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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Binding and Translocation of Substrate Allosterically Promotes Functional Interactions Within the AlkB–AlkG Electron Transfer Complex

The alkane monooxygenase AlkB and rubredoxin AlkG form an electron transfer complex that hydroxylates terminal alkanes to produce alcohols. The recent cryoEM study of Fontimonas thermophila AlkB-AlkG complex revealed its architecture, including a dodecane (D12) substrate at the active site. However, FtAlkBG molecular mechanism of action of remains unknown. Here, in this study, we examined its dynamics and interactions by multiscale computations, including molecular dynamics simulations, elastic network models, and QM/MM of the oxygen activation mechanism at the AlkB catalytic site. D12 maintained stable interactions within the catalytic site during two MD runs, coordinated by hydrophobic residues L263-L264, I267, I133. A third extended run revealed that D12 could translocate to a membrane-exposed site near S49/F46 along a hydrophobic channel gated by I54. During this translocation, D12 was temporarily stabilized at intermediate sites IS1 (lined by I27/L30-G31/G50/L53-I54/P59/S124/A127-V128) and IS2 (I33-G34/L37/L45-F46/S49) before nearly exiting the protein, and diffused back to the active site, assisted by L30. Substrate binding and translocation across those intermediate sites affects the coupling between the iron centers in AlkBG, and interfacial interactions between AlkB-AlkG. The channel was further connected to the cytosol, near two surface-exposed arginines, potentially allowing for O 2 passage. The allosteric effects between D12 putative entry site, catalytic site and AlkB-AlkG interface were analyzed by ENM-based methods which confirmed the cooperative perturbation-responses and strongly correlated movements of residues belonging to those distal regions. Our study provides new mechanistic insights into key sites and their interactions that could be targeted for developing AlkB-variants with desirable alkane conversion functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

luoyunan/ECNet: First release

ECNet (evolutionary context-integrated neural network) is a deep learning model that guides protein engineering by predicting protein fitness from the sequence. It integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe.

Luo, Yunan↗
NASA NTRS1977

Lageos orbital acquisition and initial assessment

Orbital elements for the satellite Lageos, launched on 4 May, 1976, were generated through use of Baker-Nunn camera data. Camera observations of the apogee kick motor provided the node and perigee, the inclination and the eccentricity, while the mean anomaly and the mean motion were determined from observations of the satellite. During the month following the launch, the Smithsonian Astrophysical Observatory and NASA laser tracking networks acquired about 110 satellite passes. The laser tracking system has been applied to a variety of orbital configurations, including elevation angles below 20 deg. Single-pass range residuals versus time were plotted for 741 laser observations of the satellite; the standard deviation of the total system noise was found to be 7 cm.

Pearlman, M. R.↗
NASA NTRS1990

Stochastic estimation of tropospheric path delays in global positioning system geodetic measurements

Water vapor radiometric and surface meteorological measurements taken during three GPS geodetic experiments are used to calculate process noise levels for random walk and first-order Gauss-Markov temporal models of tropospheric path delays. Entire wet and combined wet and dry zenith delays at each network site are estimated simultaneously with the geodetic parameters without prior calibration. The path delays and corresponding baseline estimates are compared to those obtained with calibrated data and stochastic residual delays. In this manner, the marginal utility of a priori tropospheric calibration is assessed given the ability to estimate the path delays directly using only the GPS data. Estimation of total zenith path delays with appropriate random walk or Gauss-Markov models yields baseline repeatabilities of a few parts in 100 million. Results suggest that GPS data alone have sufficient strength to resolve centimeter-level zenith path delay fluctuations over periods of a few minutes.

Tralli, David M.↗
NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗
DOE OSTI2023

Analytic Theory for the Dynamics of Wide Quantum Neural Networks

Here, parametrized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems. To date, much of the results on their performance on practical problems are heuristic in nature. In particular, the convergence rate for the training of quantum neural networks is not fully understood. Here, we analyze the dynamics of gradient descent for the training error of a class of variational quantum machine learning models. We define wide quantum neural networks as parametrized quantum circuits in the limit of a large number of qubits and variational parameters. Then, we find a simple analytic formula that captures the average behavior of their loss function and discuss the consequences of our findings. For example, for random quantum circuits, we predict and characterize an exponential decay of the residual training error as a function of the parameters of the system. Finally, we validate our analytic results with numerical experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Nth-plant scenario for forest resources and short rotation woody crops: Biorefineries and depots in the contiguous US

Estimating the US potential of woody material is of vital importance to ensure cost-effective supply logistics and develop a sustainable bioenergy and bioproducts industry. We analyzed a mature conversion technology for woody resources for the contiguous US that takes advantage of economies of scale: the nth-plant. Here, we developed a database to quantify the total accessible woody biomass within a distributed network of preprocessing depots and biorefineries considering both quality specifications for conversion and a target cost to compete with fossil fuels. We considered two categories of woody biomass: 1) forest residues from trees, tops and limbs produced from conventional thinning and timber harvesting operations as well as non-timber tree removal; and 2) short rotation woody crops such as poplar, willow, pine, and eucalyptus. A mixed integer linear programming model was developed to analyze scenarios with woody feedstock blends at variable biomass ash contents and cost targets at the biorefinery. When considering a target cost of 85.51 dollars/dry ton (2016$) at the biorefinery, the maximum accessible biomass from forest residues in 2040 remained constant at 106 million dry tons regardless of ash targets. Including short rotation woody crops as part of the blend increased the total accessible biomass to 153 and 195 million dry tons at ash targets of 1% and 1.75%, respectively. We concluded from our analysis that woody resources could address about 55% of EPA’s (Environmental Protection Agency) target of 16 billion gallons of cellulosic biofuel.

09 BIOMASS FUELS↗
DOE OSTI2021

ECNet is an evolutionary context-integrated deep learning framework for protein engineering

Abstract Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 β-lactamase and identified variants with improved ampicillin resistance with high success rates.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1988

Digital carrier demodulation for the DSN Advanced Receiver

The digital in-phase and quadrature (I and Q) carrier demodulation for the Deep Space Network's (DSN) Advanced Receiver is described and analyzed. The system provides coherent demodulation for a variety of modulation formats including Binary Phase Shift Keying (BPSK), BPSK with a carrier residual, Quadrature Phase Shift Keying (QPSK), Offset-QPSK (OQPSK), and Minimum Shift Keying (MSK). The focus is on the theory and hardware design of the halfband filters which are the integral part of the demodulator. The underlying theory of the filters is summarized, a breadboard hardware design is described, and a VLSI implementation is proposed which significantly decreases the hardware. A second important problem analyzed is DC-offset in the demodulator. This is a serious problem which translates into bias error in the residual carrier phase detector. The dynamic range of the complex mixer is analyzed using a probabilistic approach. It is deduced that the resulting static phase error is less than 0.2 deg when the ratio of carrier power to noise power in the demodulator input bandwidth is -72 dB or higher. Thus, the static phase error is negligible at a carrier power to noise spectral density of 0 dB-Hz for a 15 MHz bandwidth demodulator.

Sadr, R.↗
NASA NTRS1996

Performance Assessment of Two GPS Receivers on Space Shuttle

Space Shuttle STS-69 was launched on September 7, 1995, carrying the Wake Shield Facility (WSF-02) among its payloads. The mission included two GPS receivers: a Collins 3M receiver onboard the Endeavour and an Osborne flight TurboRogue, known as the TurboStar, onboard the WSF-02. Two of the WSF-02 GPS Experiment objectives were to: (1) assess the ability to use GPS in a relative satellite positioning mode using the receivers on Endeavour and WSF-02; and (2) assess the performance of the receivers to support high precision orbit determination at the 400 km altitude. Three ground tests of the receivers were conducted in order to characterize the respective receivers. The analysis of the tests utilized the Double Differencing technique. A similar test in orbit was conducted during STS-69 while the WSF-02 was held by the Endeavour robot arm for a one hour period. In these tests, biases were observed in the double difference pseudorange measurements, implying that biases up to 140 m exist which do not cancel in double differencing. These biases appear to exist in the Collins receiver, but their effect can be mitigated by including measurement bias parameters to accommodate them in an estimation process. An additional test was conducted in which the orbit of the combined Endeavour/WSF-02 was determined independently with each receiver. These one hour arcs were based on forming double differences with 13 TurboRogue receivers in the global IGS network and estimating pseudorange biases for the Collins. Various analyses suggest the TurboStar overall orbit accuracy is about one to two meters for this period, based on double differenced phase residuals of 34 cm. These residuals indicate the level of unmodeled forces on Endeavour produced by gravitational and nongravitational effects. The rms differences between the two independently determined orbits are better than 10 meters, thereby demonstrating the accuracy of the Collins-determined orbit at this level as well as the accuracy of the relative positioning using these two receivers.

Schroeder, Christine A.↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
DOE OSTI2022

METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2018

Polymer-Filled Metal Foams for Contamination Resistant Aircraft Leading Edges

Adhesion of contaminants has been identified as a ubiquitous issue for aeronautic exterior surfaces. Organic coatings have been demonstrated to enable reduction in insect residue adhesion as well as a reduction in ice adhesion strength. These approaches however, have yet to demonstrate sufficient durability to enable application to commercial aircraft wing leading edges. High durability materials have been developed in parallel to these efforts; contaminant adhesion issues are not addressed. This work describes a first attempt to identify a solution to the seemingly competing issues of contaminant adhesion and durability. A co-continuous network of organic and metallic domains may provide greater durability, while still enabling the changes in surface-contaminant interactions that have been realized. To that end, unique properties have been achieved through fabrication of composite metal foam materials. Likewise, extensive research has been dedicated toward development of polymeric coatings for mitigation of insect residue adhesion.This work describes initial results from infusion of a low surface free energy epoxy resin into a composite metal foam substrate for the purpose of generating a durable contaminant adhesion-resistant material.

Robbins, Samuel J.↗
DOE OSTI2023

Hierarchical Self-Assembly of Carbon Dots into High-Aspect-Ratio Nanowires

Here, we report a spontaneous and hierarchical self-assembly mechanism of carbon dots prepared from citric acid and urea into nanowire structures with large aspect ratios (>50). Scattering-type scanning near-field optical microscopy (s-SNOM) with broadly tunable mid-IR excitation was used to interrogate details of the self-assembly process by generating nanoscopic chemical maps of local wire morphology and composition. s-SNOM images capture the evolution of wire formation and the complex interplay between different chemical constituents directing assembly over the nano- to microscopic length scales. We propose that residual citrate promotes tautomerization of melamine surface functionalities to produce supramolecular shape synthons comprised of melamine-cyanurate adducts capable of forming long-range and highly directional hydrogen-bonding networks. This intrinsic, heterogeneity-driven self-assembly mechanism reflects synergistic combinations of high chemical specificity and long-range cooperativity that may be harnessed to reproducibly fabricate functional structures on arbitrary surfaces.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1986

Phase lock acquisition for sampled data PLLs using the sweep technique

Simulation results of the swept-acquisition performance of residual carrier phase-locked loops (PLLs) are reported. The loops investigated are sampled data counterparts of the continuous time type II and III loops currently in use in Deep Space Network receivers. It was found that sweep rates of 0.2 B(sub L)(2) to 0.4 B(sub L)(2) Hz/s can be used, depending on the loop parameters and loop signal-to-noise ratio (SNR), where B(sub L) is the one-sided loop noise bandwidth. Type III loops are shown to be not as reliable as type II loops for acquisition using this technique, especially at low SNRs.

Aguirre, S.↗
NASA NTRS1986

An assessment of the long-term drift in SBUV total ozone data, based on comparison with the Dobson network

Six years of total ozone data derived from the Nimbus 7 Solar Backscatter Ultraviolet (SBUV) experiment are compared with results from 41 Dobson stations to determine year-to-year changes in addition to the monthly bias change for individual stations and for the network. The SBUV measurements are found to drift against the Dobson stations with a linear trend of .38 + or - .13 percent/yr. The cause of the drift could not be separated between residual uncorrected drift in the SBUV instrument, limited sensitivity of the SBUV to increases in tropospheric ozone, and the effect of local increases in pollution level on individual Dobson stations.

Fleig, Albert J.↗
NASA NTRS1990

Precision orbit determination for the Geosat Exact Repeat Mission

Precise ephemerides have been determined for the U.S. Navy Geosat Exact Repeat Mission (ERM) using an improved gravity-field model, PTGF-4A (Shum et al. 1989). The Geosat orbits were computed in a terrestrial reference system which is tied to the reference system defined by satellite laser ranging (SLR) to Lageos through a survey between the Tranet Doppler receiver and the SLR system located at Wettzell, FRG. The remaining Doppler tracking station coordinates were estimated simultaneously with the geopotential in the PTGF-4A solution. In this analysis, three continuous 17-day Geosat orbits, which were computed using the 46-station Tranet data and global altimeter crossover data, have a crossover residual rms of 20 cm, indicating that the Geosat radial orbit error is of the order of 20 cm. The orbits computed based on data collected by a 7-station OPNET tracking network and crossover data have the same level of accuracy.

Shum, C. K.↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗