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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators

Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit heterogeneous properties, requiring multiple sensors to handle functions with minimal regularity. To address this issue, discretization-invariant neural operators have been used, allowing the sampling of diverse input functions with different sensor locations. However, existing frameworks still require an equal number of sensors for all functions. We propose a novel distributed approach to further relax the discretization requirements and solve the heterogeneous dataset challenges. Our method involves partitioning the input function space and processing individual input functions using independent and separate neural networks. A centralized neural network is used to handle shared information across all output functions. This distributed methodology reduces the number of gradient descent back-propagation steps, improving efficiency while maintaining accuracy. Here, we demonstrate that the corresponding neural network is a universal approximator of continuous nonlinear operators and present three numerical examples to validate its performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Impact of mixed grain size sediment input on braided river Morphodynamics: Insights from experimental and numerical modeling

The rate of sediment supply has significant impacts on river morphology, making it crucial to understand the geomorphic changes and grain size distribution dynamics in rivers. However, the effects of varying grain size sediment input on morphological changes in braided channels remain poorly understood. This study is the first to investigate the bar development and sediment sorting processes in braided channels with non-uniform sediment inputs using both numerical and experimental approaches. We applied a two-dimensional numerical model, Nays2DH to confirm and generalize experimental results. The model reproduced key experiment results, including 1) stream elevation changes, and 2) grain size distribution. Using this validated model, we explored the morphological changes and sorting process in a braided river with sediment inputs. The numerical experiments demonstrate that sediment input controls the elevation of the stream bed and the grain size distribution. Notably, both the elevation and grain-size distribution become relatively stable in downstream of the channel. Additionally, the simulation results suggest that an increased sediment supply leads to greater channel complexity, with bed surface armoring decreasing.

Nays2DH↗
DOE OSTI2025

Systematic many-fermion Hamiltonian input scheme and spectral calculations on quantum computers

We present a novel input scheme for general second-quantized Hamiltonians of relativistic or non-relativistic many-fermion systems. This input scheme incorporates the fermionic anticommutation relations, particle number variations, and respects the symmetries of the Hamiltonian. Based on our input scheme, we propose a hybrid quantum-classical framework for spectral calculations on future quantum hardwares. We provide explicit circuit designs and the associated gate cost. We demonstrate our hybrid framework by solving the low-lying spectra of 42 Ca and 46 Ca. Our input scheme provides new pathways to solving the spectra and dynamics of the relativistic and nonrelativistic many-fermion systems via first-principles approaches.

Hybrid spectral calculation framework↗
DOE OSTI2025

MontePy: a Python library for reading, editing, and writing MCNP input files.

The Monte Carlo N-Particle (MCNP) radiation transport code is a highly capable and accurate code with a long legacy. MCNP uses the Monte Carlo simulation process to simulate the path of particles (e.g., neutrons, photons, charged particles, etc.), and their interaction with materials. It is widely used in nuclear engineering, high-energy physics, and other fields. Its origins in the mid-twentieth century predate many modern software conventions. MCNP users provide an input file to MCNP, which it then uses to create an internal representation of the simulation problem. These input files originally had to be stored as punchcard decks, and the user manual still uses the terminology of cards and decks, despite moving beyond punchcards. MCNP predates nearly all modern human readable markup or data serialization languages, such as the extensible Markup Language (XML), the Standard Generalized Markup Language (SGML), YAML (YAML Ain’t Markup Language), and Javascript Object Notation (JSON). Due to this, MCNP uses an entirely custom defined syntax language for its input, making off-the-shelf libraries for XML, YAML, and JSON impossible to use for scripting various operations on MCNP input files (Kulesza et al., 2022).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Getting to the root of the problem: Soil carbon and microbial responses to root inputs within a buried paleosol along an eroding hillslope in southwestern Nebraska, USA

Large quantities of soil carbon (C) can persist within paleosols for millennia due to burial and subsequent isolation from plant-derived inputs, atmospheric conditions, and microbial activity at the modern surface. Erosion exposes buried soils to modern root-derived C influx via root exudation and root turnover, thus stimulating microbial activity leading to SOC decomposition and accumulation through organo-mineral stabilization of modern C. With this study we aim to quantify how modern root-derived C inputs impact paleosol C decomposition and stabilization across varying degrees of isolation from modern surface conditions in southwestern Nebraska, USA, where hillslope erosion is bringing a buried Late-Pleistocene-early Holocene paleosol (the “Brady Soil”) closer to the modern surface. We collected Brady Soil samples from 0.2m, 0.4m, and 1.2m below the modern surface and conducted two lab-based incubations. Soils were amended with either (1) a lab-synthesized mixture of low molecular weight compounds (12 atom% 13 C), or (2) 13 C enriched root residues (92 atom% 13 C), in 30-day and 240-day incubation experiments, respectively. Here we determined microbial responses to synthetic root exudates and residues by partitioning the 13 C label from Brady Soil C, including measurements of total, root, and primed C respiration, microbial biomass C (MBC), microbial C use efficiency (CUE). To assess the capacity of isolated paleosols to accrue modern plant C, we used Nano-scale Secondary Ion Mass Spectrometry imaging. We found that: (1) adding root-derived C inputs primed Brady Soil C across all depths, and was mediated by depth and composition of root additions; (2) root-derived C inputs stimulated microbial biomass C (MBC) growth similarly across depths, but the magnitude of CUE and MBC varied by chemistry of root-derived additions; (3) new particulate organic matter was incorporated into mineral-associated pools over time; (4) material from the added root residues was found in association with bacterial cells and fungal hyphae as well as with soil aggregate and mineral surfaces. Our study shows that paleosols defy expectations of C content and reactivity with depth, and changes in land cover and climate will expose buried paleosols to modern surface conditions, increasing respired C. This work highlights the importance of evaluating the role resurfacing buried soils through landscape change plays in C cycle feedbacks to the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SparcleQC: Automated Input File Creation for QM/MM Studies of Protein:Ligand Complexes

SparcleQC is a Python package that, given a protein:ligand complex in the Protein Data Bank (PDB) file format, can create quantum mechanics/molecular mechanics (QM/MM)-like input files for the electronic structure theory packages PSI4, QChem, and NWChem. The resulting input files include quantum mechanical representations of the ligand and a small section of the protein, surrounded by point charges that represent the rest of the protein. Creation of these QM/MM input files includes cutting and capping the QM subregion, obtaining point charges for the protein, and adjusting charges at the QM/MM boundary; and each of these tasks are automated by the software. In this article, we describe the details of SparcleQC’s procedure, show examples of the Python API, and explain additional features that are helpful in protein:ligand interaction studies. Finally, we show that SparcleQC enables automated preparation of input files for QM/MM calculations, which can return can return accurate interaction energies in minutes, while a fully quantum mechanical computation on the protein:ligand complex could take days, if it is even possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Historical Land Management Alters New Soil Carbon Inputs by Annual and Perennial Bioenergy Crops

Bioenergy and bioproduct markets are expanding to meet demand for climate-friendly goods and services. Perennial biomass crops are particularly well suited for this goal because of their high yields, low input requirements, and potential to increase soil carbon (C). However, it is unclear how much C is allocated into belowground pools by perennial bioenergy crops and whether the belowground benefits vary with nitrogen (N) fertilizer inputs. Using in situ 13 C pulse-chase labeling, we tested whether the sterile perennial grass Miscanthus × giganteus (miscanthus) or annual maize transfers more photosynthetic C to belowground pools. The experiment took place at two sites in Central and Northwest (NW) Iowa with different management histories and two nitrogen (N) fertilizer rates (0 and 224 kg N ha −1 year −1 ) to determine if the fate of plant-derived soil C depends on soil fertility and crop type (perennial or annual). Maize allocated a greater percentage of total new 13 C to roots than miscanthus, but miscanthus had greater new 13 C in total and belowground plant biomass. We found strong interactions between site and most soil measurements—including new 13 C in mineral and particulate soil organic matter (SOM) pools—which appears to be driven by differences in historical fertilizer management. The NW Iowa site, with a history of manure inputs, had greater plant-available nutrients (phosphorus, potassium, and ammonium) in soils, and resulted in less 13 C from miscanthus in SOM pools compared to maize (approximately 64% less in POM and 70% less in MAOM). In more nutrient-limited soils (Central site), miscanthus transferred 4.5 times more 13 C than maize to the more stable mineral-associated SOM pool. Our results suggest that past management, including historical manure inputs that affect a site's soil fertility, can influence the net C benefits of bioenergy crops.

13C↗
DOE OSTI2024

Rhizosphere microbial community structure in high-producing, low-input switchgrass families

Switchgrass ( Panicum virgatum L.) is a native, low-input North American perennial crop primarily grown for bioenergy, livestock forage, and industrial fiber. To achieve no-input switchgrass production that meets biomass needs, several switchgrass genotypes have been identified that have a low or negative response to nitrogen fertilizer, i . e ., the biomass accumulation with added nitrogen is less than or equal to that when grown without nitrogen. In order to improve the viability of low-input switchgrass production, a more detailed understanding of the biogeochemical mechanisms active in these select genotypes is needed. 16S and ITS amplicon sequencing and qPCR of key functional genes were applied to switchgrass rhizospheres to elucidate microbial community structure in high-producing, no-input switchgrass families. Rhizosphere microbial community structure differed strongly between sites, and nitrogen responsiveness.

Stonoha-Arther, Christina (ORCID:0000000263964507)↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Historical land management alters new soil carbon inputs by annual and perennial bioenergy crops

Bioenergy and bioproduct markets are expanding to meet demand for climate friendly goods and services. Perennial biomass crops are particularly well suited for this goal because of their high yields, low input requirements, and potential to increase soil carbon (C). However, it is unclear how much C is allocated into belowground pools by perennial bioenergy crops and whether the belowground benefits vary with nitrogen (N) fertilizer inputs. Using in situ 13C pulse-chase labeling, we tested whether the sterile perennial grass Miscanthus × giganteus ( miscanthus ) or annual maize transfers more photosynthetic C to belowground pools. The experiment took place at two sites in Central and Northwest (NW) Iowa with different management histories and two nitrogen (N) fertilizer rates (0 and 224 kg N ha-1 yr-1) to determine if the fate of plant-derived soil C depends on soil fertility and crop type (perennial or annual). Maize allocated a greater percentage of total new 13C to roots than miscanthus, but miscanthus had greater new 13C in total and belowground plant biomass. We found strong interactions between site and most soil measurements – including new 13C in mineral and particulate soil organic matter (SOM) pools –which appear to be driven by differences in historical fertilizer management. The NW Iowa site, with a history of manure inputs, had greater plant-available nutrients (phosphorus, potassium, and ammonium) in soils, and resulted in less 13C from miscanthus in SOM pools compared to maize (approximately 64% less in POM and 70% less in MAOM). In more nutrient-limited soils (Central site), miscanthus transferred 4.5 times more 13C than maize to the more stable mineral-associated SOM pool. Our results suggest that past management, including historical manure inputs that affect a site’s soil fertility, can influence the net C benefits of bioenergy crops.

Keiser, Ashley [Stockbridge School of Agriculture,↗
DOE OSTI2025

Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads in extreme idling conditions

Abstract. Floating offshore wind turbine (FOWT) systems are subject to complex environmental loads, with significant potential for damage in extreme storm conditions. Design simulations in these conditions are required to assess the survivability of the device with some level of confidence. Aero-hydro-servo-elastic engineering tools can be used with a reasonable balance of accuracy and computational efficiency. The models require many input parameters to describe the air and water conditions, the system properties, and the load calculations. Each of these parameters has some possible range, due to either statistical uncertainty or variations with time. Variation in the input parameters can have important effects on the uncertainty in the resulting loads, but it is not practical to perform detailed assessments of the impact of this uncertainty for every input parameter. This work demonstrates a method to identify the input parameters that have the most impact on the loads to focus further inspection. The process is done specifically for extreme storm load cases defined in the International Electrotechnical Commission design requirements for floating offshore wind turbines. The analysis was performed using the International Energy Agency Wind 15 MW offshore reference wind turbine atop the University of Maine VolturnUS-S reference platform in two US offshore wind regions, the Gulf of Maine and Humboldt Bay. It was found that the direction of incident waves and current, yaw misalignment, and the length of mooring line sections were among the primary sensitivities.

17 WIND ENERGY↗
NASA NTRS1974

On the design of optimal input signals in system identification

The problem of designing optimal inputs in the identification of linear systems with unknown random parameters is considered using a Bayesian approach. The information matrix, which is positive definite for the class of systems analyzed, gives a measure of performance for the system inputs. The computation of the optimal closed-loop input mappings is shown to be a nontrivial exercise in adaptive control. Deterministic optimal inputs are shown to be easily computable. Numerical examples are given. A Kalman filter is used to estimate the parameters. A necessary condition for the Kalman filter not to diverge when applying linear feedback is also given.

Lopez-Toledo, A. A.↗
NASA NTRS1979

INDES User's guide multistep input design with nonlinear rotorcraft modeling

The INDES computer program, a multistep input design program used as part of a data processing technique for rotorcraft systems identification, is described. Flight test inputs base on INDES improve the accuracy of parameter estimates. The input design algorithm, program input, and program output are presented.

Source record↗
NASA NTRS1981

High-speed multiplexing of keyboard data inputs

A high speed multiplexing system is described in which keyboard entered data is sequentially and automatically sampled by the multiplexing system for input to a computer. A sequencer is provided which sequentially and automatically controls the multiplexer to sample each keyboard input in accordance with a predetermined sampling sequence. Whenever keyboard entered data appears on input lines to the multiplexer, the system inputs the keyboard data to the computer during a brief time interval in which the multiplexer remains at the particular keyboard address or port. Thus, a high speed sampling circuit is provided whereby the only operator action required is data entry through a keyboard. Priority or interrupt systems are not required.

Anderson, T. O.↗
NASA NTRS1983

Input generator for Denton 3-dimensional turbomachine-blade-row analysis code

A users manual is presented for a computer program that prepares the bulk of the input data set required for the Denton three dimensional turbomachine blade row analysis code. The Denton input is generated from a minimum of geometry and flow variable information by using cubic spline curve fitting procedures. The features of the program are discussed. The input is described and special instructions are included to assist in its preparation. Sample input and output are included.

Glassman, A. J.↗
NASA NTRS1984

Input-output characterization of an ultrasonic testing system by digital signal analysis

The input/output characteristics of an ultrasonic testing system used for stress wave factor measurements were studied. The fundamentals of digital signal processing are summarized. The inputs and outputs are digitized and processed in a microcomputer using digital signal processing techniques. The entire ultrasonic test system, including transducers and all electronic components, is modeled as a discrete-time linear shift-invariant system. Then the impulse response and frequency response of the continuous time ultrasonic test system are estimated by interpolating the defining points in the unit sample response and frequency response of the discrete time system. It is found that the ultrasonic test system behaves as a linear phase bandpass filter. Good results were obtained for rectangular pulse inputs of various amplitudes and durations and for tone burst inputs whose center frequencies are within the passband of the test system and for single cycle inputs of various amplitudes. The input/output limits on the linearity of the system are determined.

Karaguelle, H.↗
NASA NTRS1987

Input error versus output error model reference adaptive control

Algorithms for model reference adaptive control were developed in recent years, and their stability and convergence properties have been investigated. Typical algorithms in continuous time involve strictly positive real conditions on the reference model, while similar discrete time algorithms do not require such conditions. It is shown how algorithms differ by the use of an input error versus an output error, and present a continuous time input error adaptive control algorithm which does not involve SPR conditions. The connections with other schemes are discussed. The input error scheme has general stability and ocnvergence properties that are similar to the output error scheme. However, analysis using averaging methods reveals some preferable convergence properties of the input error scheme. Several other advantages are also discussed.

Bodson, Marc↗
NASA NTRS1989

Input preshaping with frequency domain information for flexible-link manipulator control

The application of an input preshaping scheme to flexible manipulators is considered. The resulting control corresponds to a feedforward term that convolves in real-time the desired reference input with a sequence of impulses and produces a vibration free output. The robustness of the algorithm with respect to injected disturbances and modal frequency variations is not satisfactory and can be improved by convolving the input with a longer sequence of impulses. The incorporation of the preshaping scheme to a closed-loop plant, using acceleration feedback, offers satisfactory disturbance rejection due to feedback and cancellation of the flexible mode effects due to the preshaping. A frequency domain identification scheme is used to estimate the modal frequencies on-line and subsequently update the spacing between the impulses. The combined adaptive input preshaping scheme provides the fastest possible slew that results in a vibration free output.

Tzes, Anthony↗