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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Factors influencing mode choice of adults with travel-limiting disability

Introduction: Despite the plethora of research devoted to analyzing the impact of disability on travel behavior, not enough studies have investigated the varying impact of social and environmental factors on the mode choice of people with disabilities that restrict their ability to use transportation modes efficiently. This research gap can be addressed by investigating the factors influencing the mode choice behavior of people with travel-limiting disabilities, which can inform the development of accessible and sustainable transportation systems. Additionally, such studies can provide insights into the social and economic barriers faced by this population group, which can help policymakers to promote social inclusion and equity. Method: This study utilized a Random Parameters Logit model to identify the individual, trip, and environmental factors that influence mode selection among people with travel-limiting disabilities. Here, using the 2017 National Household Travel Survey data for New York State, which included information on respondents with travel-limiting disabilities, the analysis focused on a sample of 8,016 people. In addition, climate data from the National Oceanic and Atmospheric Administration were integrated as additional explanatory variables in the modeling process. Results: The results revealed that people with disabilities may be inclined to travel longer distances walking in the absence of suitable accommodation facilities for other transportation modes. Furthermore, people were less inclined to walk during summer and winter, indicating a need to consider weather conditions as a significant determinant of mode choice. Moreover, low-income people with disabilities were more likely to rely on public transport or walking. Conclusion: Based on this study’s findings, transportation agencies could design infrastructure and plan for future expansions that is more inclusive and accessible, thus catering to the mobility needs of people with travel-limiting disabilities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Functionalized Graphene via a One-Pot Reaction Enabling Exact Pore Sizes, Modifiable Pore Functionalization, and Precision Doping

Functionalizing graphene with exact pore size, specific functional groups, and precision doping poses many significant challenges. Current methods lack precision and produce random pore sizes, sites of attachment, and amounts of dopant, leading to compromised structural integrity and affecting graphene’s applications. In this work, we report a strategy for the synthesis of functionalized graphitic materials with modifiable nanometer-sized pores via a Pictet–Spengler polymerization reaction. This one-pot, four-step synthesis uses concepts based on covalent organic frameworks (COFs) synthesis to produce crystalline two-dimensional materials that were confirmed by PXRD, TEM measurements, and DFT studies. These new materials are structurally analogous to doped graphene and graphene oxide (GO) but, unlike GO, maintain their semiconductive properties when fully functionalized.

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

A Consistent Representation of Cloud Overlap and Cloud Subgrid Vertical Heterogeneity

Many global climate models underestimate the cloud cover and overestimate the cloud albedo, especially for low-level clouds. We determine how a correct representation of the vertical structure of clouds can fix part of this bias. We use the 1D McICA framework and focus on low-level clouds. Using Large Eddy Simulations results as reference, we propose a method based on exponential-random overlap that represents the cloud overlap between layers and the subgrid cloud properties over several vertical scales, with a single value of the overlap parameter. Starting from a coarse vertical grid, representative of atmospheric models, this algorithm is used to generate the vertical profile of the cloud fraction with a finer vertical resolution, or to generate it on the coarse grid but with subgrid heterogeneity and cloud overlap that ensures a correct cloud cover. Doing so we find decorrelation lengths are dependent on the vertical resolution, except if the vertical subgrid heterogeneity and interlayer overlap are taken into account coherently. We confirm that the frequently used maximum-random overlap leads to a significant error by underestimating the low-level cloud cover with a relative error of about 50%, that can lead to an error of SW cloud albedo as big as 70%. Not taking into account the subgrid vertical heterogeneity of clouds can cause a relative error of 20% in brightness, assuming the cloud cover is correct.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Marker-free carotenoid-enriched rice generated through targeted gene insertion using CRISPR-Cas9

Targeted insertion of transgenes at pre-determined plant genomic safe harbors provides a desirable alternative to insertions at random sites achieved through conventional methods. Most existing cases of targeted gene insertion in plants have either relied on the presence of a selectable marker gene in the insertion cassette or occurred at low frequency with relatively small DNA fragments (<1.8 kb). Here, we report the use of an optimized CRISPR-Cas9-based method to achieve the targeted insertion of a 5.2 kb carotenoid biosynthesis cassette at two genomic safe harbors in rice. We obtain marker-free rice plants with high carotenoid content in the seeds and no detectable penalty in morphology or yield. Whole-genome sequencing reveals the absence of off-target mutations by Cas9 in the engineered plants. These results demonstrate targeted gene insertion of marker-free DNA in rice using CRISPR-Cas9 genome editing, and offer a promising strategy for genetic improvement of rice and other crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Learning local, quenched disorder in plasticity and other crackling noise phenomena

Abstract When far from equilibrium, many-body systems display behavior that strongly depends on the initial conditions. A characteristic such example is the phenomenon of plasticity of crystalline and amorphous materials that strongly depends on the material history. In plasticity modeling, the history is captured by a quenched, local and disordered flow stress distribution. While it is this disorder that causes avalanches that are commonly observed during nanoscale plastic deformation, the functional form and scaling properties have remained elusive. In this paper, a generic formalism is developed for deriving local disorder distributions from field-response (e.g., stress/strain) timeseries in models of crackling noise. We demonstrate the efficiency of the method in the hysteretic random-field Ising model and also, models of elastic interface depinning that have been used to model crystalline and amorphous plasticity. We show that the capacity to resolve the quenched disorder distribution improves with the temporal resolution and number of samples.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Stabilities and defect-mediated lithium-ion conduction in a ground state cubic Li 3 N structure

A stable ground state structure with cubic symmetry of Li 3 N (c-Li 3 N) is found by ab initio initially symmetric random-generated crystal structure search method. Gibbs free energy, calculated within quasi-harmonic approximation, shows that c-Li 3 N is the ground state structure for a wide range of temperature. The c-Li 3 N structure has a negative thermal expansion coefficient at temperatures lower than room temperature, due mainly to two transverse acoustic phonon modes. This c-Li 3 N phase is a semiconductor with an indirect band gap of 1.90 eV within hybrid density functional calculation. We also investigate the migration and energetics of native point defects in c-Li 3 N, including lithium and nitrogen vacancies, interstitials, and anti-site defects. Lithium interstitials are found to have a very low migration barrier (~0.12 eV) and the lowest formation energy among all possible defects. Thus, the ionic conduction in c-Li 3 N is expected to occur via an interstitial mechanism, in contrast to that in the well-known α-Li 3 N phase which occurs via a vacancy mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Simultaneous quantification of uranium( VI ), samarium, nitric acid, and temperature with combined ensemble learning, laser fluorescence, and Raman scattering for real-time monitoring

In this work, laser-induced fluorescence spectroscopy (LIFS), Raman spectroscopy, and a stacked regression ensemble was developed for near real-time quantification of uranium(VI) (1–100 μg mL –1 ), samarium (0–200 μg mL –1 ) and nitric acid (0.1–4 M) with varying temperature (20 °C–45 °C). LIFS applications range from fundamental lab-scale studies to real-time process monitoring at industrial levels, such as nuclear reprocessing applications, provided the phenomena affecting the fluorescence spectrum are accounted for (e.g., absorption, quenching, complexation). Multiple chemometric models were examined and compared to a more traditional multivariate regression approach called partial least squares (PLS). Results obtained on synthetic samples selected using D-optimal experimental design indicated that a stacked regression method, which included ridge regression, random forest, PLS, and an eXtreme gradient boost algorithm, successfully measured uranium(VI) concentrations directly in nitric acid without measuring luminescence lifetimes or standard addition. The top model resulted in percent root-mean-square error of prediction values of 5.2, 1.9, 3.0, and 2.3% for U(VI), Sm 3+ , HNO 3 , and temperature, respectively. The approach may be useful for quantifying fluorescent fission products (e.g., Sm 3+ ) to provide information on burnup of irradiated nuclear fuel. This novel framework reinforces the applicability of LIFS for real-time applications in nuclear fuel cycle applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

A lightweight network anomaly detection technique

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 and NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall–magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

42 ENGINEERING↗
DOE OSTI2016

SU-F-T-615: Comparison of Plan Quality for Linac-Based Stereotactic Radiosurgery (SRS) Using Single- and Multi-Isocenter Techniques

Purpose: To compare the plan quality of linear accelerator (linac)-based stereotactic radiosurgery (SRS) using single-isocenter volumetric arc therapy (SI-VMAT), restricted single-isocenter dynamic-arc (RSI-DARC), and multi-isocenter DARC (MI-DARC) techniques. Methods: Fifteen SRS cases were randomly selected and re-planned using the SI-VMAT (Pinnacle), RSI-DARC (iPlanNet) and MI-DARC (iPlanNet). The number of planning target volumes (PTVs) for each plan ranged from 1 to 6. For SI-VMAT, a single isocenter and 3-4 VMAT beams are used for all PTVs, while for MI-DARC, each PTV has its own isocetner with 3 DARC beams. RSI-DARC uses one isocnter with 3-6 DARC beams to irradiate all PTVs within 2.5-cm radius. Both SI-DARC and RSI-DARC plans were optimized manually. The prescription dose was 20 Gy to each PTV. The maximal dose was 25 Gy for RSI-DARC and MI-DARC, but could not be controlled for SI-VMAT due to the nature of VMAT planning. Plan quality indexes including PTV coverage, mean dose of PTV (PTVmean) and tissue (Tmean), V12Gy, conformity index (CI), and V10Gy/VPTV were calculated and compared. Results: Full PTV coverage was achieved for all three techniques. Using the MI-DARC plans as the gold standard, the PTVmean of the SI-VMAT plans was 12.5%±8.3% (mean±standard deviation) higher, in comparison to 0.7%±1.4% for the RSI-DARC plans. Similar trend was observed for other indexes including V12Gy (39.4%±27.3% vs. 9.3%±7.8%), Tmean (35.0%±26.8% vs. 2.8%±3.4%), and V10Gy/VPTV (42.2%±31.5% vs. 9.9%±8.2%). CI is comparable (6.2%±14.2% vs. 6.3%±7.2%). Assuming the treatment time is proportional to the number of isocenters, the reduction of the treatment time in comparison to MI-DARC was 70% for SI-VMAT and 42% for RSI-DARC. Conclusion: Although the SI-VMAT can save a considerable amount of treatment time, the plan indexes also significantly deviates from the gold standard, MI-DARC. RSI-DARC, on the other hand, provides a good compromise between the treatment time and plan quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Machine Learning of Commercial and Residential Load Components in Northwestern United States

The impacts of weather attributes on commercial and residential electricity demands and their components in the northwestern United States were examined. Two machine learning methods, regression tree (RT), and random forest (RF), were integrated and compared. Both RT and RF models provide reliable prediction of commercial cooling load. RF models particularly yield higher accuracy with reduced overfitting.

Zhou, Huifen↗
DOE OSTI2023

DT-HYDRO

The software solves the time dependent, one dimensional (1D) coupled mass and momentum balance equations governing the elastic flow of water through the penstock, turbine and draft tube in a hydroelectric facility using a high order finite volume based method. The numerical method is based on the Kurganov-Tadmor central method paired with the Monotonic Upstream-centered Scheme for Conservation Laws (MUSCL). This solution method accurately resolves the fast transient behavior of the flow, including water hammer. Additionally, the software estimates the full 3D flow field within the turbine chamber in real time, a feat that is made possible by leveraging pre-computed CFD results by utilizing a reduced order modeling method based on an efficient randomized singular value decomposition (SVD) driven proper orthogonal decomposition (POD) with POD-mode weight regression. The reduced order model of the 3D flow is directly coupled to the 1D elastic flow model so the entire flow field through the penstock and turbine system is resolved quickly and with high fidelity.

Gurecky, William [Oak Ridge National Laboratory (O↗
DOE OSTI2020

Effects of Testosterone Supplementation on Ghrelin and Appetite During and After Severe Energy Deficit in Healthy Men

Abstract Background Severe energy deficits cause interrelated reductions in testosterone and fat free mass. Testosterone supplementation may mitigate those decrements, but could also reduce circulating concentrations of the orexigenic hormone ghrelin, thereby exacerbating energy deficit by suppressing appetite. Objective To determine whether testosterone supplementation during severe energy deficit influences fasting and postprandial ghrelin concentrations and appetite. Design and methods Secondary analysis of a randomized, double-blind trial that determined the effects of testosterone supplementation on body composition changes during and following severe energy deficit in nonobese, eugonadal men. Phase 1 (PRE-ED): 14-day run-in; phase 2: 28 days, 55% energy deficit with 200 mg testosterone enanthate weekly (TEST; n = 24) or placebo (PLA; n = 26); phase 3: free-living until body mass recovered (end-of-study; EOS). Fasting and postprandial acyl ghrelin and des-acyl ghrelin concentrations and appetite were secondary outcomes measured during the final week of each phase. Results Fasting acyl ghrelin concentrations, and postprandial acyl and des-acyl ghrelin concentrations increased in PLA during energy deficit then returned to PRE-ED values by EOS, but did not change in TEST (phase-by-group, P < 0.05). Correlations between changes in free testosterone and changes in fasting acyl ghrelin concentrations during energy deficit (ρ = -0.42, P = 0.003) and body mass recovery (ρ = -0.38; P = 0.01) were not mediated by changes in body mass or body composition. Transient increases in appetite during energy deficit were not affected by testosterone treatment. Conclusions Testosterone supplementation during short-term, severe energy deficit in healthy men prevents deficit-induced increases in circulating ghrelin without blunting concomitant increases in appetite. Clinical Trials Registration www.clinicaltrials.gov NCT02734238 (registered 12 April 2016).

Karl, J. Philip↗
DOE OSTI2018

Different measures of speckle and coherence at the output of a multimode optical fiber

Excitation of a multimode fiber with a focused spatially coherent light of finite bandwidth results in a partially coherent light at the output of the fiber. In this report, we observe the properties of speckle and classical coherence of such light with analytical theory, numerical modeling, and experimentally. Of particular interest is the relationship between measures of coherence and speckle and their dependence on input source bandwidth and fiber length. Speckle contrast is easy to measure experimentally and there exist at least two different methods to generate ensembles of random speckles. We demonstrate that speckle contrast evaluated over the ensemble of external diffusers is related to the number of effective modes—one of the characteristics of beam global coherence. The other speckle contrast measure evaluated over the ensemble of random bends and twists of the fiber is related to residual coherence, which is the pedestal on the average modulus of the complex degree of the coherence function on the output endface of the fiber.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

A GENERAL FRAMEWORK FOR ENHANCING SPARSITY OF GENERALIZED POLYNOMIAL CHAOS EXPANSIONS

Compressive sensing has become a powerful addition to uncertainty quantification when only limited data are available. In this paper, we provide a general framework to enhance the sparsity of the representation of uncertainty in the form of generalized polynomial chaos expansion. We use an alternating direction method to identify new sets of random variables through iterative rotations so the new representation of the uncertainty is sparser. Consequently, we increase both the efficiency and accuracy of the compressive-sensing-based uncertainty quantification method. We demonstrate that the previously developed iterative method to enhance the sparsity of Hermite polynomial expansion is a special case of this general framework. Moreover, we use Legendre and Chebyshev polynomial expansions to demonstrate the effectiveness of this method with applications in solving stochastic partial differential equations and high-dimensional (O(100)) problems.

Yang, Xiu↗