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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Isotope effects on energy transport in the core of ASDEX-Upgrade tokamak plasmas: Turbulence measurements and model validation

Design and operation of future tokamak fusion reactors using a deuterium–tritium 50:50 mix requires a solid understanding of how energy confinement properties change with ion mass. This study looks at how turbulence and energy transport change in L-mode plasmas in the ASDEX Upgrade tokamak when changing ion species between hydrogen and deuterium. For this purpose, both experimental turbulence measurements and modeling are employed. Local measurements of ion-scale (with wavevector of fluctuations perpendicular to the B-field k⊥< 2 cm−1, k⊥ρs< 0.2, where ρs is the ion sound Larmor radius using the deuterium ion mass) electron temperature fluctuations have been performed in the outer core (normalized toroidal flux ρTor=0.65−0.8) using a multi-channel correlation electron cyclotron emission diagnostic. Lower root mean square perpendicular fluctuation amplitudes and radial correlation lengths have been measured in hydrogen vs deuterium. Measurements of the cross-phase angle between a normal-incidence reflectometer and an ECE signal were made to infer the cross-phase angle between density and temperature fluctuations. The magnitude of the cross-phase angle was found larger (more out-of-phase) in hydrogen than in deuterium. TRANSP power balance simulations show a larger ion heat flux in hydrogen where the electron-ion heat exchange term is found to play an important role. These experimental observations were used as the basis of a validation study of both quasilinear gyrofluid trapped gyro-Landau fluid-SAT2 and nonlinear gyrokinetic GENE codes. Linear solvers indicate that, at long wavelengths (k⊥ρs<1), energy transport in the deuterium discharge is dominated by a mixed ion-temperature-gradient (ITG) and trapped-electron mode turbulence while in hydrogen transport is exclusively and more strongly driven by ITG turbulence. The Ricci validation metric has been used to quantify the agreement between experiments and simulations taking into account both experimental and simulation uncertainties as well as four different observables across different levels of the primacy hierarchy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

Analyses and forecasts with LAWS winds

Horizontal fluxes of atmospheric water vapor are studied for summer months during 1989 and 1992 over North and South America based on analyses from European Center for Medium Range Weather Forecasts, US National Meteorological Center, and United Kingdom Meteorological Office. The calculations are performed over 20 deg by 20 deg box-shaped midlatitude domains located to the east of the Rocky Mountains in North America, and to the east of the Andes Mountains in South America. The fluxes are determined from operational center gridded analyses of wind and moisture. Differences in the monthly mean moisture flux divergence determined from these analyses are as large as 7 cm/month precipitable water equivalent over South America, and 3 cm/month over North America. Gridded analyses at higher spatial and temporal resolution exhibit better agreement in the moisture budget study. However, significant discrepancies of the moisture flux divergence computed from different gridded analyses still exist. The conclusion is more pessimistic than Rasmusson's estimate based on station data. Further analysis reveals that the most significant sources of error result from model surface elevation fields, gaps in the data archive, and uncertainties in the wind and specific humidity analyses. Uncertainties in the wind analyses are the most important problem. The low-level jets, in particular, are substantially different in the different data archives. Part of the reason for this may be due to the way the different analysis models parameterized physical processes affecting low-level jets. The results support the inference that the noise/signal ratio of the moisture budget may be improved more rapidly by providing better wind observations and analyses than by providing better moisture data.

Wang, Muyin↗
NASA NTRS2013

Spatial Statistical Data Fusion (SSDF)

As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is fundamentally different than other approaches to data fusion for remote sensing data because it is inferential rather than merely descriptive. All approaches combine data in a way that minimizes some specified loss function. Most of these are more or less ad hoc criteria based on what looks good to the eye, or some criteria that relate only to the data at hand.

Braverman, Amy J.↗
NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗
NASA NTRSDate not supplied

Hayabusa2#’s Exploration to Asteroids 2001 CC21 and 1998 KY26 Provides Key Insights Into Planetary Defense

The Hayabusa2 extended mission, nicknamed Hayabusa2# (SHARP: Small Hazardous Asteroid Reconnaissance Probe), started its mission after the Hayabusa2 spacecraft successfully returned in December 2020 to the Earth with Ryugu's samples and released the Sample Return Capsule containing it. Hayabusa2#’s key operations include flying by Near Earth Asteroid (98943) 2001 CC21 and rendezvousing with Near-Earth Asteroid 1998 KY26. The flyby is planned to be in 2026, and the rendezvous will be in 2031. Detailed investigations of these targets enable maturing key knowledge and technologies necessary for planetary defense. This paper reviews the characteristics of these targets and strategies to maximize outcomes from the mission. The spacecraft will fly by 2001 CC21 at a speed of ~5 km/s in 2026. Given constraints on the spacecraft condition, not designed to perform a flyby operation, operational plans require careful assessments to maximize the proximity observations of this asteroid. The ~700 m diameter asteroid's shape is currently unknown, though lightcurve observations suggest the asteroid is elongated. While the spin pole is not constrained well, the spin period is about 5.02 h with uncertainties of 0.01 h [1]. The taxonomic class may be L-type, which implies the presence of Calcium-aluminum-rich inclusions (CAI), one of the primitive materials in the solar system [2], although there are studies suggesting an S-type. Therefore, this must be carefully examined. Photometric and spectroscopic observations will strongly constrain this asteroid’s properties. Our rendezvous target, 1998 KY26, is a ~30 m diameter roundish object spinning at a spin period of 10.7 min, which may represent one of the common groups of Near Earth Asteroids in size and composition that give a higher likelihood of threatening the Earth. Radar and optical observations reconstructed this asteroid's shape with high uncertainties and inferred that its surface composition might be similar to that of carbonaceous asteroids [3]. No spacecraft has ever visited such small bodies. Hayabusa2# visiting 1998 KY26 will be the first mission to document its geophysical properties, offering strong insights into what most frequent invaders look like [4].

Hayabusa2#↗
DOE OSTI2025

Analysis of Polarized Dust Emission Using Data from the First Flight of SPIDER

Using data from the first flight of Spider and from the Planck High Frequency Instrument, we probe the properties of polarized emission from interstellar dust in the Spider observing region. Component-separation algorithms operating in both the spatial and harmonic domains are applied to probe their consistency and to quantify modeling errors associated with their assumptions. Analyses of diffuse Galactic dust emission spanning the full Spider region demonstrate (i) a spectral energy distribution that is broadly consistent with a modified-blackbody (MBB) model with a spectral index of β d = 1.45 ± 0.05 (1.47 ± 0.06) for E (B)-mode polarization, slightly lower than that reported by Planck for the full sky; (ii) an angular power spectrum broadly consistent with a power law; and (iii) no significant detection of line-of-sight polarization decorrelation. Tests of several modeling uncertainties find only a modest impact (~10% in σ r ) on Spider's sensitivity to the cosmological tensor-to-scalar ratio. The size of the Spider region further allows for a statistically meaningful analysis of the variation in foreground properties within it. Assuming a fixed dust temperature T d = 19.6 K, an analysis of two independent subregions of that field results in inferred values of β d = 1.52 ± 0.06 and β d = 1.09 ± 0.09, which are inconsistent at the 3.9σ level. Furthermore, a joint analysis of Spider and Planck 217 and 353 GHz data within one subregion is inconsistent with a simple MBB at more than 3σ, assuming a common morphology of polarized dust emission over the full range of frequencies. This evidence of variation may inform the component-separation approaches of future cosmic microwave background polarization experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

Lee, S. C.↗
NASA NTRS1996

Observations and Analysis of Atmospheric Hydroxyl

Ground-based measurements of sunlight absorption at the OH P(sub 1)(l) resonance line at 308 nm have been made on a continuous basis at Fritz Peak, Colorado. The derived OH vertical column abundances show the persistence of a new seasonal regime which began in 1991. The fall minimum has been consistently depressed about 10-15% below the 1980-1990 average fall values. While the initial onset of depressed fall abundances occurred a few months after the Pinatubo eruption, there has been no fall OH recovery correlating with decreased amounts of volcanic aerosol found since spring 1993. The Colorado data also continues to exhibit an AM-PM asymmetry which varies seasonally, approximately in phase with local total ozone. These observations were presented at the Front Range AGU meeting in February 1996 and were published in Geophysical Research Letters in July 1996 (preprint enclosed). An update through the fall of 1996, when morning abundances were found to be extremely low, was presented at the Fall 1996 AGU meeting (abstract attached). A PEPSIOS instrument of identical design is currently operational and has been used since April 1996 for OH column measurements at New Mexico Tech, Socorro, NM. Title for both instruments was transferred from Florida Atlantic University to New Mexico Tech in February of 1996. Comparative measurements from the two instruments for April-July 1996 indicate small differences in OH column abundances, with New Mexico (34 deg N) abundances about 10% above Colorado (40 deg N) values for comparable solar zenith angles. A more detailed comparison will require at least one full year of data from both locations. New Mexico measurements were obtained on June 10, 1996, concurrently with a balloon launch of the NASA STRAT mission from Fort Sumner, New Mexico. We hope to make use of STRAT measurements H2O, CH4, and O3 which are particularly relevant to OH photochemistry. Additional work at New Mexico Tech involves a comparison of P(sub 1)(1) and Q(sub 1)(3) absorption by the method of Doppler shift of solar limb spectra. These are being used to infer path weighted temperatures and for validations studies on the standard method of analysis using the single P(sub 1)(1) line. Results were presented at the Fall 1996 AGU meeting (abstract attached). A graduate student in the Physics Department at New Mexico Tech has been supported since August 1996. The student is investigating column OH behavior using the NCAR 2-D model of the middle atmosphere. Graduate student support was not available until the start of the second year (Nov. 15, 1996), therefore funds have been transferred from the allocation for the research associate, who resigned from the project July 1, 1996.

Minschwaner, Ken R.↗
NASA NTRS2012

Telescope Alignment From Sparsely Sampled Wavefront Measurements Over Pupil Subapertures

Alignment of two-element telescopes is a classic problem. During recent integration and test of the Space Interferometry Mission s (SIM s) Astrometric Beam Combiner (ABC), the innovators were faced with aligning two such telescope subsystems in the presence of a further complication: only two small subapertures in each telescope s pupil were accessible for measuring the wavefront with a Fizeau interferometer. This meant that the familiar aberrations that might be interpreted to infer system misalignments could be viewed only over small sub-regions of the pupil, making them hard to recognize. Further, there was no contiguous surface of the pupil connecting these two subapertures, so relative phase piston information was lost; the underlying full-aperture aberrations therefore had an additional degree of ambiguity. The solution presented here is to recognize that, in the absence of phase piston, the Zygo measurements primarily provide phase tilt in the subaperture windows of interest. Because these windows are small and situated far from the center of the (inaccessible) unobscured full aperture, any aberrations that are higher-order than tilt will be extremely high-order on the full aperture, and so not necessary or helpful to the alignment. Knowledge of the telescope s optical prescription allows straightforward evaluation of sensitivities (subap mode strength per unit full-aperture aberration), and these can be used in a predictive matrix approach to move with assurance to an aligned state. The technique is novel in every operational way compared to the standard approach of alignment based on full-aperture aberrations or searching for best rms wavefront. This approach is closely grounded in the observable quantities most appropriate to the problem. It is also more intuitive than inverting full phase maps (or subaperture Zernike spectra) with a ray-tracing program, which must certainly work in principle, but in practice met with limited success. Even if such classical alignment techniques became practical, the techniques reported here form a reassuringly transparent and intuitive check on the course of the alignment with very little computational effort.

Bloemhof, Eric E.↗
DOE OSTI2020

High-volume and -adiabat capsule (“HVAC”) ignition: Lowered fuel compression requirements using advanced Hohlraums

Lower-than-expected deuterium–tritium fuel areal densities have been experimentally inferred across a variety of high-convergence, nominally low-adiabat implosion campaigns at the National Ignition Facility (NIF) using cylinder-shaped Hohlraums [Hurricane et al., Phys. Plasmas 26, 052704 (2019)]. A leading candidate explanation is the presence of atomic mix between the fuel and ablator from hydrodynamic instability growth [Clark et al., Phys. Plasmas 26, 050601 (2019)], leading to reduced fuel compressibility and an effectively higher (in-flight) fuel adiabat α. Tolerating a high-α implosion can be obtained with significantly higher capsule absorbed energy Ecap according to the one-dimensional (1-D) ignition-threshold-factor analytic scaling [S. Atzeni and J. Meyer-ter-Vehn, Nucl. Fusion 41, 465 (2001)], ITF∼Ecap·α−1.8. Recent experiments with large Al shells in rugby-shaped Hohlraums have established high laser-capsule coupling efficiencies of ≽ 30% [Ping et al., Nat. Phys. 15, 138 (2019)], enabling a path to Ecap≽ 0.5 MJ at the NIF and increased performance margin M ≡ ITF − 1. The ability to operate at high adiabat with large capsules using nonstandard Hohlraums leads to the predicted onset of a volume-ignition mode, defined as when both the entire fuel is the “hot spot” and inertial confinement is principally provided by the ablator compared with the compressed fuel. Such an ignition mode, normally reserved for high-Z targets, e.g., double shells [Amendt et al., Phys. Plasmas 14, 056312 (2007)], is predicted to lead to lower fuel convergence and less exposure to mix due to the intended high adiabat—but at the expense of ∼3–4 × reduced (1-D) yield compared with conventional central hot-spot ignition designs.

Amendt, Peter (ORCID:0000000174021777)↗
DOE OSTI2021

The scalar chemical potential in cosmological collider physics

Non-analyticity in co-moving momenta within the non-Gaussian bispectrum is a distinctive sign of on-shell particle production during inflation, presenting a unique opportunity for the “direct detection” of particles with masses as large as the inflationary Hubble scale ( H ). However, the strength of such non-analyticity ordinarily drops exponentially by a Boltzmann-like factor as masses exceed H . In this paper, we study an exception provided by a dimension-5 derivative coupling of the inflaton to heavy-particle currents, applying it specifically to the case of two real scalars. The operator has a “chemical potential” form, which harnesses the large kinetic energy scale of the inflaton, \( {\overset{\cdot }{\phi}}_0^{1/2}\approx 60H \) , to act as an efficient source of scalar particle production. Derivative couplings of inflaton ensure radiative stability of the slow-roll potential, which in turn maintains (approximate) scale-invariance of the inflationary correlations. We show that a signal not suffering Boltzmann suppression can be obtained in the bispectrum with strength f NL ~ \( \mathcal{O} \) (0 . 01–10) for an extended range of scalar masses \( \lesssim {\overset{\cdot }{\phi}}_0^{1/2} \) , potentially as high as 10 15 GeV, within the sensitivity of upcoming LSS and more futuristic 21-cm experiments. The mechanism does not invoke any particular fine-tuning of parameters or breakdown of perturbation-theoretic control. The leading contribution appears at tree-level , which makes the calculation analytically tractable and removes the loop-suppression as compared to earlier chemical potential studies of non-zero spins. The steady particle production allows us to infer the effective mass of the heavy particles and the chemical potential from the variation in bispectrum oscillations as a function of co-moving momenta. Our analysis sets the stage for generalization to heavy bosons with non-zero spin.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2000

1DVAR Analysis of Temperature and Humidity Using GPS Radio Occultation Data

The Global Positioning System enables positioning in 3 dimensions about our planet. It has been operational since 1994. Twenty-four satellites are used to aclile\,e this performance. The signals sent by these satellites are electromagnetic waves travelling through our atmosphere down to the small receivers used by the civilian community and the military. Because of varying meteorological conditions (namely, temperature and humidity changes along the ray path), the rays do not travel in a straight line. They bend towards the surface. As a consequence, the ray path between two points is longer than a straight line, and the time it takes for a signal to travel this distance is longer. In 1995, a small GPS receiver was launched on a satellite (GPS/MET). It become possible to perform radio occultations around the Earth: the source - one of the 24 GPS satellites - is seen by the receiver as it rises or sets around the other side of the Earth. When the source disappears, the receiver progressively loses the signals. By measuring accurately the time delay between the emission and the reception of the signal, it is possible to infer which part of the delay is due to the atmosphere. We use GPS/MET data to retrieve temperature and humidity profiles simultaneously. A specific method is implemented: it combines information from numerical forecasts and GPS observations in an optimal way. Comparing the result with an independent source of observations (weather balloons), we demonstrate that GPS data have the potential to improve weather analyses. We also show that improved temperature and humidity profiles can be obtained using information from a forecast model. This confirms results obtained in this study using simulated data.

Poli, Paul↗
DOE OSTI2021

Characterization of Self-Magnetic Pinch (SMP) radiographic diode performance on RITS-6 at Sandia National Laboratories: 1) Diode Dynamics, DC Heating to extend Radiation Pulse

Radiographic diodes focus an intense electron beam to a small spot size to minimize the source area of energetic photons for radiographic interrogation. The self-magnetic pinch (SMP) diode has been developed as such a source and operated as a load for the RITS-6 Inductive Voltage Adder (IVA) driver. While experiments support the generally accepted conclusion that a 1:1 aspect diode (cathode diameter equals anode-cathode gap) delivers optimum SMP performance, such experiments also show that reducing the cathode diameter, while reducing spot size, also results in reduced radiation dose, by as much as 50%, and degraded shot reproducibility. Analyzation of the effective electron impingement angle on the anode converter with time made possible by a newly developed dose-rate array diagnostic indicates that fast-developing oscillations of the angle are correlated with early termination of the radiation pulse on many of the smaller-diameter SMP shots. This behavior as a function of relative cathode size persists through experiments with output voltages and currents up to 11.5 MV and 225 kA, respectively, and with spot sizes below ~ few mm. Since simulations to date have not predicted such oscillatory behavior, considerable discussion of the angle-behavior of SMP shots is made to lend credence to the inference. There is clear anecdotal evidence that DC heating of the SMP diode region leads to stabilization of this oscillatory behavior. This is the first of two papers on the performance of the SMP diode on the RITS-6 accelerator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1997

Applications of Data Assimilation to Analysis of the Ocean on Large Scales

It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height anomalies. Because of its potential for large scale synoptic coverage of the deep ocean, acoustic travel time data should be a natural complement to satellite altimetry. Satellite data give us vertical integrals associated with thermodynamic and dynamic processes.

Miller, Robert N.↗
NASA NTRSDate not supplied

Ground Testing of the MISSE-16 Materials

The harsh space environment imposes very stringent requirements upon spacecraft materials, especially those located on exterior surfaces of space objects in low Earth orbit (LEO). As humankind moves from space exploration to space commercialization, these materials may have to last for 15–20 years without considerable degradation of their optical, electrical, mechanical, and thermal properties. Operational requirements dictate that these materials continue to function according to expectations. Hence, we must understand the effects of the space environment on materials currently in use as well as on untested materials. For the years, the Materials International Space Station Experiment Flight Facility (MISSE-FF) have flown many different materials to investigate the effect of LEO space weather exposure on the performance and durability of materials and devices. The MISSE-16 scientific team members plan to launch fifteen different novel and well-characterized spacecraft-relevant materials to the LEO environment for a duration of six months during the MISSE-16 mission. Changes in spectral reflectivity will be measured throughout the mission as the samples are exposed to the space environment. The same chemical damage the produces changes in optical reflectance also causes changes in numerous other physical properties such as electrical conductivity, mechanical strength, and chemical reactivity. Correlation of the changes in each of these properties as a function of radiation type, flux, and fluence allows various material properties to be inferred from one experimentally tractable measurement: color change. Flying identical test fixtures on the ram, zenith, and wake positions of the MISSE-FF, collecting spectrally resolved images of the materials, and concomitantly measuring the ambient space environment allows deconvolution of the material effects which occur as a result of exposure to neutral atomic oxygen (AO), unfiltered solar ultraviolet (UV) radiation, and electrons. The flight experiment will function as a ground truth reference for our team’s ongoing laboratory-based space weather-material interactions experiments. Comparison of the MISSE-16 data with extensive testing of “flight-duplicate” samples under simulated space weather conditions will enable development of chemical models for prediction of material degradation. This paper discusses preliminary results from the ground test campaign including measurements of UV/VIS/IR reflectance spectra, bidirectional reflectance distribution function (BRDF), surface potential decay, and surface morphology under electron and AO exposure.

Elena A. Plis↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗