Search NASA⌕ Search

SEARCH · Search NASA

Results for “unknowns”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Binary Analysis with Architecture and Code Section Detection using Supervised Machine Learning

When presented with an unknown binary, which may or may not be complete, having the ability to determine information about it is critical to future reverse engineering, particularly in discovering the binary’s intended use and potentially malicious nature. This paper details techniques to both identify the machine architecture of the binary, as well as to locate the important code segments within the file. This identification of unknown binaries makes use of a technique called byte histogram in addition to various machine learning (ML) techniques, which we call “What is it Binary” or WiiBin. Benefits of byte histograms reflect the simplicity of calculation and do not rely on file headers or metadata, allowing for acceptable results when only a small portion of the original file is available. Utilizing WiiBin, we were able to accurately (>80%) determine the architecture of test binaries with as little as a 20% contagious portion of the file present. We were also able to determine the location of code sections within a binary by utilizing the WiiBin framework. Ultimately, the more information that can be gleaned from a binary file, the easier it is to successfully reverse engineer.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A Butterfly-Accelerated Volume Integral Equation Solver for Broad Permittivity and Large-Scale Electromagnetic Analysis

In this work, a butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N log 1.5 N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

42 ENGINEERING↗
DOE OSTI2024

Gray-Box Modeling for Distribution Systems With Inverter-Based Resources: Integrating Physics-Based and Data-Driven Approaches

Here, in this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, we utilize partial physical knowledge of the system, including the inverters’ structures and control diagrams, as well as the equivalent network model simplified through Kron reduction. The white-box model containing unknown parameters is then constructed with mathematical equations and an optimization-based method is subsequently employed to identify these unknown parameters within the white-box model. Next, the graybox modeling framework is then constructed by embedding the output variables of the white-box model into the input vector of a black-box model (represented using a neural network). Finally, the black-box section is trained using the collected input-output datasets and the gray-box model is then obtained. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.

42 ENGINEERING↗
DOE OSTI2022

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Distributionally Robust Variational Quantum Algorithms With Shifted Noise

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is the high sensitivity of quantum noise to environmental changes, and its propensity to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step towards improving the reliability of VQAs influenced by real-time noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace —a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. Here, to address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Effects of processing conditions on flash sintering of commercial ZrN

Flash sintering (FS) is a potentially rapid and low-cost manufacturing technique for advanced ceramics. There are still many unknowns about ceramic FS, especially for highly conductive high-temperature ceramics (HTC), which often display decreased bulk conductivity with increasing temperature. This study qualitatively characterizes the flash behavior of highly conductive HTC materials using zirconium nitride (ZrN) as an example. The effects of processing parameters (e.g., DC voltage/electrical field strength, voltage ramp rate, post-flash holding time, mechanical pressure, sample milling, and choice of electrode materials) on ZrN flash behavior are also qualitatively studied and linked to samples microstructure and hardness. It is observed that best densification was achieved using 5 min Spex-milled ZrN powder under 25 MPa of applied pressure and constant 8 V DC. The potential mechanism for ZrN FS and the similarity and difference from FS of conventional oxides like YSZ have been proposed based on experimental observations, and the directions for future research are also pointed out.

Flash Sintering↗
DOE OSTI2023

Microdiverse bacterial clades prevail across Antarctic wetlands

Antarctica's extreme environmental conditions impose selection pressures on microbial communities. Indeed, a previous study revealed that bacterial assemblages at the Cierva Point Wetland Complex (CPWC) are shaped by strong homogeneous selection. Yet which bacterial phylogenetic clades are shaped by selection processes and their ecological strategies to thrive in such extreme conditions remain unknown. Here, we applied the phyloscore and feature-level βNTI indexes coupled with phylofactorization to successfully detect bacterial monophyletic clades subjected to homogeneous (HoS) and heterogenous (HeS) selection. Remarkably, only the HoS clades showed high relative abundance across all samples and signs of putative microdiversity. The majority of the amplicon sequence variants (ASVs) within each HoS clade clustered into a unique 97% sequence similarity operational taxonomic unit (OTU) and inhabited a specific environment (lotic, lentic or terrestrial). Our findings suggest the existence of microdiversification leading to sub-taxa niche differentiation, with putative distinct ecotypes (consisting of groups of ASVs) adapted to a specific environment. We hypothesize that HoS clades thriving in the CPWC have phylogenetically conserved traits that accelerate their rate of evolution, enabling them to adapt to strong spatio-temporally variable selection pressures. Variable selection appears to operate within clades to cause very rapid microdiversification without losing key traits that lead to high abundance. Variable and homogeneous selection, therefore, operate simultaneously but on different aspects of organismal ecology. The result is an overall signal of homogeneous selection due to rapid within-clade microdiversification caused by variable selection. It is unknown whether other systems experience this dynamic, and we encourage future work evaluating the transferability of our results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Conifers depend on established roots during drought: results from a coupled model of carbon allocation and hydraulics

Trees may survive prolonged droughts by shifting water uptake to reliable water sources, but it is unknown if the dominant mechanism involves activating existing roots or growing new roots during drought, or some combination of the two. To gain mechanistic insights on this unknown, a dynamic root-hydraulic modeling framework was developed that set up a feedback between hydraulic controls over carbon allocation and the role of root growth on soil–plant hydraulics. The new model was tested using a 5 yr drought/heat field experiment on an established pi~non-juniper stand with root access to bedrock groundwater. Owing to the high carbon cost per unit root area, modeled trees initialized without adequate bedrock groundwater access experienced potentially lethal declines in water potential, while all of the experimental trees maintained nonlethal water potentials. Simulated trees were unable to grow roots rapidly enough to mediate the hydraulic stress, particularly during warm droughts. Alternatively, modeled trees initiated with root access to bedrock groundwater matched the hydraulics of the experimental trees by increasing their water uptake from bedrock groundwater when soil layers dried out. Therefore, the modeling framework identified a critical mechanism for drought response that required trees to shift water uptake among existing roots rather than growing new roots.

Mackay, D. Scott↗
DOE OSTI2023

Human-in-the-Loop Motion Control of a Two-DOF Hydraulic Backhoe Powered by the Hybrid Hydraulic Electric Architecture (HHEA)

Abstract The Hybrid Hydraulic-Electric Architecture (HHEA) combines the respective power density and control advantages of hydraulic and electric actuation to save energy for off-road vehicles. It uses a set of selectable common pressure rails to transmit the majority of power and electric actuation to modulate that power. As it is critical that off-road vehicles can perform tasks dexterously and exactly as commanded by the operator, the switchings between discrete pressure rails pose a potential challenge for smooth and precise motion. A control strategy consisting of a backstepping nominal control and least norm transition control has previously been developed to address this issue. It has been tested on 1 degree of freedom (DOF) testbeds where known trajectories were able to be tracked precisely. This paper presents the implementation of the HHEA motion control strategy on a 2-DOF backhoe operated by a human operator via a 2-DOF joystick. Unlike previous studies, the duty cycle is unknown beforehand and the decision to change pressure rails is taken in real-time. The efficacy of the motion control strategy has been validated experimentally. Several strategies to improve the user interface: control in workspace coordinates, pressure feedback, and velocity field-based task specification, have also been implemented and demonstrated to make operating the multiple DOF, HHEA actuated machine more intuitive to novice operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A chiral switchable photovoltaic ferroelectric 1D perovskite

Spin and valley degrees of freedom in materials without inversion symmetry promise previously unknown device functionalities, such as spin-valleytronics. Control of material symmetry with electric fields (ferroelectricity), while breaking additional symmetries, including mirror symmetry, could yield phenomena where chirality, spin, valley, and crystal potential are strongly coupled. Here we report the synthesis of a halide perovskite semiconductor that is simultaneously photoferroelectricity switchable and chiral. Spectroscopic and structural analysis, and first-principles calculations, determine the material to be a previously unknown low-dimensional hybrid perovskite (R)-(-)-1-cyclohexylethylammonium/(S)-(+)-1 cyclohexylethylammonium) PbI 3 . Optical and electrical measurements characterize its semiconducting, ferroelectric, switchable pyroelectricity and switchable photoferroelectric properties. Temperature dependent structural, dielectric and transport measurements reveal a ferroelectric-paraelectric phase transition. Circular dichroism spectroscopy confirms its chirality. The development of a material with such a combination of these properties will facilitate the exploration of phenomena such as electric field and chiral enantiomer–dependent Rashba-Dresselhaus splitting and circular photogalvanic effects

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ultrafast solid-liquid intercalation enabled by targeted microwave energy delivery

In chemical reactions, the breaking and formation of chemical bonds usually need external energy to overcome the activation barriers. Conventional energy delivery transfers energy from heating sources via various media, hence losing efficiency and inducing side reactions. In contrast, microwave (MW) heating is known to be highly energy efficient through dipole interaction with polar media, but how exactly it transmits energy to initiate chemical reactions has been unknown. Here, we report a rigorous determination of energy delivery mechanisms underlying MW-enabled rapid hydrothermal synthesis, by monitoring the structure and temperature of all the involved components as solid-liquid intercalation reaction occurs using in situ synchrotron techniques. We reveal a hitherto unknown direct energy transmission between MW irradiation source and the targeted reactants, leading to greatly reduced energy waste, and so the ultrafast kinetics at low temperature. These findings open up new horizons for designing material synthesis reactions of high efficiency and precision.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quieting a noisy antenna reproduces photosynthetic light-harvesting spectra

Photosynthesis achieves near unity light-harvesting quantum efficiency yet it remains unknown whether there exists a fundamental organizing principle giving rise to robust light harvesting in the presence of dynamic light conditions and noisy physiological environments. Here, we present a noise-canceling network model that relates noisy physiological conditions, power conversion efficiency, and the resulting absorption spectra of photosynthetic organisms. Using light conditions in full solar exposure, light filtered by oxygenic phototrophs, and light filtered under seawater, we derived optimal absorption characteristics for efficient solar power conversion. We show how light-harvesting antennae can be tuned to maximize power conversion efficiency by minimizing excitation noise, thus providing a unified theoretical basis for the observed wavelength dependence of absorption in green plants, purple bacteria, and green sulfur bacteria.

Science & Technology - Other Topics↗
DOE OSTI2020

Diazotrophs Show Signs of Restoration in Amazon Rain Forest Soils with Ecosystem Rehabilitation

Biological nitrogen fixation can be an important source of nitrogen in tropical forests that serve as a major CO 2 sink. Extensive deforestation of the Amazon is known to influence microbial communities and the biogeochemical cycles they mediate. However, it is unknown how diazotrophs (nitrogen-fixing microorganisms) respond to deforestation and subsequent ecosystem conversion to agriculture, as well as whether they can recover in secondary forests that are established after agriculture is abandoned. To address these knowledge gaps, here we combined a spatially explicit sampling approach with high-throughput sequencing of nifH genes. The main objectives were to assess the functional distance decay relationship of the diazotrophic bacterial community in a tropical forest ecosystem and to quantify the roles of various factors that drive the observed changes in the diazotrophic community structure. We observed an increase in local diazotrophic diversity (α-diversity) with a decrease in community turnover (β-diversity), associated with a shift in diazotrophic community structure as a result of the forest-to-pasture conversion. Both diazotrophic community turnover and structure showed signs of recovery in secondary forests. Changes in the diazotrophic community were primarily driven by the change in land use rather than differences in geochemical characteristics or geographic distances. The diazotroph communities in secondary forests resembled those in primary forests, suggesting that at least partial recovery of diazotrophs is possible following agricultural abandonment. IMPORTANCE The Amazon region is a major tropical forest region that is being deforested at an alarming rate to create space for cattle ranching and agriculture. Diazotrophs (nitrogen-fixing microorganisms) play an important role in supplying soil N for plant growth in tropical forests. It is unknown how diazotrophs respond to deforestation and whether they can recover in secondary forests that establish after agriculture is abandoned. Using high-throughput sequencing of nifH genes, we characterized the response of diazotrophs’ β-diversity and identified major drivers of changes in diazotrophs from forest-to-pasture and pasture-to-secondary-forest conversions. Studying the impact of land use change on diazotrophs is important for a better understanding of the impact of deforestation on tropical forest ecosystem functioning, and our results on the potential recovery of diazotrophs in secondary forests imply the possible restoration of ecosystem functions in secondary forests.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Exploring life’s hidden majority: microbial dark matter symposium highlights

The Microbial Dark Matter Symposium held on August 28–29, 2025, in Laguna Beach, Orange County, CA, convened a multidisciplinary group of scientists to address the vast unknowns in microbial life—from uncultured taxa and uncharacterized proteins to elusive viruses and spacefaring microbes. Set against a scenic coastal backdrop, the symposium highlighted advances in single-cell genomics, proximity ligation sequencing, and artificial intelligence-ready bioinformatics, while also probing the limits of microbial persistence, metabolism, and ecological distribution. Sessions explored microbial dark matter from multiple dimensions: cultivability, where new strategies are enabling recovery of elusive microbes; functional ambiguity, where metagenomic dark zones are illuminated by computational annotation; and genomic representation, where single-cell methods bridge gaps left by shotgun community sequencing. Researchers shared breakthroughs in identifying atmospheric microbiomes, “dark oxygen” production in groundwater ecosystems, and microbial survival on the International Space Station. The symposium emphasized integration of methods, disciplines, and ecosystems, advancing a collective push to illuminate the microbial dark matter on Earth and beyond. By highlighting emerging tools, pressing questions, and cross-domain insights, the symposium underscored the need for collaborative, open, and adaptive approaches to study the microbial unknown. The meeting marks a pivotal moment in microbiology, where cultivating knowledge of the uncultivated promises transformative understanding of life, everywhere.

Podar, Mircea [ORNL] (ORCID:0000000327760205)↗
DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Parameter Estimation for Decoding Sensor Signals

This paper introduces a parameter estimation approach for decoding digital sensor signals in a cyber-physical system. For unknown or not fully characterized digital sensor data, it can be difficult to decipher a desired signal from background or noise. In a cyber-physical system with networked sensors, we can leverage knowledge of the physical system to inform the decoding of the digital signals. This work in progress is a case study on deciphering commercial vehicle on-board sensor networks that communicate through the Controller Area Network (CAN). By understanding the stock vehicle sensor network, a vehicle can be extended into a scalable research platform with minimal instrumentation. Our challenge was to localize desired sensor signals encoded in network traffic that included other sensor data, control messages, as well as encoding and security overhead. Due to the vehicle’s unknown sensor network, our approach developed methods to efficiently analyze and identify key signals despite the large state-space for potential signal embeddings.

Nice, Matthew↗
DOE OSTI2025

sparse_bias

This is a python package used to fit an unknown function from data that potential contains systematic biases related to metadata. The model fits the unknown function and uses a Bayesian horseshoe prior model to impose sparsity on the bias terms. This code has been generalized from research code developed for AIACHNE into a package that should have more general application in a wider class of statistical models.

Walton, Noah↗