Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “extrapolation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Added value of site load measurements in probabilistic lifetime extension: a Lillgrund case study

Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

17 WIND ENERGY↗
DOE OSTI2026

Scaled Implementation of Smart Charge Management for Electric Vehicles

Smart charge management (SCM) has become a critical strategy for mitigating potential grid impacts and reducing electricity costs for all customers. This study will evaluate the economics of implementing light-duty EV SCM at scale across the United States. This work will enhance distribution system analysis by estimating SCM implementation costs, exploring viable business cases, and developing a framework for national-level applications. The primary methodology involves leveraging detailed grid modeling from a specific service territory and using spatial extrapolation techniques to generalize findings to other regions. The analysis will develop key metrics to quantify the costs and benefits of SCM, including implementation costs relative to strategy and scale, the cost of distribution system upgrades with and without SCM, and the percentage of peak-load reduction. The objective is to produce a comprehensive report and a parameterized framework that enables utilities to self-assess the value of SCM in their own service territories. This will support the development of cost-effective charging strategies, accelerate the energization of new EV chargers, and facilitate the seamless integration of EVs into the nation's power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1993

Cross Sections for Electron Scattering by Ground State Ba; Elastic Scattering and Excitation of the ...6s6p (sup 1)P(sub 1) Level

Differential cross sections for elastic and inelastic (6s6p (sup 1)P(sub 1)) electron scattering from ground state Ba atom have been measured at 5eV, 10eV, 15eV, and 20eV impact energies in the angular range from 0(sup 0) to 130(sup 0). Extrapolation to the larger angles have been performed using theoretical calculations as a guide, and integral and momentum transfer cross sections were derived. Theoretical calculations based on two channel clos coupling and relativistic and non-relativistic distorted wave methods have been compared with the present results. Good agreement between experiment and various theoretical results is found at small scattering angles but significant deviations exist at larger scattering angles.

electon scattering extrapolation scattering angles↗
NASA NTRSDate not supplied

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↗
DOE OSTI2024

A Pseudo Planar Heterojunction Structure for Eco‐Friendly Printable Organic Solar Cells Achieving 19.05% Efficiency

Abstract Obtaining a well‐accurate vertical distribution active layer morphology through the air‐printing process is an essential task for achieving efficient scalable large‐area organic solar cells (OSCs). In this target, the desired and controllable pseudo planar heterojunction (PPHJ) active layer structure with suitable phase separation is developed by pre‐deposited D18‐Cl layer under the PM6:BTP‐eC9 film via an eco‐friendly manufacturing method. The addition of the D18‐Cl regulates molecular crystallization and leads to an ideal vertical stratification while simultaneously suppressing voltage loss, optimizing energetic disorder, and carrier management. Impressively, the optimal PPHJ devices perform superior power conversion efficiencies (PCEs) of 19.05% (100 nm), 17.33% (300 nm), and 14.14% (4 cm 2 ) compared to the BHJ devices. Importantly, the PPHJ OSCs also exhibit an impressive extrapolated T 80 (the time required to reach 80% of initial PCE) of long‐time storage and operational stability, as well as thermal stability.

Chemistry↗
DOE OSTI2020

Steric Impediment of Ion Migration Contributes to Improved Operational Stability of Perovskite Solar Cells

The operational instability of perovskite solar cells (PSCs) is understood to mainly originate from the migration of ionic species (or charged defects) under a potential gradient. Compositional engineering of the “A” site cation of the ABX3 perovskite structure has been shown to be an effective route to improve the stability of PSCs. Here, the effect of size-mismatch-induced lattice distortions on the ion migration energetics and operational stability of PSCs is investigated. It is determined that the size mismatch of the mixed “A” site composition films and devices leads to a steric effect to impede the migration pathways of ions to increase the activation energy of ion migration, which is demonstrated through multiple theoretical and experimental evidence. Consequently, the mixed composition devices exhibit significantly improved thermal stability under continuous heating at 85 °C and operational stability under continuous 1 sun illumination, with an extrapolated lifetime of 2011 h, compared to the 222 h of the reference device.

14 SOLAR ENERGY↗
DOE OSTI2023

Simultaneous Enhancement of Efficiency And Operational-Stability of Mesoscopic Perovskite Solar Cells via Interfacial Toughening

The combined effects of compact TiO 2 (c-TiO 2 ) electron-transport layer (ETL) are investigated without and with mesoscopic TiO 2 (m-TiO 2 ) on top, and without and with an iodine-terminated silane self-assembled monolayer (SAM), on the mechanical behavior, opto–electronic properties, photovoltaic (PV) performance, and operational-stability of solar cells based on metal-halide perovskites (MHPs). The interfacial toughness increases almost threefold in going from c-TiO 2 without SAM to m-TiO 2 with SAM. This is attributed to the synergistic effect of the m-TiO 2 /MHP nanocomposite at the interface and the enhanced adhesion afforded by the iodine-terminated silane SAM. The combination of m-TiO 2 and SAM also offers a significant beneficial effect on the photocarriers extraction at the ETL/MHP interface, resulting in perovskite solar cells (PSCs) with power-conversion efficiency (PCE) of over 24% and 20% for 0.1 and 1 cm 2 active areas, respectively. These PSCs also have exceptionally long operational stability lives extrapolated T80 (duration at 80% initial PCE retained) is ≈18 000 and 10 000 h for 0.1 and 1 cm 2 active areas, respectively. In conclusion, postmortem characterization and analyses of the operational-stability-tested PSCs are performed to elucidate the possible mechanisms responsible for the long operational-stability.

14 SOLAR ENERGY↗
DOE OSTI2020

Modeling and simulation of gas separations with spiral–wound membranes

Models for gas separations with spiral–wound membranes are developed and found to exhibit good agreement with experiments performed on N 2 /O 2 mixtures. The two–dimensional (2D) model can be accurately approximated by a one–dimensional (1D) surrogate model when the spacer widths are chosen to make the channel pressure drops small. Subsequently, the separation of propane/propylene mixtures from the recycle purge stream of a polypropylene reactor is investigated. Assuming ideal gas is found to lead to significant overestimations in membrane stage cuts (sometimes more than 10%), an extent comparable to that associated with extrapolating constant olefin permeance from a low–pressure condition. While olefin permeance can change significantly with pressure, using a constant–permeance formulation can result in a small (< 2.5%) underprediction in stage cut if the value for the permeance is taken from the feed condition. Finally, membrane properties and costs necessary for a viable separation process are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Spanning surface interaction length scales for the design of industrial antifouling materials

Fouling of surfaces introduces significant operational and economic challenges in marine and membrane systems. While empirical fouling assays and surface science methodologies have independently introduced fouling resistant methodologies and functionalities within each of these respective application spaces, they remain limited by their inability to extrapolate findings across length scales and inform materials design strategies. This perspective emphasizes the need to bridge the macroscale fouling assays conducted in the marine community and molecular-scale insights obtained from the membranes community. Here, by uniting these approaches, generalized design strategies can be developed across applications, leading to the realization of effective antifouling materials over a wide spectrum of operating conditions and foulant types. Standardized and high-throughput methodologies are proposed as tools to unify research efforts to drive the development of next-generation antifouling coatings and treatments.

fouling assays↗
DOE OSTI2021

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2% mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5% of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.

25 ENERGY STORAGE↗
DOE OSTI2022

Microbial maintenance energy quantified and modeled with microcalorimetry

Refining the energetic costs of cellular maintenance is essential for predicting microbial growth and survival in the environment. In this work, we evaluate a simple batch culture method to quantify energy partitioning between growth and maintenance using microcalorimetry and thermodynamic modeling. The constants derived from the batch culture system were comparable to those that have been reported from meta-analyses of data derived from chemostat studies. The model accurately predicted temperature-dependent biomass yield and the upper temperature limit of growth for Desulfovibrio alaskensis G20, suggesting the method may have broad application. An Arrhenius temperature dependence for the specific energy consumption rate, inferred from substrate consumption and heat evolution, was observed over the entire viable temperature range. By combining this relationship for specific energy consumption rates and observed specific growth rates, the model describes an increase in nongrowth associated maintenance at higher temperatures and the corresponding decrease in energy available for growth. This analytical and thermodynamic formulation suggests that simply monitoring heat evolution in batch culture could be a useful complement to the recognized limitations of estimating maintenance using extrapolation to zero growth in chemostats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Energy-resolved mass spectrometry as a tool for identification of lignin depolymerization products

Lignin is the largest source of bio-based aromatic compounds in nature, and its valorization is essential to the sustainability of lignocellulosic biorefining. Characterizing lignin-derived compounds remains challenging due to the heterogeneity of this biopolymer. Tandem mass spectrometry is a promising tool for lignin structural analytics, as fragmentation patterns of model compounds can be extrapolated to identify characteristic moieties in complex samples. This work extends previous resonance excitation type collision-induced dissociation (CID) methods that identified lignin oligomers containing β-Ο-4, β-5, and β-β bonds, to also identify characteristics of 5-5, β-1, and 4-Ο-5 dimers, enabled by quadrupole time-of-flight (QTOF) CID with energy-resolved mass spectrometry (ERMS). Overall, beam type ERMS offers in-depth structural information and could ultimately contribute to tools for high-throughput lignin dimer identification.

09 BIOMASS FUELS↗
DOE OSTI2024

Utilizing digitized occurrence records of Midwestern feral Cannabis sativa to develop ecological niche models

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility-oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021–2040 and 2061–2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Estimating carrying capacity for juvenile salmon using quantile random forest models

Abstract Establishing robust methods and metrics to evaluate habitat quality is critical for the recovery of endangered Pacific salmonids ( Oncorhynchus spp.). A variety of modeling approaches are used for status and trend monitoring of anadromous species throughout the Pacific Northwest, USA, but current methods may fail to capture the complex relationship between fish and habitat and are often limited in predictive power beyond specific watersheds. Further, the focus on species distribution and abundance is not easily manipulated to predict carrying capacity and traditional stock‐recruitment analyses are reliant on long‐term data which are not always available. In this study, we developed a quantile random forest model to provide estimates of habitat carrying capacity for Chinook salmon ( O. tshawytscha ) parr during the summer months, at both the site and watershed scale. Quantile random forest models allow for the consideration of noisy data, correlated variables, and non‐linear relationships: common features in fish–habitat datasets. We leveraged Columbia Habitat Monitoring Program data to select habitat co‐variates and predict capacity at those sites. We also identified a set of globally available attributes to extrapolate capacity estimate predictions throughout wadeable streams within the Columbia River basin. Total capacity estimates for watersheds closely matched estimates from alternative fish productivity models. Carrying capacity estimates based on quantile random forest models, like those presented here, provide managers a framework to guide the identification, prioritization, and development of habitat rehabilitation actions to recover salmon populations.

See, Kevin E.↗
DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Solvation Effects on the Dielectric Constant of 1 M LiPF6 in Ethylene Carbonate: Ethyl Methyl Carbonate 3:7

We report the dielectric constant of 1 M LiPF 6 in EC:EMC 3:7 w/w (ethylene carbonate/ethyl methyl carbonate) in addition to neat EC:EMC 3:7 w/w. Using three Debye relaxations, the static permittivity value, or dielectric constant, is extrapolated to 18.5, which is compared to 18.7 for the neat solvent mixture. The EC solvent is found to strongly coordinate with the Li + cations of the salt, which results in a loss of dielectric contribution to the electrolyte. However, the small amplitude and large uncertainty in relaxation frequency for EMC cloud definitive identification of the Li + solvation shell. Importantly, the loss of the free EC permittivity contribution due to Li + solvation is almost completely balanced by the positive contribution of the associated LiPF 6 salt, demonstrating that a significant quantity of dipolar ion pairs exists in 1 M LiPF 6 in EC:EMC 3:7.

25 ENERGY STORAGE↗
DOE OSTI2023

Connecting suborganismal data to bioenergetic processes: killifish embryos exposed to a dioxin-like compound

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal level responses that influence population dynamics. In this work, we describe a novel application of Dynamic Energy Budget (DEB), theory in the context of a toxicity framework (Adverse Outcome Pathways, AOP) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP Key Events (KEs) to DEB processes through “damage” that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and use DEB models to predict sublethal and lethal effects of young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters points to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗