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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Tracing U.S. fuel life-cycle greenhouse gas emissions in a multi-sector dynamics model using LC-GCAM

Model-based analysis of fuel pathways is essential for informing energy and environmental policy. Two major model types are typically used: multi-sector dynamics models, which capture the broader energy-economy, such as GCAM (Global Change Analysis Model), and life cycle assessment models, such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation). Each has distinct strengths and limitations, and recent studies increasingly adopt hybrid approaches to harness the advantages of both. However, such integration is often time-consuming and complicated by inconsistencies in system boundaries and technology definitions. We present LC-GCAM, a new tool that enables estimation of life-cycle greenhouse gas emissions and primary energy use for any fuel pathway represented in GCAM. We apply LC-GCAM to 300 scenarios designed to explore key uncertainties affecting the life-cycle performance of future fuel options in the U.S. freight sector. To evaluate LC-GCAM, we compare its results with those from GREET for nine fuel types in a 2030 reference scenario. When input assumptions are modestly aligned, LC-GCAM and GREET estimates typically agree within 10% (absolute sum-based mean absolute percentage error). LC-GCAM offers a flexible and efficient approach to generating life-cycle metrics within an integrated modeling framework, supporting robust policy analysis across a wide range of interacting energy system uncertainties.

Wolfram, Paul↗
DOE OSTI2024

Analysis of BOSS galaxy data with weighted skew-spectra

We present the first application of the weighted skew-spectra to analyze non-Gaussian information in galaxy survey data. Using the tree-level galaxy skew-spectra together with the one-loop power spectrum multipoles, we analyze the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) galaxy clustering data, and target our search towards the equilateral bispectrum shape of primordial non-Gaussianity. We use the Effective Field Theory model for the galaxy power spectrum and bispectrum, and account for systematic effects, such as the survey geometry. From our likelihood analysis, we find f NL equil = -34 +296 -334 at 68% CL, consistent with previous works, while systematic errors from our treatment of the survey geometry lead to an unreliable estimation of f NL ortho . As a result, we further constrain the bias and counterterm parameters, while keeping the cosmology fixed to Planck 2018 values. As a check, we also validate our analysis pipeline using theNseries simulation suite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Distributed State Estimation Over Time-Varying Graphs: Exploiting the Age-of-Information

Here, we study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-time-scale distributed observer that works under mild assumptions. Specifically, our communication model only requires strong-connectivity to be preserved over non-overlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. We also develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a "freshness-index" that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.

42 ENGINEERING↗
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗
DOE OSTI2021

On the Use of Field RR Lyrae as Galactic Probes. V. Optical and Radial Velocity Curve Templates

We collected the largest spectroscopic catalog of RR Lyrae (RRLs) including ≈20,000 high-, medium-, and low-resolution spectra for ≈10,000 RRLs. We provide the analytical forms of radial velocity curve (RVC) templates. These were built using 36 RRLs (31 fundamental—split into three period bins—and five first-overtone pulsators) with well-sampled RVCs based on three groups of metallic lines (Fe, Mg, Na) and four Balmer lines (H$α$, H$β$, H$γ$, H$δ$). We tackled the long-standing problem of the reference epoch to anchor light-curve and RVC templates. For the V-band, we found that the residuals of the templates anchored to the phase of the mean magnitude along the rising branch are ~35% to ~45% smaller than those anchored to the phase of maximum light. For the RVC, we used two independent reference epochs for metallic and Balmer lines and we verified that the residuals of the RVC templates anchored to the phase of mean RV are from 30% (metallic lines) up to 45% (Balmer lines) smaller than those anchored to the phase of minimum RV. We validated our RVC templates by using both the single-point and the three phase point approaches. We found that barycentric velocities based on our RVC templates are two to three times more accurate than those available in the literature. We applied the current RVC templates to Balmer lines RVs of RRLs in the globular NGC 3201 collected with MUSE at VLT. We found the cluster barycentric RV of $V$$γ$ = 496.89 ± 8.37(error) ± 3.43 (standard deviation) km s -1 , which agrees well with literature estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range–height indicator (RHI) scans

Abstract. This study extends the relative calibration adjustment technique for calibration of weather radars to higher-frequency radars as well as range–height indicator (RHI) scans. The calibration of weather radars represents one of the most dominant sources of error for their use in a variety of fields including quantitative precipitation estimation and model comparisons. While most weather radars are routinely calibrated, the frequency of calibration is often less than required, resulting in miscalibrated time periods. While full absolute calibration techniques often require the radar to be taken offline for a period of time, there have been online calibration techniques discussed in the literature. The relative calibration adjustment (RCA) technique uses the statistics of the ground clutter surrounding the radar as a monitoring source for the stability of calibration but has only been demonstrated to work at S- and C-band for plan-position indicator (PPI) scans at a constant elevation. In this work the RCA technique is modified to work with higher-frequency radars, including Ka-band cloud radars. At higher frequencies the properties of clutter can be much more variable. This work introduces an extended clutter selection procedure that incorporates the temporal stability of clutter and helps to improve the operational stability of RCA for relatively higher-frequency radars. The technique is also extended to utilize range–height scans from radars where the elevation is varied rather than the azimuth. These types of scans are often utilized with research radars to examine the vertical structure of clouds. The newly extended technique (eRCA) is applied to four Department of Energy Atmospheric Radiation Measurement (DOE ARM) weather radars ranging in frequency from C- to Ka-band. Cross comparisons of three co-located radars with frequencies C, X, and Ka at the ARM Cloud, Aerosol, and Complex Terrain Interactions (CACTI) site show that the technique can determine changes in calibration. Using an X-band radar at the ARM Eastern North Atlantic (ENA) site, we show how the technique can be modified to be more resilient to clutter fields that show increased variability, in this case due to sea clutter. The results show that this technique is promising for a posteriori data calibration and monitoring.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Nuclear Criticality Safety Integral Experiment Covariance Determination

Integral benchmarks for criticality safety and nuclear data validation require expensive uncertainty quantification studies. Commonly, the uncertainty quantification ignores correlations between experiments that share components. Experiments such as the TEX (Thermal/Epithermal eXperiments) campaigns consist of many shared parts between experiments, such as fuel, which creates a strong correlation in their errors. While these correlations are known to exist, they are often not estimated due to the complexity of such calculations. This paper describes a software package that uses an intuitive method of determining the covariance for each of the experimental components, providing a correlation matrix for each family of parts across the multiple cases examined within a benchmark. The code uses the TEX-HEU campaign as a proof of concept, and we show that the correlations can be calculated with information commonly found in ICSBEP (International Criticality Safety Benchmark Evaluation Project) benchmarks. The estimated covariances are used in χ 2 trending studies to evaluate their impact on nuclear data validation. The covariance determination code can be easily integrated into current benchmark evaluations as well as reevaluating legacy benchmark uncertainties. Uncertainty correlation calculations should become the baseline for criticality safety integral experiment benchmarks and can now be easily calculated with the described software package.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

LTAU (Loss Trajectory Analysis for Uncertainty) is a technique for estimating the uncertainty in a model's predictions for any regression task by approximating the cumulative distribution function (CDF) of the model's errors over the course of training. The approximated CDF, combined with a similarity search algorithm in the model's descriptor space, can then be used to estimate the likelihood that the model's predictions on any given test point will fall below a chosen threshold. LTAU-FF (LTAU in atomistic Force Fields) is the application of LTAU specifically for use with atomistic force fields.

Vita, JoshuaA↗
DOE OSTI2026

Regional inertia dynamics of U.S. interconnections: An event-based measurement approach

Power grid inertia plays a vital role in frequency stability following large disturbances, yet its distribution across the U.S. grid is highly uneven. While interconnection-wide inertia benchmarks are useful, they can mask regional variability driven by resource mix, network coupling, and geographic separation. This paper extends event-driven inertia estimation to the regional scale using field measurements from the Frequency Monitoring Network (FNET/GridEye). Starting from balancing authority and independent system operator footprints, candidate regions are refined using a composite coherency score that combines frequency-trajectory shape similarity, timing spread, and lead/lag behavior to ensure dynamic consistency. A filtered sliding difference method (FSDM) is then used to construct regional frequency trajectories, detect disturbance onset, and compute robust regional rate-of-change of frequency (RoCoF). Regional, local, and interconnection inertia are estimated by combining RoCoF with event power imbalance, and additional indicators (regional-to-system inertia ratio and inertial-support arrival time) quantify regional-to-interconnection coupling and relative regional contributions. The method is demonstrated on eleven regions across the Eastern Interconnection (EI) and the Western Electricity Coordinating Council (WECC), with the Electric Reliability Council of Texas (ERCOT) used for validation. In ERCOT, estimates compared against energy management system (EMS) values achieve a mean absolute percentage error of 17.94%. WECC exhibits consistently shorter inertial-support arrival times (0.15–0.3 s) than EI (0.7–1.1 s), highlighting contrasting coupling and disturbance-propagation behavior. Overall, the results reveal pronounced spatial heterogeneity in inertia and coupling, underscoring the value of regional monitoring for both operational decision-making and long-term system planning.

Disturbance events↗
DOE OSTI2023

Insights From Dayflow: A Historical Streamflow Reanalysis Dataset for the Conterminous United States

Abstract Reconstructed historical streamflow time series can supplement limited streamflow gauge observations. However, there are common challenges of typical modeling approaches: process‐based hydrologic models can be data/computation‐intensive, and statistics‐based models can be region/stream‐specific. Here we present a nationally scalable modeling framework integrating the simulated runoff from the Variable Infiltration Capacity (VIC) model with the Routing Application for Parallel computatIon of Discharge (RAPID) routing model leveraging high‐performance computing. We demonstrate an efficient method of assimilating streamflow at US Geological Survey (USGS) streamflow monitoring sites using a simple hierarchical approach in the VIC‐RAPID framework. The result is a reconstructed 36‐year (1980–2015) daily and monthly streamflow dataset (Dayflow) at ∼2.7 million NHDPlusV2 stream reaches in the conterminous US (CONUS). We perform a comprehensive evaluation at 7,526 USGS sites and characterize their error statistics. The results demonstrate that 49% of the USGS sites demonstrate Kling–Gupta Efficiency (KGE) > 0.5 and 58% of the sites show percentage bias within ±20% for the daily naturalized streamflow. Streamflow data assimilation across CONUS shows an overall improvement over naturalized streamflow, notably in the western semiarid‐to‐arid regions. Comparison to other national and global streamflow reanalysis datasets such as the National Water Model and Global Reach‐scale A priori Discharge Estimates for SWOT demonstrates improved KGE, reduced bias, and directions for Dayflow improvements. Investigations of error statistics with key hydrologic, hydroclimatic, and geomorphologic basin characteristics reveal region‐specific patterns which may help improve future framework applications. Overall, Dayflow may enable a better understanding of hydrologic conditions in a changing environment, especially in locations currently not represented by streamflow monitoring networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recalculation of Soil Bulk Density Used in the Scorpius MCNP6 Model

The soil composition used in the Scorpius MCNP6.2 (Ref. 1) model was recently recalculated. Originally, it was “determined using the United States Geological Survey (USGS) Mercury Core Library and the Nevada National Security Site U.S. Geological Survey Databases (NNSS USGS) and Nevada National Security Site Petrographic, Geochemical, and Geophysical Database (NNSS PGG),” but the details had been lost. Reference 2 documents the updated soil composition determination. Reference 2 estimated the grain density of the soil instead of the bulk density. This report corrects that error. I am indebted to Garrett Euler (EES-17) for reading Ref. 2, pointing out its errors, and guiding me through the correct calculations. This report is organized as follows. For completeness, the composition calculations from Ref. 2 are repeated: Sec. II discusses the data that are reported in the PGG Access database, and Sec. III uses the data in the PGG Access database to compute the composition of the soil. Section IV estimates the bulk density of the soil. Section V evaluates the soil wall thickness in the Scorpius model. Section VI estimates the effect of the soil density change on previously calculated results. Section VII presents a summary and conclusions. Input files and output files are listed in appendices.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

A Bayesian analysis of classical shadows

Abstract The method of classical shadows proposed by Huang, Kueng, and Preskill heralds remarkable opportunities for quantum estimation with limited measurements. Yet its relationship to established quantum tomographic approaches, particularly those based on likelihood models, remains unclear. In this article, we investigate classical shadows through the lens of Bayesian mean estimation (BME). In direct tests on numerical data, BME is found to attain significantly lower error on average, but classical shadows prove remarkably more accurate in specific situations—such as high-fidelity ground truth states—which are improbable in a fully uniform Hilbert space. We then introduce an observable-oriented pseudo-likelihood that successfully emulates the dimension-independence and state-specific optimality of classical shadows, but within a Bayesian framework that ensures only physical states. Our research reveals how classical shadows effect important departures from conventional thinking in quantum state estimation, as well as the utility of Bayesian methods for uncovering and formalizing statistical assumptions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Open Source Fault-tolerant Grid Frequency Measurement for Solar Inverters

The Discrete Fourier transform (DFT) based measurement algorithms are one of the most common measurement algorithms for grid parameter estimation such as rms, phase angle, frequency. Over the past few years, many DFT based algorithms have been developed to enhance its measurement accuracy under steady-state and/or dynamic grid conditions. For example, an adaptive band-pass filter utilizing exponential modulation filter has been proposed to reduce measurement errors at the presence of large frequency deviations. Measurement accuracy of different algorithms including FIR filter, extended Kalman filtering (EKF), and enhanced DFT method have been compared in detail under different grid conditions. Two artificial signals that have 90-degree phase difference were constructed by the Clarke transformation to address the frequency spectrum leakage of DFT. A multi-module approach was developed to enhance both steady-state and dynamic measurement accuracies, in which each module was developed to eliminate some specific errors. Besides DFT-based measurement algorithms, some signal model-based algorithms have been developed to further improve the accuracy under dynamic conditions. However, a key drawback of the state-of-the-art algorithms is that they cannot perform measurements accurately during system transient faults. In the Blue Cut Fire event, there was a phase angle jump of about 26 degrees in the voltage waveform during the transient fault. The phase angle jump fault will cause waveform discontinuity, and these algorithms will fail to provide reliable measurements during this period because they typically assume the waveform to be measured is continuous, no matter what method (DFT, PLL, EKF, FIR, or Taylor WLS) is used for estimation. In fact, the measurement errors during the system transient faults like phase-jump is not required in the IEEE Standard. As a result, although a measurement instrument can pass the strict IEEE Standard, it could still be the source of the problem in the future if we have similar system transient faults, which could happen again. Therefore, developing the fault-tolerant measurement technology is the key to solve the problem.

14 SOLAR ENERGY↗
DOE OSTI2020

Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from 1000 pressure signals recorded at local ranges (<20 km) with scaled distance up to 8000 m/kg 1/3 . Additionally, the measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

58 GEOSCIENCES↗
DOE OSTI2023

Consequences and benefits of utilizing continuous vibro-impact representations in constrained pipeline conveying fluid systems

Here, the effectiveness of continuous vibro-impact forcing representations for the cantilevered pipe that conveys fluid is explored and analyzed. The previously accepted forcing model utilizing a smoothened trilinear spring is estimated using three continuous forcing representations, namely, polynomial, rational polynomial, and hyperbolic tangent. The accuracy of the estimated forcing functions is investigated and analyzed by calculating the root mean square error, and bifurcation diagrams are generated and compared to the nominal system. Additionally, the dynamic response of the system is further characterized using Poincare maps, power spectra, and basins of attraction. Once all continuous forcing representations are analyzed and compared to the nominal system, the computational cost of each method is examined, and further limitations of the hyperbolic tangent method are discovered. It is proved that the hyperbolic tangent forcing representation most accurately captures the dynamic response of the pipeline, and the least accurate representation is the rational polynomial representation. Additionally, considerable computational cost is saved when employing the hyperbolic tangent representation compared to the discontinuous representation.

42 ENGINEERING↗