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All sources NASA NTRS DOE OSTI

DOE OSTI2022

CeTaN 3 and CeNbN 3 : Prospective Nitride Perovskites with Optimal Photovoltaic Band Gaps

Perovskites constitute an exceptionally tunable materials family with diverse applications in electronics, optoelectronics, energy, and quantum technologies. Out of the thousands of known perovskites, the majority of compounds are oxides, halides, and chalcogenides. In contrast, only two nitride perovskites are currently known. In this work we perform a thorough ab initio computational screening of possible nitride perovskites, and we identify two new compounds, CeNbN 3 and CeTaN 3 , with band gaps in the near-infrared to visible range, depending on temperature. In their room-temperature orthorhombic phase, we predict that both compounds exhibit direct or quasidirect band gaps in the range 1.1–2.0 eV, with the Pnma phases matching the Shockley–Queisser limit for photovoltaic energy conversion efficiency. Further, these compounds are also predicted to be strong light absorbers, with absorption coefficients surpassing those of high-performance semiconductors such as GaAs and CH 3 NH 3 PbI 3 . The present findings reveal a potentially new class of nitride semiconductors with promise for electronics, optoelectronics, and light harvesting and for integration with existing nitride-based lighting technology.

36 MATERIALS SCIENCE↗

NASA NTRS2012

Prediction of Broadband Shock-Associated Noise Including Propagation Effects Originating NASA

An acoustic analogy is developed based on the Euler equations for broadband shock-associated noise (BBSAN) that directly incorporates the vector Green s function of the linearized Euler equations and a steady Reynolds-Averaged Navier-Stokes solution (SRANS) to describe the mean flow. The vector Green s function allows the BBSAN propagation through the jet shear layer to be determined. The large-scale coherent turbulence is modeled by two-point second order velocity cross-correlations. Turbulent length and time scales are related to the turbulent kinetic energy and dissipation rate. An adjoint vector Green s function solver is implemented to determine the vector Green s function based on a locally parallel mean flow at different streamwise locations. The newly developed acoustic analogy can be simplified to one that uses the Green s function associated with the Helmholtz equation, which is consistent with a previous formulation by the authors. A large number of predictions are generated using three different nozzles over a wide range of fully-expanded jet Mach numbers and jet stagnation temperatures. These predictions are compared with experimental data from multiple jet noise experimental facilities. In addition, two models for the so-called fine-scale mixing noise are included in the comparisons. Improved BBSAN predictions are obtained relative to other models that do not include propagation effects.

Miller, Steven↗

NASA NTRSDate not supplied

A Machine Learning Approach to Design Shape Memory Alloys for NASA Applications

Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions including ternary, quaternary, or higher additions being considered. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature of NiTi alloys by varying the alloy composition and processing conditions. In addition, low hysteresis and an acceptable recoverable transformation strain are required. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches. We present results from our machine learning approach for designing new SMAs with target properties within our range of interest. Our developed models are capable of accurately predicting the transition temperature, hysteresis, and transformation strain of SMAs across a wide range of compositions. This approach has the potential to significantly accelerate the discovery and design of new SMA materials.

Shreyas Jaikumar Honrao↗

DOE OSTI2022

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning—specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/).

59 BASIC BIOLOGICAL SCIENCES↗

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 a 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 the baseline case. Numerical simulations are compared to wind tunnel results of a BLI inlet 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 freestream Mach number of 0.85 and a Reynolds number of approximately 2 million based on the length of the fan-face diameter. The numerical simulations with and without wind tunnel walls are performed, quantifying effects of the tunnel walls on the BLI inlet flow measurements. The wind tunnel test evaluated several different combinations of jet locations and mass flow rates as well as a vortex generator (VG) vane case. The numerical simulations will be performed on a single jet configuration for varying actuator mass flow rates at a fix inlet mass flow condition. Validation of the numerical simulations for the VG vane case will also be performed for varying inlet mass flow rates. Overall, the numerical simulations were able to predict the baseline circumferential flow distortion, DPCPavg, very well for comparisons made within the designed operating range of the BLI inlet. However the CFD simulations did predict a total pressure recovery that was 0.01 lower than the experiment. 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 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 miss alignment 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 that was 0.01 lower than the experiment as was seen in the baseline. Comparison of the flow features the jet case revealed that the CFD predicted a much larger vortex at the engine fan-face when compare to the experiment.

Allan Brian G.↗

NASA NTRS2000

Finite Element Model Development and Validation for Aircraft Fuselage Structures

The ability to extend the valid frequency range for finite element based structural dynamic predictions using detailed models of the structural components and attachment interfaces is examined for several stiffened aircraft fuselage structures. This extended dynamic prediction capability is needed for the integration of mid-frequency noise control technology. Beam, plate and solid element models of the stiffener components are evaluated. Attachment models between the stiffener and panel skin range from a line along the rivets of the physical structure to a constraint over the entire contact surface. The finite element models are validated using experimental modal analysis results. The increased frequency range results in a corresponding increase in the number of modes, modal density and spatial resolution requirements. In this study, conventional modal tests using accelerometers are complemented with Scanning Laser Doppler Velocimetry and Electro-Optic Holography measurements to further resolve the spatial response characteristics. Whenever possible, component and subassembly modal tests are used to validate the finite element models at lower levels of assembly. Normal mode predictions for different finite element representations of components and assemblies are compared with experimental results to assess the most accurate techniques for modeling aircraft fuselage type structures.

Buehrle, Ralph D.↗

NASA NTRS1953

Investigation at supersonic speeds of the variation with Reynolds number and Mach number of the total, base, and skin-friction drag of seven boattail bodies of revolution designed for minimum wave drag

An investigation has been conducted in the Langley 9-inch supersonic tunnel to determine the effect of the variation with Reynolds number and Mach number of the total, base, and skin-friction drag at zero lift of seven boattail bodies of revolution designed for minimum wave drag according to NACA TN 2550. The tests covered a Reynolds number range from approximately 1.0 x 10(6) to 10.0 x 10(6) at Mach numbers of 1.62, 1.93, and 2.41, respectively. The results show that base drag and, in general, the total drag increase with increasing values of the ratio of base area to maximum area B/S(sub max), although the results reported in NACA TN 3054 showed that the wave drag decreased with increasing values of B/S(sub max). The laminar skin-friction drag is in agreement with the theoretical predictions used, and, within the Mach number range of these tests, the simple Blasius incompressible theory gives a satisfactory prediction. Except for values of B/S (sub max) near 1, the Reynolds number of transition increases with increasing Mach number and as this ratio approaches 1 this variation is seen to reverse. These variations in Reynolds number of transition with Mach number appear to be associated with the changes in pressure gradient over the rear of the bodies.

Bromm, August F , Jr↗

DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗

DOE OSTI2020

Evidence of Kramer extrapolation inaccuracy for predicting high field Nb 3 Sn properties

Future applications requiring high magnetic fields, such as the proposed Future Circular Collider, demand a substantially higher critical current density, J c , at fields ≥16 T than is presently available in any commercial strand, so there is a strong effort to develop new routes to higher J c Nb 3 Sn. As a consequence, evaluating the irreversibility field (H irr ) of any new conductor to ensure reliable performance at these higher magnetic fields becomes essential. To predict the irreversibility field for Nb 3 Sn wires, critical current measurements, I c , are commonly performed in the 12-15 T range and the Kramer extrapolation is used to predict higher field properties. Here, the Kramer extrapolation typically models the contribution only for sparse grain boundary pinning, yet Nb 3 Sn wires rely on a high density of grain boundaries to provide the flux pinning that enables their high critical current density. However, whole-field range VSM measurements up to 30 T recently showed for Nb 3 Sn RRP® wires that the field dependence of the pinning force curve significantly deviates from the typical grain boundary shape, leading to a 1-2 T overestimation of Hirr when extrapolated from the typical mid-field data taken only up to about 15 T. In this work we characterized a variety of both RRP® and PIT Nb 3 Sn wires by transport measurements up to 29 T at the Laboratoire National des Champs Magnétiques Intenses (LNCMI), part of the European Magnetic Field Laboratory in Grenoble, to verify whether or not such overestimation is related to the measurement technique and whether or not it is a common feature across different designs. Indeed we also found that when measured in transport the 12-15 T Kramer extrapolation overestimates the actual H irr in both types of conductor with an inaccuracy of up to 1.6 T, confirming that high field characterization is a necessary tool to evaluate the actual high field performance of each Nb 3 Sn wire.

36 MATERIALS SCIENCE↗

NASA NTRS1983

Measurements of galactic plane gamma-ray emission in the energy range 10-80 MeV

A spark chamber gamma ray telescope was developed and flown to observe diffuse gamma ray emission from the central region of the galaxy. The extension of observations down to 10 MeV provides important new data indicating that the galactic diffuse gamma ray spectrum continues as a power law down to about 10 MeV, an observation in good agreement with recent theoretical predictions. Data from other experiments in the range from 100 keV to 10 MeV show a significant departure from the extension of the power-law fit to the medium energy observations reported here, possibly indicating that a different mechanism may be responsible for the emissions below and above a few MeV. The intensity of the spectrum above 10 MeV implies a galactic electron spectrum which is also very intense down to about 10 MeV. Electrons in this energy range cannot be observed in the solar cavity because of solar modulation effects. The galactic gamma ray data are compared with recent theoretical predictions. Previously announced in STAR as N83-17444

Bertsch, D. L.↗

NASA NTRS1982

Measurements of galactic plane gamma ray emission in the energy range from 10 - 80 MeV

Bertsch, D. L.↗

DOE OSTI2022

Octane Requirements of Lean Mixed-Mode Combustion in a Direct-Injection Spark-Ignition Engine

Here, this study investigates the octane requirements of a hybrid flame propagation and controlled autoignition mode referred to as mixed-mode combustion (MMC), which allows for strong control over combustion parameters via a spark-initiated deflagration phase. Due to the throughput limitations associated with both experiments and 3-D computational fluid dynamics calculations, a hybrid 0-D and 1-D modeling methodology was developed, supported by experimental validation data. This modeling approach relied on 1-D, two-zone engine simulations to predict bulk in-cylinder thermodynamic conditions over a range of engine speeds, compression ratios, intake pressures, trapped residual levels, fueling rates, and spark timings. Those predictions were then transferred to a 0-D chemical kinetic model, which was used to evaluate the autoignition behavior of fuels when subjected to temperature–pressure trajectories of interest. Finally, the predicted autoignition phasings were screened relative to the progress of the modeled deflagration-based combustion in order to determine if an operating condition was feasible or infeasible due to knock or stability limits. The combined modeling and experimental results reveal that MMC has an octane requirement similar to modern stoichiometric spark-ignition engines in that fuels with high research octane number (RON) and high octane sensitivity (S) enable higher loads. Experimental trends with varying RON and S were well predicted by the model for 1000 and 1400 rpm, confirming its utility in identifying the compatibility of a fuel’s autoignition behavior with an engine configuration and operating strategy. However, the model was not effective in predicting (nor designed to predict) operability limits due to cycle-to-cycle variations, which experimentally inhibited operation of some fuels at 2000 rpm. Putting the operable limits and efficiency from MMC in the context of a state-of-the-art engine, the MMC showed superior efficiencies over the range investigated, demonstrating the potential to further improve fuel economy.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Neural operator prediction of linear instability waves in high-speed boundary layers

Here, we investigate if neural operators can predict the linear evolution of instability waves in high-speed boundary layers. To this end, we extend the design of the DeepOnet to ensure accurate and robust predictions, and also to perform data assimilation. In particular, we train DeepONet to take as inputs an upstream disturbance and a downstream location of interest, and to provide as output the perturbation field downstream in the boundary layer. DeepONet thus approximates the linearized and parabolized Navier-Stokes operator for this flow. For successful application to the high-speed boundary layer problem, we add sample weighting and Fourier input features to the regular DeepONet formulation. Once trained, the DeepOnet can perform fast and accurate predictions of the downstream disturbances within the range of training frequencies (inside the distribution). In addition, we show that DeepONet can solve the inverse problem, where downstream wall measurements are adopted as input, and a trained network can predict the upstream disturbances that led to these observations. This capability, along with the forward predictions, allows us to perform a full data assimilation cycle efficiently: starting from wall-pressure data, we predict the upstream disturbance using the inverse DeepONet and its evolution using the forward DeepONet. Finally, we introduce three new metrics to benchmark the training, evaluation and break-even cost of neural operators.

42 ENGINEERING↗

DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.

convolutional neural networks↗

NASA NTRS1973

Design of a bleed system for a Mach 3.5 inlet

An analytical bleed system design procedure is applied to a Mach 3.5, axisymmetric, translating centerbody inlet. The design features a traveling centerbody bleed system with a ducting arrangement separating low-, medium-, and high-pressure bleed. Three forward plenums and nine throat plenums are installed on the centerbody to meet the bleed requirements in the started Mach range between 1.6 and 3.5. The cowl contains four stationary plenums, three for forward and one for throat bleed. To achieve maximum bleed plenum pressure and thereby minimize bleed drag all bleed holes are inclined 20 deg to the surface except in the cowl throat region. Here the requirement of high stability margin with minimum total pressure recovery penalty resulted in 90 deg bleed holes. The bleed hole diameter varies from bleed plenum to bleed plenum to achieve the most efficient boundary layer control, while the bleed exits are sized to operate at the highest possible plenum pressure without unchoking the bleed holes. Bleed flow rates, bleed plenum pressures, and boundary layer development along the cowl and centerbody are predicted over the entire started Mach range.

Syberg, J.↗

NASA NTRS1979

A summary and evaluation of semi-empirical methods for the prediction of helicopter rotor noise

Existing prediction techniques are compiled and described. The descriptions include input and output parameter lists, required equations and graphs, and the range of validity for each part of the prediction procedures. Examples are provided illustrating the analysis procedure and the degree of agreement with experimental results.

Pegg, R. J.↗

NASA NTRS1961

An Investigation of Wing and Aileron Loads Due to Deflected Inboard and Outboard Ailerons on a 4-Percent-Thick 30 deg Sweptback Wing at Transonic Speeds

An investigation has been conducted in the Langley 16-foot transonic tunnel to determine the changes in wing loading characteristics due to deflections of a plain faired flap-type inboard aileron, a plain faired flap-type outboard aileron, and a slab-sided thickened trailing edge outboard aileron. The test wing was 4 percent thick and had 30 sweep of the quarter chord, an aspect ratio of 3.0, a taper ratio of 0.2, and NACA 65A004 airfoil sections. The loading characteristics of the deflected ailerons were also investigated. The model was a sting-mounted wing-body combination, and pressure measurements over one wing panel (exposed area) and the ailerons were obtained for angles of attack from 0 to 20 at deflections up to +/- 15 deg for Mach numbers between 0.80 and 1.03. The test Reynolds number based on the wing mean aerodynamic chord was about 7.4 x 10(exp 6). The results of the investigation indicated that positive deflection of the plain faired flap-type inboard aileron caused significant added loading over the wing sections outboard of the aileron at all Mach numbers for model angles of attack from 0 deg or 4 deg up to 12 deg. Positive deflection of the two outboard ailerons (plain faired and slab sided with thickened trailing edge) caused significant added loading over the wing sections inboard of the ailerons for different model angle-of-attack ranges at the several test Mach numbers. The loading shapes over the ailerons were irregular and would be difficult to predict from theoretical considerations in the transonic speed range. The longitudinal and lateral center-of-pressure locations for the ailerons varied only slightly with increasing angle of attack and/or Mach number. Generally, the negative slopes of the variations of aileron hinge-moment coefficient with aileron deflection for all three ailerons varied similarly with Mach number at the test angles of attack.

Whitcomb, Charles F.↗

NASA NTRSDate not supplied

Progress on Inverse Estimation Technique of Non-Linear Pitch Damping Coefficient Curves Using Free-Flight CFD Generated Trajectories

Characterization of entry vehicle pitch damping coefficient curves is crucial to ensure appropriate re-entry and overall mission success. The pitch damping coefficient (C_(m_q )+C_(m_α ̇ )) is used to encapsulate the oscillatory growth or decay of a body during a trajectory. The inverse estimation technique utilizes an existing Free-Flight CFD (FF-CFD) dataset and wraps a reconstruction algorithm in an optimizer. The reconstruction integrates the planar equations of motion derived by Schoenenberger, Queen [1] using Python’s scipy.integrate.solve_ivp. The optimizer’s objective function is the normalized 𝐿2 residual of the angle of attack peaks between the reconstructed trajectory and the original data produced with FF-CFD. Inclusion of the peak times in this residual calculation allows for simultaneous optimization of the pitch moment coefficient, C_(m_α ). This residual equation is shown below in Eq. 1. The optimizer scipy.optimize.minimize was used with the gradient-based Powell method for the analysis presented, however the differential evolution method was investigated as means of comparison, and was found to produce marginally lower residual values with prohibitively longer run times. Further, the pitch damping curve is found by fitting a cubic interpolation function to a set of (α, (C_(m_q )+C_(m_α ̇ ))) control points, where the α points are held constant and the (C_(m_q )+C_(m_α ̇ )) values are the optimized parameters. The pitch moment curve uses a linear interpolation between the minimum and maximum α in the dataset. FF-CFD generated trajectories using the Dragonfly capsule geometry with the Genesis ballistic range model parameters were simulated and used for this analysis. These FF-CFD trajectories simulate planar motion, as restricted by the reconstructing the equations of motion, of three different cases: 1-DoF (free-to-pitch), 2-DoF (free-to-pitch and heave), and 3-DoF (free-to-pitch, heave, and decelerate). Pitch damping coefficient curves generated using this inverse estimation curve technique with FF-CFD 1-DoF Dragonfly data are found in Fig. 1. Preliminary results reconstructing ballistic range shots using these FF-CFD derived predictions of the pitch damping curve (Fig. 1) are shown in Fig. 2. It should be noted that the ballistic range shot used a Genesis model whereas the FF-CFD data used a Dragonfly geometry, however these geometries are similar.

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