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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Computational Modeling as a Design Tool in Microelectronics Manufacturing

Plans to introduce pilot lines or fabs for 300 mm processing are in progress. The IC technology is simultaneously moving towards 0.25/0.18 micron. The convergence of these two trends places unprecedented stringent demands on processes and equipments. More than ever, computational modeling is called upon to play a complementary role in equipment and process design. The pace in hardware/process development needs a matching pace in software development: an aggressive move towards developing "virtual reactors" is desirable and essential to reduce design cycle and costs. This goal has three elements: reactor scale model, feature level model, and database of physical/chemical properties. With these elements coupled, the complete model should function as a design aid in a CAD environment. This talk would aim at the description of various elements. At the reactor level, continuum, DSMC(or particle) and hybrid models will be discussed and compared using examples of plasma and thermal process simulations. In microtopography evolution, approaches such as level set methods compete with conventional geometric models. Regardless of the approach, the reliance on empricism is to be eliminated through coupling to reactor model and computational surface science. This coupling poses challenging issues of orders of magnitude variation in length and time scales. Finally, database development has fallen behind; current situation is rapidly aggravated by the ever newer chemistries emerging to meet process metrics. The virtual reactor would be a useless concept without an accompanying reliable database that consists of: thermal reaction pathways and rate constants, electron-molecule cross sections, thermochemical properties, transport properties, and finally, surface data on the interaction of radicals, atoms and ions with various surfaces. Large scale computational chemistry efforts are critical as experiments alone cannot meet database needs due to the difficulties associated with such controlled experiments and costs.

Meyyappan, Meyya↗
NASA NTRS2002

High-Resolution Gravity and Time-Varying Gravity Field Recovery using GRACE and CHAMP

This progress report summarizes the research work conducted under NASA's Solid Earth and Natural Hazards Program 1998 (SENH98) entitled High Resolution Gravity and Time Varying Gravity Field Recovery Using GRACE (Gravity Recovery and Climate Experiment) and CHAMP (Challenging Mini-satellite Package for Geophysical Research and Applications), which included a no-cost extension time period. The investigation has conducted pilot studies to use the simulated GRACE and CHAMP data and other in situ and space geodetic observable, satellite altimeter data, and ocean mass variation data to study the dynamic processes of the Earth which affect climate change. Results from this investigation include: (1) a new method to use the energy approach for expressing gravity mission data as in situ measurements with the possibility to enhance the spatial resolution of the gravity signal; (2) the method was tested using CHAMP and validated with the development of a mean gravity field model using CHAMP data, (3) elaborate simulation to quantify errors of tides and atmosphere and to recover hydrological and oceanic signals using GRACE, results show that there are significant aliasing effect and errors being amplified in the GRACE resonant geopotential and it is not trivial to remove these errors, and (4) quantification of oceanic and ice sheet mass changes in a geophysical constraint study to assess their contributions to global sea level change, while the results improved significant over the use of previous studies using only the SLR (Satellite Laser Ranging)-determined zonal gravity change data, the constraint could be further improved with additional information on mantle rheology, PGR (Post-Glacial Rebound) and ice loading history. A list of relevant presentations and publications is attached, along with a summary of the SENH investigation generated in 2000.

Shum, C. K.↗
NASA NTRS2005

Enabling Pinpoint Landing on Mars

This slide presentation reviews the entry, descent and landing (EDL) of a pinpoint landing (PPL) on Mars. These PPL missions will be required to deliver about 1000 kg of useful payload to the surface of Mars, therefore soft landings are of primary interest. The landing sites will be in a mid to to high latitude with possible sites about 2.5 km above the martian mean surface altitude. The applicable EDL is described and reviewed in phases. The evaluation approach is reviewed and the requirements for an accurate landing are reviewed. The descent of the unguided aeroshell entry phase dispersion due to trajectory and ballistic coefficient variations are shown in charts. These charts view the dispersions from three entries, Entry from orbit, and two types of direct entry. There is discussion of the differences in steerable subsonic parachute control vs dispersions, and the propulsive phase delta velocity vs dispersions. Data is presented for the three trajectory phases (i.e., Aeroshell, supersonic and subsonic chute) for Direct entry and low orbit entry. The results of the analysis is presented, including possibilities for mitigation of dispersions. The analysis of the navigation error is summarized, and the trajectory biasing for martian winds is assessed.

aerodynamic↗
NASA NTRS2017

Remote Sensing of Multiple Cloud Layer Heights Using Multi-Angular Measurements

Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASAs airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross-correlations between this set and co-located sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allow retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSPs CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC(exp. 4)RS) campaign. RSP retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 nm and 1880 nm and their combination. The 1880-nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption.

Sinclair, Kenneth↗
NASA NTRS2017

A Celestial Reference Frame Based on Kalman Filtering

In this study, we investigate a novel approach to the determination of celestial reference frames (CRF). Instead of a constant model for radio sources positions, we adopt a time series representation, which allows temporal variations of radio source coordinates to be taken into account. In particular, the added flexibility is beneficial for radio sources with extended structure. We compute our time series-based CRF solutions by Kalman filtering and smoothing radio source positions, which are initially obtained from single-session VLBI analysis. The temporal resolution of the estimated CRF coordinates is identical to that of the input data, i.e. usually 1-4 days. The magnitude of the coordinate variations is controlled by the amount of process noise applied in the filter, which is in turn derived from analyzing the Allan standard deviation of the corresponding radio source coordinate time series. Measures have been developed to reduce the impact of observation errors and datum effects on the noise model.

Soja, B.↗
NASA NTRS2020

ANC of Radiated Sound from a Small UAS Rotor using Virtual Error Sensors

Small unmanned aerial systems (UAS) are becoming increasingly common for private, military, and commercial use, increasing community noise exposure. Reducing the noise produced by UAS could help improve community acceptance. Active noise control (ANC) might be used to attenuate noise produced by UAS, however, traditional ANC systems require a physical sensor in the far-field, which is not feasible. This paper assesses a virtual error sensing (VES) method that eliminates the need for a far-field sensor. This paper describes the proposed VES strategy, and presents numerical simulations and experimental results that highlight the benefits and limitations of the approach. Results for the VES system alone and with an ANC approach are presented and discussed. Experimental testing focused on attenuating the tonal noise produced by one 2-bladed rotor with a tip radius of 11.9 cm. Pressure variations caused by blade rotation were measured in the near and far-field using electret microphones and externally polarized condenser microphones, respectively. The filtered-x least mean squares algorithm was used in conjunction with the VES system to attenuate the far-field response. Experimental results show reductions between 6-13 dB at varying far-field locations and rotation rates.

virtual error sensor↗
NASA NTRS2015

GCR Simulator Reference Field and a Spectral Approach for Laboratory Simulation

The galactic cosmic ray (GCR) simulator at the NASA Space Radiation Laboratory (NSRL) is intended to deliver the broad spectrum of particles and energies encountered in deep space to biological targets in a controlled laboratory setting. In this work, certain aspects of simulating the GCR environment in the laboratory are discussed. Reference field specification and beam selection strategies at NSRL are the main focus, but the analysis presented herein may be modified for other facilities. First, comparisons are made between direct simulation of the external, free space GCR field and simulation of the induced tissue field behind shielding. It is found that upper energy constraints at NSRL limit the ability to simulate the external, free space field directly (i.e. shielding placed in the beam line in front of a biological target and exposed to a free space spectrum). Second, variation in the induced tissue field associated with shielding configuration and solar activity is addressed. It is found that the observed variation is likely within the uncertainty associated with representing any GCR reference field with discrete ion beams in the laboratory, given current facility constraints. A single reference field for deep space missions is subsequently identified. Third, an approach for selecting beams at NSRL to simulate the designated reference field is presented. Drawbacks of the proposed methodology are discussed and weighed against alternative simulation strategies. The neutron component and track structure characteristics of the simulated field are discussed in this context.

Slaba, Tony C.↗
DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of ferromagnetic electrode length and thickness on Magnetic Tunnel Junction-Based Molecular Spintronic Devices (MTJMSD)

A knowledge gap exists about the impact of variation in length and thickness of ferromagnetic(FM) electrodes on molecular spintronics devices’ ability to manifest molecular coupling impact. Magnetic Tunnel Junction-Based Molecular Spintronic Devices (MTJMSDs) are potential candidates for inventing highly correlated materials and devices and investigating the fundamental science of organic spintronics. This paper reports our experi- mental observations providing the dramatic impact of variation in ferromagnetic electrode length and thickness on paramagnetic molecule-based MTJMSD. Room temperature transport studies were performed to investigate the effect of FM electrode thickness. On the other hand, magnetic force microscopy measurements were con- ducted to understand the effect of FM electrode length extending beyond the molecular junction area, i.e., the site where paramagnetic molecules bridged between two FM. In the strong molecular coupling regime, transport study suggested thickness variation caused ~1000 to million-fold differences in junction conductivity. MFM study revealed near-zero magnetic contrast for pillar-shaped MTJMSD without any extended FM electrode. However, MFM images showed a multitude of microscopic magnetic phases on cross junction shaped MTJMSD where FM electrodes extended beyond the junction area. To understand the intriguing experimental results, we conducted an in-depth theoretical study using Monte Carlo Simulation (MCS) approach. MCS study utilized a Heisenberg atomic model of cross junction shaped MTJMSD to gain insights about room temperature transport and MFM experimental observations of microscopic MTJMSD. To make this study applicable for a wide variety of MTJMSDs, we systematically studied the effect of variation in molecular coupling strength between magnetic molecules and ferromagnetic (FM) electrodes of various dimensions. Here, our theoretical results suggest 25 atoms thick FM electrode show very weak molecular coupling impact and of the same order of experimentally studied FM electrode dimensions showing weak effect. Also, MCS results agree with experimentally observed multiple magnetic phases on lengthy FM electrodes of an MTJMSD.

42 ENGINEERING↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗
DOE OSTI2023

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Abstract In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the Large Hadron Collider (LHC), there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE’s limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in 18.30 ± 0.04 μ s , making it one of the fastest methods for this task up to now.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A detailed study of pre-heating effects in electron beam melting powder bed fusion process

Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.

36 MATERIALS SCIENCE↗
NASA NTRS1984

A variable structure approach to robust control of VTOL aircraft

This paper examines the application of variable structure control theory to the design of a flight control system for the AV-8A Harrier in a hover mode. The objective in variable structure design is to confine the state trajectories to a subspace of the total state space. The motion in this subspace is insensitive to system parameter variations and external disturbances that lie in the range space of the control. A switching type of control law results from the design procedure. The control system was designed to track a vector-valued velocity command. For comparison, a proportional controller was designed using optimal linear regulator theory. Both controllers were evaluated for their transient response performance using a linear model; then a nonlinear simulation study of a hovering approach to landing was conducted. The variable structure controller outperformed its linear counterpart in the presence of wind disturbances and plant parameter uncertainties afforded by the simulation.

Calise, A. J.↗
DOE OSTI2026

ML-Based Pebble Power Reconstruction for Pebble Bed Reactor Analysis

Pebble power reconstruction has been explored to complement the conventional homogenized modeling approach in pebble bed reactor (PBR) analysis, as detailed heterogeneous geometry calculations are computationally expensive. The random distribution of pebble fuels within the core challenges the application of conventional pin power reconstruction methods. To address this, we introduce a machine learning approach based on the transformer model, composed of encoder and decoder layers, to estimate the flux and power form functions for reconstructing individual pebble neutron fluxes and powers. The homogeneous neutron flux distribution within each spectral zone (SZ) is obtained from finite element solutions of global diffusion or transport calculations. Verification tests demonstrate that the trained transformer model accurately predicts power form functions over a range of conditions, including variations in pebble enrichment, location, type, SZ size, and burnup. In particular, verification using a three-dimensional PBR benchmark with burned pebbles shows good agreement in heterogeneous pebble power distributions between Griffin and Serpent. These results highlight the potential of applying conventional pin power reconstruction approaches to PBR cores with randomly distributed pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1980

A piloted simulator investigation of static stability and stability/control augmentation effects on helicopter handling qualities for instrument approach

A ground simulator experiment was conducted on the Flight Simulator for Advanced Aircraft at Ames Research Center to investigate the influence of several static stability and stability/control augmentation design parameters on helicopter flying qualities during terminal area operations in instrument conditions. Effects of light turbulence were included. Two levels of static stability in each rotational axis (pitch, roll, yaw) were examined for a hingeless rotor configuration. The variations in pitch and roll were: (1) stable and (2) neutral static stability; in yaw there were two stable levels. Four types of stability/control augmentation were also examined for the lower level of static stability in each axis. This latter investigation covered three helicopter rotor types: hingeless, articulated, and teetering. Four pilots performed a total of 105 evaluations of these parameters for a representative VOR instrument approach task. Pilot rating results indicate the acceptability of neutral static stability longitudinally and laterally and the need for pitch-roll attitude augmentation to achieve a satisfactory system.

Lebacqz, J. V.↗
DOE OSTI2023

Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution

Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.

Bretas, Arturo Suman↗