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Results for “Real Analytic Functions”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Microwave landing system modeling with application to air traffic control automation

Compared to the current instrument landing system, the microwave landing system (MLS), which is in the advanced stage of implementation, can potentially provide significant fuel and time savings as well as more flexibility in approach and landing functions. However, the expanded coverage and increased accuracy requirements of the MLS make it more susceptible to the features of the site in which it is located. An analytical approach is presented for evaluating the multipath effects of scatterers that are commonly found in airport environments. The approach combines a multiplane model with a ray-tracing technique and a formulation for estimating the electromagnetic fields caused by the antenna array in the presence of scatterers. The model is applied to several airport scenarios. The reduced computational burden enables the scattering effects on MLS position information to be evaluated in near real time. Evaluation in near real time would permit the incorporation of the modeling scheme into air traffic control automation; it would adaptively delineate zones of reduced accuracy within the MLS coverage volume, and help establish safe approach and takeoff trajectories in the presence of uneven terrain and other scatterers.

Poulose, M. M.↗
DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.

Drgona, Jan↗
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

CUDO: closed-form universal dwell-time optimization for computer-controlled optical surfacing

Precision optical figuring demands fast and accurate dwell time optimization to reach nanometer- and sub-nanometer-level accuracy in next-generation optical systems. We introduce CUDO (closed-form universal dwell-time optimization), the first, to the best of our knowledge, unified closed-form analytical framework that supports both function-form and matrix-form dwell time models in computer-controlled optical surfacing (CCOS). In contrast to traditional methods, which rely on iterative optimization and hyperparameter tuning, our framework derives direct analytical solutions with no adjustable parameters. This approach unifies the solution principles of existing methods within a single mathematical model, delivering three key advantages: (1) accuracy on par with, or superior to, iterative solvers, (2) substantial reduction in computation time, and (3) numerical robustness. Comparative studies with prior art confirm that closed-form solutions achieve equivalent residual error while removing runtime bottlenecks. By simplifying the implementation and enabling real-time, scalable deployment, CUDO establishes a practical foundation for future deterministic fabrication of large-aperture and high-performance optics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Approximating Density Probability Distribution Functions Across Cosmologies

Using a suite of self-similar cosmological simulations, we measure the probability distribution functions (PDFs) of real-space density, redshift-space density, and their geometric mean. We find that the real-space density PDF is well-described by a function of two parameters: ns, the spectral slope, and σL, the linear rms density fluctuation. For redshift-space density and the geometric mean of real- and redshift-space densities, we introduce a third parameter, ${s}_{L}=\sqrt{\langle {\left({{dv}}_{\mathrm{pec}}^{L}/{dr}\right)}^{2}\rangle }/H$. We find that density PDFs for the LCDM cosmology is also well-parameterized by these three parameters. As a result, we are able to use a suite of self-similar cosmological simulations to approximate density PDFs for a range of cosmologies. We make the density PDFs publicly available and provide an analytical fitting formula for them.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Impact of Humidity and Temperature on the Stability of the Optical Properties and Structure of MAPbI3, MA0.7FA0.3PbI3 and (FAPbI3)0.95(MAPbBr3)0.05 Perovskite Thin Films

In situ real-time spectroscopic ellipsometry (RTSE) measurements have been conducted on MAPbI3, MA0.7FA0.3PbI3, and (FAPbI3)0.95(MAPbBr3)0.05 perovskite thin films when exposed to different levels of relative humidity at given temperatures over time. Analysis of RTSE measurements track changes in the complex dielectric function spectra and structure, which indicate variations in stability influenced by the underlying material, preparation method, and perovskite composition. MAPbI3 and MA0.7FA0.3PbI3 films deposited on commercial fluorine-doped tin oxide coated glass are more stable than corresponding films deposited on soda lime glass directly. (FAPbI3)0.95(MAPbBr3)0.05 films on soda lime glass showed improved stability over the other compositions regardless of the substrate, and this is attributed to the preparation method as well as the final composition.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Dynamics of Symmetry-Breaking Stacking Boundaries in Bilayer MoS 2

Crystal symmetry of two-dimensional (2D) materials plays an important role in their electronic and optical properties. Engineering symmetry in 2D materials has recently emerged as a promising way to achieve novel properties and functions. The noncentrosymmetric structure of monolayer transition metal dichalcogenides (TMDCs), such as molybdenum disulfide (MoS 2 ), has allowed for valley control via circularly polarized optical excitation. In bilayer TMDCs, inversion symmetry can be controlled by varying the stacking sequence, thus providing a pathway to engineer valley selectivity. Here, we report the in situ integration of AA' and AB stacked bilayer MoS 2 with different inversion symmetries by creating atomically sharp stacking boundaries between the differently stacked domains, via thermal stimulation and electron irradiation, inside an atomic-resolution scanning transmission electron microscopy. The setup enables us to track the formation and atomic motion of the stacking boundaries in real time and with ultrahigh resolution which enables in-depth analysis on the atomic structure at the boundaries. In conjunction with density functional theory calculations, we establish the dynamics of the boundary nucleation and expansion and further identify metallic boundary states. Our approach provides a means to synthesize domain boundaries with intriguing transport properties and opens up a new avenue for controlling valleytronics in nanoscale domains via real-time patterning of domains with different symmetry properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Energy Infrastructure Dependency Curves Simulation Tool User’s Guide

Foundational studies of infrastructure interdependencies have attempted to make sense of the complex relationships among critical infrastructure. Beyond understanding the complexity of these interconnections, owner/operators and decision makers have a continuing need for a situational awareness capability tool to move beyond a conceptual understanding toward a more quantified, visual analytic method to enable more detailed real-world analysis and prioritization.The purpose and development of the Energy Infrastructure Dependency Curves Simulation Tool is to provide a means to display and analyze the complexities of infrastructure dependencies, in a way that is dynamic and can be easily manipulated. The tool focuses on the connections between two infrastructure assets, a natural gas-processing plant that relies on electric power, and an electric power-generating plant that depends on natural gas to produce electricity. The tool allows the user to enter information characterizing each asset and the dependence relationships, including details about core operations, backup function, shutdown, and resumption of service. Output is provided as a visualization of the performance of each asset over time, displaying impact, stages of operation, and resumption to full service. Input can be modified to enable the user to see how different scenarios or decisions would change the outcome. The intent of the tool is to provide the user with the ability to make informed decisions regarding operation of individual asset.This report presents an overview of the concepts used for developing the Energy Infrastructure Dependency Curves Simulation Tool and a detailed user guide that provides instructions on how to use the tool. Finally, three example scenarios are presented to illustrate dependency curve outputs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Parity-odd four-point correlation function from the DESI data release 1 luminous red galaxy sample

The parity-odd four-point function provides a unique probe of fundamental symmetries and potential new physics in the large-scale structure of the Universe. We present measurements of the parity-odd four-point function using the Dark Energy Spectroscopic Instrument (DESI) DR1 luminous red galaxy (LRG) sample and assess its detection significance. Our analysis considers both auto- and cross-correlations, using two complementary approaches to the covariance: (i) the full analytic covariance matrix applied to the uncompressed data vector, and (ii) a compressed data vector combined with a hybrid covariance matrix constructed from simulations and analytic estimates. When using the full analytic covariance matrix without corrections, we observe apparent auto-correlation signals with significance up to 4⁢𝜎. However, this excess is also consistent with a mismatch between the statistical fluctuations estimated from the simulations and those present in the real data. Our findings therefore suggest that the parity-odd signal in the current DESI DR1 LRG sample is consistent with zero. We note, however, that the low completeness of this sample may have a non-negligible impact on the detection sensitivity. Future data releases with improved completeness will be crucial for further investigation.

Hou, Jiamin [Ludwig-Maximilians-Universität; Unive↗
NASA NTRSDate not supplied

Flight Testing of In-Time Safety Assurance Technologies for UAS Operations

Ongoing research at NASA is driven by a strategic plan defined by the Aeronautics Research Mission Directorate and a vision for future In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. In both visions, system safety awareness and provision are expanded through increased access to relevant data; integrated analysis and predictive capabilities; improved real-time detection and alerting of domain-specific hazards; decision support, and in some cases, automated risk mitigation strategies. One primary research focus is to develop means by which more timely (i.e., “in-time”) actions may be taken to mitigate precursors, anomalies, or trends that are observed during operations. In this paper, we describe such means as a collection of Services, Functions, and Capabilities (SFCs) that are supported by an underlying information system. For example, an integrated risk assessment capability is envisioned that continuously monitors safety-related metrics and margins and recommends timely operational changes. Assessment functions and/or services can be based on data analytics and predictive models derived from heterogeneous data sets that span relevant indicator metrics and their time histories. Likewise, on-board functions can identify and reduce susceptibility to precursor conditions that have led (and can lead) to aircraft loss-of-control or out-of-control accidents. This paper summarizes development and testing of such an information system tailored to hazards anticipated for future highly autonomous flight missions near and over densely populated areas. Testing is accomplished via simulation and by using small, unmanned aircraft operating over a test range at NASA’s Langley Research Center. Flight plans and test scenarios are defined to emulate several use-cases, including package delivery; reconnaissance; fire management; and urban air taxi vertiport operations. Two test phases are summarized with Phase 1 occurring in (2019-2020) and Phase 2 ongoing (2021-present). Results focus on SFC performance, technology readiness level assessment, and requirements discovery/validation. Companion papers are cited throughout for additional details on the recent testing.

safety management↗
DOE OSTI2024

Tank Waste Characterization: History, Challenges, and Success Stories

The preparation and chemical and radiochemical analysis of Hanford tank waste samples can be performed with standard laboratory equipment and instruments as relatively routine processes that are not particularly challenging. Rather, the main challenges of tank waste characterization are associated with radiological dose and sampling limitations. Accurate, representative and effective sampling techniques are difficult with the waste tanks because they were not designed for routine sampling. There are a finite number of sampling locations for each tank based on riser positioning, depth and the operational functionality of the sampling riser. For example, in one recently emptied SST, there was one riser that was found to have had concrete dumped down it, thereby eliminating that sampling port. Additionally, the waste within the tank; especially true for the saltcake and sludge, is not homogenous. The ability to adequately mix a million-gallon double shell tank (DST) is a concern for data reproducibility. Another real challenge that must be addressed for sampling single shell tanks, is how to dissolve the salt cake waste in a compromised (leaking) SST. These physical constraints mean that uncertainty in the representativeness of samples must be considered when applying analytical results to the bulk contents of the tank. The tank waste is highly radioactive and thus can only be handled initially by facilities that can receive samples into concrete-shielded hot cells with remote operation with an example provided in Figure 1. The shielding protects the worker from the radiological dose while mineral oil windows and remotely operated manipulators enables the samples to be handled. At Hanford, analytical laboratories with these hot cell capabilities are limited to the Pacific Northwest National Laboratory and the main Hanford operations support laboratory, 222-S Laboratory. Because of their highly radioactive nature, samples must be sufficiently diluted to facilitate their analysis outside of a shielded cell. In some cases, this means some accuracy must be compromised to complete the analysis beyond that normally encountered for non-radioactive material.

Waste Characterization, BBI, PHOENIX: Tank Farms: ↗
NASA NTRS2013

Faster Array Training and Rapid Analysis for a Sensor Array Intended for an Event Monitor in Air

Environmental monitoring, in particular, air monitoring, is a critical need for human space flight. Both monitoring and life support systems have needs for closed loop process feedback and quality control for environmental factors. Monitoring protects the air environment and water supply for the astronaut crew and different sensors help ensure that the habitat falls within acceptable limits, and that the life support system is functioning properly and efficiently. The longer the flight duration and the farther the destination, the more critical it becomes to have carefully monitored and automated control systems for life support. There is an acknowledged need for an event monitor which samples the air continuously and provides near real-time information on changes in the air. Past experiments with the JPL ENose have demonstrated a lifetime of the sensor array, with the software, of around 18 months. We are working on a sensor array and new algorithms that will incorporate transient sensor responses in the analysis. Preliminary work has already showed more rapid quantification and identification of analytes and the potential for faster training time of the array. We will look at some of the factors that contribute to demonstrating faster training time for the array. Faster training will decrease the integrated sensor exposure to training analytes, which will also help extend sensor lifetime.

sensing array↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

(2, 2) Scattering and the celestial torus

Analytic continuation from Minkowski space to (2, 2) split signature spacetime has proven to be a powerful tool for the study of scattering amplitudes. Here we show that, under this continuation, null infinity becomes the product of a null interval with a celestial torus (replacing the celestial sphere) and has only one connected component. Spacelike and timelike infinity are time-periodic quotients of AdS 3 . These three components of infinity combine to an S 3 represented as a toric fibration over the interval. Privileged scattering states of scalars organize into SL(2, $\mathbb{R}$) L ×SL(2, $\mathbb{R}$) R conformal primary wave functions and their descendants with real integral or half-integral conformal weights, giving the normally continuous scattering problem a discrete character.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Real Time Monitoring of the Dynamic Intracluster Diffusion of Single Gold Atoms into Silver Nanoclusters

Alloying metal materials with heterometal atoms is an efficient way to diversify the function of materials, but in-depth understanding of the dynamic heterometallic diffusion inside the alloying materials is rather limited, especially at the atomic level. Here, we report the real-time monitoring of the dynamic diffusion process of a single gold (Au) atom into an atomically precise silver nanocluster (Ag NC), Ag 25 (MHA) 18 (MHA = 6-mercaptohexanoic acid), by using in situ UV–vis absorption spectroscopy in combination with mass and tandem mass spectrometry. We found that the Au heteroatom first replaces the Ag atom at the surface Ag 2 (MHA) 3 motifs of Ag 25 (MHA) 18 . After that, the Au atom diffuses into the surface layer of the icosahedral Ag 13 kernel and finally occupies the center of the alloy NCs to form the thermodynamically stable [email protected] 24 (MHA) 18 product. Density functional theory (DFT) calculations reveal that the key thermodynamic driving force is the preference of the Au heteroatom for the central site of alloy NCs. Furthermore, the real-time monitoring approach developed in this study could also be extended to other metal alloy systems to reveal the reaction dynamics of intracluster diffusion of heteroatoms, as well as the formation mechanisms of metal alloy nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

MolSym : A Python package for handling symmetry in molecular quantum chemistry

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program’s environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree–Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. Furthermore, this package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Neural control of magnetic suspension systems

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Gray, W. Steven↗