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At least 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Predictions of core plasma performance for the SPARC tokamak

SPARC is designed to be a high-field, medium-size tokamak aimed at achieving net energy gain with Ion Cyclotron Range-of-Frequencies (ICRF) as its primary auxiliary heating mechanism. Empirical predictions with conservative physics indicate that SPARC baseline plasmas would reach Q~11, well above its mission objective of Q>2. To build confidence that SPARC will be successful, physics-based integrated modeling has also been performed. The TRANSP code coupled with the theory-based TGLF turbulence model and EPED predictions for pedestal stability find that Q~9 is attainable in standard H-mode operation and confirms Q>2 operation is feasible even with adverse assumptions. In this analysis, ion cyclotron waves are simulated with the full wave TORIC code and alpha heating is modeled with the Monte-Carlo fast ion NUBEAM module. Detailed analysis of expected turbulence regimes with linear and nonlinear CGYRO simulations is also presented, demonstrating that profile predictions with the TGLF reduced model are in reasonable agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2006

Numerical Modeling of Flow Control in a Boundary-Layer-Ingesting Offset Inlet Diffuser at Transonic Mach Numbers

This paper will investigate the validation of the NASA developed, Reynolds-averaged Navier-Stokes (RANS) flow solver, OVERFLOW, for a boundary-layer-ingesting (BLI) offset (S-shaped) inlet in transonic flow with passive and active flow control devices as well as a baseline case. Numerical simulations are compared to wind tunnel results of a BLI inlet experiment conducted at the NASA Langley 0.3-Meter Transonic Cryogenic Tunnel. Comparisons of inlet flow distortion, pressure recovery, and inlet wall pressures are performed. The numerical simulations are compared to the BLI inlet data at a free-stream Mach number of 0.85 and a Reynolds number of approximately 2 million based on the fanface diameter. The numerical simulations with and without tunnel walls are performed, quantifying tunnel wall effects on the BLI inlet flow. A comparison is made between the numerical simulations and the BLI inlet experiment for the baseline and VG vane cases at various inlet mass flow rates. A comparison is also made to a BLI inlet jet configuration for varying actuator mass flow rates at a fixed inlet mass flow rate. Overall, the numerical simulations were able to predict the baseline circumferential flow distortion, DPCP avg, very well within the designed operating range of the BLI inlet. A comparison of the average total pressure recovery showed that the simulations were able to predict the trends but had a negative 0.01 offset when compared to the experimental levels. Numerical simulations of the baseline inlet flow also showed good agreement with the experimental inlet centerline surface pressures. The vane case showed that the CFD predicted the correct trends in the circumferential distortion levels for varying inlet mass flow but had a distortion level that was nearly twice as large as the experiment. Comparison to circumferential distortion measurements for a 15 deg clocked 40 probe rake indicated that the circumferential distortion levels are very sensitive to the symmetry of the flow and that a misalignment of the vanes in the experiment could have resulted in this difference. The numerical simulations of the BLI inlet with jets showed good agreement with the circumferential inlet distortion levels for a range of jet actuator mass flow ratios at a fixed inlet mass flow rate. The CFD simulations for the jet case also predicted an average total pressure recovery offset that was 0.01 lower than the experiment as was seen in the baseline. Comparisons of the flow features for the jet cases revealed that the CFD predicted a much larger vortex at the engine fan-face when compare to the experiment.

Allan, Brian G.↗
DOE OSTI2023

Platinum Surface Oxide and Oxygen Reduction Reaction Kinetics during Transient Fuel Cell Operation

Pt surface oxide coverage (POC) and Oxygen Reduction Reaction (ORR) were measured under a range of operating conditions in a Proton Exchange Membrane Fuel Cell (PEMFC). Sensitivity analysis suggests that adsorption of sulfonate groups of the ionomer on the Pt surface plays an important role in POC and ORR kinetics. Although both Pt oxide growth and ORR activity decay follow a logarithm of time behavior, it is found that ORR kinetics are affected by at least two different types of Pt oxide. A semi-empirical ORR kinetic model is proposed taking into account the effect of ionomer and Pt oxide types. The model is capable of providing a quantitative prediction of POC and ORR activity over a range of potential, temperature, relative humidity, and time that is relevant to normal PEMFC operation.

Electrochemistry↗
DOE OSTI2025

Enhancing Short-Range Weather Forecasts through Temporal Variation Encoding: A Multiperiod Embedding Approach

Machine learning (ML) techniques have emerged as promising approaches to improve regional weather forecast accuracy and reliability through data-driven methods. We propose a novel ML-based weather forecasting model, the Multiperiod Embed Net (MPENet). A key distinguishing feature of MPENet is its explicit utilization of the inherent cyclic nature in weather dynamics, unlike the autoregressive strategies commonly used in other ML weather forecasting approaches. Critical cyclic structures are identified via Fourier analyses of dynamic time series. Cyclicity in the convolutional representation is achieved by transforming one-dimensional time series of meteorological variables into two-dimensional tensors based on identified periods. This approach enables the model to leverage intrinsic weather patterns, enhancing regional forecast performance. To demonstrate the effectiveness of MPENet, we conduct a comparative analysis with Nvidia’s FourCastNet. Both models are trained on High-Resolution Rapid Refresh (HRRR) data from 2015 to 2022, over a 192 km × 192 km region in Tennessee. The comparisons are performed locally at two specific locations known to have different weather dynamics due to orographic effects: Crossville, on the relatively flat Cumberland Plateau with fewer topographic airflow disruptions, and Oak Ridge, in the ridge-and-valley region, where airflow is heavily influenced by surrounding valleys and mountains. Our results indicate that FourCastNet achieves strong accuracy at very short lead times, while MPENet maintains competitive skill and shows advantages in capturing temporal evolution over longer periods. Cross-correlation analyses of MPENet and FourCastNet predictions with the HRRR data suggest that encoding critical cyclicity into the network architecture leads to improvements in the forecasting skill.

Artificial intelligence↗
NASA NTRS2005

Trajectories and energy transfer of saltating particles onto rock surfaces : application to abrasion and ventifact formation on Earth and Mars

The interaction between saltating sand grains and rock surfaces is assessed to gauge relative abrasion potential as a function of rock shape, wind speed, grain size, and planetary environment. Many kinetic energy height profiles for impacts exhibit a distinctive increase, or kink, a few centimeters above the surface, consistent with previous field, wind tunnel, and theoretical investigations. The height of the kink observed in natural and wind tunnel settings is greater than predictions by a factor of 2 or more, probably because of enhanced bouncing off hard ground surfaces. Rebounded grains increase the effective flux and relative kinetic energy for intermediate slope angles. Whether abrasion occurs, as opposed to simple grain impact with little or no mass lost from the rock, depends on whether the grain kinetic energy (EG) exceeds a critical value (EC), as well as the flux of grains with energies above EC. The magnitude of abrasion and the shape change of the rock over time depends on this flux and the value of EG > EC. Considering the potential range of particle sizes and wind speeds, the predicted kinetic energies of saltating sand hitting rocks overlap on Earth and Mars. However, when limited to the most likely grain sizes and threshold conditions, our results agree with previous work and show that kinetic energies are about an order of magnitude greater on Mars.

planet sufraces↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗
DOE OSTI2021

Nucleon-nucleon potentials from Δ-full chiral effective-field-theory and implications

We closely investigate NN potentials based upon the Δ-full version of chiral effective-field-theory. We find that recently constructed NN potentials of this kind, which (when applied together with three-nucleon forces) were presented as predicting accurate binding energies and radii for a range of nuclei from A = 16 to A = 132 and providing accurate equations of state for nuclear matter, yield a χ 2 /datum of 60 for the reproduction of the pp data below 100 MeV laboratory energy. This χ 2 is more than three times what the Hamada-Johnston potential of the year of 1962 achieved already some 60 years ago. We perceive this historical fact as concerning in view of the current emphasis on precision. We are able to trace the very large χ 2 as well as the apparent success of the potentials in nuclear structure to unrealistic predictions for P-wave states, in which the Δ-full next-to-next-to-leading order (NNLO) potentials are off by up to 40 times the NNLO truncation errors. In fact, we show that the worse the description of the P-wave states, the better the predictions in nuclear structure. Thus, these potentials cannot be seen as the solution to the outstanding problems in current microscopic nuclear structure physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1983

Space shuttle entry longitudinal aerodynamic comparisons of flights 1-4 with preflight predictions

The Space Shuttle orbiter flight test program has required the aerodynamicist to take a new approach in determining flight characteristics. A conventional flight test program of slowly and cautiously approaching more severe flight conditions was not possible with the orbiter. On the first four orbital flights, The orbiter entered the atmosphere at approximately Mach 29 and decelerated through the Mach range (the subsonic portion of flight had also been flown by another orbiter vehicle during the Approach and Landing Test Program). Certification for these flights was achieved by an extensive wind tunnel test and analysis program. The initial series of flights of the orbiter were heavily instrumented for the purpose of obtaining accurate aerodynamic data. The flight data derived from the entry Mach range provided comparisons between flight and wind tunnel derived predicted data in the areas of both aerodynamic performance and longitudinal trim.

Romere, P. O.↗
DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Validation of a Polyimide Foam Model for Use in Transmission Loss Applications

The work described in this paper was focused on the use of a new polyimide foam in a double wall sound transmission loss application. Recall that polyimide foams are functionally attractive, compared to polyurethane foams, for example, owing to their fire resistance. The foam considered here was found to have a flow resistivity that was too high for conventional acoustical applications, and as a result, it was processed by partial crushing to lower the flow resistivity into an acceptable range. Procedures for measuring the flow resistivity and Young s modulus of the material have been described, as was an inverse characterization procedure for estimating the remaining Biot parameters based on standing wave tube measurements of transmission loss and absorption coefficient. The inverse characterization was performed using a finite element model implementation of the Biot poro-elastic material theory. Those parameters were then used to predict the sound transmission loss of a double panel system lined with polyimide foam, and the predictions were compared with full-scale transmission loss measurements. The agreement between the two was reasonable, especially in the high and low frequency limits; however, it was found that the SEA model resulted in an under-prediction of the transmission loss in the mid-frequency range. Nonetheless, it was concluded that the performance of polyimide foam could be predicted using conventional poro-elastic material models and that polyimide foam may offer an attractive alternative to other double wall linings in certain situations: e.g., when fire resistance is a key issue. Future work will concentrate on reducing the density of the foam to values similar to those used in current aircraft sidewall treatments, and developing procedures to improve the performance of the foam in transmission loss applications.

Hong, Kwanwoo↗
DOE OSTI2023

Quantifying the Relevance of Long-Range Forces for Crystal Nucleation in Water

Understanding nucleation from aqueous solutions is of fundamental importance in a multitude of fields, ranging from materials science to biophysics. The complex solvent-mediated interactions in aqueous solutions hamper the development of a simple physical picture, elucidating the roles of different interactions in nucleation processes. In this study, we make use of three complementary techniques to disentangle the role played by short- and long-range interactions in solvent-mediated nucleation. Specifically, the first approach we utilize is the local molecular field (LMF) theory to renormalize long-range Coulomb electrostatics. Second, we use well-tempered metadynamics to speed up rare events governed by short-range interactions. Third, the deep learning-based State Predictive Information Bottleneck approach is employed in analyzing the reaction coordinate of the nucleation processes obtained from the LMF treatment coupled with well-tempered metadynamics. We find that the two-step nucleation mechanism can largely be captured by the short-range interactions, while the long-range interactions further contribute to the stability of the primary crystal state under ambient conditions. Furthermore, by analyzing the reaction coordinate obtained from the combined LMF-metadynamics treatment, we discern the fluctuations on different time scales, highlighting the need for long-range interactions when accounting for metastability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1972

Experimental and predicted cavitation performance of an 80.6 deg helical inducer in high temperature water

The cavitating performance of a stainless steel 80.6 degree flat-plate helical inducer was investigated in water over a range of liquid temperatures and flow coefficients. A semi-empirical prediction method was used to compare predicted values of required net positive suction head in water with experimental values obtained in water. Good agreement was obtained between predicted and experimental data in water. The required net positive suction head in water decreased with increasing temperature and increased with flow coefficient, similar to that observed for a like inducer in liquid hydrogen.

Kovich, G.↗
DOE OSTI2023

Capturing the Coverage Dependence of Aromatics’ Adsorption through Mean-Field Models

To capture the dominant interactions (surface-mediated and through-space steric) in catalytic hydrodeoxygenation systems, coverage-dependent mean-field models of aromatic adsorption are developed on Pt(111) and Ru(0001). We derive three key insights from this work: (1) we can universally apply mean-field models to capture the coverage-dependent behavior of oxygenated aromatics on transition metal surfaces, (2) we can deconvolute surface-mediated and throughspace steric interactions from the mean-field model, and (3) we can develop relatively accurate models that predict the adsorption energy of aromatics on transition metal surfaces for the full coverage range using the work function at lowest modeled coverage. In conclusion, our approach enables the rapid prediction of coverage-dependent behavior of oxygenated aromatics on transition metal surfaces, reducing the computational cost associated with these studies by an order of magnitude.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗
NASA NTRS2000

State Relationships of Laminar Permanently-Blue Opposed-Jet Hydrocarbon-Fueled Diffusion Flames

The structure and state relationships of laminar soot-free (permanently-blue) diffusion flames at various strain rates were studied experimentally using an opposed-jet configuration, motivated by the importance of soot-free hydrocarbon-fueled diffusion flames for many practical applications. Measurements of gas velocities, temperatures and compositions were carried out along the stagnation stream line. Flame conditions studied included propylene- and 1,3-butadiene-fueled opposed-jet diffusion flames having a stoichiometric mixture fractions of 0.7 and strain rates of 60-240/s at normal temperature and pressure. It was found that oxygen leakage to fuel-rich conditions and carbon monoxide leakage to fuel-lean conditions both increased as strain rates increased. Furthermore, increased strain rates caused increased fuel concentrations near the flame sheet decreased peak gas temperatures, and decreased concentrations of carbon dioxide and water vapor throughout the flames. State relationships for major gas species and gas temperatures for these flames were found to exist over broad ranges of strain rates. In addition, current measurements, as well as previous measurements and predictions of ethylene-fueled permanently-blue diffusion flames, all having a stoichiometric mixture fraction of 0.7, were combined to establish generalized state relationships for permanently-blue diffusion flames for this stoichiometric mixture fraction. The combined measurements and predictions support relatively universal generalized state relationships for N2, CO2, H2O and fuel over a broad range of strain rates and fuel types. State relationships for O2 in the fuel-rich region, and for CO in the fuel-lean region, however, are functions of strain rate and fuel type. State relationships for H2 and temperature exhibit less universality, mainly due to the increased experimental uncertainties for these variables. The existence of state relationships for soot-free hydrocarbon-fueled diffusion flames provides potential for significant computational simplifications for modeling purposes in many instances, allowing for effects of finite-rate chemistry while avoiding time-consuming computations of Arrhenius expressions.

Lin, K.-C.↗
NASA NTRS2000

State Relationships of Laminar Permanently-Blue Opposed-Jet Hydrocarbon-Fueled Diffusion Flames

The structure and state relationships of laminar soot-free (permanently-blue) diffusion flames at various strain rates were studied experimentally using an opposed-jet configuration, motivated by the importance of soot-free hydrocarbon-fueled diffusion flames for many practical applications. Measurements of gas velocities, temperatures and compositions were carried out along the stagnation stream line. Flame conditions studied included propylene- and 1,3-butadiene-fueled opposed-jet diffusion flames having a stoichiometric mixture fractions of 0.7 and strain rates of 60-240 s (exp -1) at normal temperature and pressure. It was found that oxygen leakage to fuel-rich conditions and carbon monoxide leakage to fuel-lean conditions both increased as strain rates increased. Furthermore, increased strain rates caused increased fuel concentrations near the flame sheet, decreased peak gas temperatures, and decreased concentrations of carbon dioxide and water vapor throughout the flames. State relationships for major gas species and gas temperatures for these flames were found to exist over broad ranges of strain rates. In addition, current measurements, as well as previous measurements and predictions of ethylene-fueled permanently-blue diffusion flames, all having a stoichiometric mixture fraction of 0.7, were combined to establish generalized state relationships for permanently-blue diffusion flames for this stoichiometric mixture fraction. The combined measurements and predictions support relatively universal generalized state relationships for N2, CO2, H2O and fuel over a broad range of strain rates and fuel types. State relationships for O2 in the fuel-rich region, and for CO in the fuel-lean region, however, are functions of strain rate and fuel type. State relationships for H2 and temperature exhibit less universality, mainly due to the increased experimental uncertainties for these variables. The existence of state relationships for soot-free hydrocarbon-fueled diffusion flames provides potential for significant computational simplifications for modeling purposes in many instances, allowing for effects of finite-rate chemistry while avoiding time-consuming computations of Arrhenius expressions.

Lin, K.-C.↗
NASA NTRS2019

Casper-1, Part 6: Uncertainty Quantification, Factor Effects, and Outlier Analysis for an On-Board Airplane Trajectory Prediction Function

This report presents data analysis results for a simulation-based approach named CASPEr (Characterization of Airplane State Prediction Error) to characterize the performance of onboard energy state and automation mode prediction functions for terminal area arrival and approach phases of flight over a wide range of conditions. In particular, the results include quantification of energy state (i.e., altitude and airspeed) prediction performance, models for prediction performance as a function of initial energy state (i.e., initial altitude, airspeed, and weight) and weather factors, and analysis of outlier prediction performance. Wind speed, wind direction, and wind gradient were found to be major factors in energy state prediction performance. Initial energy and gust intensity were also significant factors in airspeed prediction performance. Furthermore, the results suggest that errors in automation mode prediction may be a major contributor to outlier prediction performance.

Torres-Pomales, Wilfredo↗
DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗