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At least 415 records · Page 23

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NASA NTRSDate not supplied

Surface-Normal Active Flow Control for Lift Enhancement and Separation Mitigation for High-Lift Common Research Model

This study explores the feasibility of using small surface-normal jets near the flap trailing edge as active aerodynamic load control on the high-lift Common Research Model wing/-body configuration. Chimera Grid Tools are utilized to generate structured curvilinear overset grids, and the Reynolds-averaged Navier-Stokes solver OVERFLOW is employed to solve for the flow-field around the geometry. The so-called microjet is initially employed across both the inboard and the outboard flaps on the pressure-side near the trailing edge. It is shown that implementing the microjet on the inboard flap is more effective compared to implementing it on the outboard flap. This is because, prior to microjet implementation, the flow on the outboard flap exhibits extensive separation, while the flow on the inboard flap exhibits moderate separation. For microjet implementation across the inboard flap only, the relationship between momentum coefficient of the microjet and lift-enhancement is found to be∆CL'1.66√Cμfor the rangeCμ= 0.00−0.012. We show that implementing a microjet with a jet velocity ratio of one, which corresponds toCμ= 0.003, can shift the linear region of the lift curve by ∆CL= 0.08. The linear shift in the lift curve is significant for enhancing airplane performance such as increasing its payload. Microjet implementation effects on the drag coefficient are also investigated through a drag decomposition analysis. Further, we employ an induced drag analysis based on the spanwise load distribution and show that the microjet-related increase in pressure drag coefficient is dominated by the increase in the induced drag while microjet implementation reduces the form drag. These preliminary results show that favorable changes in aerodynamic performance can be achieved by using the surface-normal jets presented in this study.

ARMD↗

NASA NTRS2021

A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization

The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network.

Belen Franch↗

NASA NTRSDate not supplied

Aerodynamic Assessment of Surface-Normal Active Flow Control for Lift Enhancement on the High-Lift Common Research Model

This study explores aerodynamic feasibility of small surface-normal pneumatic jets (micro-jets) for lift enhancement on the high-lift Common Research Model. To date, lift enhancement studies using microjets have been conducted at wind tunnel Reynolds numbers less than three and half million. This paper computationally investigates microjet effects at a flight Reynolds number of24.6million. Prior to microjet activation, it is shown that flow features such as trailing edge (TE) flow separation on both inboard and outboard flaps have noticeable sensitivity to Reynolds number. At the flight Reynolds number, the onset of TE flow separation shifts downstream resulting in less flow separation compared to at the wind tunnel Reynolds numberof3.26million. The effects are insignificant for lift enhancement due to microjet on the inboard flap. However, the effects are significant for lift enhancement on the outboard flap. Microjets are shown to be more effective in circulation control on the outboard flap at the higher Reynolds number. For a microjet with velocity ratio of one, lift enhancement is increased by17%at the flight Reynolds number. This highlights the sensitivity of microjet performance to the flow features of the configuration it is implemented on. Microjet lift enhancement trends at both Reynolds numbers are found to be consistent in their ability to move the lift curve upwards in the linear regime, with the lift enhancement related to microjet momentum coefficient throughΔ𝐶𝐿=𝐾√︁𝐶𝜇. Analysis of the impact microjets have on drag shows that microjets improve the configuration’s Oswald efficiency by5.4%and2.5%at the wind tunnel and flight Reynolds numbers respectively, while providing lift enhancement. Microjet effects on the trim drag are found to be less than one percent of the total drag. The results of this study highlight the potential of small surface-normal pneumatic jets for improved performance at high-lift conditions.

ARMD↗

NASA NTRS2023

High-Pressure, Normal-Gravity Droplet Combustion Experimental Hardware

The High-Pressure, Trans-Critical (HPTC) 1-g Liquid Droplet Combustion Experiments will assess the autoignition and combustion of liquid fuel droplets at elevated temperatures and pressures. These experiments are being conducted at the NASA Glenn Research Center in a manner similar to experiments performed in Glenn’s Zero Gravity Research Facility (ZGF) using the B drop vehicle. The modification to the test capabilities of the ZGF experiments in the new experimental configuration will allow researchers to continue performing normal-gravity liquid droplet combustion experiments without requiring the use of the ZGF test equipment. These normal-gravity experiments will be conducted at lower pressures than in the ZGF because the pressure vessel for this setup is limited to a maximum working pressure of 7.74 atm (99.1 psig) at 366.5 K (200 °F). This experimental configuration will enable development of a similar capability for conducting high-pressure combustion and supercritical oxidation research on the International Space Station. Experiments will be conducted with the capability of altering the ambient pressure, temperature, and gas concentrations, as well as the fuel type for each test. For these experiments, the test vessel operates at pressures up to 7.5 atm (95.5 psig), temperatures up to 1,073 K (1,472 °F) inside the insulated oven, and oxygen concentrations up to 30 percent.

Droplet Combustion↗

NASA NTRSDate not supplied

The Contribution of Pilots to Resilience in Normal Operations: A Survey Approach

Much of our knowledge about human performance in flight safety has come from the analysis of undesired events, whether accidents, incidents, or crew behaviors identified via flight exceedance monitoring or observational techniques. In recent years, there has been an acknowledgement that operational personnel are not merely sources of “human error”, but also make a unique human contribution to safe outcomes. In a few celebrated cases, this takes the form of “heroic saves”, but on many more occasions, operational personnel contribute to safety through everyday, often-unnoticed actions that turn potentially hazardous situations into non-events. An emerging approach to safety, frequently referred to as “Safety II,” proposes that the positive human contribution is an important and largely untapped source of safety information. Some airlines have successfully trained observers to identify and record the positive behaviors exhibited by the crew over the course of a flight. In other cases, flight crew are interviewed about good practices or positive behaviors. However, each of these methods are relatively limited in scale and resource intensive. A survey could provide a relatively low-cost approach to systematically gather this information on a larger scale. The primary purpose of the research was to develop and assess a surveys methodology for assessing crews' activities in normal flights and the operational perturbations encountered during normal operations. We hope that such a survey could be both a research tool as well as a safety management aid for the aviation industry. We collected responses concerning revenue flights from two groups of airline pilots (N = 25 & N= 65). The results indicated that relatively few flights proceeded exactly as in the original flight plan. Pilots routinely anticipated and adapted to changing circumstances. We will review the challenges encountered in developing the survey and summarize preliminary findings from two administrations of the survey to airline pilots.

human contribution safety↗

NASA NTRSDate not supplied

The Contribution of Pilots to Resilience in Normal Operations. Part II: A closer look at briefings: Anticipation and Monitoring Also Known as Planning and Coordination

human contribution safety↗

DOE OSTI2021

ANOMALOUS SKIN EFFECT STUDY OF NORMAL CONDUCTING FILM

For the radiofrequency (RF) applications of normal conducting film with large mean free path at high frequency and low temperature, the anomalous skin effect differs considerably from the normal skin effect with field decaying exponentially in the film. Starting from the relationship be-tween the current and the electric field (E field) in the film, the amplitude of E field along the film depth is calculated and is found to be non-monotonic. The surface impedance is found to have a minimum value at certain film thickness.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

Dai, Enyan↗

DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [Chicago U.] (ORCID:0009000218901379)↗

DOE OSTI2025

Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought

Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield ($Φ_F$) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and $Φ_F$ to drought need further study. This study normalized SIF as SIFn to account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFn anomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and $Φ_F$ during a 2019 drought in the North China Plain (NCP). Here, the results show SIFn provides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and $Φ_F$. In the first two weeks of drought, SIFn decreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFn outperformed other indicators, proving sensitive to early drought detection. SIFn was also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFn anomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and $Φ_F$ anomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll ($Φ_F$) are impacted by drought and influence SIF at different stages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Non-perturbative Approach to Computing Seismic Normal Modes in Rotating Planets

In this work, a continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard “radial” algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales.

58 GEOSCIENCES↗

DOE OSTI2024

Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques

Here, we study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗

DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Prediction of the Wave Normal Angle of Proton‐Band EMIC Waves Near Geosynchronous Orbit

We investigate how the wave normal angle (WNA) and polarization of proton-band electromagnetic ion cyclotron (EMIC) waves change as they travel from their source to Earth. This paper marks a significant milestone as the first full-wave simulation of proton-band EMIC waves reflecting from the ionosphere. Our findings show that the WNA can change rapidly during propagation, primarily due to plasma inhomogeneities, such as variations in the Alfvén speed. The wave polarization is strongly related to the WNA, consistent with theory. Newly generated EMIC waves near the equator propagate with a WNA of 0° , then the WNA gradually shifts to 90° as they move toward Earth. In contrast, reflecting waves having 90° of WNA at Earth maintain a relatively larger WNA even near the magnetic equator. As a result, only the newly generated waves close to the source, where the magnetic latitude is less than approximately 20° , show left-handed polarization, while linear polarization remains dominant throughout the rest of the propagation.

EMIC wave↗

DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Aspects of the normal state resistivity of cuprate superconductors

Planar normal state resistivity data taken from three families of cuprate superconductors are compared here with theoretical calculations from the recent extremely correlated Fermi liquid theory (ECFL). The two hole doped cuprate materials LSCO and BSLCO and the electron doped material LCCO have yielded rich data sets at several densities δ and temperatures T, thereby enabling a systematic comparison with theory. The recent ECFL resistivity calculations for the highly correlated t-t'-J model by us give the resistivity for a wide set of model parameters. After using X-ray diffraction and angle resolved photoemission data to fix parameters appearing in the theoretical resistivity, only one parameter, the magnitude of the hopping t, remains undetermined. For each data set, the slope of the experimental resistivity at a single temperature-density point is sufficient to determine t, and hence the resistivity on absolute scale at all remaining densities and temperatures. This procedure is demonstrated to give a fair account of the entire data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗