying that the theoretical and experimental data are actually consistent, at least when considering the raw data from experiments. Similar analyses of typical signals from low-temperature experiments indicate that an HOOOOH intermediate – identified by recent theory but absent from earlier interpretations – yields modest effects that are smaller than, but may have contributed to, the scatter in data among different experiments. More generally, the findings demonstrate that modern chemical theories and experiments have progressed to a point where meaningful comparison requires joint consideration of their data simultaneously. The third sub-system explored builds a larger web of interconnected reaction systems in an attempt to achieve data redundancy and demonstrate how interpreting coupled reaction systems is necessary to accurately determine many key rate constants. The ability of the MSI method to interpret raw experimental data and untangle rate constant reaction systems is demonstrated. The study also reinforces how implementing theory into the model structure is imperative to yield results that are consistent with experimental data as well as theoretical calculations and achieve physically realistic branching ratios. Finally, this work will present how results from all the studied reaction systems culminate into a complex hydrogen/syngas combustion model validated against data from various combustion experiments.

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