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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data-model files associated with the manuscript "Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California"

This package contains the data, simulation setups, notebooks and figures used in “Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California” (Xu et al., 2025). In this study, we selected Elkhorn Slough, a tidal estuary, in California, to investigate the impact of wetland restoration and sea level rise on coastal hydrology using the process-based coastal hydrologic model, Advanced Terrestrial Simulator (ATS), informed by site-specific data. We designed a novel modeling workflow for incorporating wetland restoration features into land cover and soil properties for the model parameterization. The validation results demonstrate a strong agreement between modeled and observed data. We studied the characteristics of coastal watershed hydrology, then focused on the surface water dynamics at two wetland sites within Elkhorn Slough, a reference site and a restored site. Our simulation results indicate that the restored site successfully maintains surface elevation, resulting in reduced surface inundation. We also examined the impact of wetland restoration under expected sea level rise over the next few decades. The low-lying Yampah Marsh, the reference site, is likely to be inundated due to future sea level rise when highest tides arrive; while a higher percentage of Hester Marsh, the restored site, would retain marsh vegetation in coming decades, regardless of tidal conditions. Our study provides important information for examining the outcome of restoration practices that include surface elevation in tidal wetlands under climate changes.Several files can be found from this data package.1. README.md: This file describes the title, journal, co-authors, abstract, repository structure and model version.2. Simulation_Setups.zip: The file contains the model configuration files (XML format) for ATS. 3. Notebooks.zip: The file contains the Jupyter notebooks for generating the pre- and post-restoration meshes and the meshes of future scenarios. 4. Figures.zip: The file contains the figures used in the manuscript.5. Data.zip: The file contains the data used to drive the model simulations, including watershed and wetlands boundaries, mesh files and references to additional datasets (e.g., meteorological forcing, tidal dataset, DEMs, land cover, soil properties). Also, it contains water level observations at the restored wetland.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Application of the Tasseled Cap concept to simulated thematic mapper data

Thematic Mapper signal counts in the six reflective bands (i.e., excluding the thermal band) are simulated using field and laboratory spectrometer measurements of a variety of crops, crop conditions, and soil types. The Dave atmospheric model and prelaunch sensor characteristics comprise the other components of the simulation. The simulated data are found to occupy essentially three dimensions, two of which are equivalent to the MSS Tasseled Cap Greennes and Brightness features, and a third which is substantially influenced by the mid-infrared bands of the TM. This new dimension is primarily related to soil characteristics, including soil moisture. The nature and characteristics of each dimension are discussed, as are some of the expected information gains (over MSS data) resulting from the additional dimensionality of the data.

Crist, E. P.↗
DOE OSTI2021

Experimental demonstration of coupled multi-peak Bragg coherent diffraction imaging with genetic algorithms

Bragg coherent diffraction imaging has the potential to provide significant insight into the structure-properties relationship for crystalline materials by imaging, with nanoscale resolution, three-dimensional strain fields within individual grains and nanoparticles. The capability of present-day synchrotrons to locate and measure a multiplicity of Bragg reflections from a single grain makes it possible to recover the full strain tensor with nanometer resolution. Recent methods for coupling reconstructions from several peaks to determine the strain tensor have been developed and applied to synthetic data, but have not been applied to experimental data. Here, using a coupled genetic reconstruction algorithm, we reconstruct an experimental data set and demonstrate improvements in the ability to resolve vector-valued displacement fields internal to the particle as compared to what is achieved with a noncoupled approach. The coupled approach developed in this work was also validated on simulated data sets. In both simulated and experimental data, reconstructions from our coupled Bragg peak algorithm show improvements over the noncoupled independent reconstruction method of 5% in terms of accuracy and 53% in terms of consistency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1972

Apollo oxygen tank stratification analysis, volume 2

An analysis of flight performance of the Apollo 15 cryogenic oxygen tanks was conducted with the variable grid stratification math model developed earlier in the program. Flight conditions investigated were the CMP-EVA and one passive thermal control period which exhibited heater temperature characteristics not previously observed. Heater temperatures for these periods were simulated with the math model using flight acceleration data. Simulation results (heater temperature and tank pressure) compared favorably with the Apollo 15 flight data, and it was concluded that tank performance was nominal. Math model modifications were also made to improve the simulation accuracy. The modifications included the addition of the effects of the tank wall thermal mass and an improved system flow distribution model. The modifications improved the accuracy of simulated pressure response based on comparisons with flight data.

Barton, J. E.↗
DOE OSTI2024

Thermal field reconstruction and compressive sensing using proper orthogonal decomposition

Model order reduction allows critical information about sensor placement and experiment design to be distilled from raw fluid mechanics simulation data. In many cases, sensed information in conjunction with reduced order models can also be used to regenerate full field variables. In this paper, a proper orthogonal decomposition (POD) inferencing method is extended to the modeling and compressive sensing of temperature, a scalar field variable. The method is applied to a simulated, critically stable, incompressible flow over a heated cylinder (Re = 1000) with Prandtl number varying between 0.001 and 50. The model is trained on pressure and temperature data from simulations. Field reconstructions are then generated using data from selected sensors and the POD model. Finally, the reconstruction error is evaluated across all Prandtl numbers for different numbers of retained modes and sensors. The predicted trend of increasing reconstruction accuracy with decreasing Prandtl number is confirmed and a Prandtl number/sensor count error matrix is presented.

Matulis, John↗
NASA NTRS1997

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

NASA Discipline Regulatory Physiology↗
NASA NTRSDate not supplied

Multiplanar Synthetic Arrays and Their Application to Full-Scale Landing Gear Noise Source Identification

Identification and quantification of noise sources generated by the landing gears of large civil transports are critical for the development of viable abatement technologies that can be applied locally to affect solely the source regions. Leveraging validated computational data sets obtained from airframe noise simulations of a full-scale B777-300ER aircraft, a frame-work based on multiplanar synthetic arrays was developed to examine landing gear sources. The position of the arrays relative to the aircraft nose or main landing gears, the number of microphones in each array, and their distribution pattern were judiciously selected to provide adequate spatial resolution of the sources in the primary flyover, sideline, and upstream directions while maintaining reasonable signal-to-noise ratio and beamwidth over a wide frequency range. Overall, seven synthetic arrays with 800 microphones each, for a total of 5,600 microphones, were used in the framework. Fully synchronous synthetic pressure records at each microphone location were obtained from the simulated data sets via a Ffowcs-Williams and Hawkings integral approach, with flow quantities on a permeable data surface enclosing the source regions used as input. Various applications of synthetic multiplanar arrays to acoustic data analysis are presented; the advantages of the framework in properly resolving and identifying the B777 landing gear sources is demonstrated by comparing three-dimensional source distribution maps with those obtained from a synthetic version of the ground-based microphone array used during flight tests of the same aircraft.

airframe noise↗
DOE OSTI2023

Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network

As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).

Wang, Jinzhen↗
DOE OSTI2025

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems

Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

In-flight polarization angle calibration for LiteBIRD: blind challenge and cosmological implications

We present a demonstration of the in-flight polarization angle calibration for the JAXA/ISAS second strategic large class mission, LiteBIRD, and estimate its impact on the measurement of the tensor-to-scalar ratio parameter, r, using simulated data. We generate a set of simulated sky maps with CMB and polarized foreground emission, and inject instrumental noise and polarization angle offsets to the 22 (partially overlapping) LiteBIRD frequency channels. Our in-flight angle calibration relies on nulling the EB cross correlation of the polarized signal in each channel. This calibration step has been carried out by two independent groups with a blind analysis, allowing an accuracy of the order of a few arc-minutes to be reached on the estimate of the angle offsets. Both the corrected and uncorrected multi-frequency maps are propagated through the foreground cleaning step, with the goal of computing clean CMB maps. We employ two component separation algorithms, the Bayesian-Separation of Components and Residuals Estimate Tool (B-SeCRET), and the Needlet Internal Linear Combination (NILC). We find that the recovered CMB maps obtained with algorithms that do not make any assumptions about the foreground properties, such as NILC, are only mildly affected by the angle miscalibration. However, polarization angle offsets strongly bias results obtained with the parametric fitting method. Once the miscalibration angles are corrected by EB nulling prior to the component separation, both component separation algorithms result in an unbiased estimation of the r parameter. While this work is motivated by the conceptual design study for LiteBIRD, its framework can be broadly applied to any CMB polarization experiment. In particular, the combination of simulation plus blind analysis provides a robust forecast by taking into account not only detector sensitivity but also systematic effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Advancing Sea Ice Predictability in E3SM with Machine Learning

Focal area(s): To improve predictions of sea ice in E3SM we propose to develop a hierarchy of data-driven models using observational and simulation data to investigate the most important Earth system drivers of sea ice variability and loss, develop surrogates that build on the reduced parameter space of important drivers, and, where appropriate, couple machine learning models with standard PDE models to capture important physical behavior at different scales. This work falls under Focal Area 2. Predictive modeling through the use of AI techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hybrid (PDE+ML) models in the context of land ice modeling

Focal Area(s): Primary focal areas are: predictive modeling through the use of AI-derived model components; advanced methods including network design/optimization/deep learning. Science Challenge: The atmospheric, ocean and ice dynamics components of the Energy Exascale Earth System Model (E3SM) are governed by Partial Differential Equations (PDEs) and significant efforts have been made during the last decades to develop such computational models. Here we propose to fundamentally improve these PDE-based codes by enhancing them with Machine Learning (ML) sub-models for complex, poorly understood physical processes in the context of ice sheet modeling. We propose to train these models with a novel approach that allows the assimilation of the different sources of data available (direct/indirect observations and possibly simulation data), improving on existing simplified models. We also highlight computational challenges originating from the coexistence of PDE-based and ML-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Preliminary Comparison of Neutron Detector Sensitivities [Poster]

Nuclear data (ND) is vital to predictive simulations such as those completed with MCNP®. ND sensitivities are used to optimize the design of benchmark experiments. Diverse benchmarks (including neutron noise) are required to further our understanding of ND. Measured and simulated data of a 4.5-kg Pu sphere was used to calculate neutron noise parameters and associated ND Sensitivities

Nuclear Criticality Safety Program (NCSP)↗
DOE OSTI2021

Biological Mechanisms May Contribute to Soil Carbon Saturation Patterns: Modeling Archive

This Modeling Archive is in support of a TES-SFA publication “Biological Mechanisms May Contribute to Soil Carbon Saturation Patterns” (Craig et al., 2021). We ran and evaluated a multi-assumption soil organic carbon (SOC) model to investigate whether alternative assumptions regarding constraints on soil microbial biomass could lead to soil carbon saturation patterns. We developed this model in the Multi-Assumption Architecture and Testbed (MAAT, https://github.com/walkeranthonyp/MAAT, tag: v1.2.1_Craig2021; Walker et al. 2018). Using MAAT, we embedded three alternative hypotheses in a microbially explicit three-pool SOC model: 1) the efficiency of mineral-associated SOC formation decreases as mineral-associated SOC approaches a maximum value (“Mineral saturation”), 2) the microbial biomass turnover rate increases with increasing microbial biomass (“Density-dependent turnover”), and 3) community carbon use efficiency decreases as microbial biomass increases toward an upper limit (“Density-dependent growth”). We ran a factorial combination of these hypotheses resulting in eight models for three different classes of model (linear decay, Michaelis-Menten decay, or reverse Michaelis-Menten decay), resulting in 24 models, 12 of which are presented or discussed in the related publication. Models were parameterized using values from previous studies with similar models (Wang et al. 2013, Wieder et al. 2014, Li et al. 2014, Georgiou et al. 2017, Hassink and Whitmore 1997) and ran to an approximate steady state (200 years) at six (6) different C input rates corresponding to 0.5, 1, 2, 4, 7, and 10 times the default input value. Further model details are available in the related publication. This archive contains output from three MAAT simulations, and scripts to run these simulations and process and plot the data. Simulations are labeled “lin”, “MM_highKm”, and “RMM_highKm” reflecting factorial runs for linear, Michealis-Menten, and reverse Michaelis-Menten models, respectively. This archive contains: • 3 R scripts prepended with “init_MAAT_” to initialize runs (1 for each simulation), • 1 csv file containing years over which to run simulations (“met_year.csv”), • 1 bash script (.bs) to run MAAT, • 6 XML files that are output from MAAT describing a run (2 for each simulation), • 3 model output csv files prepended by “out_” (1 for each simulation), and • 1 analysis R script for reproducing figures 3 and 4 in Craig et al. 2021. See included user guide (Craig_2021_modeling_archive_20210315.pdf) for file organization details.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improved Treatment of Host-galaxy Correlations in Cosmological Analyses with Type Ia Supernovae

Improving the use of Type Ia supernovae (SNe Ia) as standard candles requires a better approach to incorporate the relationship between SNe Ia and the properties of their host galaxies. Using a spectroscopically confirmed sample of ~1600 SNe Ia, we develop the first empirical model of underlying populations for SNe Ia light-curve properties that includes their dependence on host-galaxy stellar mass; we find a significant correlation between stretch population and stellar mass (99.9% confidence) and a weaker correlation between color and stellar mass (90% confidence). These populations are important inputs to simulations that are used to model selection effects and correct distance biases within the BEAMS with Bias Correction (BBC) framework. Here we improve BBC to also account for SNe Ia-host correlations, and we validate this technique on simulated data samples. Here, we recover the input relationship between SNe Ia luminosity and host-galaxy stellar mass (the mass step, γ) with a bias of 0.004 ±0.001 mag, which is a factor of 5 improvement over previous methods that have a γ bias of ~0.02 ± 0.001 mag. We adapt BBC for a novel dust-based model of intrinsic brightness variations, which results in a greatly reduced mass step for data (γ = 0.017 ± 0.008) and for simulations (γ = 0.006 ± 0.007). Analyzing simulated SNe Ia, the biases on the dark energy equation of state, w, vary from Δw = 0.006(5) to 0.010(5) with our new BBC method; these biases are significantly smaller than the 0.02(5) w bias using previous BBC methods that ignore SNe Ia-host correlations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1978

A digital strategy for manometer dynamic enhancement

Application of digital signal processing techniques to improve the non-linear dynamic characteristics of a sonar-type mercury manometer is described. The dynamic enhancement strategy quasi-linearizes the manometer characteristics and improves the effective bandwidth in the context of a wind-tunnel pressure regulation system. Model identification data and real-time hybrid simulation data demonstrate feasibility of approach.

Stoughton, J. W.↗
NASA NTRS1982

Discrimination of growth and water stress in wheat by various vegetation indices through a clear a turbid atmosphere

Reflectance data were obtained over a drought-stressed and a well-watered wheat plot with a hand-held radiometer having bands similar to the MSS bands of the LANDSAT satellites. Data for 48 clear days were interpolated to yield reflectance values for each day of the growing season, from planting until harvest. With an atmospheric path radiance model and LANDSAT-2 calibration data, the reflectance were used to simulate LANDSAT digital counts (not quantized) for the four LANDSAT bands for each day of the growing season, through a clear (approximately 100 km meteorological range) and a turbid (approximately 10 km meteorological range) atmosphere. Several ratios and linear combinations of bands were calculated using the simulated data, then assessed for their relative ability to discriminate vegetative growth and plant stress through the two atmospheres. The results show that water stress was not detected by any of the indices until after growth was retarded, and the sensitivity of the various indices to vegetation depended on plant growth stage and atmospheric path radiance.

Jackson, R. D.↗