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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Transfer function models for using empirical and physics-based simulation signal response data

In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part geometry specimens or in real parts. Typically, surface fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or cylindrical specimens. If a nondestructive evaluation (NDE)technique is required to provide a reliably detectable flaw size, denoted asa90/95, for induced flaws in a part, then a direct method for qualifying the NDE procedure is to use appropriate induced flaw specimens and perform NDE procedure demonstration on the specimens. Probability of detection (POD)analysis of the empirical data may provide estimation of a90/95. This approach is described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where embedded tight cracklike induced flaws are to be detected reliably using a signal response based NDE procedure. Here, it is assumed that it is not practical to make surface or embedded induced flaw specimens in part geometry or configuration. Therefore, a direct POD demonstration testing cannot be undertaken. It is also assumed that simulation of signal response is possible for both surface and embedded induced flaws in part geometry specimens using a physics-based model. The proposed approach for NDE procedure qualification uses artificial flaws in simple geometry and part geometry specimens, and induced flaws in the same type of simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws in simple geometry and part geometry specimens. Moreover, simulated signal response data is generated for surface and embedded flaws. Thus, a case of five signal response versus flaw size datasets is considered. Three of the datasets are empirical and two datasets are physics model-based simulation datasets. A method of devising and using transfer function calculation dataset blocks to estimate the either the reliably detectable flaw size or the demonstration flaw size is provided.

Nondestructive evaluation↗
NASA NTRSDate not supplied

Utilizing Experimental-based Line Positions for Semi-empirical IR Line Lists

Molecular IR line lists computed from semi-empirically refined ab initio potential energy surface and high quality ab initio dipole moment surface may have line position accuracy typically within 0.01 - 0.05 cm−1. For high resolution spectroscopy databases, it is necessary to integrate the computed theoretical IR intensities with the line positions accurately determined from experiments and (or) reliably derived from Effective Hamiltonian models. However, it is not a trivial task, and the choices in reality are heavily contingent upon the coverage, consistency, and accuracy of the data available for a specific molecule or isotopologue. We will present several approaches applied to recent Ames IR line lists of CO 2 1, N 2 O 2 , and OCS to demonstrate how challenging such integration may become, and what we have learned From these projects. Discussions will focus on the relation between each different scenario and the corresponding choice or solution, including their advantages and limits, how the semi-empirical IR line lists can help, and what else may be needed for future improvements. We will emphasize that the “Theory+Experiment” synergy may still play significant role in the determination of the best line positions.

Effective Hamiltonian models↗
NASA NTRS2023

Comparative Accuracies of Models for Drag Prediction During Geomagnetically Disturbed Periods: A First Principles Model Versus Empirical Models

We examine the accuracy of density prediction by the first principles model Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) developed by the National Center for Atmospheric Research and compare it to the accuracy of three empirical models: Jacchia 71, the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Extended 2000 (NRLMSIS), Jacchia 1971, and Jacchia-Bowman 2008. Comparisons are made for three large storms: the October 2003 storm, the March 2013 storm, and the March 2015 storm. To evaluate the accuracy of these models we use tracking data for nine space objects in low Earth orbit. Additionally, we evaluate the accuracy of the TIEGCM and NRLMSIS with data from high precision accelerometers on the Challenging Minisatellite Payload (CHAMP) and Gravity field and Circulation Explorer (GOCE) satellites. The goal is to assess the use of a first principles model as a potential tool for forecasting satellite drag during large magnetic storms. For the storms considered, we found the TIEGCM, JB2008, and NRLMSIS models to be substantially more accurate than the Jacchia 71 model. The accuracies of the TIEGCM and JB2008 models were similar, but overall, the TIEGCM was more accurate. We found smaller differences for TIEGCM versus CHAMP than for NRLMIS for the Halloween Storm, and smaller differences than results published for JB2008 and the assimilative model HASDM. The empirical models are at present more practical for operational purposes, but the TIEGCM, developed as a research model, with a greater focus on operational use offers the potential for improved utility during stressing conditions.

R. L. Walterscheid↗
DOE OSTI2020

Vertex Reordering for Real-world Graphs and Applications: An Empirical Evaluation

Vertex reordering is a way to improve locality in graph computations. Given an input (or ``natural'') order, reordering aims to compute an alternate permutation of the vertices that is aimed at maximizing a locality-based objective. Given decades of research on this topic, there are tens of graph reordering schemes, and there are also several linear arrangement ``gap'' measures for treatment as objectives. However, a comprehensive empirical analysis of the efficacy of the ordering schemes against the different gap measures, and against real-world applications is currently lacking. In this study, we present an extensive empirical evaluation of up to 11 ordering schemes, taken from different classes of approaches, on a set of 34 real-world graphs emerging from different application domains. Our study is presented in two parts: a) a thorough comparative evaluation of the different ordering schemes on their effectiveness to optimize different linear arrangement gap measures, relevant to preserving locality; and b) extensive evaluation of the impact of the ordering schemes on two real-world, parallel graph applications, namely, community detection and influence maximization. Our studies show a significant divergence among the ordering schemes (up to $40\times$ between the best and the poor) in their effectiveness to reduce the gap measures; and a wide ranging impact of the ordering schemes on various aspects including application runtime (up to $4\times$), memory and cache use, load balancing, and parallel work and efficiency. The comparative study also help reveal the nuances of a parallel environment (compared to serial) on the ordering schemes and their role in optimizing applications.

Barik, Reet↗
DOE OSTI2025

Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO 2 and UO 2

The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which is well suited for capturing phonons and phonon-related properties. Our approach is based on direct comparisons of the second- and third-order irreducible derivatives (IDs) between an EIP and the Born–Oppenheimer potential within density functional theory (DFT) calculations. IDs fully exploit space group symmetry and allow for training without redundant information. We demonstrate the fidelity of our approach in the context of ThO 2 and UO 2 , where we optimize parameters of an embedded-atom method potential in addition to core–shell interactions. Our EIPs provide thermophysical properties in good agreement with DFT and outperform widely utilized EIPs for phonon dispersion and thermal conductivity predictions. Reasonable estimates of thermal expansion and formation energies of Frenkel pairs are also obtained.

empirical interatomic potential↗
DOE OSTI2026

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Addressing the "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the measured energy consumption of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 41,129 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (trainable parameters), 8 "shapes", and 14 depths on both CPUs and GPUs using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use. Our analysis uncovers a surprising, hardware-mediated non-linear relationship between energy efficiency and network design, challenging the assumption that reducing the number of parameters or FLOPs is the best way to achieve greater energy efficiency. We propose a straightforward and effective energy model that accounts for network size, computing, and memory hierarchy. Highlighting the need for cache-considerate algorithm development, we suggest a codesign approach to energy efficient network, algorithm, and hardware design. This work contributes to the fields of sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Projected Climate Impacts to South African Maize and Wheat Production in 2055: A Comparison of Empirical and Mechanistic Modeling Approaches

Crop model-specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs' median-projected maize and wheat yield changes were 3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water-use efficiency gains simulated by the MMs under elevated CO2 accounted for much of the EMMM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EMMM comparisons to provide a fuller picture of crop-climate response uncertainties.

DSSAT↗
NASA NTRS2021

Nonlinear Unsteady Aerodynamic Modeling Using Empirical Orthogonal Functions

Empirical orthogonal function modeling is explained and applied to identify compact discrete-time nonlinear unsteady aerodynamic models from data generated by an unsteady three-dimensional compressible Navier-Stokes flow solver for an airfoil undergoing various pitching motions. Model structures, model parameter estimates, and model parameter uncertainty estimates for nondimensional lift, drag, and pitching moment coefficient models were determined autonomously and directly from the data. Prediction tests using data that were not used in the modeling process showed that the identified models exhibited excellent prediction capability, which is a strong indicator of an accurate model.

empirical↗
NASA NTRS2015

Empirical InfraRed Line Lists for Five SO2 Isotopologues: 32/33/34/36S16O2 and 32S18O2

Using the latest published, empirically refined potential energy surface (PES) Ames-1 and purely ab initio CCSD(T)/aug-cc-pVQZ dipole moment surface (DMS), we have computed Infrared line lists for five symmetric isotopologues of sulfur dioxide: 32S16O2 (626), 33S16O2 (636), 34S16O2 (646), 36S16O2 (666), and 32S18O2 (828). The line lists are based on J=0-80 rovibrational variational calculations with E’ ≤ 8000 cm-1. The 34S16O2 and 33S16O2 line lists are compared to the experiment-based models in the HIgh-resolution TRANsmission molecular absorption database (HITRAN2012, http://www.cfa.harvard.edu/hitran/) and the Cologne Database for Molecular Spectroscopy, CDMS (http://www.astro.uni-koeln.de/cdms/). The accuracy for computed 646 band origins is similar to what has been reported for the main isotopologue, i.e. 0.01 – 0.03 cm-1 for bands up to 5500 cm-1. For rovibrational transitions, the 646 line position and intensity deviation patterns are much simpler and more self-consistent than those of the main isotopologue 626. The discrepancies are mainly found for higher Ka/J transitions. 626 and 646 exhibit comparable line position and intensity agreement for lower Ka/J transitions. The line position deviations for the 636 purely rotational band are parallel to those of 626 and 646, while its line intensity deviations do not show branching patterns as we found in the 626 and 646 cases. Predictions for the other minor isotopologues are expected to exhibit similar accuracy. These line lists are accurate enough to provide alternatives for missing bands of 626 and the minor isotopologues. It may significantly facilitate the laboratory spectroscopic measurement and analysis, as well as to identify these isotopologues in various astrophysical environments.

Sulfur Dioxide↗
DOE OSTI2025

First‐Order Empirical Interpolation Method for Real‐Time Solution of Parametric Time‐Dependent Nonlinear PDEs

ABSTRACT We present a model reduction approach for the real‐time solution of time‐dependent nonlinear partial differential equations (PDEs) with parametric dependencies. A major challenge in constructing efficient and accurate reduced‐order models for nonlinear PDEs is the efficient treatment of nonlinear terms. We address this by unifying the implementation of hyperreduction methods to deal with nonlinear terms. Furthermore, we introduce a first‐order empirical interpolation method (EIM) to provide an efficient approximation of the nonlinear terms in time‐dependent PDEs. We demonstrate the effectiveness of our approach on the Allen–Cahn equation, which models phase separation, and the Buckley–Leverett equation, which describes two‐phase fluid flow in porous media. Numerical results highlight the accuracy, efficiency, and stability of the proposed method compared with both the Galerkin–Newton approach and hyper‐reduced models using the standard EIM.

Nguyen, Ngoc Cuong [Center for Computational Engin↗
DOE OSTI2026

Out With the Old: Empirical Trends in U.S. Land‐Based Wind Turbine Decommissioning and Repowering

A growing number of wind turbines (WTs) across the globe are now reaching or exceeding their expected service lifetime; WT decommissioning is on the rise. Accordingly, questions pertaining to WT end-of-life have risen in importance in policy and practice. Yet, research on the various factors relating to WT decommissioning is relatively sparse. Moreover, the key assumptions underpinning that prior research (e.g., the lifespan of WTs, characteristics of WTs being decommissioned, and whether the site is repowered with new WTs) have never been empirically tested across a large set of decommissioned WTs. Leveraging a uniquely comprehensive and spatially explicit dataset of decommissioned WTs in the United States, this research analyzes spatial, technological, and temporal trends in WT decommissioning and develops a novel predictive model for WT decommissioning. Our analysis pinpoints more than 12,400 WTs that have been fully decommissioned in the United States., the majority of which have been relatively old (> 30 years) and small (< 200 kW). While a WT's age alone is a good predictor of the likelihood of decommissioning, other factors such as the size of the WT and recent performance are also important and significant predictors. Most sites where decommissioning has occurred have seen subsequent repowering, with repowered plants featuring substantially fewer WTs (−86 on average) and higher rated plant capacity (+62 MW on average). Many existing WTs in the U.S. are approaching the end of their expected life with roughly 7500 being 20 or more years old. Findings can help policymakers and stakeholders begin preparing for this potential wave of future decommissioning and repowering.

Decommissioning / End-of-life↗
DOE OSTI2024

Empirical Characterization and Modeling of Cohesive – to – Adhesive Shear Fracture Mode Transition due to Increased Adhesive Layer Thicknesses of Fiber Reinforced Composite Single – Lap Joints

Here, to ensure a strong adhesive bond, most standards and adhesive manufacturers specify a maximum adhesive gap of 1 mm when bonding fiber reinforced composite structures. In manufacturing large components, such as joining two halves of wind turbine blades, meeting this gap tolerance specification is impractical; gaps larger than 10 mm are common in large adhesively bonded composite structures using state-of-the-art manufacturing techniques. Currently, there is a lack of fundamental understanding of the failure mechanics of adhesive gaps larger than 3 mm. To create such understanding, glass fiber - acrylic thermoplastic composite panels bonded using different epoxy adhesives within single-lap joint samples with adhesive thicknesses of 0.1 mm, 0.3 mm, 1 mm, 3 mm, 5 mm, and 10 mm were sheared to failure. A transition from cohesive to adhesive failure was observed to occur about 1 mm to 3 mm joint thicknesses. Plotting the shear stress normalized by the ratio of the joint width to thickness as a function of the joint thickness normalized by the joint length is shown to result in the ability to fit simple empirically derived models of the cohesive-to-adhesive failure transition, regardless of the adhesive. Furthermore, using these normalized variables, all the observed cohesively failed specimens collapse to a single master curve, as do the adhesively failed specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An empirical investigation of organic software product lines

Abstract Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 10 20 products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An empirical analysis of supply offers in the ERCOT operating reserves markets

Here, this paper seeks to improve theoretical and empirical understanding of supplier dynamics in wholesale markets for operating reserves, which have been understudied compared to energy markets. We begin by identifying several economic factors that unit owners may consider when submitting offers into operating reserves auctions in two-stage, co-optimized markets common across much of North America. Next, we analyze historical offer data from the Electric Reliability Council of Texas (ERCOT) market to assess whether actual reserve market behavior aligns with expectations based on economic theory, as well as with commonly used assumptions in electricity market modeling efforts. We find that the aggregate supply of operating reserves in ERCOT varies meaningfully over time, becoming more expensive during summer afternoons, which is consistent with theoretical expectations but contradicts the typical modeling assumption of temporally invariant reserve offers. Analysis of offers made by individual units uncovers additional insights, such as the existence of large offer pattern differences by unit owner and the tendency of battery storage units to submit very low offer prices. We conclude by discussing how our findings can be integrated into electricity market modeling assumptions to improve alignment with observed operating reserve offer inputs and pricing outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Association of residential energy efficiency retrofits with indoor environmental quality, comfort, and health: A review of empirical data

This paper reviews empirical data from evaluations of the influence of residential energy efficiency retrofits on indoor environmental quality conditions and self-reported thermal comfort and health. The data were extracted from 36 studies described in 44 papers plus two reports. Nearly all reviewed studies were performed in Europe or United States. Most studies evaluated retrofits of homes with low-income occupants. Indoor radon and formaldehyde concentrations tended to increase after retrofits that did not add whole-house mechanical ventilation. Study-average indoor concentrations of nitrogen dioxide and volatile organic compounds other than formaldehyde increased and decreased with approximately equal frequency. Average indoor temperatures during winter typically increased after retrofits, usually by less than 1.5 °C. Dampness and mold, usually based on occupant's reports, almost always decreased after retrofits. Subjectively reported thermal comfort, thermal discomfort, non-asthma respiratory symptoms, general health, and mental health nearly always improved after retrofits. For asthma symptoms, the evidence of improvement slightly outweighed the evidence of worsening. There was insufficient evidence to determine whether changes in thermal comfort and health outcomes varied depending on the type of energy efficiency retrofit. The published research has numerous limitations including a lack of data from retrofits in warm-humid climates and minimal data on changes in objective health outcomes. Suggestions for future research are provided.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Automated workflow for non-empirical Wannier-localized optimal tuning of range-separated hybrid functionals

Here, we introduce an automated workflow for generating non-empirical Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functionals. WOT-SRSH functionals have been shown to yield highly accurate fundamental band gaps, band structures, and optical spectra for bulk and 2D semiconductors and insulators. Our workflow automatically and efficiently determines the WOT-SRSH functional parameters for a given crystal structure and composition, approximately enforcing the correct screened long-range Coulomb interaction and an ionization potential ansatz. In contrast to previous manual tuning approaches, our tuning procedure relies on a new search algorithm that only requires a few hybrid functional calculations with minimal user input. We demonstrate our workflow on 23 previously studied semiconductors and insulators, reporting the same high level of accuracy. By automating the tuning process and improving its computational efficiency, the approach outlined here enables applications of the WOT-SRSH functional to compute spectroscopic and optoelectronic properties for a wide range of materials.

Gant, Stephen E. [University of California, Berkel↗
DOE OSTI2020

Cybersecurity Vulnerability Mitigation Framework through Empirical Paradigm: Enhanced Prioritized Gap Analysis

Existing cybersecurity vulnerability assessment tools were designed based on the policies and standards defined by organizations such as the U.S. Department of Energy and the National Institute of Standards and Technology (NIST). Frameworks such as the cybersecurity capability maturity model (C2M2) and the NIST Cybersecurity Framework (CSF) are often used by the critical infrastructure owners and operators to determine the cybersecurity maturity of their facility. Although these frameworks are exceptional at performing qualitative cybersecurity analysis and identifying vulnerabilities, they do not provide a means to perform prioritized mitigation of those vulnerabilities in order to achieve a desired cybersecurity maturity. To address that challenge, we developed a framework and software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). This paper presents the detailed architecture of CyFEr’s enhanced prioritized gap analysis (EPGA) methodology and its application to CSF. The efficacy of the presented framework is demonstrated by comparing against existing similar models and testing against the cyber injects from a real-world cyber-attack that targeted industrial control systems (ICS) in critical infrastructures.

Gourisetti, Sri Nikhil G.↗
DOE OSTI2020

A rigorous non-empirical theoretical analysis of the 2p XPS of NiO: Is it necessary to invoke nonlocal screening?

The role of ligand field effects and many-body effects arising from angular momentum coupling and shake effects for the XPS of NiO, as a representative transition metal oxide, have been investigated using rigorous, non-empirical wave functions for single site cluster models. Here it is shown that important features of the XPS arise from proper treatment of angular momentum coupling and it is unnecessary to invoke non-local screening to explain the experimental XPS. Contrary to the usual understanding, it is shown that inclusion of shake excitations in the many body wavefunctions is responsible for the high BE satellites that are observed. The contribution of covalent mixing of metal and ligand orbitals in the closed shells to screening of core-holes is demonstrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗