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At least 523 records · Page 29

Modeling Measurement Error in Dose-Response Models of Community Annoyance to Low-Noise Supersonic Flight

The primary research goal of the forthcoming NASA Quesst mission community test campaign is to collect representative community response data in support of the development of supersonic overflight noise certification standards. Beginning in 2026, NASA will fly the novel X-59 demonstrator aircraft over select communities in United States in order to demonstrate the possibility of low-noise supersonic flight over land and to collect objective measurements and subjective data on the perceptual experience of this new noise source. It is believed that a regression of a binary perceptual response (‘highly annoyed’ or ‘not’) on estimated noise levels (doses, measured in decibels) will provide a useful dose-response relationship for regulators. However, as these estimated doses will be subject to measurement error, naïve estimators of regression coefficients are inconsistent and slopes may be subject to attenuation bias. In this presentation, I contrast functional modeling of measurement error via simulation extrapolation (SIMEX) with structural Bayesian measurement error models. These methods are applied to available data collected during two NASA risk reduction studies in California in 2011 and Texas in 2018. I’ll conclude noting that in the presence of nonnegligible measurement errors, probabilities of annoyance may be overpredicted for low noise levels and underpredicted for high noise levels, therefore, methods of correcting for measurement error will be necessary to improve the utility of the dose-response relationship for policy-making purposes.

simulation↗

Retrospective Analysis of NIST Standard Reference Material 1450, Fibrous Glass Board, for Thermal Insulation Measurements

Thermal conductivity data acquired previously for the establishment of Standard Reference Material (SRM) 1450, Fibrous Glass Board, as well as subsequent renewals 1450a, 1450b, 1450c, and 1450d, are re-analyzed collectively and as individual data sets. Additional data sets for proto-1450 material lots are also included in the analysis. The data cover 36 years of activity by the National Institute of Standards and Technology (NIST) in developing and providing thermal insulation SRMs, specifically high-density molded fibrous-glass board, to the public. Collectively, the data sets cover two nominal thicknesses of 13 mm and 25 mm, bulk densities from 60 kg∙m -3 to 180 kg∙m -3 , and mean temperatures from 100 K to 340 K. The analysis repetitively fits six models to the individual data sets. The most general form of the nested set of multilinear models used is given in the following equation: λ(ρ, T) = α 0 + α 1 ρ +α 2 T + α 3 T 3 +α 4 e - ($\frac{T-a_{5}}{a_{6}}$) 2 = where λ(ρ,T) is the predicted thermal conductivity (W∙m -1 ∙K -1 ), ρ is the bulk density (kg∙m -3 ), T is the mean temperature (K) and $a_i$ (for i = 1, 2, … 6) are the regression coefficients. The least squares fit results for each model across all data sets are analyzed using both graphical and analytic techniques. The prevailing generic model for the majority of data sets is the bilinear model in ρ and T. λ(ρ, T) = α 0 + α 1 ρ +α 2 T One data set supports the inclusion of a cubic temperature term and two data sets with low-temperature data support the inclusion of an exponential term in T to improve the model predictions. Physical interpretations of the model function terms are described. Recommendations for future renewals of SRM 1450 are provided. An Addendum provides historical background on the origin of this SRM and the influence of the SRM on external measurement programs.

regression analysis↗

Alpha-helical hydrophobic polypeptides form proton-selective channels in lipid bilayers

Proton translocation is important in membrane-mediated processes such as ATP-dependent proton pumps, ATP synthesis, bacteriorhodopsin, and cytochrome oxidase function. The fundamental mechanism, however, is poorly understood. To test the theoretical possibility that bundles of hydrophobic alpha-helices could provide a low energy pathway for ion translocation through the lipid bilayer, polyamino acids were incorporated into extruded liposomes and planar lipid membranes, and proton translocation was measured. Liposomes with incorporated long-chain poly-L-alanine or poly-L-leucine were found to have proton permeability coefficients 5 to 7 times greater than control liposomes, whereas short-chain polyamino acids had relatively little effect. Potassium permeability was not increased markedly by any of the polyamino acids tested. Analytical thin layer chromatography measurements of lipid content and a fluorescamine assay for amino acids showed that there were approximately 135 polyleucine or 65 polyalanine molecules associated with each liposome. Fourier transform infrared spectroscopy indicated that a major fraction of the long-chain hydrophobic peptides existed in an alpha-helical conformation. Single-channel recording in both 0.1 N HCl and 0.1 M KCl was also used to determine whether proton-conducting channels formed in planar lipid membranes (phosphatidylcholine/phosphatidylethanolamine, 1:1). Poly-L-leucine and poly-L-alanine in HCl caused a 10- to 30-fold increase in frequency of conductive events compared to that seen in KCl or by the other polyamino acids in either solution. This finding correlates well with the liposome observations in which these two polyamino acids caused the largest increase in membrane proton permeability but had little effect on potassium permeability. Poly-L-leucine was considerably more conductive than poly-L-alanine due primarily to larger event amplitudes and, to a lesser extent, a higher event frequency. Poly-L-leucine caused two populations of conductive events, one in the 0.1-0.5 pA range, and one in the 1.0-5.0 pA range, whereas nearly all events caused by poly-L-alanine were in the 0.1-0.5 pA range at an applied voltage of +60 mV. The channel-like activity appeared to switch between conductive and nonconductive states, with most open-times in the range of 50-200 ms. We conclude that hydrophobic polyamino acids produce proton-conducting defects in lipid bilayers that may be used to model functional proton channels in biological membranes.

NASA Discipline Exobiology↗

Constructing a Simulation Surrogate with Partially Observed Output

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. In conclusion, the proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.

42 ENGINEERING↗

Accurate Measurements of the Dielectric Constant of Seawater at L Band

This paper describes measurements of the dielectric constant of seawater at a frequency of 1.413 GHz, the center of the protected band (i.e., passive use only) used in the measurement of sea surface salinity from space. The objective of the measurements is to accurately determine the complex dielectric constant of seawater as a function of salinity and temperature. A resonant cylindrical microwave cavity in transmission mode has been employed to make the measurements. The measurements are made using standard seawater at salinities of 30, 33, 35, and 38 practical salinity units over a range of temperatures from 0 degree C to 35 degree C in 5 degree C intervals. Repeated measurements have been made at each temperature and salinity. Mean values and standard deviations are then computed. The total error budget indicates that the real and imaginary parts of the dielectric constant have a combined standard uncertainty of about 0.3 over the range of salinities and temperatures considered. The measurements are compared with the dielectric constants obtained from the model functions of Klein and Swift and those of Meissner and Wentz. The biggest differences occur at low and high temperatures.

Measurements↗

Determining initial enrichment, burnup, and cooling time of pressurized-water reactor spent fuel assemblies by analyzing passive gamma spectra measured at the Clab interim-fuel storage facility in Sweden

The purpose of the Next Generation Safeguards Initiative (NGSI)–Spent Fuel (SF) project is to strengthen the technical toolkit of safeguards inspectors and/or other interested parties. The NGSI–SF team is working to achieve the following technical goals more easily and efficiently than in the past using nondestructive assay measurements of spent fuel assemblies: (1) verify the initial enrichment, burnup, and cooling time of facility declaration; (2) detect the diversion or replacement of pins; (3) estimate the plutonium mass [which is also a function of the variables in (1)]; (4) estimate the decay heat; and (5) determine the reactivity of spent fuel assemblies. Since August 2013, a set of measurement campaigns has been conducted at the Central Interim Storage Facility for Spent Nuclear Fuel (Clab), in collaboration with Swedish Nuclear Fuel and Waste Management Company (SKB). One purpose of the measurement campaigns was to acquire passive gamma spectra with high-purity germanium and lanthanum bromide scintillation detectors from Pressurized Water Reactor and Boiling Water Reactor spent fuel assemblies. The absolute 137 Cs count rate and the 154 Eu/ 137 Cs, 134 Cs/ 137 Cs, 106 Ru/ 137 Cs, and 144 Ce/ 137 Cs isotopic ratios were extracted; these values were used to construct corresponding model functions (which describe each measured quantity’s behavior over various combinations of burnup, cooling time, and initial enrichment) and then were used to determine those same quantities in each measured spent fuel assembly. Furthermore, the results obtained in comparison with the operator declared values, as well as the methodology developed, are discussed in detail in the paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Nuclear collective excitations in a two-phase coexistence region

The relation between collective modes and phase transitions in nuclear matter is examined. The dispersion relations for the low-lying excitations in a linear approach are evaluated within a Landau-Fermi liquid scheme by assuming coexisting phases in thermodynamical equilibrium. Temperature and isospin composition are used as relevant parameters. The in-medium nuclear interaction is provided by a recently proposed density functional model. The low density liquid-gas phase transition is taken as a typical situation for examination. We found significative modifications in the energy spectrum, within a certain range of temperatures and isospin asymmetry, due to the separation of matter into independent phases. The influence of the electromagnetic interaction over the dispersion relation of these collective excitations is also examined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Establishing the Maximum Collectivity in Highly Deformed N = Z Nuclei

The lifetimes of the first excited 2 + states in the N = Z nuclei 80 Zr, 78 Y, and 76 Sr have been measured using the γ-ray line shape method following population via nucleon-knockout reactions from intermediate-energy rare-isotope beams. The extracted reduced electromagnetic transition strengths yield new information on where the collectivity is maximized and provide evidence for a significant, and as yet unexplained, odd-odd vs even-even staggering in the observed values. As a result, the experimental results are analyzed in the context of state-of-the-art nuclear density-functional model calculations.

59 ≤ A ≤ 89↗

Amplification of ion acoustic wave in an inhomogeneous plasma through nonlinear wave-particle interaction with drift wave turbulence

A study on the amplification of ion acoustic wave in an inhomogeneous plasma has been made on the basis of a nonlinear wave-particle interaction process called plasma maser effect. The drift wave instability, which is a universal phenomenon of an inhomogeneous confined plasma system, is found to be strongly in phase relation with thermal particles and may transfer its wave energy nonlinearly through a modulated field to ion acoustic wave. Considering a Maxwellian distribution function model for inhomogeneous plasmas under the standard local approximation, we have estimated the growth rate for ion acoustic wave, which is obtained by using the nonlinear dispersion relation. It has been found that amplification of ion acoustic wave is possible at the expense of drift wave turbulent energy. This result may be particularly important for stability of various drift modes in magnetically confined plasma as well as for transport of momentum and energy in such inhomogeneous systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

Abstract The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and architecture used. Further, the lack of thorough ablation studies makes it hard to discern which components are most critical to success. In this work, we show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified Swin Transformer V2 (SwinV2) on ERA5 data and find that it attains superior skill in terms of mean-square errors of deterministic forecasts when compared against the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS). Almost all DL–NWP systems share a core set of hyperparameters and design decisions. To aid and expedite future DL–NWP research, we present an in-depth, systematic exploration of different loss functions, model sizes and depths, patch sizes, and multistep training objectives. We also examine the model performance with metrics beyond the typical accuracy (ACC) and RMSE and investigate how the performance scales with model size. Through our open-source code, scoring pipelines, and models, we share our findings on key aspects of the training pipeline. These ablations reduce the necessity for expensive hyperparameter tuning and lower the barrier to entry for future DL–NWP research. Significance Statement This study investigates the potential of using large-scale transformer-based models for weather prediction, showing that it is possible to achieve high forecast accuracy with simpler, off-the-shelf architectures. By training a minimally modified SwinV2 transformer on ERA5 data, we show that the model achieves competitive forecast skill in terms of mean-square error for key variables, outperforming the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS) at all lead times. Our findings suggest that effective training strategies, such as multistep fine-tuning and channel-weighted losses, significantly enhance the model’s performance. However, we also highlight that these improvements come with trade-offs in other areas, such as ensemble spread and high-frequency spatial detail. This work highlights the promise of deep learning in improving weather forecasts, which could lead to better preparedness and response to weather events, ultimately benefiting society by providing more reliable weather predictions.

Willard, Jared D. [Lawrence Berkeley National Labo↗

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗

A priori filtered chemical source term modeling for LES of high Karlovitz number premixed flames

Assumptions behind closure models for the filtered source term are studied a priori using results from DNS of turbulent n-heptane/air premixed flames at varying Karlovitz numbers. Simulations with both detailed chemistry and tabulated chemistry, as well as unity and non-unity Lewis numbers, are used to determine if finite-rate chemistry and differential diffusion effects affect the filtered chemical source terms. While the unfiltered source term shows large fluctuations, the filtered source terms from detailed chemistry and tabulated chemistry are in good agreement at sufficiently large filter widths (Δ ≳ l F ). Using the concept of optimal estimators, it is shown that a tabulation approach using the filtered progress variable and its variance can predict accurately the filtered chemical source terms. Finally, the filtered source terms from the DNS are compared to predictions from two commonly assumed sub-filter probability density function models. Both models show deviations from the filtered DNS source terms but predict accurately the mean turbulent flame speed. In conclusion, the results illustrate the potential of using simple tabulated chemistry approaches based on presumed PDFs for LES of premixed flames in the thin and distributed reaction zones regimes.

33 ADVANCED PROPULSION SYSTEMS↗

Vertical canopy gradients of respiration drive plant carbon budgets and leaf area index

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model – Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R dark ). We ran global and point simulations varying the canopy gradient of R dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

59 BASIC BIOLOGICAL SCIENCES↗

Extending plasma transport theory to strong coupling through the concept of an effective interaction potential

A method for extending traditional plasma transport theories into the strong coupling regime is presented. Like traditional theories, this is based on a binary scattering approximation, but where physics associated with many body correlations is included through the use of an effective interaction potential. The latter is simply related to the pair-distribution function. Modeling many body effects in this manner can extend traditional plasma theory to orders of magnitude stronger coupling. Theoretical predictions are tested against molecular dynamics simulations for electron-ion temperature relaxation as well as diffusion in one component systems. Emphasis is placed on the connection with traditional plasma theory, where it is stressed that the effective potential concept has precedence through the manner in which screening is imposed. The extension to strong coupling requires accounting for correlations in addition to screening. Limitations of this approach in the presence of strong caging are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Microfluidic electrochemical cell for in situ structural characterization of amorphous thin-film catalysts using high-energy X-ray scattering

Porous, high-surface-area electrode architectures are described that allow structural characterization of interfacial amorphous thin films with high spatial resolution under device-relevant functional electrochemical conditions using high-energy X-ray (>50 keV) scattering and pair distribution function (PDF) analysis. Porous electrodes were fabricated from glass-capillary array membranes coated with conformal transparent conductive oxide layers, consisting of either a 40 nm–50 nm crystalline indium tin oxide or a 100 nm–150 nm-thick amorphous indium zinc oxide deposited by atomic layer deposition. These porous electrodes solve the problem of insufficient interaction volumes for catalyst thin films in two-dimensional working electrode designs and provide sufficiently low scattering backgrounds to enable high-resolution signal collection from interfacial thin-film catalysts. For example, PDF measurements were readily obtained with 0.2 Å spatial resolution for amorphous cobalt oxide films with thicknesses down to 60 nm when deposited on a porous electrode with 40 µm-diameter pores. This level of resolution resolves the cobaltate domain size and structure, the presence of defect sites assigned to the domain edges, and the changes in fine structure upon redox state change that are relevant to quantitative structure–function modeling. The results suggest the opportunity to leverage the porous, electrode architectures for PDF analysis of nanometre-scale surface-supported molecular catalysts. In addition, a compact 3D-printed electrochemical cell in a three-electrode configuration is described which is designed to allow for simultaneous X-ray transmission and electrolyte flow through the porous working electrode.

36 MATERIALS SCIENCE↗

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Analysis of Ku-band cross section at low incidence angles

This study is using airborne Ku-band data to address questions which have implications for both model function development and for advancing our physical understanding of the sea surface. Concurrent measurements of ocean directional spectra, significant wave height, and mean surface roughness are made using the capabilities of the radar ocean wave spectrometer (ROWS). The NASA/GSFC's ROWS is a 15 GHz pulse compressed radar which is a radar sensor designed to measure the direction of the long wave components using spectral analysis of the tilt induced reflectively modulation. The ROWS are modified to cycle at 50 Hz for the scanning spectrometer antenna and a wide beamwidth nadir altimeter mode. This change allows the sensor to simultaneously measure directional wave spectra, wave height, mean square slope parameter, and small scale surface roughness. The surface stress caused by the wind is widely believed to be the predominant quantity related to the Ku-band radar cross section for a wide range of incidence angles. The complete coverage in the quasi specular region provided by one sensor is essential to understand the uncertainties between the scattering model and what is happening on the surface. For this presentation, special attention is devoted to sort out some measurement of the anisotropy associated with the band of high frequencies. Using the other geophysical parameters, comparisons are then made with the classic spectral form currentlyused to describe the wind impact on the sea surface.

Chapron, B.↗

Towards an Optimal Noise Versus Resolution Trade-Off in Wind Scatterometry

A scatterometer is a radar that measures the normalized radar cross section sigma(sup 0) of the Earth's surface. Over the ocean this signal is related to the wind via the geophysical model function (GMF). The objective of wind scatterometry is to estimate the wind vector field from sigma(sup 0) measurements; however, there are many subtleties that complicate this problem-making it difficult to obtain a unique wind field estimate. Conventionally, wind estimation is split into two stages: a wind retrieval stage in which several ambiguous solutions are obtained, and an ambiguity removal stage in which ambiguities are chosen to produce an appropriate wind vector field estimate. The most common approach to wind field estimation is to grid the scatterometer swath into wind vector cells and estimate wind vector ambiguities independently for each cell. Then, field wise structure is imposed on the solution by an ambiguity selection routine. Although this approach is simple and practical, it neglects field wise structure in the retrieval step and does not account for the spatial correlation imposed by the sampling. This makes it difficult to develop a theoretically appropriate noise versus resolution trade-off using pointwise retrieval. Fieldwise structure may be imposed in the retrieval step using a model-based approach. However, this approach is generally only practical if a low order wind field model is applied, which may discard more information than is desired. Furthermore, model-based approaches do not account for the structure imposed by the sampling. A more general fieldwise approach is to estimate all the wind vectors for all the WVCs simultaneously from all the measurements. This approach can account for structure of the wind field as well as structure imposed by the sampling in the wind retrieval step. Williams and Long in 2010 developed a fieldwise retrieval method based on maximum a posteriori estimation (MAP). This MAP approach can be extended to perform a noise versus resolution trade-off, and deal with ambiguity selection. This paper extends the fieldwise MAP estimation approach and investigates both the noise versus resolution trade-off as well as ambiguity removal in the fieldwise wind retrieval step. The method is then applied to the Sea Winds scatterometer and the results are analyzed. This paper extends the fieldwise MAP estimation approach and investigates both the noise versus resolution trade-off as well as ambiguity removal in the fieldwise wind retrieval step. The method is then applied to the Sea Winds scatterometer and the results are analyzed.

soil moisture↗