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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Interpretable boosted-decision-tree analysis for the Majorana Demonstrator

The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditional analysis. An interpretability study reveals the machine's decision-making logic, allowing us to learn from the machine to feedback to the traditional analysis. In this work, we have presented the first machine learning analysis of the data from the Majorana Demonstrator; this is also the first interpretable machine learning analysis of any germanium detector experiment. Two gradient boosted decision tree models are trained to learn from the data, and a game-theory-based model interpretability study is conducted to understand the origin of the classification power. By learning from data, this analysis recognizes the correlations among reconstruction parameters to further enhance the background rejection performance. By learning from the machine, this analysis reveals the importance of new background categories to reciprocally benefit the standard Majorana analysis. This model is highly compatible with next-generation germanium detector experiments like LEGEND since it can be simultaneously trained on a large number of detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

EvoProtGrad (Directed Evolution for Proteins with Gradients) [SWR-23-48]

A Python package for directed evolution on a protein sequence with gradient-based discrete Markov chain monte carlo (MCMC). Users are able to compose custom models that map sequence to function with pretrained models, including protein language models (PLMs), to guide and constrain search. Our package natively integrates with the HuggingFace platform and supports PLMs from transformers. Our MCMC sampler identifies promising amino acids to mutate via model gradients taken with respect to the input (i.e., sensitivity analysis). We allow users to compose their own custom target function for MCMC by leveraging the Product of Experts MCMC paradigm. Each model is an "expert" that contributes its own knowledge about the protein's fitness landscape to the overall target function. The sampler is designed to be more efficient and effective than brute force and random search while maintaining most of the generality and flexibility. Additional information can be found in the related publication: https://iopscience.iop.org/article/10.1088/2632-2153/accacd

Emami, Patrick↗
DOE OSTI2021

Applying Time-Parallelization to Turbulent Flows

Parallelization of the temporal domain is explored for the solution of turbulent flows. Multigrid reduction-in-time (MGRIT) is used to advance the large-scale fluid dynamics in time sequentially on the coarsest space-time grid but propagate the information in time parallel on all other levels. The goal of this process is to accurately and efficiently resolve the coarse-scale turbulence structure and use that to drive the fine-scales of the turbulent flow. The extra forcing from nonlinear multigrid facilitates the coupling and interaction between fine and coarse scales, through which the multiscale nonlinear physics is properly captured. Adaptive mesh refinement is employed to finely resolve only the regions with strong gradients, which provides further computational efficiency. The underlying computational fluid dynamics solver is a fourth-order finite-volume scheme with the standard 4-stage Runge-Kutta method. An advanced approach is devised and implemented to enable MGRIT to solve highly turbulent flows successfully. Furthermore, the method is applied to solve a Taylor-Green vortex problem and a doubleshear-layer turbulent mixing flow. Results are promising, validating that MGRIT with the filtering approach has the potential to efficiently solve general turbulent flows.

Computational Fluid Dynamics↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Analytical estimates of radial segregation in Bridgman growth from low-level steady and periodic accelerations

Approximate analytical solutions are developed that are mathematically tractable but provide the elements of CFD models for estimating convective flows subjected to low-level accelerations. The estimates are applicable to dilute systems that have horizontal temperature gradients in the vertical Bridgman configuration. The Navier-Stokes momentum equation is solved in 1D and subsequently in 2D by the first-order perturbation method. The analysis used is the case of plane-front solidification of a dilute system in the vertical thermally stable configuration. The model is found to predict the degree of radial segregation within a factor of two for a range of material and processing parameters. The method provides detailed information on the effects of processing on solute distribution in the grown crystal which are of interest in the orbital experiments emphasizing the control of radial segregation.

Naumann, Robert J.↗
NASA NTRS2003

Comparison of Flow-Dependent and Static Error Correlation Models in the DAO Ozone Data Assimilation System

In a data assimilation system the forecast error covariance matrix governs the way in which the data information is spread throughout the model grid. Implementation of a correct method of assigning covariances is expected to have an impact on the analysis results. The simplest models assume that correlations are constant in time and isotropic or nearly isotropic. In such models the analysis depends on the dynamics only through assumed error standard deviations. In applications to atmospheric tracer data assimilation this may lead to inaccuracies, especially in regions with strong wind shears or high gradient of potential vorticity, as well as in areas where no data are available. In order to overcome this problem we have developed a flow-dependent covariance model that is based on short term evolution of error correlations. The presentation compares performance of a static and a flow-dependent model applied to a global three- dimensional ozone data assimilation system developed at NASA s Data Assimilation Office. We will present some results of validation against WMO balloon-borne sondes and the Polar Ozone and Aerosol Measurement (POAM) III instrument. Experiments show that allowing forecast error correlations to evolve with the flow results in positive impact on assimilated ozone within the regions where data were not assimilated, particularly at high latitudes in both hemispheres and in the troposphere. We will also discuss statistical characteristics of both models; in particular we will argue that including evolution of error correlations leads to stronger internal consistency of a data assimilation ,

Wargan, K.↗
DOE OSTI2024

In tube condensation of low global warming potential refrigerants in an axial micro-fin aluminum tube

Environmental regulations have driven the development of refrigerants with low global warming potential (GWP). To design heat exchangers using these new refrigerants, data are needed concerning the heat transfer coefficient and pressure drop in two-phase flow. Another change is the increasing use of aluminum tubes rather than copper tubes to reduce heat exchanger cost. Hence, this study presents an experimental investigation of flow condensation using an expanded axial micro-fin aluminum tube with a fin-tip diameter of 5.96 mm. Here, the experiments included single compounds R-32, R-1234yf, and R-1234ze(E), zeotropic mixtures with low glide (R-454B), and zeotropic mixtures with high-glide (R-454C and R-455A). Experiments were conducted at condensation temperatures ranging from 40 °C to 50 °C, reduced pressures ranging from 0.21 to 0.55, and mass fluxes ranging from 150 to 350 kg/(m 2 s). Data obtained for these refrigerants constitute one of the first reports for high-glide refrigerants using axial micro-fin aluminum tubes. An evaluation of heat transfer degradation of zeotropic mixtures due to mass transfer resistance at the liquid/vapor interface is presented. This information can be used to design heat exchangers for next generation air conditioning and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Say where you sample: Increasing site selection transparency in urban ecology

Urban ecological studies have the potential to extend our understanding of socio-ecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists’ ability to assess the internal and external validity of studies’ findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields’ ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding and Predictability of Integrated Mountain Hydroclimate (Workshop Report)

Mountainous systems cover approximately 23% of Earth’s land and are distributed across all continents. They can capture and store atmospheric moisture that is then cycled through the terrestrial surface and subsurface system, released to downstream communities, and cycled back to the atmosphere. Mountain hydroclimate—characterized by steep gradients, geological, ecological, and biogeochemical diversity—is influenced by topographic forcing and elevated warming and susceptible to large subseasonal to multidecadal variability and rapid changes. Terrestrial hydrological and biogeochemical cycles also experience cascading effects from global warming impacts, such as multidecadal declines in mountain snowpack, longer growing seasons, and increased frequency and severity of extreme events like droughts and wildfires. However, little is known about the effects of these impacts and their feedbacks on climate systems and surface-subsurface compartments. Also unknown are the full implications of changing hydroclimate and extreme events on hydro biogeochemical cycles across atmosphere, terrestrial, and human systems in mountain regions and beyond. This knowledge gap is critical, given human reliance on mountain systems for stable water supply and quality. Mountain systems’ increasing vulnerability to climate change and human perturbations motivates the need to improve understanding of integrated mountain hydroclimate (IMHC) systems and their feedbacks and impacts on humans across scales. However, due to large heterogeneity and strong gradients, coupled natural-human processes in mountain regions present significant challenges for observations, modeling, predictions, and projections. Motivated by gaps in mountain hydroclimate understanding, observations, and modeling and the need for credible projections of future changes, the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program organized a virtual workshop on “Understanding and Predictability of Integrated Mountain Hydroclimate.” Sponsored by BER’s Earth and Environmental Systems Sciences Division (EESSD), the workshop aimed to inform and catalyze EESSD’s growing interests in enhancing predictive understanding of IMHC. Organizers structured the workshop to identify (1) knowledge gaps, (2) observational and modeling challenges, (3) short-term (1 to 3 years) and long-term (10 years and beyond) research opportunities, and (4) strategies for fostering collaboration and coordination. To address the outstanding challenges of IMHC, the workshop included two sessions organized by disciplinary, cross-disciplinary, and crosscutting science topics. The disciplinary and cross-disciplinary topics focused on essential IMHC elements: atmosphere, terrestrial, and human systems and their interactions. Breakout sessions on disciplinary and cross-disciplinary topics facilitated identification of crosscutting topics and central emerging themes. Session 1 focused on connecting existing DOE investments to accelerate progress related to scientific challenges in understanding mountain hydroclimate. In Session 2, participants further explored key Session 1 takeaways through the lens of multiagency collaborations and coordination.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Analysis of the vector magnetic fields of complex sunspots

An analysis of the vector magnetic field in the delta-configurations of two complex sunspot groups is presented, noting several characteristics identified in the delta-configurations. The observations of regions 2469 (S12E80) and 2470 (S21E83) took place in May, 1980 with a vector magnetograph, verified by optical viewing. Longitudinal magnetic field plots located the delta-configurations in relation to the transverse field neutral line. It is shown that data on the polarization yields qualitative information on the magnetic field strengths, while the azimuth of the transverse field can be obtained from the relative intensities of linear polarization measurements aligned with respect to the magnetograph analyses axis at 0 and 90 deg, and at the plus and minus 45 deg positions. Details of the longitudinal fields are discussed. A strong, sheared transverse field component is found to be a signature of strong delta. A weak delta is accompanied by a weak longitudinal gradient with an unsheared transverse component of variable strength.

Patty, S. R.↗
NASA NTRS2004

Putting Priors in Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Srivastava, Ashok N.↗
DOE OSTI2024

Training quantum neural networks using the quantum information bottleneck method

Abstract We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error ε that requires O ( log 2 ⁡ ( 1 / ϵ ) + 1 / δ 2 ) queries to a purification of the input density operator if its spectrum is supported on { 0 } ⋃ [ δ , 1 − δ ] for δ > 0 and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial.

Çatlı, Ahmet Burak (ORCID:0000000152294141)↗
DOE OSTI2024

Imaging extended single crystal lattice distortion fields with multi-peak Bragg ptychography

Recent advances in phase-retrieval-based x-ray imaging methods have demonstrated the ability to reconstruct 3D distortion vector fields within a nanocrystal by using coherent diffraction information from multiple crystal Bragg reflections. However, these works do not provide a solution to the challenges encountered in imaging lattice distortions in crystals with significant defect content that result in phase wrapping. Moreover, these methods only apply to isolated crystals smaller than the x-ray illumination, and therefore cannot be used for imaging of distortions in extended crystals. We introduce multi-peak Bragg ptychography which addresses both challenges via an optimization framework that combines stochastic gradient descent and phase unwrapping methods for robust image reconstruction of lattice distortions and defects in extended crystals. Our work uses modern automatic differentiation toolsets so that the method is easy to extend to other settings and easy to implement in high-performance computers. This work is particularly timely given the broad interest in using the increased coherent flux in fourth-generation synchrotrons for innovative material research.

36 MATERIALS SCIENCE↗
NASA NTRS1999

STS-99 Crew Interviews: Gorie

Live footage of a preflight interview with Pilot Dominic L. Pudwell Gorie is seen. The interview addresses many different questions including why Gorie became an astronaut, the events that led to his interest, and his career path. Other interesting information that this one-on-one interview discusses is the purpose for the Shuttle Radar Topography Mission (SRTM). The main interest is on the importance of this SRTM flight, the knowledge we will learn gain from the 3D topographic map of the Earth, and the possible similarity to the tethered Satellite System Flight. The two antennas that will be taking the pictures, the involvement of the National Imagery and Mapping Agency (NIMA), mass deployment and retraction, gravity gradient force, flight cast maneuvers, EARTHCAM, a student-controlled camera on the Endeavour Orbiter, and Gorie's responsibility during this 24 hour mission.

Source record↗
DOE OSTI2021

Sample environment effects on synchrotron-measured temperature profiles in an approximant of optical floating zone crystal growth

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quantitative information about the sample thermal profile during crystal growth. Building on recent work demonstrating that in situ synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models, and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The distribution and selective decontamination of carbon-14 from nuclear graphite

This work investigates 14 C release behaviour from UK Magnox reactor sourced irradiated graphite under relatively low temperature oxidation conditions: a 1% oxygen in argon atmosphere at temperatures ranging from 600°C to 700°C, and durations from 4 to 120 hours. A method is used for construction of a detailed 14 C release profile with mass loss. The 14 C release profile is predictable between samples, and an empirical release profile is derived to predict releases to higher mass losses than in this work. An accelerated 14 C release rate at small mass losses is observed, indicative of a 14 C-enriched surface region with a depleting concentration gradient into the material, and the source terms and complexities associated with predicting where the 14 C arose from are discussed. Selective decontamination of a fraction of the 14 C is possible, with the limitation of a reducing efficiency with mass loss and time. This work demonstrates a method for determining the distribution of 14 C between near-surface regions and the bulk material in differing sources of irradiated graphite, and provides data in support of making an informed assessment for the adoption of low temperature thermal treatment and isotopic reduction of irradiated graphite wastes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Differentiable Earth mover’s distance for data compression at the high-luminosity LHC

Abstract The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Biocrust Mosses and Cyanobacteria Exhibit Distinct Carbon Uptake Responses to Variations in Precipitation Amount and Frequency

ABSTRACT Dryland organisms exhibit varied responses to changes in precipitation, including event size, frequency, and soil moisture duration, influencing carbon uptake and reserve management strategies. This principle, central to the pulse‐reserve paradigm, has not been thoroughly evaluated in biological soil crusts (biocrusts), essential primary producers on dryland surfaces. We conducted two experiments to investigate carbon uptake in biocrusts under different precipitation regimes. In the first, we applied a gradient of watering amounts to biocrusts dominated by moss or cyanobacteria, hypothesising distinct pulse‐response strategies. The second experiment extended watering treatments over three months, varying pulse size and frequency. Our results revealed distinct carbon uptake patterns: moss crusts exhibited increased CO 2 uptake with larger, less frequent watering events, whereas cyanobacteria crusts maintained similar carbon uptake across all event sizes. These findings suggest divergent pulse‐response strategies across biocrust types, with implications for modelling dryland carbon dynamics and informing land management under changing precipitation regimes.

Young, Kristina E. [USDA‐ARS Jornada Experimental ↗