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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales-picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure–function relationships require atomistic knowledge of the populated states in the ensemble. Here, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCC FLUS and LULCC Demeter , respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCC FLUS and LULCC Demeter , and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Extreme flood return levels in a U.S. mid-Atlantic estuary using 40-year fluvial-coastal model simulations

Using an integrated watershed-coastal modeling framework, we conducted long-term historical simulations (1980-2019) of fluvial and coastal flooding in the Delaware Bay and River, a vulnerable estuarine system in the U.S., at high spatial resolutions. By applying process-based models, we generated physically consistent and spatially detailed estimates of estuarine, riverine, and surge-driven extreme water level compared to previous studies that used field data only. We then evaluated changes in the magnitude of flood events using the 40-year simulations and detrended Floodwater Depth values with stationary extreme value analysis. Our detailed assessment of spatial-varying extreme values revealed how different flood-generation mechanisms can dominate various zones in the estuary. The datasets produced through this work will be valuable for long-term flood hazard mitigation planning in coastal communities in the Delaware Bay and River region. Additionally, this work will serve as a benchmark for other coastal flood hazard modeling communities worldwide, aiding them in systematically modeling long-term and continuous extreme flood events.

Coastal and Riverine Flooding↗
NASA NTRS1981

AIRCL: A programmed system for generating NC tapes for airplane models. User's manual

A computer program is presented which calculates the cutter location file needed to machine models of airplane wings or wing-fuselage combinations on numerically controlled machine tools. Input to the program is a data file consisting of coordinates on the fuselage and wing. From this data file, the program calculates tool offsets, determines the intersection between wing and fuselage tool paths, and generates additional information needed to machine the fuselage and/or wing. Output from the program can be post processed for use on a variety of milling machines. Information on program structure and methodology is given as well as the user's manual for implementation of the program.

Akgerman, N.↗
NASA NTRS2020

Simulation and Modeling of Cold-Wall Hypersonic Turbulent Boundary Layers on Flat Plate

Direct numerical simulations (DNS) of flat-plate, zero-pressure-gradient turbulent boundary layers are presented for nominal freestream Mach numbers of 11 and 14 and a highly cooled wall (wall-to-recovery temperature of approximately 0.2). The flow conditions of the DNS are representative of the experimental data for a Mach 11.1 turbulent boundary layer on a flat plate that was tested at Calspan–University of Buffalo Research Center (CUBRC) and the operational conditions of the AEDC Hypervelocity Tunnel No. 9 at Mach 14. The wall shear stress and turbulent heat flux predicted by DNS show good comparisons with those measured at CUBRC and those modeled with a Baldwin-Lomax model. The generated DNS database extends the range of Reynolds numbers from those typical of previous DNS studies for hypersonic boundary-layer flows and confirms the validity of Morkovin’s hypothesis within the relatively broader range of Reynolds numbers considered in this study. A priori assessment of turbulent heat flux models with DNS data shows that the algebraic energy flux (AEF) formulation gives better predictions of the streamwise turbulent heat flux than the commonly used constant turbulent Prandtl number model while both models give similarly good predictions of the wall-normal turbulent heat flux. A posteriori assessment shows that the AEF model improves the prediction of the temperature profiles while keeping a good prediction of the velocity field.

Junji Huang↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
DOE OSTI2022

Gradient Field Detection Using Interference of Stimulated Microwave Optical Sidebands

Here, we demonstrate that stimulated microwave optical sideband generation using parametric frequency conversion can be utilized as a powerful technique for coherent state detection in atomic physics experiments. The technique has advantages over traditional absorption or polarization rotation-based measurements and enables the isolation of signal photons from probe photons. We outline a theoretical framework that accurately models sideband generation using a density matrix formalism. Using this technique, we demonstrate a novel intrinsic magnetic gradiometer that detects magnetic gradient fields between two spatially separated vapor cells by measuring the frequency of the beat note between sidebands generated within each cell. The sidebands are produced with high efficiency using parametric frequency conversion of a probe beam interacting with 87 Rb atoms in a coherent superposition of magnetically sensitive hyperfine ground states. Interference between the sidebands generates a low-frequency beat note whose frequency is determined by the magnetic field gradient between the two vapor cells. In contrast to traditional gradiometers the intermediate step of measuring the magnetic field experienced by the two vapor cells is unnecessary. We show that this technique can be readily implemented in a practical device by demonstrating a compact magnetic gradiometer sensor head with a sensitivity of 25 fT/cm/ √ Hz with a 4.4 cm baseline, while operating in a noisy laboratory environment unshielded from Earth’s field.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1977

A simulation model for wind energy storage systems. Volume 1: Technical report

A comprehensive computer program for the modeling of wind energy and storage systems utilizing any combination of five types of storage (pumped hydro, battery, thermal, flywheel and pneumatic) was developed. The level of detail of Simulation Model for Wind Energy Storage (SIMWEST) is consistent with a role of evaluating the economic feasibility as well as the general performance of wind energy systems. The software package consists of two basic programs and a library of system, environmental, and load components. The first program is a precompiler which generates computer models (in FORTRAN) of complex wind source storage application systems, from user specifications using the respective library components. The second program provides the techno-economic system analysis with the respective I/O, the integration of systems dynamics, and the iteration for conveyance of variables. SIMWEST program, as described, runs on the UNIVAC 1100 series computers.

Warren, A. W.↗
NASA NTRS2006

Program Synthesizes UML Sequence Diagrams

A computer program called "Rational Sequence" generates Universal Modeling Language (UML) sequence diagrams of a target Java program running on a Java virtual machine (JVM). Rational Sequence thereby performs a reverse engineering function that aids in the design documentation of the target Java program. Whereas previously, the construction of sequence diagrams was a tedious manual process, Rational Sequence generates UML sequence diagrams automatically from the running Java code.

Barry, Matthew R.↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Modeling and Power-Hardware-in-the-Loop Validation of Synchronous Machine Governor

This paper introduces the development of a high-fidelity gas turbine governor model using a programmable logic controller for power-hardware-in-the-loop (PHIL) validation. The governor model is integrated with the National Renewable Energy Laboratory's (NREL) PHIL test bed, featuring a 2-MVA synchronous machine and a 2.5-MW variable-speed drive, to emulate NG-driven HRSGs and CTs under various operational scenarios. The primary objective of this research is to study the grid-connected and islanding operations of conventional generation sources, with representative startup sequences including turbine purge, ignition, speed ramp-up, synchronization, and breaker closure. Preliminary results of the generator governor model on NREL PHIL platform, particularly using the 2.5-MW dynamometer system, offered significant insights into the modeling techniques, hardware integration, scaling, and real-world simulation dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

A modified Finite Element-Transfer Matrix for control design of space structures

The Finite Element-Transfer Matrix (FETM) method was developed for reducing the computational efforts involved in structural analysis. While being widely used by structural analysts, this method does, however, have certain limitations, particularly when used for the control design of large flexible structures. In this paper, a new formulation based on the FETM method is presented. The new method effectively overcomes the limitations in the original FETM method, and also allows an easy construction of reduced models that are tailored for the control design. Other advantages of this new method include the ability to extract open loop frequencies and mode shapes with less computation, and simplification of the design procedures for output feedback, constrained compensation, and decentralized control. The development of this new method and the procedures for generating reduced models using this method are described in detail and the role of the reduced models in control design is discussed through an illustrative example.

Tan, T.-M.↗
DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Pynta-An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics

Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. Here, we present Pynta, a workflow software for automating the calculation of surface and gas–surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The METLIN small molecule dataset for machine learning-based retention time prediction

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Abstract Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt 3 Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Evolving, dynamical models for collapsed-core globular clusters - M15 and NGC 6624

The stellar populations of the collapsed-core globular clusters M15 and NGC 6624 are investigated by fitting observed surface-brightness and projected velocity-dispersion profiles. The present evolving cluster models were generated by the direct Fokker-Planck method and incorporate realistic stellar mass spectra and energy input from binaries formed by three-body interactions. An evolved power-law mass function with nonluminous remnants of maximum mass 1.0-1.4 solar mass is adopted. M15 is found to be best fitted by postcollapse evolving models with a mass-function slope of x = 0.9 (where x = 1.35 corresponds to the Salpeter mass function) and a maximum remnant mass of 1.3 solar mass. Postcollapse core oscillations, driven by energy input from hard binaries, can produce sufficiently rapid core expansion to explain the resolution of the core of M15 by HST observations reported by Lauer et al. (1991).

Grabhorn, Robert P.↗
NASA NTRS1989

Distributed Finite Element Analysis Using a Transputer Network

The principal objective of this research effort was to demonstrate the extraordinarily cost effective acceleration of finite element structural analysis problems using a transputer-based parallel processing network. This objective was accomplished in the form of a commercially viable parallel processing workstation. The workstation is a desktop size, low-maintenance computing unit capable of supercomputer performance yet costs two orders of magnitude less. To achieve the principal research objective, a transputer based structural analysis workstation termed XPFEM was implemented with linear static structural analysis capabilities resembling commercially available NASTRAN. Finite element model files, generated using the on-line preprocessing module or external preprocessing packages, are downloaded to a network of 32 transputers for accelerated solution. The system currently executes at about one third Cray X-MP24 speed but additional acceleration appears likely. For the NASA selected demonstration problem of a Space Shuttle main engine turbine blade model with about 1500 nodes and 4500 independent degrees of freedom, the Cray X-MP24 required 23.9 seconds to obtain a solution while the transputer network, operated from an IBM PC-AT compatible host computer, required 71.7 seconds. Consequently, the $80,000 transputer network demonstrated a cost-performance ratio about 60 times better than the $15,000,000 Cray X-MP24 system.

Watson, James↗