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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
NASA NTRS1963

New requirements in photo interpretation.

Photo interpretation shifting, from image identification to processes of imagery associations, to meet needs of extraterrestrial investigation

PHOTOGRAPH INTERPRETATION↗
NASA NTRS2014

IKOS: A Framework for Static Analysis based on Abstract Interpretation (Tool Paper)

The RTCA standard (DO-178C) for developing avionic software and getting certification credits includes an extension (DO-333) that describes how developers can use static analysis in certification. In this paper, we give an overview of the IKOS static analysis framework that helps developing static analyses that are both precise and scalable. IKOS harnesses the power of Abstract Interpretation and makes it accessible to a larger class of static analysis developers by separating concerns such as code parsing, model development, abstract domain management, results management, and analysis strategy. The benefits of the approach is demonstrated by a buffer overflow analysis applied to flight control systems.

Abstract Interpretation↗
NASA NTRS2017

Static Analysis Using Abstract Interpretation

Short presentation about static analysis and most particularly abstract interpretation. It starts with a brief explanation on why static analysis is used at NASA. Then, it describes the IKOS (Inference Kernel for Open Static Analyzers) tool chain. Results on NASA projects are shown. Several well known algorithms from the static analysis literature are then explained (such as pointer analyses, memory analyses, weak relational abstract domains, function summarization, etc.). It ends with interesting problems we encountered (such as C++ analysis with exception handling, or the detection of integer overflow).

Static Analysis↗
DOE OSTI2015

Interpreting ~1 Hz magnetic compressional waves in Mercury's inner magnetosphere in terms of propagating ion‐Bernstein waves

Abstract We show that ~1 Hz magnetic compressional waves observed in Mercury's inner magnetosphere could be interpreted as ion‐Bernstein waves in a moderate proton beta ~0.1 plasma. An observation of a proton distribution with a large planetary loss cone is presented, and we show that this type of distribution is highly unstable to the generation of ion‐Bernstein waves with low magnetic compression. Ray tracing shows that as these waves propagate back and forth about the magnetic equator; they cycle between a state of low and high magnetic compression. The group velocity decreases during the high‐compression state leading to a pileup of compressional wave energy, which could explain the observed dominance of the highly compressional waves. This bimodal nature is due to the complexity of the index of refraction surface in a warm plasma whose upper branch has high growth rate with low compression, and its lower branch has low growth/damping rate with strong compression. Two different cycles are found: one where the compression maximum occurs at the magnetic equator and one where the compression maximum straddles the magnetic equator. The later cycle could explain observations where the maximum in compression straddles the equator. Ray tracing shows that this mode is confined within ±12° magnetic latitude which can account for the bulk of the observations. We show that the Doppler shift can account for the difference between the observed and model wave frequency, if the wave vector direction is in opposition to the plasma flow direction. We note that the Wentzel‐Kramers‐Brillouin approximation breaks down during the pileup of compressional energy and that a study involving full wave solutions is required.

Boardsen, S. A.↗
DOE OSTI2015

The response of ENSO flavors to mid‐Holocene climate: Implications for proxy interpretation

Abstract The response of El Niño–Southern Oscillation (ENSO) to mid‐Holocene boundary conditions remains an open question: paleoclimate proxies and climate model simulations do not agree in the magnitude of the reduction of ENSO variability, while recent proxy evidence from fossil corals from the central Pacific show that the reduction in mid‐Holocene ENSO variability compared to the end of the twentieth century is not different from the reduction during other Holocene time intervals. This is inconsistent with the interpretation of lake and ocean sediment records from the eastern Pacific, which show a significant reduction compared to all other Holocene periods. In order to reconcile the seemingly conflicting proxy evidence from the eastern and central Pacific, we hypothesize that ENSO remained active during the mid‐Holocene; however, there was a change in the spatial pattern of the sea surface temperature anomalies, also known as ENSO flavors. Using National Center for Atmospheric Research's Community Climate System Model version 4 forced with mid‐Holocene orbital conditions, we find that the frequency of occurrence of the strongest eastern Pacific (EP) events decreases in the mid‐Holocene and their variance is reduced by ∼30%, while the frequency of central Pacific (CP) events slightly increases and their variance does not change. We also find a shift in the seasonality of EP events, but not in that of CP events. Lastly, mid‐Holocene EP events develop more slowly and decay faster. The differential response of ENSO flavors to mid‐Holocene forcing is remotely forced by the West Pacific, where a weakening of the trade winds in early boreal spring in the mid‐Holocene initiates an anomalous downwelling annual Kelvin wave, which reaches the eastern Pacific during the ENSO development season, weakens the upper ocean stratification, and results in reduced ENSO upwelling feedback. The simulated reduction in the EP flavor versus the CP flavor in the mid‐Holocene is consistent with proxy evidence: The teleconnection patterns of the two flavors with temperature, precipitation, and salinity are distinct, and proxies from different regions of the Pacific might be recording variability associated with only one of the flavors, or some combination of their relative effects.

Karamperidou, Christina↗
DOE OSTI2018

A New Method for Interpreting Nonstationary Running Correlations and Its Application to the ENSO‐EAWM Relationship

Abstract We here propose a new statistical method to interpret nonstationary running correlations by decomposing them into a stationary part and a first‐order Taylor expansion approximation for the nonstationary part. Then, this method is applied to investigate the nonstationary behavior of the El Niño–Southern Oscillation (ENSO)‐East Asian winter monsoon (EAWM) relationship, which exhibits prominent multidecadal variations. It is demonstrated that the first‐order approximation of the nonstationary part can be expressed to a large extent by the impact of the nonlinear interaction between the Atlantic Multidecadal Oscillation (AMO) and ENSO (AMO*Niño3.4) on the EAWM. Therefore, the nonstationarity in the ENSO‐EAWM relationship comes predominantly from the impact of an AMO modulation on the ENSO‐EAWM teleconnection via this key nonlinear interaction. This general method can be applied to investigate nonstationary relationships that are often observed between various different climate phenomena.

Geng, Xin↗
DOE OSTI2024

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

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

A multidimensional stability framework enhances interpretation and comparison of carbon cycling response to disturbance

Abstract The concept of stability is central to the study and sustainability of vital ecosystem goods and services as disturbances increase globally. While ecosystem ecologists, including carbon (C) cycling scientists, have long‐considered multiple dimensions of disturbance response, our discipline lacks an agreed‐upon analytical framework for characterizing multidimensional stability. Here, we advocate for the broader adoption of a standardized and normalized multidimensional stability framework for analyzing disturbance response. This framework includes four dimensions of stability: the degree of initial change in C fluxes (i.e., resistance ); rate (i.e., resilience ) and variability (i.e., temporal stability ) of return to pre‐disturbance C fluxes; and the extent of return to pre‐disturbance C fluxes (i.e., recovery ). Using this framework, we highlight findings not readily seen from analysis of absolute fluxes, including trade‐offs between initial and long‐term C flux responses to disturbance; different overall stability profiles among fluxes; and, using a pilot dataset, similar relative stability of net primary production following fire and insect disturbances. We conclude that ecosystem ecologists’ embrace of a unifying multidimensional stability framework as a complement to approaches focused on absolute C fluxes could advance global change research by aiding in the novel interpretation, comprehensive synthesis, and improved forecasting of ecosystems’ response to an increasing array of disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Statistical interpretation of the Amerithrax “morph” assay results

In the Amerithrax investigation PCR–based “morph assays” were used to link the anthrax letters with the RMR–1029 flask at USAMRIID. Quantitative data reported for several of these assays are not consistent with Poisson sampling statistics, but instead exhibit “Taylor's Law” behavior where the variance greatly exceeds the mean. A plausible statistical model for this behavior can explain the large number of observed negative and “inconclusive” findings, and implies a high likelihood that a repository sample could contain a “morph” mutant at concentrations well above the nominal detection limit but nonetheless give a negative or inconclusive test result. A Bayesian framework relates the assay results to the probability that a sample actually contains all four morph mutants, even though it tested negative for at least one. The analysis implies that the observed false negative rate actually does not significantly weaken the conclusion that the morph assays correctly excluded all but the stocks derived from RMR–1029 as possible sources of the letter powders, at least when the test results were unambiguous. In conclusion, these findings expand upon and resolve some of the issues cited in recent reviews, and indicate the importance of developing a rigorous statistical framework for interpreting “morph” assay data.

59 BASIC BIOLOGICAL SCIENCES↗