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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring the hidden valley at MATHUSLA

Hidden valley models naturally predict numerous long-lived particles, the distinctive signatures of which would be compelling evidence for a hidden valley scenario. As these are typically low-energy particles, they pose a challenge in terms of passing energy triggers in traditional searches at the Large Hadron Collider. The MATHUSLA (MAssive Timing Hodoscope for Ultra-Stable neutraL pArticles) experiment is specifically designed for the purpose of detecting long-lived particles. It also has the capability of detecting lower energy particles relative to ATLAS and CMS. In this paper, we assess MATHUSLA's potential for effectively probing hidden valley models. As a benchmark, we assume the hidden valley sector communicates with Standard Model sectors via a heavy vector propagator that couples to Standard Model quarks as well as hidden valley quarks. We model the showering and hadronization in the hidden valley sector using PYTHIA and study the detector acceptance as a function of the hidden valley meson's lifetime. We find that MATHUSLA possesses significant capabilities to explore previously uncharted parameter space within hidden valley models.

Hidden sector↗
NASA NTRS1972

The use of remote sensors to relate biological and physical indicators to environmental and public health problems

Relationships between biological, ecological and botanical structures, and disease organisms and their vectors which might be detected and measured by remote sensing are determined. In addition to the use of trees as indicators of disease or potential disease, an attempt is made to identify environmental factors such as soil moisture and soil and water temperatures as they relate to disease or health problems and may be detected by remote sensing. The following three diseases and one major health problem are examined: Malaria, Rocky Mountain spotted fever, Encephalitis and Red Tide. It is shown that no single species of vascular plant nor any one environmental factor can be used as the indicator of disease or health problems. Entire vegetation types, successional stages and combinations of factors must be used.

Source record↗
NASA NTRS1982

Modeling lateral acceleration effects on pilot performance

Attendant to the direct side force maneuver of a Vectored Force Fighter is the transverse acceleration imposed on the pilot. This lateral acceleration (Gy), when combind with a positive Gz stress, is a potential source of pilot tracking performance impairment. A research effort to investigate these performance decrements includes experimental as well as anaytical pilot performance modeling using the Optimal Control Model.

Korn, J.↗
NASA NTRS1986

Flight management concepts compatible with air traffic control

With the advent of airline deregulation and increased competition, the need for cost efficient airline operations is critical. This paper summarizes past research efforts and planned research thrusts toward the development of compatible flight management and air traffic control systems that promise increased operational effectiveness and efficiency. Potential capacity improvements resulting from a time-based ATC simulation (fast-time) are presented. Advanced display concepts with time guidance and velocity vector information to allow the flight crew to play an important role in the future ATC environment are discussed. Results of parametric sensitivity analyses are also presented that quantify the fuel/cost penalties for idle-thrust mismodeling and wind-modeling errors.

Morello, S. A.↗
NASA NTRS1988

Meteorological atlas of the Southern Hemisphere lower stratosphere for August and September 1987

Southern Hemisphere meteorological data for the months of August and September 1987 in the lower stratosphere are shown. National Meteorological Center (NMC) data, Total Ozone Mapping Spectrometer (TOMS) data, and Goddard Laboratory for Atmospheres (GLA) data are used to display polar stereographic projections of 200 to 100 mb vertical mean temperatures, 100 mb zonal mean geopotential height perturbations, total ozone, Ertel's potential vorticity (Epv), and 50 to 30 mb vertical mean temperatures. In addition, latitude height cross sections at 65 W of potential temperature, Epv, geostrophic isotachs, and temperature are also shown. Finally, a longitude height cross section at 65 S of temperature and geostrophic wind vectors is also shown.

Newman, P. A.↗
DOE OSTI2022

A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Sensitivity to longitudinal vector boson scattering and doubly charged Higgs boson production in $W^± W^± jj$ at future hadron colliders

We study the sensitivity to longitudinal vector boson scattering at a 27-, 50-, and 100-TeV pp collider using events containing two leptonically decaying same-electric-charge 𝑊 bosons produced in association with two jets. The baseline FCC-hh detector parametrization within the D ELPHES framework is used under the assumption of fully efficient pileup mitigation. A tightly constrained phase space with a dijet mass greater than 2 TeV is considered in order to suppress the impact of potential instrumental backgrounds. Based on this setup, the expected sensitivity to the production of longitudinally polarized same-sign 𝑊 boson pairs is evaluated. Additionally, expected limits are set on doubly charged Higgs bosons produced via vector boson fusion processes and decaying to same-sign 𝑊 boson pairs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

NeuroSymbolic Approaches as a Vector for Assured Artificial Intelligence

The deployment of artificial intelligence systems in critical applications requires higher levels of assurance for safety, security, and interpretability. While neurosymbolic (NESY) approaches combining neural networks with symbolic reasoning offer potential advantages for assured AI, existing differentiable neurosymbolic frameworks face significant limitations including computational overhead and performance constraints. This report investigates the ISED (InferSampleEstimateDescend) framework as an alternative approach that enables neurosymbolic learning without requiring endtoend differentiability. We evaluate ISED’s utility for geointelligence applications by comparing neurosymbolic models against standard neural networks on aircraft classification tasks using the RarePlanes and MTARSI imagery datasets. Our results demonstrate that while ISEDbased models achieve slightly lower accuracy (89.7% vs 92.1% on RarePlanes; 91.1% vs 92.5% on MTARSI), they provide critical explainability capabilities that enable tracing incorrect predictions back to specific attribute misclassifications. We also present an automated pipeline that generates both attributeclass mappings and neurosymbolic model architectures from natural language descriptions, significantly reducing the manual effort required for NESY model deployment. These findings suggest that ISED offers a promising direction for developing assured AI systems where interpretability and reasoning transparency are prioritized alongside performance.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Identification of central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar

Rift Valley Fever (RVF) is a mosquito-borne virus that affects livestock and humans in Africa. Landsat TM data are shown to be effective in identifying dambos, intermittently flooded areas that are potential mosquite breeding sites, in an area north of Nairobi, Kenya. Positive results were obtained from a limited test of flood detection in dambos with airborne high resolution L, C, and X band multipolarization SAR imagery. L and C bands were effective in detecting flooded dambos, but LHH was by far the best channel for discrimination between flooded and nonflooded sites in both sedge and short-grass environments. This study demonstrates the feasibility of a combined passive and active remote sensing program for monitoring the location and condition of RVF vector habitats, thus making future control of the disease more promising.

Pope, K. O.↗
DOE OSTI2021

Synthetic Evaluation of Infrasonic Multipole Waveform Inversion

Acoustic source inversions estimate the mass flow rate of volcanic explosions or yield of chemical explosions and provide insight into potential source directionality. However, the limitations of applying these methods to complex sources and their ability to resolve a stable solution have not been investigated in detail. We perform synthetic infrasound waveform inversions that use 3-D Green’s functions for a variety of idealized and realistic deployment scenarios using both a flat plane and Yasur volcano, Vanuatu as examples. We investigate the ability of various scenarios to retrieve the input source functions and relative amplitudes for monopole and multipole (monopole and dipole) inversions. Infrasound waveform inversions appear to be a robust method to quantify mass flow rates from simple sources (monopole) using deployments of infrasound sensors placed around a source, but care should be taken when analyzing and interpreting results from more complex acoustic sources (multipole) that have significant directional components. In the examples we consider the solution is stable for monopole inversions with a signal-to-noise ratio greater than five and the dipole component is small. For most scenarios investigated, the vertical dipole component of the multipole explosion source is poorly constrained and can impact the ability to recover the other source term components. Because multipole inversions are ill-posed for many deployments, a low residual does not necessarily mean the proper source vector has been recovered. Synthetic studies are able to help investigate the limitations and place bounds on information that may be missing using monopole and multipole inversions for potentially directional sources.

58 GEOSCIENCES↗
NASA NTRS1985

Reduction method for thermal analysis of complex aerospace structures

A reduction method which combines classical Rayleigh-Ritz modal superposition techniques with contemporary finite-element methods is applied to transient nonlinear thermal analysis of aerospace structures. The essence of the method is the use of a few thermal modes from eigenvalue analyses as basis vectors to represent the temperature response in the structure. The method is used to obtain approximate temperature histories for a portion of the Shuttle orbiter wing subject to reentry heating and for a large space antenna reflector subject to heating associated with a low Earth orbit. The reduction method has excellent potential for significant size reduction for radiation-dominated problems such as the antenna reflector. However, for conduction-dominated problems such as the Shuttle wing, especially those with complex spatial and temporal variations in the applied heating, additional work appears necessary to find alternate sources of basis vectors which will permit significant problem size reductions.

Shore, C. P.↗
DOE OSTI2023

Malicious Cyber Activity Detection using Zigzag Persistence

In this study we synthesize zigzag persistence from topological data analysis with autoencoder-based approaches to detect malicious cyber activity, and derive analytic insights. Cybersecurity aims to safeguard computers, networks, and servers from various forms of malicious attacks, including network damage, data theft, and activity monitoring. We focus on the cybersecurity domain and investigate the detection of malicious activity using log data. We consider the dynamics of the log data and explore the changing topology of a hypergraph representation of this data to gain insights into the underlying activity. These hypergraphs capture complex interactions between processes, together with their temporal information. To study the changing topology we use zigzag persistence, which captures how topological features persist at multiple dimensions over time. We observe that this detects malicious activity in a cyber data set. To automate this detection we implement an autoencoder trained on a vectorization of the resulting zigzag persistence barcodes. Our experimental results demonstrate the effectiveness of the autoencoder in detecting malicious activity. Overall, this study highlights the potential of zigzag persistence and its combination with temporal hypergraphs for analyzing cybersecurity log data and detecting malicious behavior.

hypergraphs, temporal hypergraph, topological data↗
NASA NTRS1972

Comparison of satellite theories

The accuracy of five mathematical models in computing a nominal orbit for the Vanguard 2 satellite by using a position velocity vector is considered. Either numerical integration or analytical theories are used in all models as well as the same force model that corresponds to a potential with the zonal harmonics to order four. The amounts of spread in the values of the total energy and the z-component of the angular momentum for a set of times are considered as measures of accuracy.

Hertz, H. G.↗
NASA NTRS1985

Model-size reduction technique for the analysis of symmetric anisotropic structures

A two-step computational procedure is presented for reducing the size of the analysis model for an anisotropic symmetric structure to that of the corresponding orthotropic structure. The key elements of the procedure are: (1) decomposition of the stiffness matrix into the sum of an orthotropic and nonorthotropic (anisotropic) parts; and (2) successive application of the finite element method and the classical Rayleigh-Ritz technique. The finite element method is first used to generate few global approximation vectors (or modes). Then the amplitudes of these modes are computed by using the Rayleigh-Ritz technique. The global approximation vectors are selected to be the solution corresponding to zero nonorthotropic matrix and its various-order derivatives with respect to an anisotropic tracing parameter (identifying the nonorthotropic material coefficients). The size of the analysis model used in generating the global approximation vectors is identical to that of the corresponding orthotropic structure. The effectiveness of the proposed technique is demonstrated by means of numerical examples and its potential for solving other quasi-symmetric problems is discussed.

Noor, A. K.↗
NASA NTRS1987

Nonpotential features observed in the magnetic field of an active region

A unique coordinated data set consisting of vector magnetograms, H-alpha photographs, and high-resolution ultraviolet images of a solar active region is used, together with mathematical models, to calculate potential and force-free magnetic field lines and to examine the nonpotential nature of the active region structure. It is found that the overall bipolar magnetic field of the active region had a net twist corresponding to net current of order 3 x 10 to the 12th A and average density of order 4 x 10 to the -4th A/sq m flowing antiparallel to the field. There were three regions of enhanced nonpotentiality in the interior of the active region; in one the field had a marked nonpotential twist or shear with height above the photosphere. The measured total nonpotential magnetic energy stored in the entire active region was of order 10 to the 32nd ergs, about 3 sigma above the noise level.

Gary, G. A.↗
NASA NTRS2015

Using Satellite Observations to Infer the Relationship Between Cold Pools and Subsequent Convection Development

Cold pools are increasingly being recognized as important players in the evolution of both shallow and deep convection; hence, the incorporation of cold pool processes into a number of recently developed convective parameterizations. Unfortunately, observations serving to inform cold pool parameterization development are limited to select field programs and limited radar domains. However, a number of recent studies have noted that cold pools are often associated with arcs-lines of shallow clouds traversing 10 100 km in visible satellite imagery. Boundary layer thermodynamic perturbations are plausible at such scales, coincident with such mesoscale features. Atmospheric signatures of features at these spatial scales are potentially observable from satellites. In this presentation, we discuss recent work that uses multi-sensor, high-resolution satellite products for observing mesoscale wind vector fluctuations and boundary layer temperature depressions attributed to cold pools produced by antecedent convection. The relationship to subsequent convection as well as convective system longevity is discussed. As improvements in satellite technology occur and efforts to reduce noise in high-resolution orbital products progress, satellite pixel level (10 km) thermodynamic and dynamic (e.g. mesoscale convergence) parameters can increasingly serve as useful benchmarks for constraining convective parameterization development, including for regimes where organized convection contributes substantially to the cloud and rainfall climatology.

thermodynamics↗
NASA NTRSDate not supplied

TPSAS-NF1676L-11456-DND

NASA’s precision scheduling and spacing research has matured to show the high potential of merging efficient flows in high density operations - Precision scheduling and efficient delay distribution minimizes early descent from enroute altitude and low-altitude vectoring - Precision merging and spacing control improves throughput - Enables the extended use of flight path optimization based procedures FAA/NASA/Industry collaboration is needed to accelerate implementation of ADS-B applications in a way that achieves the full benefit of NextGen operations to those that equip in high density airspace Integrated precision scheduling and spacing will to offer immediate and systematic benefits to airlines in many environments, even with different equipage levels and traffic densities

Harry Swenson↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗