Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Adaptation models”

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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.

Data Assimilation↗
DOE OSTI2024

Quantum mechanical model of crossing and anti-crossing points in 3D full-band Monte Carlo simulations

This work presents a 3D quantum mechanics based model to address the physics at band structure crossing/anti-crossing points in full band Monte Carlo (FBMC) simulations. The model solves the Krieger and Iafrate (KI) equations in real time using pre-computed coefficients at k-points spatially sampled within the first Brillouin zone. Solving the KI equations in real time makes this model applicable for all electric fields, which enables its use in FBMC device simulations. In this work, a two-level refinement scheme is used to aggressively sample regions in proximity to band crossings for accurate solutions to the KI equations and coarsely sample everywhere else to limit the number of k-points used. The presented sampling method is demonstrated on the band structure of silicon but is effective for the band structure of any semiconductor material. Next, the adaptation of the fully quantum KI model into the semi-classical FBMC method is discussed. Finally, FBMC simulations of hole transport in 4H silicon carbide with and without the KI model are performed. Results along different crystallographic directions for a wide range of electric fields are compared to previously published simulation and experimental values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Security and Trust Metrics for Edge Computing

The present state of edge computing is an environment of different computing capabilities connected via a wide variety of communication paths. The energy grid is relying upon distributed energy devices connected at the edge of the internet. Consider the scenario where each edge device is customer-owned distributed energy resource (DER) that is connected via a trustworthy link to a grid service provider. Each DER keeps a local simple trust record of interactions. Information protection is provided by internet https standards, however, trust must be evaluated throughout operation. This paper presents a model for representing and evaluating trust in general and applied to the energy grid as a key example. Actors on the edge may interact with each other as well as with a central datacenter.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Adaptive Patching for High-resolution Image Segmentation with Transformers

Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear sequence of tokens. For high-resolution images, e.g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation. The solution is to either use custom complex multi-resolution models or approximate attention schemes. We take inspiration from Adapative Mesh Refinement (AMR) methods in HPC by adaptively patching the images, as a pre-processing step, based on the image details to reduce the number of patches being fed to the model, by orders of magnitude. This method has a negligible overhead, and works seamlessly with any attention-based model, i.e. it is a pre-processing step that can be adopted by any attention-based model without friction. We demonstrate superior segmentation quality over SoTA segmentation models for realworld pathology datasets while gaining a geomean speedup of 6.9× for resolutions up to 64K2, on up to 2, 048 GPUs.

Zhang, Enzhi↗
DOE OSTI2020

The Separation and Hα Contrasts of Massive Accreting Planets in the Gaps of Transitional Disks: Predicted Hα Protoplanet Yields for Adaptive Optics Surveys

We present a massive accreting gap planet model that ensures large gaps in transitional disks are kept dust free by the scattering action of three coplanar quasi-circular planets in a 1:2:4 mean motion resonance (MMR). This model uses the constraint of the observed gap size, and the dust-free nature of the gap, to determine within ∼10% the possible orbits for three massive planets in an MMR. Calculated orbits are consistent with the observed orbits and Hα emission (the brightest line to observe these planets) for LkCa 15 b, PDS 70 b, and PDS 70 c within observational errors. Moreover, the model suggests that the scarcity of detected Hα planets is likely a selection effect of the current limitations of non-coronagraphic, low (<10%) Strehl, Hα imaging with adaptive optics (AO) systems used in past Hα surveys. We predict that as higher Strehl AO systems (with high-performance custom coronagraphs; like the 6.5 m Magellan Telescope MagAO-X system) are utilized at Hα, the number of detected gap planets will substantially increase by more than tenfold. For example, we show that >25 ± 5 new Hα “gap planets” are potentially discoverable by a survey of the best 19 transitional disks with MagAO-X. Detections of these accreting protoplanets will significantly improve our understanding of planet formation, planet growth and accretion, solar system architectures, and planet–disk interactions.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1996

Theoretical model of HZE particle fragmentation by hydrogen targets

The fragmenting of high energy, heavy ions (HZE particles) by hydrogen targets is an important, physical process in several areas of space radiation research. In this work quantum mechanical optical model methods for estimating cross sections for HZE particle fragmentation by hydrogen targets are presented. The cross sections are calculated using a modified abrasion-ablation collision formalism adapted from a nucleus-nucleus collision model. Elemental and isotopic production cross sections are estimated and compared with reported measurements for the breakup of neon, sulphur, and iron, nuclei at incident energies between 400 and 910 Mev/nucleon. Good agreement between theory and experiment is obtained.

Townsend, L.W.↗
DOE OSTI2024

DESS (Differential Equation System Solver) [SWR-24-48]

The Differential Equation System Solver (DESS) is a Rust crate implementing fixed-step and adaptive-step solvers and designed especially for modeling physical systems. Seven explicit ordinary differential equation (ODE) solver methods have been added so far: Euler’s, Heun’s, Midpoint, Ralston’s, Classic Runge-Kutta, Bogacki-Shampine, and Cash-Karp. These comprise five fixed-step methods and two adaptive-step methods. Few solver packages are implemented in the Rust ecosystem and none are intended specifically for physical system modeling, so the goal of DESS is to create a Rust ODE solver crate designed to easily specify and model physical systems with modular, configurable solver options. In addition to allowing users to directly input equations to solve, DESS allows users to optionally specify and define relationships between nodes in their system, which the package then translates into a system of equations via the Rust macro system, leading to simpler and more intuitive code.

Steuteville, Robin↗
NASA NTRS1994

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar

This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.

Lai, Jonathan Y.↗
NASA NTRS1992

Computational technology for high-temperature aerospace structures

The status and some recent developments of computational technology for high-temperature aerospace structures are summarized. Discussion focuses on a number of aspects including: goals of computational technology for high-temperature structures; computational material modeling; life prediction methodology; computational modeling of high-temperature composites; error estimation and adaptive improvement strategies; strategies for solution of fluid flow/thermal/structural problems; and probabilistic methods and stochastic modeling approaches, integrated analysis and design. Recent trends in high-performance computing environment are described and the research areas which have high potential for meeting future technological needs are identified.

Noor, A. K.↗
NASA NTRS1987

Influence of the g-force on the circumnutations of sunflower hypocotyls

Circumnutations of hypocotyls of sunflower (Helianthus annuus L. cv. Californicus) were studied under 1 g and 3 g conditions. Root mean square values of the hypocotyl deviation from the plumbline and period of the movements were determined from calculations of the autocorrelation functions of the movements. The amplitude and the period of the circumnutations increased under 3 g as compared to 1 g. A transition from 3 to 1 g or vice versa also caused changes in period and amplitude of the movements. The results are interpreted as a support for the idea that gravity influences the circumnutation parameters in this sunflower variety. A comparison is made with published results on the dwarf sunflower cv. Teddy Bear where the force influence is very small or negligible. Simulations of a model for circumnutations show movements which are in qualitative agreement with the experimental results, provided adaptation to g-levels is included in the model. Finally, the results are discussed with the recent Spacelab-experiment (SL1) as a background.

NASA Discipline Number 40-30↗
NASA NTRSDate not supplied

The Effect of Stochastic Acceleration on the Dynamics of Solar Energetic Particles in the Heliosphere

Solar Energetic Particles (SEPs) are crucial in space weather phenomena, impacting space-based technologies and human activities in space. Understanding the dynamics of SEPs, including their acceleration and transport mechanisms, is essential for improving predictive models and mitigating adverse effects. This study investigates the impact of stochastic acceleration (SA) on the dynamics of SEPs within the heliosphere. By integrating stochastic acceleration into a SEP transport model, we assess its impact relative to diffusive shock acceleration (DSA). The investigation utilizes the Adaptive Mesh Particle Simulator (AMPS) within the Space Weather Modeling Framework (SWMF) to simulate SEP dynamics, incorporating solar wind parameters and turbulence from the Alfven Wave Solar Model (AWSoM). To do this, we solve the focused transport equation to model the propagation of SEPs along magnetic field lines, extending these lines to 5 AU to incorporate the potential effects of pitch angle scattering beyond 1 AU. This aspect could notably impact the decay phase of a SEP event. Modeling the transport of SEPs is coupled with the simulation of solar wind dynamics, the interplanetary magnetic field, and the characteristics of Alfven wave turbulence. This presentation covers the modeling approach used in this research, the characterization of the impact of the stochastic acceleration, and the role of pitch angle scattering at various heliocentric distances on the dynamics of the SEP events' decay phase.

SEP↗
DOE OSTI2026

MSD CoP Webinar: Modeling the Operations of Reservoir Systems with LLMs and Inverse Reinforcement Learning

Context: This panel featured three presentations centered on the common theme of applying LLMs and inverse reinforcement learning (IRL) to capture the complex human-environment interactions that are central to the operation of reservoir systems. Dr. Wyatt Arnold will kick off the webinar with a talk on how analyzing LLM chain-of-thought reasoning reveals sophisticated quantitative justification and risk awareness, showing promise as a bridge between quantitative models and value-driven water management decisions. Next, Dr. Matteo Giuliani will build on this with a discussion demonstrating that AI- and IRL-driven approaches can infer the trade-offs between flood control and water supply using historical observations. Finally, Dr. Rohan Singh Wilkho will close the webinar with a talk establishing IRL as a generalizable diagnostic tool for decoding decision-making in managed hydrologic and human-infrastructure systems. Across the three presentations, the application of LLMs and IRL opens new possibilities for the development of adaptive, transparent, and human-aware models supporting water management in an increasingly uncertain future. Presenters: Wyatt Arnold (Politecnico di Milano); Matteo Giuliani (Politecnico di Milano); Rohan Singh Wilkho (Cornell University) Moderator: Patrick M. Reed (MSD CoP Facilitation Team); Stefano Galelli (MSD CoP AI Working Group Co-Chair); David Gold (MSD CoP AI Working Group Co-Chair) This webinar was held on: June 23rd, 2026 from 12-1 PM EST.

Arnold, Wyatt [Politecnico di Milano]↗
DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗
NASA NTRS1994

(abstract) SEQ_POINTER: Next Generation, Planetary Spacecraft Remote Sensing Science Observation Design Tool

Implementation of a multimission tool, SEQ_POINTER, under the auspices of the JPL Multimission Operations Systems Office (MOSO) is in progress. This version has been designed to address the limitations experienced on previous versions when being adopted to a new mission and spacecraft. The tool has been modularly designed with subroutine interface structures to support interchangeable celestial body and spacecraft definition models. The computational and graphics modules have also been designed to interface with data collected from previous spacecraft, or on-going observations, which describe the surface of each target body. These enhancements make SEQ_POINTER a candidate for low-cost mission usage, when a remote sensing science observation design capability is required.

remote↗
NASA NTRS1993

Controlling a truck with an adaptive critic temporal difference CMAC design

In this study, CMAC (Cerebellar Model Articulated Controller) neural architectures are shown to be viable for the purposes of real-time learning and control. An adaptive critic temporal difference neurocontrol design has been implemented that learns in real-time how to back up a trailer truck along a fixed straight line trajectory. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neurocontrollers on-line in real-time on a MS-DOS PC 386.

Shelton, Robert O.↗
NASA NTRSDate not supplied

The Influence of Adaptive Mesh Refinement on the Prediction of Vortex Interactions about a Generic Missile Airframe

The complex interaction of forebody and wing vortices significantly impacts missile aerodynamics. The formation of these vortices involves smooth regions of the geometry or geometric discontinuities like leading edges, trailing edges, tips, and corners. Regions of supersonic flow and complex shock topologies interact with boundary layers and vortices. Smooth-body separation and 3D viscous effects strain current Reynolds-averaged Navier-Stokes (RANS) techniques. The quantification and control of discretization error is critical to obtaining reliable simulation results and often turbulence model assessments are made in the presence of unquantified (and potentially large) discretization errors. Two mesh adaptation schemes are applied to steady RANS simulations. Multiscale unstructured mesh adaptation is applied to control interpolation error estimates of the Mach field, which resolves boundary layers, vortices, and shocks. A dual-mesh approach with overset communication is applied between an expert-crafted near-body unstructured mesh and an adaptive off-body Cartesian mesh refined with Q-criterion scaled by the strain tensor magnitude. A generic missile configuration is examined in a supersonic flow field to show the interaction of mesh adaptation and turbulence model. Turbulence model modifications for rotational correction and a quadratic constitutive relationship show a strong influence on adaptive mesh refinement and predicted rolling moment.

slender body aerodynamics↗
NASA NTRS1991

Adaptive snakes - Control of damping and material parameters

The stability of active contour models or 'snakes' is studied. It is shown that the modification of snake parameters using adaptive systems improves both the stability of the snakes and the boundaries obtained. The adaptive snakes perform better with images of varying contrasts, noisy images and images with different curvatures along the boundaries. The computational costs at each iteration for the adaptive snakes is still of order N, where N is the number of points on the snakes. Comparisons of the results for non-adaptive and adaptive snakes are shown using both computer simulations and satellite images.

Samadani, Ramin↗
NASA NTRS2015

A CFD-Based Study of the Feasibility of Adapting an Erosion Burner Rig for Examining the Effect of CMAS Deposition Corrosion on Environmental Barrier Coatings

Thermodynamic and computational fluid dynamics modeling has been conducted to examine the feasibility of adapting the NASA-Glenn erosion burner rigs for use in studies of corrosion of environmental barrier coatings by the deposition of molten CMAS. The effect of burner temperature, Mach number, particle preheat, duct heating, particle size, and particle phase (crystalline vs. glass) were analyzed. Detailed strategies for achieving complete melting of CMAS particles were developed, thereby greatly improving the probability of future successful experimental outcomes.

Deposition Studies↗