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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ATR-SEIRAS Reveals Potential Inversion and Associated Electron Transfer Kinetics in the Reduction of Surface-Confined Anthraquinone

The detection of stable semiquinone radicals on an anthraquinone (AQ) layer chemically grafted to an electrode surface in aqueous electrolytes has been elucidated by using attenuated total reflection surface enhanced infrared absorption spectroscopy (ATR-SEIRAS). In very alkaline conditions (pH 13), the reduction of the AQ involves no proton transfer, but surface sensitive infrared spectroscopy reveals that the anthraquinone dianion forms a strong hydrogen bonding network with coadsorbed water, leading to irreversible features in the voltammetry. The potential dependence of the IR band assigned to the AQ radical is consistent with the enhanced hydrogen bonding network causing increased stabilization of the quinone radical and supports the predicted response of a system under mild potential inversion, whereby the formal potential for the reduction of the anthraquinone radical is positive of the reduction potential of the neutral AQ molecule. Time-resolved ATR-SEIRAS is used to measure the transient formation of the AQ •– radical, from which rate constant information can be extracted using the Butler–Volmer model involving two one-electron transfers without a direct disproportionation reaction. The potential dependence of the rate constants is consistent with the potential inversion and can be used to qualitatively simulate the measured cyclic voltammograms. In conclusion, the thermodynamic and kinetic analyses re-emphasize long established deficiencies associated with using one-electron reaction formalisms to characterize multi-electron systems.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2025

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Orbital Inverse Faraday and Cotton-Mouton Effects in Hall Fluids

We report two light-induced orbital magnetization effects in quantum Hall (QH) fluids, stemming from their transverse response. The first is a purely transverse contribution to the inverse Faraday effect (IFE), where circularly polarized light induces a dc magnetization by stirring the charged fluid. This contribution dominates the IFE in the QH regime. The second is the orbital inverse Cotton-Mouton effect (ICME), in which linearly polarized light generates a dc magnetization. Since the applied field in the ICME does not break time-reversal symmetry, the induced magnetization directly probes the chiral orbital response of the fluid at the driving frequency. We estimate that the resulting magnetization lies in the range of 0.5–10 Bohr magnetons per charge carrier in materials such as graphene and transition-metal dichalcogenides (TMDs) in the QH regime. Finally, we show that the induced magnetization is accompanied by a local correction to the static particle density, enabling optical quantum printing of density profiles into the QH fluid.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

INTEGRATE – Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project developed a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. Training data was obtained from improved turbulence and transition models for RANS and hybrid RANS/LES solvers with machine-learned physics-based data-augmented corrections and then using the resulting neural-network(s) augmented RANS model to run thousands of 2-D and 3-D CFD simulations.

17 WIND ENERGY↗
DOE OSTI2024

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning

The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Mesospheric Temperature Inversion Layers: Recent Observations from UARS ISAMS and MLS

This paper presents an observational study of the mesospheric temperature inversion layer with Upper Atmosphere Research Satellite (UARS) Improved Stratospheric and Mesospheric Sounder (ISAMS) and Microwave Limb Sounder (MLS). The satellite data show that the temperature inversion layer can be generated from deep penetration of planetary waves in the mesosphere.

mesospheric temperature inversion layers UARS ISAM↗
NASA NTRS2012

Collective Evidence for Inverse Compton Emission from External Photons in High-Power Blazars

We present the first collective evidence that Fermi-detected jets of high kinetic power (L(sub kin)) are dominated by inverse Compton emission from upscattered external photons. Using a sample with a broad range in orientation angle, including radio galaxies and blazars, we find that very high power sources (L(sub kin) > 10(exp 45.5) erg/s) show a significant increase in the ratio of inverse Compton to synchrotron power (Compton dominance) with decreasing orientation angle, as measured by the radio core dominance and confirmed by the distribution of superluminal speeds. This increase is consistent with beaming expectations for external Compton (EC) emission, but not for synchrotron self Compton (SSC) emission. For the lowest power jets (L(sub kin) < 10(exp 43.5) erg /s), no trend between Compton and radio core dominance is found, consistent with SSC. Importantly, the EC trend is not seen for moderately high power flat spectrum radio quasars with strong external photon fields. Coupled with the evidence that jet power is linked to the jet speed, this finding suggests that external photon fields become the dominant source of seed photons in the jet comoving frame only for the faster and therefore more powerful jets.

INVERSE↗
NASA NTRS2021

Constrained Inversion of a Microwave Snowpack Emission Model Using Dictionary Matching: Applications for GPM Satellite

This article presents a new algorithmic framework for multilayer inversion of the dense media radiative transfer (DMRT) equations of snowpack emission, with particular emphasis on the role of high-frequency microwave channels above 60 GHz. The approach relies on dictionary matching and locally constrained least squares. The results demonstrate that the algorithm can invert the DMRT model and retrieve depth, density, and grain size of a single-layer snowpack when dependencies of density and grain size on depth are properly accounted for. However, as the number of layers increases, the sensitivity of the inversion to observation noise grows markedly. Using observations, over the Great Plains in the United States, from the microwave imager onboard the global precipitation measurement (GPM, 10-166 GHz) core satellite, the initial results demonstrate that under a clear-sky condition and no vegetation canopy, the algorithm is capable to retrieve the snow depth and water equivalent of seasonal snow with a mean absolute error (MAE) of less than 0.15 m--when compared to the high-resolution analysis data from the SNOw Data Assimilation System (SNODAS).

Snow↗
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.

entry↗
NASA NTRS2023

Equivalent Area Targets for Inverse Design Optimization With Changing Low-Boom Cruise Conditions

This paper provides theoretical and numerical justifications for inverse design optimization of reversed equivalent area (A e,r ) of low-boom supersonic aircraft when the cruise condition changes during the optimization iterations. A modified linear theory for steady flow around a supersonic projectile is used to establish the accuracy of A e,r -based body-of-revolution approximation of a low-boom supersonic aircraft for undertrack sonic boom analysis using computational fluid dynamics off-body pressure. As a result, designing a low-boom shape of the off-body pressure at three body lengths below the aircraft is equivalent to designing a low-boom A e,r shape. A Bezier curve with eight control points is used to define an A e,r target for inverse design optimization of A e,r of a supersonic aircraft. To make an A e,r target matchable by a low-boom supersonic aircraft, the A e,r target must have the minimal perceived level of decibels (PLdB) for sonic boom and satisfy two constraints defined by the cruise condition, effective length, and two nondimensional parameters. Numerical results are used to verify that, for different cruise conditions, all optimized A e,r targets with PLdB below 70 can be approximately generated by scaling one of them if the targets have the same nondimensional parameters and approximately the same effective length.

multidisciplinary optimization↗
NASA NTRS2023

Optimal Estimation Inversion of Ionospheric Electron Density from GNSS-POD Limb Measurements: Part I-Algorithm and Morphology

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (N e ). In this study, we developed an optimal estimation (OE) method to retrieve N e profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the N e inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large N e oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric N e morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the N e variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.

GNSS constellation↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.

42 ENGINEERING↗
DOE OSTI2024

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

Here, this study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Insights into the Surface Electronic Structure and Catalytic Activity of InO x /Au(111) Inverse Catalysts for CO 2 Hydrogenation to Methanol

In this article, the direct conversion of carbon dioxide (CO 2 ) into methanol (CH 3 OH) via low-temperature hydrogenation is crucial for recycling anthropogenic CO 2 emissions and producing fuels or high value chemicals. Nevertheless, it continues to be a great challenge due to the trade-off between selectivity and catalytic activity. For CO 2 hydrogenation, In 2 O 3 catalysts are known for their high CH 3 OH selectivity. Subsequent studies explored depositing metals on In 2 O 3 to enhance CO 2 conversion. Despite extensive research on metal (M) supported In 2 O 3 catalysts, the role of In-M alloys and M/In 2 O 3 interfaces in CO 2 activation and CH 3 OH selectivity remains unclear. In this work, we have examined the behavior of In/Au(111) alloys and InO x /Au(111) inverse systems during CO 2 hydrogenation using synchrotron-based ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) and catalytic tests in a batch reactor. Indium forms alloys with Au(111) after deposition. The In-Au(111) alloys display high reactivity towards CO 2 and can dissociate the molecule at room temperature to generate InO x nanostructures. At very low coverages of In (≤ 0.05 ML), the InO x nanostructures are not stable under CO 2 hydrogenation conditions and the active In-Au(111) alloys produces mainly CO and little methanol. An increase in indium coverage to 0.3 ML led to stable InOx nanostructures under CO 2 hydrogenation conditions. These InO x /Au(111) catalysts displayed a high selectivity (~ 80 %) towards CH 3 OH production and an activity for CO 2 conversion that was at least 10 times larger than that of plain In 2 O 3 or Cu(111) and Cu/ZnO(000$\overline{1)}$ benchmark catalysts. The results of AP-XPS show that InO x /Au(111) produces methanol via methoxy intermediates. Inverse oxide/metal catalysts containing InOx open up a possibility for improving CO 2 → CH 3 OH conversion in processes associated with the control of environmental pollution and the production of high value chemicals.

36 MATERIALS SCIENCE↗