Deep learning detection and quantification of volcanic thermal signals in infrared satellite data

Thesis (M.S.) University of Alaska Fairbanks, 2024Volcanic eruptions pose hazards to human lives and livelihoods (Loughlin et al., 2015). To mitigate these hazards, volcano monitoring groups aim to detect signs of unrest and eruption as early as possible. Prior to eruption volcanoes may show various signals of unrest, including: increased surface temperatures, surface deformation, increased seismicity, increased degassing, and more. Here we focus on one approach to monitor volcanic unrest: detecting high-temperature localized volcanic heat emissions, otherwise known as hotspots. The presence of hotspots can indicate subsurface and surface volcanic processes that precede, or coincide with, eruptions. Space-borne infrared sensors can identify hotspots in near-real-time; however, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN-based model to detect volcanic hotspots in VIIRS (Visible Infrared Imaging Radiometer Suite) imagery. We find that HotLINK achieves an accuracy of 96% when evaluated on a validation dataset of ~1,700 unseen images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska, and 95% when evaluated on a test dataset of ~3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). Additional testing on ~700 labeled MODIS images demonstrates that our model is applicable to this sensor's data as well, achieving an accuracy of 98%. We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to a threshold-based method, MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. In particular, analysis of the Mount Veniaminof record demonstrates that HotLINK is able to detect subtle hotspot signals that are coincident with elevated seismicity, potentially indicative of surface heating due to shallow magma intrusion and/or degassing. We identify three advantages to our model over its predecessors: (1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime imagery; (2) the incorporation of probabilistic predictions for each detection that provide a measure of detection confidence; and (3) its transferability to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application. HotLINK is able to identify eruptions and potentially precursory warming signals in infrared satellite data, making it a valuable tool for monitoring volcanoes and tracking their heat released over time.National Science Foundation Prediction of and Resilience against Extreme Events (PREEVENTS, award number 1855126) awardChapter 1: Introduction -- Chapter 2: Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the Hotspot Learning and Identification Network -- Chapter 3: Overall conclusions -- References -- Appendix -- A. U-net code -- B. Image augmentation validation -- C. Optimizing hysteresis thresholds -- D. Optimizing MIROVA thresholds -- E. Additional HotLINK detection examples

Development of a Diesel Surrogate Fuel Library

[EN] Diesel fuel is composed of a complex mixture of hundreds of hydrocarbons that vary globally depending on crude oil sources, refining processes, legislative requirements and other factors. In order to simplify the study of this fuel, researchers create surrogate fuels to mimic the physical and chemical properties of Diesel fuels. This work employed the commercial software Reaction Workbench - Surrogate Blend Optimizer (SBO) to develop a Surrogate Fuel Library containing 18 fuels. Within the fuel library, the cetane number ranges from 35 to 60 (in increments of 5) at threshold soot index (TSI) levels representative of low, baseline and high sooting tendency fuels (TSI = 17, 31 and 48, respectively). The Surrogate Fuel Library provides the component blend ratios and predicted properties for cetane number, threshold soot index, lower heating value, density, kinematic viscosity, molar hydrogen-to-carbon ratio and distillation curve temperatures from T-10 to T-90. A market petroleum Diesel fuel with a cetane number of 50 and a threshold soot index of 31 was selected as the Baseline Diesel Fuel. The combustion, physical and chemical properties of the Baseline Diesel Fuel were precisely matched by the Baseline Surrogate Fuel. To validate the SBO predicted fuel properties, a set of five surrogate fuels, deviating in cetane number and threshold soot index, were blended and examined with ASTM tests. Good agreement was obtained between the SBO predicted and ASTM measured fuel properties. To further validate the Surrogate Fuel Library, key properties that were effected by altering the component blend ratios to control cetane number and TSI were compared to a set of five market Diesel fuels with good results. These properties included density, viscosity, energy density and the T-10 and T-90 distillation temperatures. The Surrogate Fuel Library provided by this work supplies Diesel engine researchers and designers the ability to analytically and experimentally vary fuel cetane number and threshold soot index with fully-representative surrogate fuels. This new capability to independently vary cetane number and threshold soot index provides a means to further enhance the understanding of Diesel combustion and design future combustion systems that improve efficiency and emissions.Szymkowicz, P.; Benajes, J. (2018). Development of a Diesel Surrogate Fuel Library. Fuel. 222:21-34. https://doi.org/10.1016/j.fuel.2018.01.112S213422

Automated Shaped Charge Design: Applying Dakota Optimization to CTH Kinetic Energy Results

Advances in computational power present an opportunity to further optimize the design of an engineered energetic system. This work presents the application of a proposed optimization scheme which combines the shock-physics hydrocode CTH with the DAKOTA optimization package to automate shaped-charge jet design. The formation of an explosively driven hypervelocity jet is highly dependent on the original shaped charge liner geometry. By parameterizing this geometry, and by developing a characteristic objective function from CTH simulations, a process can be established where the Dakota code iteratively builds an optimal shaped charge. This work attempts to use this methodology to reproduce a reference geometry. This is done by characterizing the liner geometry with two parabolas and post-processing an objective function from the kinetic energy profile of the resulting jet. Multi-dimensional parameter studies, gradient optimizations and genetic algorithms are used to probe the parameter space

Deep learning for Gaussian process tomography model selection using the ASDEX Upgrade SXR system

Gaussian process tomography (GPT) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in a tokamak, given some model for the underlying physical processes involved. GPT can also be used, thanks to Bayesian formalism, to perform model selection -- i.e., comparing different models and choosing the one with maximum evidence. However, the computations involved in this particular step may become slow for data with high dimensionality, especially when comparing the evidence for many different models. Using measurements collected by the ASDEX Upgrade Soft X-ray (SXR) diagnostic, we train a convolutional neural network (CNN) to map SXR tomographic projections to the corresponding GPT model whose evidence is highest. We then compare the network's results, and the time required to calculate them, with those obtained through analytical Bayesian formalism. In addition, we use the network's classifications to produce tomographic reconstructions of the plasma emissivity profile, whose quality we evaluate by comparing their projection into measurement space with the existing measurements themselves

ANALYTICAL AND EXPERIMENTAL INVESTIGATION OF MULTI-COMPONENT SURROGATE DIESEL FUELS

Diesel fuel is composed of a complex mixture of hundreds of hydrocarbons that vary globally depending on crude oil sources, refining processes, legislative requirements and other factors. In order to simplify the study of this fuel, researchers create surrogate fuels with a much simpler composition, in an attempt to mimic and control the physical and chemical properties of Diesel fuel. The first surrogates were single-component fuels such as n-heptane and n-dodecane. Recent advancements have provided researchers the ability to develop multi-component surrogate fuels and apply them to both analytical and experimental studies. The systematic application of precisely controlled surrogate fuels promises to further enhance our understanding of Diesel combustion, efficiency, emissions and particulates and provide tools for investigating new and alternative engine combustion systems. This thesis employed analytical and experimental methods to develop, validate and study a library of multi-component surrogate Diesel fuels. The first step was to design a surrogate fuel to precisely match the physical and chemical properties of a full-range petroleum Diesel fuel with 50 cetane number and a typical threshold soot index value of 31. The next step was to create a Surrogate Fuel Library with 18 fuels that independently varied two key fuel properties: cetane number and threshold soot index. Within the fuel library cetane number ranged from 35 to 60 at three threshold soot index levels of 17, 31 and 48 (low, mid-range and high). Extensive ASTM fuel property tests showed that good agreement with important physical and chemical properties of petroleum Diesel fuel such as density, viscosity, heating value and distillation curve. An experimental investigation was conducted to evaluate the combustion, emissions, soot and exhaust particles from the petroleum Diesel fuel and the matching surrogate fuel. A fully-instrumented single-cylinder Diesel engine was operated with combustion strategies including Premixed Charge Compression Ignition (PCCI), Low-Temperature Combustion (LTC) and Conventional Diesel Combustion (CDC). For combustion, the ignition delay, low-temperature (first stage) and high temperature (second stage) heat-release matched very well. Gaseous emissions, soot and exhaust particles maintained good agreement as exhaust gas recirculation and combustion phasing were varied. This thesis demonstrated that fully representative Diesel surrogate fuels could be tailored with the proper blending of the following hydrocarbon components: n-hexadecane, 2,2,4,4,6,8,8-heptamethylnonane, decahydronaphthalene and 1-methylnaphthalene. It was also established that the volumetric blending fractions of these four components could be varied to independently control the fuel cetane number and threshold soot index while retaining the combustion, physical and chemical properties of full-range petroleum Diesel fuel. The Surrogate Fuel Library provided by this thesis supplies Diesel engine researchers and designers the ability to analytically and experimentally vary fuel cetane number and threshold soot index. This new capability to independently vary two key fuel properties provides a means to further enhance the understanding of Diesel combustion and design future combustion systems that improve efficiency and emissions.El combustible diésel está compuesto por cientos de hidrocarburos cuya presencia y proporción varía dependiendo del origen del crudo, del proceso de refinado, de los requerimientos legislativos, y de muchos otros factores. Para evitar las dificultades que produce esta variabilidad y complejidad en su composición, en los estudios sistemáticos, los investigadores suelen trabajar con combustibles de sustitución, mucho más sencillos, pero que reproducen las propiedades químicas y físicas del gasóleo. Los primeros combustibles de sustitución estuvieron formados por un solo componente, como el n-heptano y el n-dodecano. Recientemente se han desarrollado combustibles de sustitución multi-componentes, que se aplican tanto a estudios experimentales como de modelado. La aplicación sistemática de combustibles de sustitución controlados con precisión es una vía prometedora para mejorar la comprensión de la combustión Diesel, su eficiencia, y sus emisiones y proporciona herramientas para la investigación de sistemas de combustión nuevos y alternativos. En esta tesis se han empleado métodos experimentales y de cálculo para desarrollar, estudiar y validar una librería de combustibles de sustitución multi-componentes. El primer combustible de sustitución se diseñó para reproducir con precisión las propiedades físicas y químicas de un gasóleo con número de cetano 50 y un índice de hollín umbral (TSI) de 31.El siguiente paso fue crear una biblioteca de combustibles de sustitución con 18 combustibles que pueden modificar independientemente dos propiedades clave del combustible: índice de cetano y TSI. En la biblioteca de combustibles el número de cetano osciló entre 35 y 60 con tres niveles de TSI iguales a 17, 31 y 48 (bajo, medio y alto rango). Los ensayos según la normativa ASTM demostraron una buena coincidencia con las propiedades del gasóleo como densidad, viscosidad, poder calorífico y curvas de destilación. Para comprobar la validez de la librería, se realizó un estudio experimental comparativo sobre el proceso de combustión, las emisiones gaseosas, hollín y partículas de un gasóleo y de su combustible de sustitución ajustado. El estudio se realizó con un motor monocilíndrico Diesel completamente instrumentado y operando con estrategias de combustión en premezcla parcial (PPCI) y de baja temperatura (LTC), además de la combustión Diesel convencional (CDC). Los parámetros de la combustión como el retraso al encendido y la liberación de calor tanto de baja como de alta temperatura se aproximaron muy bien. Las emisiones de gases, hollín y partículas también fueron similares al variar el nivel de EGR y la fase de la combustión. La tesis demuestra que se pueden encontrar combustibles de sustitución perfectamente representativos de un gasóleo corriente, en base a mezclas apropiadas de n-hexadecano, 2,2,4,4,6,8,8-heptamethylnonano, decahidronaftaleno y 1-metilnaftaleno. Asimismo, se concluye que variando la proporción de estos cuatro componentes se puede controlar independientemente el número de cetano y el índice de hollín umbral, a la vez que se mantienen las propiedades físico-químicas y de combustión del gasóleo. La librería de combustibles de sustitución definida en esta tesis es una herramienta a disposición de los investigadores para profundizar en el conocimiento de la combustión diésel y avanzar en el diseño de sistemas futuros de combustión con mejor rendimiento y menores emisiones.El combustible Diesel està compost per centenars d'hidrocarburs, la presència i proporció dels quals varia depenent de l'origen del cru, del procés de refinat, dels requeriments legislatius, i de molts altres factors. Per a evitar les dificultats que produeix aquesta variabilitat i complexitat en la seua composició, en els estudis sistemàtics, els investigadors solen treballar amb combustibles de substitució, molt més senzills, però que reprodueixen les propietats químiques i físiques del gasoil. Els primers combustibles de substitució van estar formats per un sol component, com el n-heptà i el n-dodecà. Recentment s'han desenvolupat combustibles de substitució multi-components, que s'apliquen tant a estudis experimentals com de modelatge. L'aplicació sistemàtica de combustibles de substitució controlats amb precisió és una via prometedora per a millorar la comprensió de la combustió Dièsel, la seua eficiència, i les seues emissions i proporciona eines per a la recerca de sistemes de combustió nous i alternatius. En aquesta tesi s'han emprat mètodes experimentals i de càlcul per a desenvolupar, estudiar i validar una llibreria de combustibles de substitució multi-components. El primer combustible de substitució es va dissenyar per a reproduir amb precisió les propietats físiques i químiques d'un gasoil amb índex de cetà 50 i un índex de sutge límit (TSI) de 31. El següent pas va ser crear una biblioteca de combustibles de substitució amb 18 combustibles que poden modificar independentment dues propietats clau del combustible: índex de cetà i TSI. En la biblioteca de combustibles l'índex de cetá va oscil·lar entre 35 i 60 amb tres nivells de TSI iguals a 17, 31 i 48 (baix, mitjà i alt rang). Els assajos segons la normativa ASTM van demostrar una bona coincidència amb les propietats del gasoil com a densitat, viscositat, poder calorífic i corbes de destil·lació. Per a comprovar la validesa de la llibreria, es va realitzar un estudi experimental comparatiu sobre el procés de combustió, les emissions gasoses, sutge i partícules d'un gasoil i del seu combustible de substitució ajustat. L'estudi es va realitzar amb un motor monocilíndric Dièsel completament instrumentat i operant amb estratègies de combustió en premescla parcial (PPCI) i de baixa temperatura (LTC), a més de la combustió Dièsel convencional (CDC). Els paràmetres de la combustió com el retard a l'encès i l'alliberament de calor tant de baixa com d'alta temperatura es van aproximar molt bé. Les emissions de gasos, sutge i partícules també van ser similars en variar el nivell d'EGR i la fase de la combustió. La tesi demostra que es poden trobar combustibles de substitució perfectament representatius d'un gasoil corrent, sobre la base de mescles apropiades de n-hexadecà, 2,2,4,4,6,8,8-heptamethylnonà, decahidronaftalé i 1-metilnaftaleno. Així mateix, es conclou que variant la proporció d'aquests quatre components es pot controlar independentment l'índex de cetà i l'índex de sutge límit, alhora que es mantenen les propietats físic-químiques i de combustió del gasoil. La llibreria de combustibles de substitució definida en aquesta tesi és una eina a la disposició dels investigadors per a aprofundir en el coneixement de la combustió Diesel i avançar en el disseny de sistemes futurs de combustió amb millor rendiment i menors emissions.Szymkowicz, P. (2017). ANALYTICAL AND EXPERIMENTAL INVESTIGATION OF MULTI-COMPONENT SURROGATE DIESEL FUELS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90406TESI

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

Environmental properties of compounds provide significant information in treating organic pollutants, which drives the chemical process and environmental science toward eco-friendly technology. Traditional group contribution methods play an important role in property estimations, whereas various disadvantages emerge in their applications, such as scattered predicted values for certain groups of compounds. In order to address such issues, an extraction strategy for molecular features is proposed in this research, which is characterized by interpretability and discriminating power with regard to isomers. Based on the Henry's law constant data of organic compounds in water, we developed a hybrid predictive model that integrates the proposed strategy in conjunction with a neural network framework. The structure of the predictive model is optimized using cross-validation and grid search to improve its robustness. Moreover, the predictive model is improved by introducing the plane of best fit descriptor as input and adopting k-means clustering in sampling. In contrast with reported models in the literature, the developed predictive model demonstrates improved generality, higher accuracy, and fewer molecular features used in its development