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At least 1,531 records · Page 85

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DOE OSTI2021

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Abstract Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Assembly of short amphiphilic peptoids into nanohelices with controllable supramolecular chirality

Abstract A long-standing challenge in bioinspired materials is to design and synthesize synthetic materials that mimic the sophisticated structures and functions of natural biomaterials, such as helical protein assemblies that are important in biological systems. Herein, we report the formation of a series of nanohelices from a type of well-developed protein-mimetics called peptoids. We demonstrate that nanohelix structures and supramolecular chirality can be well-controlled through the side-chain chemistry. Specifically, the ionic effects on peptoids from varying the polar side-chain groups result in the formation of either single helical fiber or hierarchically stacked helical bundles. We also demonstrate that the supramolecular chirality of assembled peptoid helices can be controlled by modifying assembling peptoids with a single chiral amino acid side chain. Computational simulations and theoretical modeling predict that minimizing exposure of hydrophobic domains within a twisted helical form presents the most thermodynamically favorable packing of these amphiphilic peptoids and suggests a key role for both polar and hydrophobic domains on nanohelix formation. Our findings establish a platform to design and synthesize chiral functional materials using sequence-defined synthetic polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites

Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The structure of dissipative dark matter halos

Dissipative dark matter self-interactions can affect halo evolution and change its structure. We perform a series of controlled N-body simulations to study impacts of the dissipative interactions on halo properties. The interplay between gravitational contraction and collisional dissipation can significantly speed up the onset of gravothermal collapse, resulting in a steep inner density profile. For reasonable choices of model parameters controlling the dissipation, the collapse timescale can be a factor of 10–100 shorter than that predicted in purely elastic self-interacting dark matter. The effect is maximized when energy loss per collision is comparable to characteristic kinetic energy of dark matter particles in the halo. Our simulations provide guidance for testing the dissipative nature of dark matter with astrophysical observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Automated experimental design of safe rampdowns via probabilistic machine learning

Abstract Typically the rampdown phase of a shot consists of a decrease in current and injected power and optionally a change in shape, but there is considerable flexibility in the rate, sequencing, and duration of these changes. On the next generation of tokamaks it is essential that this is done safely as the device could be damaged by the stored thermal and electromagnetic energy present in the plasma. This works presents a procedure for automatically choosing experimental rampdown designs to rapidly converge to an effective rampdown trajectory. This procedure uses probabilistic machine learning methods paired with acquisition functions taken from Bayesian optimization. In a set of 2022 experiments at DIII-D, the rampdown designs produced by our method maintained plasma control down to substantially lower current and energy levels than are typically observed. The actions predicted by the model significantly improved as the model was able to explore over the course of the experimental campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. In this study, we developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Optimizing collimator positions using bayesian optimization in the Fermilab MI-8 transfer line

Collimators are used to minimize losses and to remove particles that would otherwise get lost downstream and irradiate the machine. Finding the optimal jaw positions is time consuming and with the upstream beam properties changing, the collimation settings would need to be readjusted each time. Therefore, a method to optimize collimator positions and to operate them at full capacity in a short time is required for loss control downstream. A study of collimator positions was conducted and a machine learning (ML) model was developed to predict optimal collimator positions. Bayesian Optimization (BO) was used to calculate new jaw positions from the ML model. The results of BO and usage of ML for better performance of the collimation system are presented in this paper.

Babacan, Betiay [Fermilab]↗

DOE OSTI2022

Classification of Ultrasonic Weld Quality using Acoustic Signatures Acquired During Manufacture

Ultrasonic welding is a process is based on generating a solid-state bond between two metals by applying moderate pressure and high intensity sound waves (20-70 kHz frequencies) at their interface. During the solid-state process numerous material, surface, instrument, and environmental factors contribute to bond formation, strength, and durability. The inherent difficulty of measuring and controlling each of these factors has, to date, made predicting bond quality elusive. In this work, a Sonics model MWB20 ultrasonic spot welder with integral base, was used to produce a variety of welds of different metal foils under varying weld conditions. Acoustic measurements were recorded throughout the measurement process. Subsequent analysis of the signals using metrics that approximate the energy dispersed during the weld proved to successfully predict weld quality. Two metrics based on normalized energy differential and Renyi entropy were developed into Python and C++ scripts for direct analysis of weld acoustic data.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Mass Transport Limitations and Kinetic Consequences of Corn Stover Deacetylation

Alkaline pretreatment of herbaceous feedstocks such as corn stover prior to mechanical refining and enzymatic saccharification improves downstream sugar yields by removing acetyl moieties from hemicellulose. However, the relationship between transport phenomena and deacetylation kinetics is virtually unknown for such feedstocks and this pretreatment process. Here, we report the development of an experimentally validated reaction–diffusion model for the deacetylation of corn stover. A tissue-specific transport model is used to estimate transport-independent kinetic rate constants for the reactive extraction of acetate, hemicellulose and lignin from corn stover under representative alkaline conditions (5–7 g L -1 NaOH, 10 wt% solids loadings) and at low to mild temperatures (4–70°C) selected to attenuate individual component extraction rates under differential kinetic regimes. The underlying transport model is based on microstructural characteristics of corn stover derived from statistically meaningful geometric particle and pore measurements. These physical descriptors are incorporated into distinct particle models of the three major anatomical fractions (cobs, husks and stalks) alongside an unsorted, aggregate corn stover particle, capturing average Feret lengths of 917–1239 μm and length-to-width aspect ratios of 1.8–2.9 for this highly heterogeneous feedstock. Individual reaction–diffusion models and their resulting particle model ensembles are used to validate and predict anatomically-specific and bulk feedstock performance under kinetic-controlled vs. diffusion-controlled regimes. In general, deacetylation kinetics and mass transfer processes are predicted to compete on similar time and length scales, emphasizing the significance of intraparticle transport phenomena. Critically, we predict that typical corn stover particles as small as ~2.3 mm in length are entirely diffusion-limited for acetate extraction, with experimental effectiveness factors calculated to be 0.50 for such processes. Debilitatingly low effectiveness factors of 0.021–0.054 are uncovered for cobs—implying that intraparticle mass transfer resistances may impair observable kinetic measurements of this anatomical fraction by up to 98%. These first-reported quantitative maps of reaction vs. diffusion control link fundamental insights into corn stover anatomy, biopolymer composition, practical size reduction thresholds and their kinetic consequences. These results offer a guidepost for industrial deacetylation reactor design, scale-up and feedstock selection, further establishing deacetylation as a viable biorefinery pretreatment for the conversion of lignocellulosics into value-added fuels and chemicals.

09 BIOMASS FUELS↗

DOE OSTI2024

Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review

The increasing global population and the challenges faced by the food production sector, including urbanization, reduction of arable land, and climatic extremes, necessitate innovative solutions for sustainable agriculture. This comprehensive review examines advancements in improving the energy performance of agricultural greenhouses, highlighting innovations in thermal and energy efficiency, particularly in heating and cooling systems. The methods include a systematic analysis of current technologies and their applications in optimizing greenhouse design and functionality. Key findings reveal significant progress in materials and techniques that enhance energy efficiency and operational sustainability. The review identifies gaps in the current knowledge, such as the need for more research on the economic viability of new materials and the development of predictive models for various climatic conditions. The conclusions emphasize the importance of integrating renewable energy technologies and advanced control systems to achieve energy-efficient and sustainable agricultural practices

Castro, Rodrigues Pascoal↗

NASA NTRS1973

Flowfield analysis for successive oblique shock wave interactions with a turbulent boundary layer

A computation procedure is described for predicting flow fields which develop when successive interactions between oblique shock waves and a turbulent boundary layer occur. Computations were carried out for axisymmetric internal flows at free stream Mach numbers = 3.82 and 2.82. The effect of boundary layer bleed was considered for the free stream Mach number = 2.82 flow. A control volume analysis is used to predict changes in the flow field across the interactions. Two bleed flow models were considered. A turbulent boundary layer program was used to compute changes in the boundary layer between the interactions. The results given are for flows with two shock wave interactions and for bleed at the second interaction site. In principle the method described may be extended to account for additional interactions. The predicted results are compared with measured results and are shown to be in good agreement when the bleed flow rate is low (on the order of 3 percent of the boundary layer mass flow), or when there is no bleed. As the bleed flow rate is increased, differences between the predicted and measured results become larger. Shortcomings of the bleed flow models at higher bleed flow rates are discussed.

Sun, C. C.↗

NASA NTRS1974

Flowfield analysis for successive oblique shock wave-turbulent boundary-layer interactions

Description of a procedure for predicting the flowfields that develop when successive interactions between oblique shock waves and a turbulent boundary layer occur. Such interactions may occur, for example, in engine inlets for supersonic aircraft. A control volume analysis is used to predict changes in the flow field across the interactions. Two bleed flow models are considered. The predicted results are compared with measured results and are shown to be in good agreement when the bleed flow rate is low, or when there is no bleed. Shortcomings of the bleed flow models at higher bleed flow rates are discussed.

Sun, C.-C.↗

NASA NTRS1976

Flowfield analysis for successive oblique shock wave-turbulent boundary layer interactions

A computation procedure is described for predicting the flowfields which develop when successive interactions between oblique shock waves and a turbulent boundary layer occur. Such interactions may occur, for example, in engine inlets for supersonic aircraft. Computations are carried out for axisymmetric internal flows at M 3.82 and 2.82. The effect of boundary layer bleed is considered for the M 2.82 flow. A control volume analysis is used to predict changes in the flow field across the interactions. Two bleed flow models have been considered. A turbulent boundary layer program is used to compute changes in the boundary layer between the interactions. The results given are for flows with two shock wave interactions and for bleed at the second interaction site. In principle the method described may be extended to account for additional interactions. The predicted results are compared with measured results and are shown to be in good agreement when the bleed flow rate is low (on the order of 3% of the boundary layer mass flow), or when there is no bleed. As the bleed flow rate is increased, differences between the predicted and measured results become larger. Shortcomings of the bleed flow models at higher bleed flow rates are discussed.

Sun, C. C.↗

NASA NTRS1976

Mathematical modelling of undrained clay behavior

The proposed general analytical model describes the anisotropic, elastoplastic, path-dependent, stress-strain properties of inviscid saturated clays under undrained conditions. Model parameters are determined by using results from strain-controlled simple shear tests on a saturated clay. The model's accuracy is evaluated by applying it to predict the results of other tests on the same clay, including monotonic and cyclic loading. The model explains the very anisotropic shear strength behavior observed for weak marine clays.

Prevost, J. H.↗

NASA NTRS1976

Display/control requirements for automated VTOL aircraft

A systematic design methodology for pilot displays in advanced commercial VTOL aircraft was developed and refined. The analyst is provided with a step-by-step procedure for conducting conceptual display/control configurations evaluations for simultaneous monitoring and control pilot tasks. The approach consists of three phases: formulation of information requirements, configuration evaluation, and system selection. Both the monitoring and control performance models are based upon the optimal control model of the human operator. Extensions to the conventional optimal control model required in the display design methodology include explicit optimization of control/monitoring attention; simultaneous monitoring and control performance predictions; and indifference threshold effects. The methodology was applied to NASA's experimental CH-47 helicopter in support of the VALT program. The CH-47 application examined the system performance of six flight conditions. Four candidate configurations are suggested for evaluation in pilot-in-the-loop simulations and eventual flight tests.

Hoffman, W. C.↗

NASA NTRS1983

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Cull, R. C.↗

NASA NTRS1989

An evaluative model of system performance in manned teleoperational systems

Manned teleoperational systems are used in aerospace operations in which humans must interact with machines remotely. Manual guidance of remotely piloted vehicles, controling a wind tunnel, carrying out a scientific procedure remotely are examples of teleoperations. A four input parameter throughput (Tp) model is presented which can be used to evaluate complex, manned, teleoperations-based systems and make critical comparisons among candidate control systems. The first two parameters of this model deal with nominal (A) and off-nominal (B) predicted events while the last two focus on measured events of two types, human performance (C) and system performance (D). Digital simulations showed that the expression A(1-B)/C+D) produced the greatest homogeneity of variance and distribution symmetry. Results from a recently completed manned life science telescience experiment will be used to further validate the model. Complex, interacting teleoperational systems may be systematically evaluated using this expression much like a computer benchmark is used.

Haines, Richard F.↗