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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Harmonic cancellation within AC low voltage distribution for a realistic office environment

An increase of non-linear loads, primarily from power electronics, has substantially increased current harmonics in commercial buildings, which contributes to decreased transformer efficiency / lifespan and poor power quality. This study uses recorded power consumption data from common miscellaneous electric loads (MELs) seen in offices, combined with detailed characterizations of example MELs, to simulate harmonic cancellation within building circuits. Typically, harmonic cancellation studies assume that AC converters operate across their rated power range. However, this study finds that common MELs operate below 40% of rated power the majority of the time when not quiescent; 89% of sampled devices never operated above 60% of rated power. Simulations using these more realistic power levels indicate current-harmonic cancellation (3rd to 13th harmonic) is significantly lower than that predicted when using full-range power assumptions, resulting in minor errors for low-order harmonics and larger errors for higher order harmonics. Furthermore, increased MELs load diversity increases harmonic cancellation, but insufficiently to eliminate errors. In contrast, blending lighting loads with MELs on the secondaries of distribution transformers improves harmonic cancellation to near those predicted by traditional methods. These results indicate that realistic power levels, as well as better characterization of harmonics from typical MELs, should be used to estimate harmonic cancellation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1986

Solar dynamic power systems for space station

The Parabolic Offset Linearly Actuated Reflector (POLAR) solar dynamic module was selected as the baseline design for a solar dynamic power system aboard the space station. The POLAR concept was chosen over other candidate designs after extensive trade studies. The primary advantages of the POLAR concept are the low mass moment of inertia of the module about the transverse boom and the compactness of the stowed module which enables packaging of two complete modules in the Shuttle orbiter payload bay. The fine pointing control system required for the solar dynamic module has been studied and initial results indicate that if disturbances from the station are allowed to back drive the rotary alpha joint, pointing errors caused by transient loads on the space station can be minimized. This would allow pointing controls to operate in bandwidths near system structural frequencies. The incorporation of the fine pointing control system into the solar dynamic module is fairly straightforward for the three strut concentrator support structure. However, results of structural analyses indicate that this three strut support is not optimum. Incorporation of a vernier pointing system into the proposed six strut support structure is being studied.

Irvine, Thomas B.↗
NASA NTRS2005

The Effects of the Uncertainty of Departures on Multi-Center Traffic Management Advisor (TMA) Scheduling

The Multi-center Traffic Management Advisor (McTMA) provides a platform for regional or national traffic flow management, by allowing long-range cooperative time-based metering to constrained resources, such as airports or air traffic control center boundaries. Part of the demand for resources is made up of proposed departures, whose actual departure time is difficult to predict. For this reason, McTMA does not schedule the departures in advance, but rather relies on traffic managers to input their requested departure time. Because this happens only a short while before the aircraft's actual departure, McTMA is unable to accurately predict the amount of delay airborne aircraft will need to take in order to accommodate the departures. The proportion of demand which is made up by such proposed departures increases as the horizon over which metering occurs gets larger. This study provides an initial analysis of the severity of this problem in a 400-500 nautical mile metering horizon and discusses potential solutions to accommodate these departures. The challenge is to smoothly incorporate departures with the airborne stream while not excessively delaying the departures.' In particular, three solutions are reviewed: (1) scheduling the departures at their proposed departure time; (2) not scheduling the departures in advance; and (3) scheduling the departures at some time in the future based on an estimated error in their proposed time. The first solution is to have McTMA to automatically schedule the departures at their proposed departure times. Since the proposed departure times are indicated in their flight times in advance, this method is the simplest, but studies have shown that these proposed times are often incorrect2 The second option is the current practice, which avoids these inaccuracies by only scheduling aircraft when a confirmed prediction of departure time is obtained from the tower of the departure airport. Lastly, McTMA can schedule the departures at a predicted departure time based on statistical data of past departure time performance. It has been found that departures usually have a wheels-up time after their indicated proposed departure time, as shown in Figure 1. Hence, the departures were scheduled at a time in the future based on the mean error in proposed departure times for their airport.

Thipphavong, Jane↗
NASA NTRS1993

An optimization-based integrated controls-structures design methodology for flexible space structures

An approach for an optimization-based integrated controls-structures design is presented for a class of flexible spacecraft that require fine attitude pointing and vibration suppression. The integrated design problem is posed in the form of simultaneous optimization of both structural and control design variables. The approach is demonstrated by application to the integrated design of a generic space platform and to a model of a ground-based flexible structure. The numerical results obtained indicate that the integrated design approach can yield spacecraft designs that have substantially superior performance over a conventional design wherein the structural and control designs are performed sequentially. For example, a 40-percent reduction in the pointing error is observed along with a slight reduction in mass, or an almost twofold increase in the controlled performance is indicated with more than a 5-percent reduction in the overall mass of the spacecraft (a reduction of hundreds of kilograms).

Maghami, Peiman G.↗
DOE OSTI2019

Measuring key Sm isotope ratios in irradiated UO 2 for use in plutonium discrimination nuclear forensics

As part of an experimental validation of a nuclear forensics methodology for Pu source reactor-type discrimination, destructive analysis has been performed here on two irradiated UO 2 pellets with different irradiation histories. Analysis has focused on measuring key Sm fission product isotope ratios used in a previously published maximum likelihood methodology to determine the most likely irradiation history of the pellets. A total of 21 Sm isotope ratios were measured within the irradiated pellets, and generally agreed within 20% of the irradiations as simulated using the Monte Carlo Radiation Transport and material depletion code, MCNP6. Results indicate the chosen approach can accurately measure the isotope ratios within 5% experimental error.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2021

Corrigendum to “Engineering Pseudomonas putida KT2440 for efficient ethylene glycol utilization” [Metab. Eng. 48 (2018) 197–207]

The sequence of the promoter used to drive expression of glcDEF operon in the engineered strain MFL185 in the original article was incorrect. As described here, we performed additional experiments that indicate expression of this operon was increased in MFL185, as intended. Ultimately, this error is immaterial with respect to the findings and conclusions reported in the original article.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Proton Transfer Dynamics in the Aprotic Proton Accepting Solvent 1-Methylimidazole

The dynamics of proton transfer to the aprotic solvent 1-methylimidazole (MeIm, proton acceptor) from the photoacid 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt (HPTS) were investigated using fast fluorescence measurements. The closely related molecule, 8- methoxypyrene-1,3,6-trisulfonic acid trisodium salt (MPTS), which is not a photoacid, was also studied for comparison. Following optical excitation, the wavelength-dependent population dynamics of HPTS in MeIm resulting from the deprotonation process were collected over the entire fluorescence emission window. Analysis of the time-dependent fluorescence spectra revealed four distinct fluorescence bands that appear and decay on different time scales. We label these four states as: protonated (P), associated I (A I ), associated II (A II ), and deprotonated (D). We find that the simple kinetic scheme of P → A I → A II → D is not consistent with the data. Instead, the kinetic scheme that describes the data has P decaying into A I , which mainly goes on to deprotonation (D), but AI can also feed into A II . A II can return to A I or decay to the ground state, but does not deprotonate within experimental error. Finally, quantum chemistry and excitedstate QM/MM Born-Oppenheimer molecular dynamics simulations indicate that A I and A II are two H-bonding conformations of MeIm to the HPTS hydroxyl, axial and equatorial, respectively.

14 SOLAR ENERGY↗
DOE OSTI2023

A Coupled Deep Learning Model for Estimating Surface NO 2 Levels from Remote Sensing Data: 15-Year Study Over the Contiguous United States

This study proposes a novel two-step deep learning (DL) model for estimating surface NO 2 concentrations using satellite data over the contiguous United States (CONUS) from 2005 to 2019. The first phase of the model uses partial convolutional neural network (PCNN), an advanced DL model that accurately imputes gaps between surface NO 2 stations and creates 5,478 daily-mean NO 2 grids (PCNN-NO 2 ) of the 2005-2019 period over the study area. We then feed the PCNN-NO 2 , along with other predictor variables, into a deep neural network (DNN) to estimate surface NO 2 levels, achieving exceptional performance with a correlation coefficient of 0.975 to 0.978, a mean absolute bias of 0.99 ppb to 1.38 ppb, and a root mean square error of 1.47 ppb to 1.97 ppb. Spatial cross-validation results also indicate strong spatial performance of PCNN-DNN surface NO 2 estimates. In addition to its accurate estimates, the PCNN-DNN model consistently generates estimated NO 2 grids without any missing values, improving the quality of various applications such as emission reduction strategies and public health studies. Between 2005 and 2019, the 5,478 daily estimated NO 2 grids over the CONUS reveal significant reductions in NO 2 levels in fourteen major urban environments: Washington D.C. (-43%), New York (-45%), Los Angeles (-38%), Chicago (-25%), Boston (-43%), Houston (-34%), Dallas (-40%), Philadelphia (-41%), Phoenix (-38%), Detroit (-20%), Denver (-23%), Atlanta (-0.7%), Cincinnati (-38%), and Pittsburgh (-56%). Furthermore, the study shows that the denser urban regions that in-situ stations are installed in, the higher the difference between in-situ observations and regional-mean NO 2 levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Adaptive stretching of representations across brain regions and deep learning model layers

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model’s representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Development of control quality factor for HVAC control loop performance assessment I—Methodology (ASHRAE RP-1587)

This article is the second paper from the ASHRAE research project RP-1587, focusing on the methodology of obtaining a control quality factor (CQF). This article presents a development of two CQFs for assessing the heating, ventilation, and air-conditioning (HVAC) control loop performance. Both CQFs are able to detect whether the control loop is able to maintain the setpoint and identify the loop's ability to handle disturbances. In addition, the reversal behaviors are assessed as well. The first CQF (CQF-Harris) is proposed based on the normalized Harris index using the recursive least squares method. This recursive least squares method is selected because of its computational efficiency compared with the maximum likelihood estimation method. The second CQF (CQF-EWMA) is based on the exponentially weighted moving average of the error ratio. The assessment scale of excellent, good, fair, bad, and failed, which indicates the quality of the HVAC control loops, is established as well. The sensitivity analysis for both CQFs is also conducted, and it provides insights on choosing the appropriate parameters to compute such CQFs. Such parameters include the sampling frequency, the length of the moving window, and the variance of the unmeasured disturbance. The field evaluations and tests of the proposed CQFs for simulated control loops and real control loops can be found in the companion paper with the title 'Development of Control Quality Factor for HVAC Control Loop Performance Assessment - III: Field Testing and Results (ASHRAE RP-1587).'

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Removing Atmospheric Fringes from Zwicky Transient Facility i-band Images using Principal Component Analysis

The Zwicky Transient Facility is a time-domain optical survey that has substantially increased our ability to observe and construct massive catalogs of astronomical objects by use of its 47 square degree camera that can observe in multiple filters. However the telescope’s i-band filter suffers from significant atmospheric fringes that reduce photometric precision, especially for faint sources and in multi-epoch co-additions. Here we present a method for constructing models of these atmospheric fringes using Principal Component Analysis that can be used to identify and remove these artifacts from contaminated images. In addition, we present the Uniform Background Indicator as a quantitative measurement of the reduced correlated background noise and photometric error present after removing fringes. We conclude by evaluating the effect of our method on measuring faint sources through the injection and recovery of artificial stars in both single-image epochs and co-additions. Our method for constructing atmospheric fringe models and applying those models to produce cleaned images is available for public download in the open source Python package fringez.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Finite and infinite matrix product states for Gutzwiller projected mean-field wave functions

Matrix product states (MPS) and “dressed” ground states of quadratic mean fields (e.g., Gutzwiller projected Slater determinants) are both important classes of variational wave functions. This latter class has played important roles in understanding superconductivity and quantum spin liquids. We present a method to obtain both the finite and infinite MPS (iMPS) representation of the ground state of an arbitrary fermionic quadratic mean-field Hamiltonian (which in the simplest case is a Slater determinant and in the most general case is a Pfaffian). We also show how to represent products of such states (e.g., determinants times Pfaffians). From this representation one can project to single occupancy and evaluate the entanglement spectra after Gutzwiller projection. We then obtain the MPS and iMPS representation of Gutzwiller projected mean-field states that arise from the variational slave-fermion approach to the S = 1 bilinear-biquadratic quantum spin chain. To accomplish this, we develop an approach to orthogonalize degenerate iMPS to find all the states in the degenerate ground-state manifold. We find the energies of the MPS and iMPS states match the variational energies closely, indicating the method is accurate and there is minimal loss due to truncation error. We then present an exploration of the entanglement spectra of projected slave-fermion states, exploring their qualitative features and finding good qualitative agreement with the respective exact ground-state spectra found from density matrix renormalization group.

1-dimensional spin chains↗
DOE OSTI2019

PPT-GPU: Scalable GPU Performance Modeling

Performance modeling is a challenging problem due to the complexities of hardware architectures. In this paper, we present PPT-GPU, a scalable and accurate simulation framework that enables GPU code developers and architects to predict the performance of applications in a fast, and accurate manner on different GPU architectures. PPT-GPU is part of the open source project, Performance Prediction Toolkit (PPT) developed at the Los Alamos National Laboratory. We extend the old GPU model in PPT that predict the runtimes of computational physics codes to offer better prediction accuracy, for which, we add models for different memory hierarchies found in GPUs and latencies for different instructions. To further show the utility of PPT-GPU, we compare our model against real GPU device(s) and the widely used cycle-accurate simulator, GPGPU-Sim using different workloads from RODINIA and Parboil benchmarks. The results indicate that the predicted performance of PPT-GPU is within a 10% error compared to the real device(s). In addition, PPT-GPU is highly scalable, where it is up to 450x faster than GPGPU-Sim with more accurate results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High-throughput sequencing of the chloroplast and mitochondrion of Chlamydomonas reinhardtii to generate improved de novo assemblies, analyze expression patterns and transcript speciation, and evaluate diversity among laboratory strains and wild isolates

Chlamydomonas reinhardtii is a unicellular chlorophyte alga that is widely studied as a reference organism for understanding photosynthesis, sensory and motile cilia, and for development of an algal-based platform for producing biofuels and bio-products. Its highly repetitive, ~205-kbp circular chloroplast genome and ~15.8-kbp linear mitochondrial genome were sequenced prior to the advent of high-throughput sequencing technologies. Here, high coverage shotgun sequencing was used to assemble both organellar genomes de novo. These new genomes correct dozens of errors in the prior genome sequences and annotations. Gen-ome sequencing coverage indicates that each cell contains on average 83 copies of the chloroplast genome and 130 copies of the mitochondrial genome. Using protocols and analyses optimized for organellar tran-scripts, RNA-Seq was used to quantify their relative abundances across 12 different growth conditions. Forty-six percent of total cellular mRNA is attributable to high expression from a few dozen chloroplast genes. RNA-Seq data were used to guide gene annotation, to demonstrate polycistronic gene expression, and to quantify splicing of psaA and psbA introns. In contrast to a conclusion from a recent study, we found that chloroplast transcripts are not edited. Unexpectedly, cytosine-rich polynucleotide tails were observed at the 3’-end of all mitochondrial transcripts. A comparative genomics analysis of eight laboratory strains and 11 wild isolates of C. reinhardtii identified 2658 variants in the organellargenomes, which is 1/10th as much genetic diversity as is found in the nucleus.

ftsH↗
DOE OSTI2018

Solid Mechanics Code Verification: Contrasting Classical and Manufactured Reference Solutions

Verification results for Sierra/SM using inexact reference solutions have often exhibited unsatisfactory convergence behavior. With an understanding of the convergence behavior for these types of tests, one can avoid falsely attributing pathologies of the test with incorrectness of the code. Simple theoretical results highlight that for an inexact reference solution two conditions must be met to observe asymptotic convergence. These conditions, and the resulting types of convergence behaviors, are further illustrated with graphical examples depicting the exact, inexact reference, and sequence of numerical solutions as vectors (in a function space). A stress concentration problem is adopted to contrast convergence behaviors when using inexact (classical linear elastic) and exact (manufactured) reference solutions. Convergence is not initially attained with the classical solution. Convergence with the manufactured solution indicates the convergence failure with the classical reference did not result from code error and provides insight on how for this problem asymptotic convergence could be attained with the classical reference solution by modifying the computational models.

42 ENGINEERING↗
DOE OSTI2024

A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations

Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (N CCN ) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and N CCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in N CCN . The potential of this algorithm is further evaluated by retrieving N CCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the N CCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

54 ENVIRONMENTAL SCIENCES↗