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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sequential flows by irrelevant operators

We explore whether one can T\overline{T} T T ¯ deform a collection of theories that are already T\overline{T} T T ¯ -deformed. This allows us to define classes of irrelevant deformations that know about subsystems. In some basic cases, we explore the spectrum that results from this procedure and we provide numerical evidence in favor of modular invariance. We also study the flow of the classical Lagrangian for free bosons and free fermions under successive deformations. Some of the models found by sequentially flowing are likely to have interesting holographic interpretations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Microalgae Conversion to Biofuels and Biochemical via Sequential Hydrothermal Liquefaction (SEQHTL) and Bioprocessing: 2020 State of Technology

A preliminary techno-economic analysis (TEA) was developed for the fiscal year 2020 state of technology (SOT) assessment to evaluate the benefits and risks for a large-scale microalga hydrothermal liquefaction (HTL) system based on most recent testing results. The focus of the study is directed toward the conversion system, which consists of five processes: two-stage sequential HTL (SEQHTL), biocrude upgrading to final fuels, bioprocessing for co-product generation, hydrogen generation, and steam cycle. In this system, algae biomass with corn stover supplement during the lower algae productivity seasons (winter, fall, and spring) to match the maximum algae seasonal production rate in summer is employed to maintain a constant plant capacity in all the seasons. Algae only (summer season) or algae/corn stover blended feedstock (other seasons) are sent to a two-stage SEQHTL process. In stage I, the carbohydrates in the feedstock are extracted and separated from the residual solid. The residual solid from stage I is further converted to biocrude in the SEQHTL stage II step. The biocrude is upgraded to final fuel products in an upgrading process. The extract stream from HTL stage I is sent to the bioprocessing section for co-product generation via fermentation of carbohydrate. Lactic acid (LA) is assumed to be the co-product based on current bioprocessing testing results.

09 BIOMASS FUELS↗
DOE OSTI2024

Sequential Monte Carlo for Cut-Bayesian Posterior Computation

We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches constructed primarily to account for model misspecification. We prove finite sample concentration bounds for estimators derived from the proposed method along with a linear tempering extension and apply these results to a realistic setting where a computer model is misspecified. We then illustrate the SMC method for inference in a modular chemical reactor example that includes submodels for reaction kinetics, turbulence, mass transfer, and diffusion. The samples obtained are commensurate with a direct-sampling approach that consists of running multiple Markov chains, with computational efficiency gains using the SMC method. Overall, the SMC method presented yields a novel, rigorous approach to computing with cut-Bayesian posterior distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Model-Based Sequential Design of Experiments for Pilot Testing of Novel Water-Lean CO2 Capture Solvent

Poster for the 2024 Fossil Energy and Carbon Management Meeting. It summarizes work done on process modeling and uncertainty quantification in preparation for the test campaign at the National Carbon Capture Center for a general audience. The poster includes sections detailing background on the EEMPA solvent, sequential design of experiments, process modeling (including results from the model), uncertainty quantification, and the goals of the test campaign.

Hedrick, Katherine↗
DOE OSTI2024

Sequential Design of Experiments for Pilot Testing of Novel Solvent System

The CCSI2 program is supporting a six-month test campaign at the National Carbon Capture Center (NCCC) for evaluation of a novel water-lean solvent. This presentation describes CCSI2’s efforts in process modeling of the solvent system for both coal and natural gas-based flue gas sources and initial uncertainty quantification (UQ) work to estimate parametric uncertainty in key sub-models of interest (e.g., thermodynamics, mass transfer, reaction kinetics). Moreover, perspective is provided on how UQ and sequential design of experiments (SDoE) tools are used to assess the impact of model uncertainty on projected process performance, use this information to optimize data collection during the campaign, and refine process models through data collection. This framework is expected to reduce the overall model uncertainty, and thus risk associated with scale-up as the process moves towards commercialization.

Morgan, Joshua↗
DOE OSTI2024

Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)

Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Effect of Intensity Fluctuations on Sequential X-ray Photon Correlation Spectroscopy at the X-ray Free Electron Laser Facilities

How materials evolve at thermal equilibrium and under external excitations at small length and time scales is crucial to the understanding and control of material properties. X-ray photon correlation spectroscopy (XPCS) at X-ray free electron laser (XFEL) facilities can in principle capture dynamics of materials that are substantially faster than a millisecond. However, the analysis and interpretation of XPCS data is hindered by the strongly fluctuating X-ray intensity from XFELs. Here we examine the impact of pulse-to-pulse intensity fluctuations on sequential XPCS analysis. We show that the conventional XPCS analysis can still faithfully capture the characteristic time scales, but with substantial decrease in the signal-to-noise ratio of the g2 function and increase in the uncertainties of the extracted time constants. We also demonstrate protocols for improving the signal-to-noise ratio and reducing the uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Rare Earth Element (REE) and Critical Mineral Fractions of Central Appalachian Coal-Related Strata Determined by 7-Step Sequential Extraction

Rare earth elements (REEs) and critical minerals (CMs) are used in many modern industries, including the automotive sector, generation and storage, clean energy, and defense. The demand for REEs is increasing, and the REE supply chain is unpredictable. The US has driven to assess non-conventional sources of REE (such as coal underclay) to identify domestic resources to stabilize this uncertainty in supply. Knowledge of the minerology, distribution, and modes of occurrence of REEs is integral to the assessment of non-conventional sources. Additionally, extraction techniques can be optimized and targeted when REE distribution in different solid fractions from source material is understood. In this study, four bituminous coal-related samples associated with the Lower and Middle Kittanning coal seams in the Appalachian Basin (US) underwent a seven-step sequential extraction procedure, primarily targeting the water-soluble, exchangeable, acid soluble, mildly reducible, moderately reducible, strongly reducible, and oxidizable fractions. The REE and other elements of interest from each extraction step were analyzed, and the percentages of element extracted from raw solids were calculated. REEs extracted from the total seven steps were reported as the extractable fraction, whereas the fractions in the residual solids were reported as the non-extractable fraction. Less than 6% of REE were extracted from three samples. Twenty-one percent of REE was extracted from the fourth sample, mainly from the steps targeting oxidizable and exchangeable phases. Co-extraction of critical metals (Co, Ni, Cu, and Zn) occurred during the oxidizable, exchangeable, acid soluble, and water-soluble steps for the four samples. In the extracted fractions, the four samples all exhibited a middle and heavy REE enrichment relative to light REE. The mobility of major cation (e.g., Ca, Fe, and P) and REE is associated with exchangeable, oxidizable, and acid soluble fractions. Non-extractable REE is likely held in refractory apatitic phases, and/or primary REE-phosphates (e.g., monazite and xenotime).

58 GEOSCIENCES↗
DOE OSTI2024

Self-Cleaning Highly Porous TiO 2 Coating Designed by Swelling-Assisted Sequential Infiltration Synthesis (SIS) of a Block Copolymer Template

Photocatalytic self-cleaning coatings with a high surface area are important for a wide range of applications, including optical coatings, solar panels, mirrors, etc. Here, we designed a highly porous TiO 2 coating with photoinduced self-cleaning characteristics and very high hydrophilicity. This was achieved using the swelling-assisted sequential infiltration synthesis (SIS) of a block copolymer (BCP) template, which was followed by polymer removal via oxidative thermal annealing. The quartz crystal microbalance (QCM) was employed to optimize the infiltration process by estimating the mass of material infiltrated into the polymer template as a function of the number of SIS cycles. This adopted swelling-assisted SIS approach resulted in a smooth uniform TiO 2 film with an interconnected network of pores. The synthesized film exhibited good crystallinity in the anatase phase. The resulting nanoporous TiO 2 coatings were tested for their functional characteristics. Exposure to UV irradiation for 1 h induced an improvement in the hydrophilicity of coatings with wetting angle reducing to unmeasurable values upon contact with water droplets. Furthermore, their self-cleaning characteristics were tested by measuring the photocatalytic degradation of methylene blue (MB). The synthesized porous TiO 2 nanostructures displayed promising photocatalytic activity, demonstrating the degradation of approximately 92% of MB after 180 min under ultraviolet (UV) light irradiation. Thus, the level of performance was comparable to the photoactivity of commercial anatase TiO 2 nanoparticles of the same quantity. Our results highlight a new robust approach for designing hydrophilic self-cleaning coatings with controlled porosity and composition.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants

High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sequential infiltration synthesis for enhancing multiple-patterning lithography

Simplified methods of multiple-patterning photolithography using sequential infiltration synthesis to modify the photoresist such that it withstands plasma etching better than unmodified resist and replaces one or more hard masks and/or a freezing step in MPL processes including litho-etch-litho-etch photolithography or litho-freeze-litho-etch photolithography.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Systems and methods for enhanced sequential power system model parameter estimation

A system for enhanced sequential power system model calibration is provided. The system is programmed to store a model of a device. The model includes a plurality of parameters. The system is also programmed to receive a plurality of events associated with the device, receive a first set of calibration values for the plurality of parameters, generate a plurality of sets of calibration values for the plurality of parameters, for each of the plurality of sets of calibration values, analyze a first event of the plurality of events using a corresponding set of calibration values to generate a plurality of updated sets of calibration values, analyze the plurality of updated sets of calibration values to determine a current updated set of calibration values, and update the model to include the current updated set of calibration values.

Wang, Honggang↗
DOE OSTI2023

CRITICAL MINERAL PARTITIONING IN COAL-HOSTED CLAYS OF THE POWDER RIVER BASIN, WY DETERMINED BY SEQUENTIAL EXTRACTION

Increasing demand for a more robust domestic supply of rare earth elements (REE) and critical minerals (CM) has led to significant investigation into unconventional sources. Coal and coal byproducts from the Powder River Basin (PRB) of Wyoming are potential sources of REE and CM. The association between strategic metals and their mineral hosts has important implications for extractability. Samples of overburden, underclays, and clay-rich partings were selected for analysis from core and bucket samples from three PRB coal mines. Quartz, illite, and kaolinite-group minerals are abundant in clay-rich portions of the PRB coal strata. We performed a five-step sequential extraction to constrain critical metal partitioning in samples of overburden and underclays (n=10). Unlike the occurrences of REE in other clay deposits, insignificant amounts of total REE + Y (TREY) were recovered in the ion-exchangeable fraction (< 0.5 ppm). A significant amount of TREY was leached from the acid-soluble fraction by 0.1M HCl, suggesting REE may be tied up within crystal lattices rather than sorbed to clay surfaces. The acid-soluble fraction contained between 3 and 250 ppm TREY, with an average TREY concentration of 60 ppm. Relative standard deviations for each extraction step were generally < ±10%. Whole-rock samples (n=72) contained greater TREY enrichment in overburden than in underclays and partings, however, the opposite is true for other CM, such as Ti and V. Other locations in the PRB show different enrichment trends. Our findings supplement a broader effort to couple REE and CM extraction with existing domestic coal production and contribute to the fundamental understanding of how these metals concentrate in low-temperature basin environments.

Stuart, Sophia↗
DOE OSTI2020

A Rapid Sequential Separation Method for Determination of Actinides and TENORM in Fracking Wastes - 20223

With recent advances in unconventional drilling technology in the US and around the world, the risks of environmental contamination and exposure due to technologically enhanced naturally occurring radioactive materials (TENORM) from fracking wastes have also surfaced. Although the Permian Basin (southeastern New Mexico, western Texas) has long been a modest producer of oil, the advent of fracking technology a decade ago turned it into hot property for producers. A 2018 assessment of undiscovered, technically recoverable continuous oil and gas resources by the U.S. Geological Survey estimates an average 46.3 billion barrels of oil and 281 trillion cubic feet of gas. This is likely to increase the use of unconventional drilling and TENORM generation. TENORM concentrations in oil and gas exploration and production waste can be several hundred to several thousand times higher than background TENORM concentrations. Both the thorium and uranium decay chains contribute to airborne radionuclides arising from the radon gas escaping the ground and subsequently decaying as airborne particulates. Additionally, radon decay products {sup 210}Pb and {sup 210}Po can build up in scale on the internal surfaces of oil and gas handling pipes and in sludge in refineries, becoming potential inhalation and ingestion hazards for workers. Furthermore, southeastern NM is also home to world's only licensed and operating transuranic nuclear waste repository, the DoE's Waste Isolation Pilot Plant (WIPP). Plutonium isotopes ({sup 239+240}Pu) and {sup 241}Am, are expected to account for more than 99% of the total radioactivity scheduled for disposal in the WIPP repository. Thus, an improved understanding of the environmental fate and transport of TENORM, liberated by unconventional drilling, is essential to assess how best to protect individuals and the environment. In this context, accurate measurement of TENORM and actinides in environmental and biological samples is essential. In this presentation, a new sequential method for the separation and pre-concentration of actinides (Pu, Am, Np) and TENORM (Po, U and Th) derived from oil and gas exploration is proposed. The TEVA method involves a rapid co-precipitation step to remove matrix interferences from the samples, followed by plutonium oxidation state adjustment to Pu (IV) and an incubation period of ∼ 1 hour at 50-60 deg. C to allow the resultant Po (II) to oxidize into Po(IV). The polonium, neptunium, thorium and plutonium are then separated on a TEVA column, while americium and uranium are separated on a TRU column. After separation, the alpha counting source was prepared by micro-precipitation with copper sulfide (CuS) for polonium and neodymium fluoride (NdF{sub 3}) for actinides. The efficiency and reliability of the procedures were tested by analyzing filter, drinking water and frack sand samples. The method is simple, robust and can be performed quickly with excellent removal of interferences, high chemical recovery and very good alpha peak resolution. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2026

Localized Heterogeneous Nucleation for Vapor‐Assisted Sequential Deposition of Metal Halide Perovskites

Vapor-assisted hybrid two-step deposition, which combines thermally evaporated inorganic layers with solution-processed organic halides to form halide perovskites, has emerged as a scalable and industry-compatible route for textured tandem photovoltaics. However, this process is often hindered by reaction-limited phase formation, particularly when compact, non-porous, and highly crystalline inorganic layers formed by thermal evaporation restrict subsequent conversion, resulting in incomplete reaction and pronounced depth-dependent heterogeneity. In this study, we introduce a strategy to regulate the inorganic precursor layer by incorporating localized heterogeneous nucleation sites. Sparsely distributed hydrophilic metal oxide species serve as effective nucleation centers during vapor deposition, enabling effective control over film morphology and crystal orientation from the early stages of growth. This tailored inorganic framework facilitates the subsequent incorporation of organic halides, alleviating reaction limitations and suppressing residual unreacted precursors. Consequently, the perovskite films exhibit improved stoichiometric uniformity and enhanced optoelectronic quality, enabling wide-bandgap perovskite solar cells with markedly improved performance and operational stability. This work provides important mechanistic insight into crystal growth engineering of vapor-deposited perovskite thin films.

nucleation↗