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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Modeling Disturbance Dynamics in Transitional and Turbulent Boundary Layers

The dynamics of an ensemble of linear disturbances in boundary-layer flows at various Reynolds numbers is studied through an analysis of the transport equations for the mean disturbance kinetic energy and energy dissipation rate. Effects of adverse and favorable pressure-gradients on the disturbance dynamics are also included in the analysis. Unlike the fully turbulent regime where nonlinear phase scrambling of the fluctuations affects the flow field even in proximity to the wall, the early stage transition regime fluctuations studied here are influenced across the boundary layer by the solid boundary. In addition, the dominating dynamics in the disturbance kinetic energy equation is governed by the energy production, pressure-transport and viscous diffusion - also in contrast to the fully turbulent regime. For the disturbance dissipation rate, a dynamic balance exists between the destruction and diffusion of dissipation.

Grosch, C. E.↗
NASA NTRS2018

Luminous Blue Variable Outbursts from the Variations of Helium Opacity

Luminous blue variables are evolved massive stars that exhibit poorly understood variability on timescales from months to years with significant mass loss rates. They typically reach an effective temperature of ≈ 9000K during outbursts. These variations and large mass loss rates are believed to be caused by the radiation force on the cooler, more opaque outer layers that can balance or even exceed the force of gravity, although the exact mechanisms for the outburst and variability are unknown and cannot be calculated in one dimension. Here we report three dimensional simulations of radiation-dominated massive stars, which show that helium opacity plays an important role for the outburst and setting the observed effective temperature ≈ 9000K. It also likely triggers the episodic mass loss at rates of 10(exp -7) – 10(exp -5)M(sub ⨀)/yr. The helium opacity peak only shows up in our three-dimensional simulations but not in one dimensional calculation because the envelope structures need to be determined self-consistently with convection. The simulations not only reproduce similar observed long time scale variabilities, but also predict that convection causes irregular envelope oscillations and 10-30% brightness variations on a typical timescale of a few days and reaches larger amplitudes for even cooler stars. These short time scale variabilities should be observed with future high cadence observations.

Helium opacities↗
DOE OSTI2020

The Future of Renewable Energy Generation: Photovoltaic Materials

There are myriad ways to harness solar energy, including, but not limited to: solar thermal, solar fuels, and photovoltaics (PV). Across the portfolio of solar energy technologies, PV is unique in that collected photons are directly converted to electricity. Traditionally, the dominant materials in PV are also the dominant semiconductor and optoelectronic materials. The cost of solar energy from PV is a product of a complex combination of non-material related factors, known as soft costs, e.g., permitting, zoning, and of balance-of-systems (BOS) costs, like inverters or other components needed to utilize cells and modules. While the soft costs are due to administrative/regulatory concerns, BOS costs are driven by details of cell/module fabrication, mounting, power electronics, and other concerns, which touch on a range of basic material science issues. There is some overlap of BOS and soft cost, as a lighter weight solar panel can reduce installations costs, as an example. At the PV-system level, the materials considerations span both electronic and structural components. The focus of this paper is the solar cell, which is the minimum operational unit device that converts light into electricity.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1979

Estimations of electron densities and temperatures in He-3 dominated plasmas

It is very useful to have a method of estimation for electron temperature and electron densities in nuclear pumped plasmas because measurements of such quantities are very difficult. This paper describes a method, based on rate equation analysis of the ionized species in the plasma and the electron energy balance. In addition to the ionized species, certain neutral species must also be calculated. Examples are given for pure helium and a mixture of helium and argon. In the HeAr case, He(+), He2(+), He/2 3S/, Ar(+), Ar2(+), and excited Ar are evaluated.

Depaola, B. D.↗
DOE OSTI2020

Replacement of Cobalt in Medical Device Sterilization: Current Trends, Opportunities and Barriers to Adoption of X-ray and E-Beam Within the Medical Device Sterilization Market

Radioactive sources, specifically Co-60, are crucially important to the medical device sterilization industry; however, their benefits must be balanced against the need to ensure that the materials are secure. Accelerator-based sterilization methods, including X-ray and E-Beam, can in many cases sufficiently replace Co-60 and ethylene oxide, the dominant modalities currently in use; however, a lack of hands-on experience with accelerators, regulations, and a lack of capacity have hampered faster adoption of these technologies. The market is likely to change, however, given public pressures against ethylene oxide and concerns over the cost and long-term supply chain robustness of Co-60. This may provide an opportunity for growth for accelerator capabilities. Current research at Sandia National Laboratories and Argonne National Laboratory, on behalf of the Department of Energy’s National Nuclear Security Administration Office of Radiological Security, is focused on developing an understanding of the medical device sterilization marketplace, the different modalities that are used to sterilize goods, and the drivers that affect the actors within it. Research at Pacific Northwest National Laboratory, in conjunction with the medical device sterilization industry, has focused on expanding the industry’s understanding of the capabilities of X-ray sterilization and developing data that can inform the industry’s consideration of a transition towards accelerator-based devices.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1979

Large-scale motion in the Venus stratosphere

To examine a postulate that the horizontal momentum exchanges in the Venusian stratosphere are quasi-nondivergent, the properties of two-dimensional turbulence on a slowly rotating sphere in a high-resolution, one-level numerical model are investigated. It is concluded that the forcing which maintains the stratospheric flow is weak and influences the dynamics far less than the inertial effects. Consequently, the behavior resembles that of vorticity-conserving, two-dimensional flows. On a slowly rotating planet like Venus, such flows are dominated by solid-body rotation and by a planetary wave of unit zonal wavenumber - this wave corresponds to the observed Y-shaped UV feature. Although these largest scales of motion stand out, the dynamic balances of the flow are fundamentally nonlinear, in contrast to the quasi-linear Rossby wave regime on rapidly rotating planets.

Rossow, W. B.↗
NASA NTRS1982

A post-Voyager view of Saturn's environment

Major results obtained by Voyager spacecraft encounters in the vicinity of Saturn are discussed, noting that the hydrogen torus dominates the Saturn magnetosphere without directly participating in its dynamics, being neutral. The average sunward location of the magnetopause has been found to generally lie beyond the orbit of Titan, and is determined by the pressure balance between the solar wind and the planetary magnetic field. Energetic electrons were observed during Voyager 1 traversal of the Saturn magnetic tail down to the magnetopause, indicating that field lines in the tail lobe of the Saturn magnetosphere are closed. The detailed analysis of Voyager 1 and 2 encounters with Saturn will continue for another two years, exploring the dynamics of the magnetosphere itself and of its interaction with the rings, satellites, and ionosphere of the planet.

Krimigis, S. M.↗
NASA NTRS1985

A method for estimating the rolling moment due to spin rate for arbitrary planform wings

The application of aerodynamic theory for estimating the force and moments acting upon spinning airplanes is of interest. For example, strip theory has been used to generate estimates of the aerodynamic characteristics as a function of spin rate for wing-dominated configurations for angles of attack up to 90 degrees. This work, which had been limited to constant chord wings, is extended here to wings comprised of tapered segments. Comparison of the analytical predictions with rotary balance wind tunnel results shows that large discrepancies remain, particularly for those angles-of-attack greater than 40 degrees.

Poppen, W. A., Jr.↗
NASA NTRS1990

Release of liquid water from the Space Shuttle

Groundbased and onboard video images of a sunlit Shuttle Orbiter water dump are interpreted as showing that the continuous 1-mm-diameter liquid stream quickly breaks up in near-vacuum to form ice/snow particles of two characteristic sizes. Discrete large droplets are most evident in the close-in photographs, and unresolved submicron 'fog' from recondensation of overexpanded evaporated water appears to dominate the ground-telescope photographs of the 2.5 km long optically detectable trail. The mean diameter of the smaller particles was estimated from the spatial distribution of visible radiance using a model of their energy balance, (small) surface roughening as they sublime, and Mie scattering of pre-dawn sunlight. The results are consistent with those from recent space-tank simulations.

Pike, C. P.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2016

Constraints on LISA Pathfinder's Self-Gravity: Design Requirements, Estimates and Testing Procedures

LISA Pathfinder satellite was launched on 3 December 2015 toward the Sun Earth first Lagrangian point (L1) where the LISA Technology Package (LTP), which is the main science payload, will be tested. LTP achieves measurements of differential acceleration of free-falling test masses (TMs) with sensitivity below 3 x 10(exp -14) m s(exp -2) Hz(exp - 1/2) within the 130 mHz frequency band in one dimension. The spacecraft itself is responsible for the dominant differential gravitational field acting on the two TMs. Such a force interaction could contribute a significant amount of noise and thus threaten the achievement of the targeted free-fall level. We prevented this by balancing the gravitational forces to the sub nm s(exp -2) level, guided by a protocol based on measurements of the position and the mass of all parts that constitute the satellite, via finite element calculation tool estimates. In this paper, we will introduce the gravitational balance requirements and design, and then discuss our predictions for the balance that will be achieved in flight.

Armano, M.↗
DOE OSTI2020

Projecting Antarctica's contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2)

Abstract. The sea level contribution of the Antarctic ice sheet constitutes a large uncertainty in future sea level projections. Here we apply a linear response theory approach to 16 state-of-the-art ice sheet models to estimate the Antarctic ice sheet contribution from basal ice shelf melting within the 21st century. The purpose of this computation is to estimate the uncertainty of Antarctica's future contribution to global sea level rise that arises from large uncertainty in the oceanic forcing and the associated ice shelf melting. Ice shelf melting is considered to be a major if not the largest perturbation of the ice sheet's flow into the ocean. However, by computing only the sea level contribution in response to ice shelf melting, our study is neglecting a number of processes such as surface-mass-balance-related contributions. In assuming linear response theory, we are able to capture complex temporal responses of the ice sheets, but we neglect any self-dampening or self-amplifying processes. This is particularly relevant in situations in which an instability is dominating the ice loss. The results obtained here are thus relevant, in particular wherever the ice loss is dominated by the forcing as opposed to an internal instability, for example in strong ocean warming scenarios. In order to allow for comparison the methodology was chosen to be exactly the same as in an earlier study (Levermann et al., 2014) but with 16 instead of 5 ice sheet models. We include uncertainty in the atmospheric warming response to carbon emissions (full range of CMIP5 climate model sensitivities), uncertainty in the oceanic transport to the Southern Ocean (obtained from the time-delayed and scaled oceanic subsurface warming in CMIP5 models in relation to the global mean surface warming), and the observed range of responses of basal ice shelf melting to oceanic warming outside the ice shelf cavity. This uncertainty in basal ice shelf melting is then convoluted with the linear response functions of each of the 16 ice sheet models to obtain the ice flow response to the individual global warming path. The model median for the observational period from 1992 to 2017 of the ice loss due to basal ice shelf melting is 10.2 mm, with a likely range between 5.2 and 21.3 mm. For the same period the Antarctic ice sheet lost mass equivalent to 7.4 mm of global sea level rise, with a standard deviation of 3.7 mm (Shepherd et al., 2018) including all processes, especially surface-mass-balance changes. For the unabated warming path, Representative Concentration Pathway 8.5 (RCP8.5), we obtain a median contribution of the Antarctic ice sheet to global mean sea level rise from basal ice shelf melting within the 21st century of 17 cm, with a likely range (66th percentile around the mean) between 9 and 36 cm and a very likely range (90th percentile around the mean) between 6 and 58 cm. For the RCP2.6 warming path, which will keep the global mean temperature below 2 ∘C of global warming and is thus consistent with the Paris Climate Agreement, the procedure yields a median of 13 cm of global mean sea level contribution. The likely range for the RCP2.6 scenario is between 7 and 24 cm, and the very likely range is between 4 and 37 cm. The structural uncertainties in the method do not allow for an interpretation of any higher uncertainty percentiles. We provide projections for the five Antarctic regions and for each model and each scenario separately. The rate of sea level contribution is highest under the RCP8.5 scenario. The maximum within the 21st century of the median value is 4 cm per decade, with a likely range between 2 and 9 cm per decade and a very likely range between 1 and 14 cm per decade.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Energetics and dynamics of resonant and nonresonant scattering in strong magnetic fields

The energetics and the dynamics resulting from electron-photon resonant and nonresonant scattering in a plasma with magnetic field about 10 to the 12th G are studied in detail. Precise analytic results are obtained in the optically thin limit, and numerical results are presented for the optically thick case. For the latter, it is found that when the equilibrium temperature T(C) is determined by the cooling/heating balance due solely to cyclotron resonant scattering, the ratio of T(C) to the superstrong field B remains fairly constant for N(e) up to about 6 x 10 to the 21st electrons/sq cm. This line-dominated region comes to an end when the extra heating from the hard continuum photons becomes competitive with the line processes and drives T(C) well above the pure line value. For parameters characteristic of GB 880205, the thickness of the line-dominated region is determined to be between 10 to the 21st and 10 to the 22nd electrons/sq cm.

Lamb, Don Q.↗
NASA NTRS1999

The Dissipation Range in Rotating Turbulence

The dissipation range energy balance of the direct interaction approximation is applied to rotating turbulence when rotation effects persist well into the dissipation range. Assuming that RoRe (exp 1/2) is much less than 1 and that three-wave interactions are dominant, the dissipation range is found to be concentrated in the wavevector plane perpendicular to the rotation axis. This conclusion is consistent with previous analyses of inertial range energy transfer in rotating turbulence, which predict the accumulation of energy in those scales.

Rubinstein, Robert↗
NASA NTRS2023

Parker Solar Probe Observations of High Plasma β Solar Wind From the Streamer Belt

In general, slow solar wind from the streamer belt forms a high plasma β equatorial plasma sheet around the heliospheric current sheet (HCS) crossing, namely, the heliospheric plasma sheet (HPS). Current Parker Solar Probe (PSP) observations show that the HCS crossings near the Sun could be full or partial current sheet (PCS) crossings, and they share some common features but also have different properties. In this work, using the PSP observations from encounters 4–10, we identify streamer belt solar wind from enhancements in plasma β, and we further use electron pitch angle distributions to separate it into HPS solar wind around the full HCS crossings and PCS solar wind in the vicinity of PCS crossings. Based on our analysis, we find that the PCS solar wind has different characteristics as compared with HPS solar wind: (a) the PCS solar wind could be non-pressure-balanced structures rather than magnetic holes, and the total pressure enhancement mainly results from the less reduced magnetic pressure; (b) some of the PCS solar wind is mirror-unstable; and (c) the PCS solar wind is dominated by very low helium abundance but varied alpha–proton differential speed. We suggest that the PCS solar wind could originate from coronal loops deep inside the streamer belt, and it is pristine solar wind that still actively interacts with ambient solar wind; thus, it is valuable for further investigations of the heating and acceleration of slow solar wind.

Slow solar wind↗
DOE OSTI2025

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗
DOE OSTI2026

Carbon source–driven metabolic and regulatory remodeling defines phenomic states in Lipomyces starkeyi

Lipomyces is a genus of oleaginous yeasts with potential for contributing to reliable biomanufacturing supply chains. However, progress in advanced strain designs and engineering efforts are still constrained by a lack of understanding of the underlying molecular drivers of Lipomyces phenotypes. To address this gap, we collected a suite of multi-omic data to dissect how carbon source availability reshapes the metabolic network, lipid allocation, and regulatory architecture of Lipomyces starkeyi. We observed that glucose promotes biosynthetic and proliferative processes supported by abundant energy and carbon intermediates, xylose enhances redox-balancing mechanisms centered on the pentose phosphate pathway, and glycerol activates respiratory metabolism, ß-oxidation, and the glyoxylate cycle. Lipid species distributions remained consistent in both nitrogen replete and depleted conditions across the carbon sources, indicating robust production mechanisms. Regulatory protein identification and network analysis revealed glycerol-driven respiratory growth favors regulatory programs integrating stress tolerance, redox balance, and lipid-associated metabolism, whereas xylose growth activates compensatory transcriptional responses aimed at maintaining mitochondrial function. Nitrogen limitation modulates the strength of these responses but does not fundamentally alter their direction, reinforcing carbon source as the dominant driver of regulatory architecture. Taken together, this data enhances the understanding of Lipomyces molecular rearrangements and provides a foundation for further development of predictive phenotypic tools in this genus.

Biotechnology↗
DOE OSTI2021

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. Here, this study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

42 ENGINEERING↗