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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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Circulating miRNA Signature Predicts Health Risks Associated with Radiation and Microgravity

The many known health risks currently associated with space travel include increased risk of cardiovascular disease, cancer, central nervous system related diseases, muscle degeneration, and changes with host-gut microbiome interactions that can have profound impact with these and other health risks. The majority of the risk from space travel stem of the two components of the space environment which are microgravity and radiation. From our earlier work (Beheshti et al, PLOS One, 2018), we predicted that there is a systemic component of the host that causes general increased health risks due to spaceflight driven by a circulating microRNA (miRNA) signature consisting of 13 miRNAs that directly regulates both p53 and TGF1. MiRNAs are small non-coding RNA molecules with a negative and post-transcriptional regulation on gene expression) are increasingly recognized as major systemic regulators of responses to stressors, including microgravity, oxidative stress, and DNA damage. In addition, due to the size and stability of miRNAs, it is known that miRNAs can circulate throughout the body and have been found in the majority of the bodily fluids including blood, urine, saliva, and tears. Here, we start to dissect the actual impact of this miRNA signature on both the radiation and microgravity components and prove that this miRNA signature actually exists in the circulation of a host. To achieve this, we obtained multiple tissues including, serum, liver, and spleen and utilizing droplet digital PCR (ddPCR), we start to show how this circulating miRNA signature impacts which component of the spaceflight. The tissue was obtained from experiments performed on C57BL/6 male mice (N=10 for each condition) that were hindlimb unloaded (HU) to simulated microgravity, irradiated with 2Gy gamma (IR), HU plus IR, and control mice under normal conditions. It was shown that these miRNAs were present in the serum as predicted by the in silico prediction from our earlier predictions. The HU vs Controls show significant increases of the predicted miRNAs in the serum for more than half of the miRNA signature, with remaining miRNAs increasing comparing to the controls close to statistical significance. IR vs control mice showed increases for the miRNAs, but not has pronounced as the HU conditions. Finally, the combination of the HU+IR vs controls showed increases for the majority of the miRNA signature. The data indicates that the miRNA signature originally predicted through in silico methods is mainly associated with the microgravity component and is circulating throughout the host resulting in a systemic impact of the miRNAs on the host. These miRNAs are shown in the literature to potentially increase health risks associated with several diseases. In addition, we have begun testing the potential of utilizing antagonists to this miRNA signature to act as a potential countermeasure to mitigate radiation impact on the organism. This work demonstrates for the first time the potential of a minimally invasive novel biomarker and countermeasure that can be used to mitigate both radiation and microgravity effects.

Beheshti, Afshin↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
NASA NTRS2021

A New Method for Predicting Turbulent Flow Reattachment Location: Application of Stratford Criterion for Reattaching Flows

A new method for predicting turbulent flow reattachment location has been developed. The method predicts the reattachment location using the surface static pressure distribution and requires only a single empirical parameter. The method is based on the well-known Stratford’s flow separation criterion, which results from an approximate solution to the equations of motion within the boundary layer. Because of the large pressure rise, the original Stratford separation criterion is not valid for reattaching flows. In order to apply the Stratford criterion for reattaching flows, the reference location was moved to the pressure drop conditions instead of pressure rise conditions. In addition, the pressure distribution was scaled using a technique that was originally introduced for supersonic base flows. A new empirical parameter was also obtained for the modified Stratford criterion. The method has been tested for 30 different separated flow configurations from the literature. The variety of test cases include subsonic flows, transonic flows, 2D flows, and axisymmetric flows, with experimental as well as high resolution numerical simulation results. The flow reattachment predictions with the new method agree very well with the reported data. The error is less than 5% and typically about 2%. The new method is an extremely simple and a quick way of finding the reattachment location. Given the complexities of the existing experimental techniques for finding the reattachment location, the method easily and quickly provides the flow reattachment location, which could be used to estimate the physical extent of separation bubbles, as a performance index for flow control methods, or to assess the predictive capability of turbulence models.

Flow reattachment location↗
NASA NTRS1976

Correlation study of theoretical and experimental results for spin tests of a 1/10 scale radio control model

A correlation study was conducted to determine the ability of current analytical spin prediction techniques to predict the flight motions of a current fighter airplane configuration during the spin entry, the developed spin, and the spin recovery motions. The airplane math model used aerodynamics measured on an exact replica of the flight test model using conventional static and forced-oscillation wind-tunnel test techniques and a recently developed rotation-balance test apparatus capable of measuring aerodynamics under steady spinning conditions. An attempt was made to predict the flight motions measured during stall/spin flight testing of an unpowered, radio-controlled model designed to be a 1/10 scale, dynamically-scaled model of a current fighter configuration. Comparison of the predicted and measured flight motions show that while the post-stall and spin entry motions were not well-predicted, the developed spinning motion (a steady flat spin) and the initial phases of the spin recovery motion are reasonably well predicted.

Bihrle, W., Jr.↗
DOE OSTI2021

Kinetically Controlled Linker Binding in Rare Earth-2,5-Dihydroxyterepthalic Acid Metal–Organic Frameworks and Its Predicted Effects on Acid Gas Adsorption

In the pursuit of highly stable and selective metal–organic frameworks (MOFs) for the adsorption of caustic acid gas species, an entire series of rare earth MOFs have been explored. Each of the MOFs in this series (RE-DOBDC; RE = La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu; DOBDC = 2,5-dihydroxyterepthalic acid) was synthesized in the tetragonal space group I4/m. Crystallized MOF samples, specifically Eu-DOBDC, were seen to have a combination of monodentate and bidentate binding when synthesized under typical reaction conditions, resulting in a contortion of the structure. However, extended crystallization times determined that this binding is kinetically controlled and that the monodentate binding option was crystallographically eliminated by extended reaction times at higher temperatures. Furthermore, this series allows for the direct study of the effect of the metal center on the structure of the of the MOF; herein, the lanthanide metal ionic radii contraction across the periodic table results in a reduction of the MOF pore size and lattice parameters. Scanning electron microscopy–energy-dispersive spectroscopy was used to investigate the stages of crystal growth for these RE-DOBDC MOFs. All MOFs, except Er-DOBDC had a minimum of two stages of growth. These analogues were demonstrated by analysis of neutron diffraction (PND) to exhibit a cooperative rotational distortion of the secondary building unit, resulting in two crystallographically distinct linker sublattices. Computational modeling efforts were used to show distinct differences on acid gas (NO 2 and SO 2 ) binding energies for RE-DOBDC MOFs when comparing the monodentate/bidentate combined linker with the bidentate-only linker crystal structures.

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

Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign

Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.

42 ENGINEERING↗
DOE OSTI2025

Hydraulic constraints to stomatal conductance in flooded trees

Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.

Plant stress↗
DOE OSTI2025

Numerical framework for integrated additive manufacturing-compression molding (AM-CM) of thermoplastic composites

Additive manufacturing-compression molding (AM-CM) has emerged as a transformative technology in advanced composite manufacturing. Additive manufacturing (AM) offers high design flexibility and the ability to produce complex geometries with precisely aligned fibers in the preferred orientation. Compression molding (CM) enhances composite materials by providing excellent dimensional stability, reduced porosity, high production rates, and a smooth surface finish. Despite these advantages, extensive integrated analysis is required to optimize processing conditions for improved fiber orientation distribution (FOD) and porosity control. Here, this study develops a comprehensive numerical model to simulate the AM-CM manufacturing process. The model isolates the effects of both the AM and CM phases while also capturing their integration. Additionally, it accounts for heat transfer, temperature-dependent viscosity, and fiber orientation in the extruded fiber-filled polymer, accurately representing material behavior during processing. This approach enables the analysis of interactions between deposited beads of complex strand shapes and their interface regions after full compression. Moreover, the model predicts key parameters such as polymer flowability, fiber orientation, and temperature evolution in AM-CM parts. By optimizing processing conditions, it facilitates a controlled and predictable microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

The role of the droplet interface in controlling the multiphase oxidation of thiosulfate by ozone

Predicting reaction kinetics in aqueous microdroplets, including aerosols and cloud droplets, is challenging due to the probability that the underlying reaction mechanism can occur both at the surface and in the interior of the droplet. Additionally, few studies directly measure the surface activities of doubly charged anions, despite their prevalence in the atmosphere. Here, deep-UV second harmonic generation spectroscopy is used to probe surface affinities of the doubly charged anions thiosulfate, sulfate, and sulfite, key species in the thiosulfate ozonation reaction mechanism. Thiosulfate has an appreciable surface affinity with a measured Gibbs free energy of adsorption of -7.3 ± 2.5 kJ mol -1 in neutral solution, while sulfate and sulfite exhibit negligible surface propensity. The Gibbs free energy is combined with data from liquid flat jet ambient pressure X-ray photoelectron spectroscopy to constrain the concentration of thiosulfate at the surface in our model. Stochastic kinetic simulations leveraging these novel measurements show that the primary reaction between thiosulfate and ozone occurs at the interface and in the bulk, with the contribution of the interface decreasing from ~65% at pH 5 to ~45% at pH 13. Additionally, sulfate, the major product of thiosulfate ozonation and an important species in atmospheric processes, can be produced by two different pathways at pH 5, one with a contribution from the interface of >70% and the other occurring predominantly in the bulk (>98%). The observations in this work have implications for mining wastewater remediation, atmospheric chemistry, and understanding other complex reaction mechanisms in multiphase environments. Future interfacial or microdroplet/aerosol chemistry studies should carefully consider the role of both surface and bulk chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Recovery of sounding rocket payloads by center-of-gravity position control.

A computerized procedure has been developed to predict dynamic pressure at parachute deployment altitude. Inputs to the program are payload geometry, center-of-gravity position, velocity, and altitude at start of reentry. Payload is assumed to be trimmed, and potential and cross-flow drag theory is used to compute trim angle-of-attack and trim drag coefficient as a function of free-stream Mach number. The computations involve only simple geometric quantities and closed-form equations. Payload flight path is assumed to be vertical, and equations of motion of the payload are integrated from start of reentry to chute deployment altitude.

Mcgarvey, J. F.↗
NASA NTRS1983

Noise Control in Propeller-Driven Aircraft

Analytical model predicts noise levels inside propeller-driven aircraft during cruise at mach 0.8. Double wall sidewalls minimize interior noise and weight. Model applied to three aircraft with fuselages of different size (wide-body, narrow-body, and small-diameter) to determine noise reductions required to achieve A-weighted sound level not to exceed 80 dB.

Rennison, D. C.↗
NASA NTRS1990

Intrusive and extrusive growth of the Mount St Helens lava dome

High-resolution, digital topographic maps of the Mount St. Helens dome derived from aerial photographs are used here to make a quantitative assessment of the partitioning of magma into endogenous intrusion and exogenous lobes. The endogenous growth is found to be predictable, which shows that the cooling dome controls its own development independently of such deep-seated factors as magma overpressure and extrusion rate. The observed regular decrease in exogenous growth rate also allows volume prediction. Knowledge of the volume can be used to determine when an ongoing eruptive event should end. Finally, the observed transition from predominantly exogenous to predominantly endogenous growth reflects the increase in crust thickness, which in turn seems to depend on long repose periods rather than some fundamental change in the character of the dome.

Fink, Jonathan H.↗
DOE OSTI2022

Designing strong and tunable magnetoelectric coupling in 2D trilayer heterostructures

Abstract The quest for electric-field control of nanoscale magnetic states such as skyrmions, which would impact the field of spintronics, has led to a challenging search for multiferroic materials or structures with strong magnetoelectric coupling and efficient electric-field control. Here we report a theoretical prediction that such phenomena can be realized in two-dimensional (2D) bilayer FE/PMM and trilayer FE/PMM/FE heterostructures (two-terminal and three-terminal devices), where FE is a 2D ferroelectric and PMM is a polar magnetic metal with strong spin–orbit coupling. Such a PMM has strong Dzyaloshinskii-Moriya interactions (DMI) that can generate skyrmions, while the FE can generate strong magnetoelectric coupling through polarization-polarization interactions. In trilayer heterostructures, contact to the metallic PMM layer enables multiple polarization configurations for electric-field control of skyrmions. We report density-functional-theory calculations for particular material choices that demonstrate the effectiveness of these arrangements, with the key driver being the polarization-polarization interactions between the PMM and FE layers. The present findings provide a method to achieve strong magnetoelectric coupling in the 2D limit and a new perspective for the design of related spintronics.

Materials Science↗

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