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Results for “predictive state estimation”

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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Low-lying dipole response of Ni 64

In this work, two complementary real-photon scattering experiments were conducted on the proton-magic 64 Ni nucleus to study the dipole response up to its neutron-separation energy of $S_n$ = 9.7 MeV. By combining both measurements, 87 $E$1 and 23 $M$1 transitions were identified above 4.3 MeV. The results of the observed $M$1 transitions were compared to shell-model calculations using two different model spaces. It was found that the inclusion of excitations across the $Z$ = 28 shell gap in the calculations has a large impact. Furthermore, average cross sections for decays to the ground state (elastic transitions) as well as to lower-lying excited states (inelastic decays) were determined. The corresponding $E$1 channel was compared to calculations within the relativistic equation of motion (REOM) framework. Whereas the calculations of highest possible complexity reproduce the fragmentation and overall behavior of the $E$1 average elastic cross section well, the predicted absolute cross sections are approximately twice as high as the experimental upper limits even though the latter also include an estimate of the inelastic-decay channel.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

DNA methylation–based age prediction and sex-specific epigenetic aging in a lizard with female-biased longevity

Sex differences in life span are widespread across animal taxa, but their causes remain unresolved. Alterations to the epigenome are hypothesized to contribute to vertebrate aging, and DNA methylation–based aging clocks allow for quantitative estimation of biological aging trajectories. Here, we investigate the influence of age, sex, and their interaction on genome-wide DNA methylation patterns in the brown anole (Anolis sagrei), a lizard with pronounced female-biased survival and longevity. We develop a series of age predictor models and find that, contrary to our predictions, rates of epigenetic aging were not slower in female lizards. However, methylation states at loci acquiring age-associated changes appear to be more “youthful” in young females, suggesting that female DNA methylomes are preemptively fortified in early life in opposition to the direction of age-related drift. Collectively, our findings provide insights into epigenetic aging in reptiles and suggest that early-life epigenetic profiles may be more informative than rates of change for predicting sex biases in longevity.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Hidden Markov model analysis of force/torque information in telemanipulation

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

Hannaford, Blake↗
NASA NTRS1994

An evaluation of the regional acid deposition model surface module for ozone uptake at three sites in the San Joaquin Valley of California

Plants and soils act as major sinks for the destruction of tropospheric ozone, especially during daylight hours when plant stomata open and are thought to provide the dominant pathway for the uptake of ozone. The present study, part of the California Ozone Deposition Experiment, compares predictions of the regional acid deposition model ozone surface conductance module with surface conductance data derived from eddy covariance measurements of ozone flux taken at a grape, a cotton, and a grassland site in the San Joaquin Valley of California during the summer of 1991. Results indicate that the model (which was developed to provide long-term large-area estimates for the eastern United States) significantly overpredicts the surface conductance at all times of the day for at least two important types of plant cover of the San Joaquin Valley and that it incorrectly partitions the ozone flux between transpiring and nontranspiring components of the surface at the third site. Consequently, the model either overpredicts or inaccurately represents the observed deposition velocities. Other results indicate that the presence of dew does not reduce the rate of ozone deposition, contradicting to model assumptions, and that model assumptions involving the dependency of stomata upon environmental temperature are unnecessary. The effects of measurement errors and biases, arising from the presence of the roughness sublayer and possible photochemical reactions, are also discussed. A simpler model for ozone surface deposition (at least for the San Joaquin Valley) is proposed and evaluated.

Massman, W. J.↗
NASA NTRS2012

Model-Based Method for Sensor Validation

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

Vatan, Farrokh↗
DOE OSTI2025

General signals for charged lepton flavor violating decays

We explore the most general phenomenology of charged lepton flavor violating (CLFV) decays of muon and tau leptons to the three body final states ($\bar{e}ee, \bar{μ}μμ, \bar{e}μμ, \bar{μ}μe, \bar{μ}ee, \bar{e}eμ$). By constructing a complete basis of operators at each dimension, we derive the most general amplitudes for these decay processes. By considering constraints from unitarity and Large Electron-Positron Collider (LEP), we show that operators of mass dimension 6 and 7 are the most likely to be observed in next generation experiments. Focusing on these dimensions, we compute the results of unpolarized (spin-averaged) decays parametrized in terms of the invariant masses of the daughter particles. We also compute the differential decay rates for polarized decays, in anticipation of the experimental search Mu3e, which expects to have a muon beam with ∼ 90% polarization, and the chiral Belle proposal, which aims to have a 70% polarized electron beam. To determine the extent to which the operators may be distinguished experimentally, we plot the differential distributions for each operator, showing that they leave only a few possible degenerate explanations. Through a statistical analysis, we estimate the number of events needed to break the degeneracies using the angular information. These results are adapted to treat ℓ → ℓ′⁢$v\bar{v}$, where the angular distribution of the outgoing charged lepton has enhanced distinguishing power. With many Standard Model extensions predicting these CLFV decays, these results will better enable upcoming searches to identify and/or constrain physics beyond the Standard Model.

phenomenology↗
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2026

Temperature and conductivity in shock compressed bridgmanite MgSiO 3 up to 2 TPa

The melting behavior and transport properties of MgSiO 3 at multi-megabar pressures remain poorly constrained despite their importance for high-pressure silicate physics. Here we report the first direct measurements of temperature and optical reflectivity in shock-compressed bridgmanite (MgSiO 3 ) using laser-driven decaying shock compression combined with velocimetry and optical pyrometry. Temperature and reflectivity data spanning approximately 4000–60 000 K were used to constrain the MgSiO 3 melting curve and to infer its electrical conductivity. We find that the MgSiO 3 melting curve becomes shallower than that of iron above 400 GPa, yielding lower melting temperatures in planetary mantles than predicted by several previous theoretical estimates. Across the solid-liquid transition, the inferred electrical conductivity increases significantly, reaching ∼2000 Ω cm −1 . These results provide experimental benchmarks for theoretical models of silicate melting and transport under extreme pressure-temperature conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Diffusion coefficients predicting facet-dependent crystallization in amorphous silicon nitride

Amorphous silicon nitride is a common material in microelectronics devices, which acts as an insulating barrier. Extended annealing times at elevated temperature can initiate crystallization of α-Si 3 ⁢ N 4 , which does not possess the same barrier properties. Molecular dynamics can resolve the fundamental mechanism for α-Si 3 ⁢N 4 crystallization and the influence of local environments. Here, we compare two interatomic potentials and conclude that these models predict structural features (e.g., angular distributions and densities) which span the range of experimental measurements. We confirmed these models reproduce experimental estimates of activation energy and leveraged these models to identify crystallization drivers. We conclude that near-T g , facet-dependent silicon nitride crystal growth rates can be predicted directly by either bulk or interfacial diffusion properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Jet substructure from dark sector showers

We examine the robustness of collider phenomenology predictions for a dark sector scenario with QCD-like properties. Pair production of dark quarks at the LHC can result in a wide variety of signatures, depending on the details of the new physics model. A particularly challenging signal results when prompt production induces a parton shower that yields a high multiplicity of collimated dark hadrons with subsequent decays to Standard Model hadrons. The final states contain jets whose substructure encodes their non-QCD origin. This is a relatively subtle signature of strongly coupled beyond the Standard Model dynamics, and thus it is crucial that analyses incorporate systematic errors to account for the approximations that are being made when modeling the signal. We estimate theoretical uncertainties for a canonical substructure observable designed to be sensitive to the gauge structure of the underlying object, the two-point energy correlator $ {e}_2^{\left(\beta \right)} $ , by computing envelopes between resummed analytic distributions and numerical results from Pythia. We explore the separability against the QCD background as the confinement scale, number of colors, number of flavors, and dark quark masses are varied. Additionally, we investigate the uncertainties inherent to modeling dark sector hadronization. Simple estimates are provided that quantify one’s ability to distinguish these dark sector jets from the overwhelming QCD background. Such a search would benefit from theory advances to improve the predictions, and the increase in statistics using the data to be collected at the high luminosity LHC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Stability, growth, and doping of In 2 (Si, Ge) 2 O 7 : Promising n -type wide-bandgap semiconductors

In this paper, we investigate, computationally and experimentally, the phase stability, electronic structure properties, and the propensity for n-type doping of In 2 X 2 O 7 (X = Si, Ge) ternary oxides. This family of materials contains promising novel wide-gap semiconductors based on their estimated high n-type Baliga figures of merit and acceptable thermal conductivity for power electronics applications. Here, we predict that both In 2 Si 2 O 7 and In 2 Ge 2 O 7 are n-type dopable, with Zr providing between 10 16 and above 10 21 cm −3 net donor concentrations under O-poor conditions, depending on the chemistry, structure (ground-state thortveitite or high-pressure pyrochlore), and synthesis temperature. To verify our predictions, we synthesize Zr-doped In 2 Ge 2 O 7 in the thortveitite structure and measure its electrical properties. Initial thin-film growth and annealing lead to polycrystalline thin films with bandgaps over 4 eV and confirm Zr doping predictions by achieving electron concentrations at 10 14 –10 16 cm −3 even under O-rich conditions. While future epitaxial growth development is still needed, this study establishes In 2 X 2 O 7 as promising n-type wide-gap semiconductors for power electronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Competing magnetic phases in Li-Fe-Ge kagome systems

Competing interlayer magnetic interactions in kagome magnets can lead to diverse magnetic phases, which enable promising topological or quantum material properties. In this work, the electronic structure and magnetic properties are studied using first-principles calculations for the Li⁢Fe6⁢G⁡e6, Li⁢Fe6⁢G⁡e4, and Li⁢Fe6⁢G⁡e5 compounds sharing the kagome F⁡e3⁢Ge layer motif but with different interlayer arrangements. For Li⁢Fe6⁢G⁡e4 and Li⁢Fe6⁢G⁡e5, the predicted magnetic ground states are collinear antiferromagnetic (AFM) states involving a mix of ferromagnetic (FM) and AFM interlayer orientations. In contrast, for Li⁢Fe6⁢G⁡e6, an incommensurate cycloidal spin spiral ground state is stabilized, although its energy is close to a collinear A-type AFM state. The analysis of magnetic RKKY exchange coupling confirms the results of calculations. The values of atomic magnetic moments are in good agreement with existing experimental estimations. Our experiments on Li⁢Fe6⁢G⁡e6 single crystals reveals AFM ordering at ∼540 K and transition to another magnetic phase with a small FM component, possibly with spin canting, below ∼270 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1972

The excitation of O2 in auroras.

Newly measured electron impact cross sections for excitation of the a super 1 Delta sub g and b super 1 Sigma (plus) sub g electronic states of O2 have been employed to predict the absolute volume emission rates from these states under auroral conditions. A secondary electron flux typical of an IBC II nighttime aurora was used, and the most important quenching processes were included in the calculations. The new excitation cross sections for the a super 1 Delta sub g and b super 1 Sigma (plus) sub g states are more than an order of magnitude larger than previous estimates and lead to correspondingly greater intensities in the atmospheric and IR atmospheric band systems. The calculated intensity ratios of the volume emission rates of 7621 A and 1.27 micron to that for 3914 A are smaller than those obtained from aircraft observations and recent rocket experiments.

Cartwright, D. C.↗
NASA NTRS1984

Dynamic compression and volatile release of carbonates

Particle velocity profiles upon shock compression and isentropic releases were measured for polycrystalline calcite. The Solenhofen limestone release paths lie, close to the Hugoniot. Calcite 3 to 2 transition, upon release, was observed, but rarefaction shocks were not detected. The equation of state is used to predict the fraction of material devolatilized upon isentropic release as a function of shock pressure. The effect of ambient partial pressure of CO2 on the calculations is demonstrated and considered in models of atmospheric evolution by impact induced mineral devolatilization. The radiative characteristics of shocked calcite indicate that localization of thermal energy occurs under shock compression. Shock entropy calculations result in a minimum estimate of 90% devolatilization upon complete release from 10 GPa. Isentropic release paths from calculated continuum Hugoniot temperatures cross into the CaO (solid) + CO2 (vapor) field at improbably low pressures. It is found that release paths from measured shock temperatures cross into the melt plus vapor field at pressures greater than .5 GPa, which suggests that devolatilization is initiated at the shear banding sites.

Tyburczy, J. A.↗
NASA NTRS1986

Dynamic compression and volatile release of carbonates

Particle velocity profiles upon shock compression and isentropic releases were measured for polycrystalline calcite. The Solenhofen limestone release paths lie close to the Hugoniot. Calcite 3 to 2 transition, upon release was observed, but rarefaction shocks were not detected. The equation of state is used to predict the fraction of material devolatilized upon isentropic release as a function of shock pressure. The effect of ambient partial pressure of CO2 on the calculations is demonstrated and considered in models of atmospheric evolution by impact induced mineral devolatilization. The radiative characteristics of shocked calcite indicate that localization of thermal energy occurs under shock compression. Shock entropy calculations result in a minimum estimate of 90 percent devolatilization upon complete release from 10 GPa. Isentropic release paths from calculated continuum Hugoniot temperatures cross into the CaO (solid) + CO2 (vapor) field at improbably low pressures. It is found that release paths from measured shock temperatures cross into the melt plus vapor field at pressures greater than .5 GPa, which suggests that devolatilization is initiated at the shear banding sites.

Tyburczy, J. A.↗
NASA NTRS1994

TDRS orbit determination by radio interferometry

In support of a NASA study on the application of radio interferometry to satellite orbit determination, MITRE developed a simulation tool for assessing interferometry tracking accuracy. The Orbit Determination Accuracy Estimator (ODAE) models the general batch maximum likelihood orbit determination algorithms of the Goddard Trajectory Determination System (GTDS) with the group and phase delay measurements from radio interferometry. ODAE models the statistical properties of tracking error sources, including inherent observable imprecision, atmospheric delays, clock offsets, station location uncertainty, and measurement biases, and through Monte Carlo simulation, ODAE calculates the statistical properties of errors in the predicted satellites state vector. This paper presents results from ODAE application to orbit determination of the Tracking and Data Relay Satellite (TDRS) by radio interferometry. Conclusions about optimal ground station locations for interferometric tracking of TDRS are presented, along with a discussion of operational advantages of radio interferometry.

Pavloff, Michael S.↗
NASA NTRS2012

Statistical Orbit Determination using the Particle Filter for Incorporating Non-Gaussian Uncertainties

The tracking of space objects requires frequent and accurate monitoring for collision avoidance. As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full probability density function (PDF) of the random orbit state. Through representing the full PDF of the orbit state for orbit maintenance and collision avoidance, we can take advantage of the statistical information present in the heavy tailed distributions, more accurately representing the orbit states with low probability. The classical methods of orbit determination (i.e. Kalman Filter and its derivatives) provide state estimates based on only the second moments of the state and measurement errors that are captured by assuming a Gaussian distribution. Although the measurement errors can be accurately assumed to have a Gaussian distribution, errors with a non-Gaussian distribution could arise during propagation between observations. Moreover, unmodeled dynamics in the orbit model could introduce non-Gaussian errors into the process noise. A Particle Filter (PF) is proposed as a nonlinear filtering technique that is capable of propagating and estimating a more complete representation of the state distribution as an accurate approximation of a full PDF. The PF uses Monte Carlo runs to generate particles that approximate the full PDF representation. The PF is applied in the estimation and propagation of a highly eccentric orbit and the results are compared to the Extended Kalman Filter and Splitting Gaussian Mixture algorithms to demonstrate its proficiency.

Mashiku, Alinda↗