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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Capturing the full benefits of bifacial modules to approach an LCOE of 3c/kWh through a regional optimization of the electrical architecture (Final Technical Report)

This is the final technical report for the Cypress Creek Renewables project focused on bifacial plant performance. The project consisted of building a heavily instrumented Bifacial test sites with several cell technologies; instrumenting >1 MW scale commercial sites with both monofacial and bifacial arrays in order to understand real-world differences; and to engage with the performance modeling and financial stakeholder community to improve accuracy and increase investor confidence. The data from the test and commercial sites has been used to validate and improve the accuracy of both physics-based and reduced order modeling software. The heavily instrumented nature has been used to understand how light contributions from the sky and reflected from the ground impact different portions of the module and array while the data from the commercial sites has directly impacted how independent engineers model production from bifacial systems. The latter point has led to an increase in over 2% compared to earlier predictions. Even this modest increase has had significant downward pressure on project LCOE. As the data and results are both public, this refined modeling and empirical evidence for a higher bifacial gain than modeled, the benefits to the broader US solar community are assured.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2002

Confronting Models with Data: The GEWEX Cloud Systems Study

The GEWEX Cloud System Study (GCSS; GEWEX is the Global Energy and Water Cycle Experiment) was organized to promote development of improved parameterizations of cloud systems for use in climate and numerical weather prediction models, with an emphasis on the climate applications. The strategy of GCSS is to use two distinct kinds of models to analyze and understand observations of the behavior of several different types of clouds systems. Cloud-system-resolving models (CSRMs) have high enough spatial and temporal resolutions to represent individual cloud elements, but cover a wide enough range of space and time scales to permit statistical analysis of simulated cloud systems. Results from CSRMs are compared with detailed observations, representing specific cases based on field experiments, and also with statistical composites obtained from satellite and meteorological analyses. Single-column models (SCMs) are the surgically extracted column physics of atmospheric general circulation models. SCMs are used to test cloud parameterizations in an un-coupled mode, by comparison with field data and statistical composites. In the original GCSS strategy, data is collected in various field programs and provided to the CSRM Community, which uses the data to "certify" the CSRMs as reliable tools for the simulation of particular cloud regimes, and then uses the CSRMs to develop parameterizations, which are provided to the GCM Community. We report here the results of a re-thinking of the scientific strategy of GCSS, which takes into account the practical issues that arise in confronting models with data. The main elements of the proposed new strategy are a more active role for the large-scale modeling community, and an explicit recognition of the importance of data integration.

Randall, David↗
NASA NTRS2006

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Quattrochi, Dale A.↗
NASA NTRS2011

Data Driven Model Development for the Supersonic Semispan Transport (S(sup 4)T)

We investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models and a data-driven system identification procedure. It is shown via analysis of experimental Super- Sonic SemiSpan Transport (S4T) wind-tunnel data using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Kukreja, Sunil L.↗
NASA NTRS2011

Data Driven Model Development for the SuperSonic SemiSpan Transport (S(sup 4)T)

In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Kukreja, Sunil L.↗
DOE OSTI2023

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2026

pyDiSCaMB : enabling the use of multipolar scattering factors in Phenix

Multipolar scattering models, such as the transferable aspherical atom model, account for atomic chemical interactions and provide a more accurate representation of experimental data. However, the simpler independent atom model (IAM), which assumes non-interacting atoms, is the only model available in the most widely used macromolecular refinement programs. This is primarily because IAM offers a hard-to-beat combination of computational efficiency and modelling power at typical macromolecular resolutions. By contrast, more accurate multipolar modelling has historically been limited due to its computational cost and the absence of an interface between software capable of calculating structure factors and gradients based on multipolar models and software designed for macromolecular refinement. This work introduces pyDiSCaMB , a Python software package designed to integrate between the computational crystallography toolbox ( cctbx ) and the quantum crystallography library DiSCaMB ( Densities in Structural Chemistry and Molecular Biology ), thus enabling multipolar scattering models in Phenix 's toolkit. The implementation, features and capabilities of pyDiSCaMB are presented, the runtimes for the calculation of structure factor and target gradients with respect to atomic parameters are explored, and Fourier images of electrostatic potential, electron density and deformation maps are computed as illustrative examples. The pyDiSCaMB library will make multipolar modelling widely available to the structural biology community, potentially transforming refinement and model-building for both crystallography and cryogenic electron microscopy (cryoEM).

MATTS data bank↗
NASA NTRS2005

Standardization Process for Space Radiation Models Used for Space System Design

The space system design community has three concerns related to models of the radiation belts and plasma: 1) AP-8 and AE-8 models are not adequate for modern applications; 2) Data that have become available since the creation of AP-8 and AE-8 are not being fully exploited for modeling purposes; 3) When new models are produced, there is no authorizing organization identified to evaluate the models or their datasets for accuracy and robustness. This viewgraph presentation provided an overview of the roadmap adopted by the Working Group Meeting on New Standard Radiation Belt and Space Plasma Models.

Barth, Janet↗
NASA NTRS2012

Detached-Eddy Simulation Based on the V2-F Model

Detached-eddy simulation (DES) based on the v(sup 2)-f Reynolds-averaged Navier-Stokes (RANS) model is developed and tested. The v(sup 2)-f model incorporates the anisotropy of near-wall turbulence which is absent in other RANS models commonly used in the DES community. The v(sup 2)-f RANS model is modified in order the proposed v(sup 2)-f-based DES formulation reduces to a transport equation for the subgrid-scale kinetic energy isotropic turbulence. First, three coefficients in the elliptic relaxation equation are modified, which is tested in channel flows with friction Reynolds number up to 2000. Then, the proposed v(sup 2)-f DES model formulation is derived. The constant, C(sub DES), required in the DES formulation was calibrated by simulating both decaying and statistically-steady isotropic turbulence. After C(sub DES) was calibrated, the v(sub 2)-f DES formulation is tested for flow around a circular cylinder at a Reynolds number of 3900, in which case turbulence develops after separation. Simulations indicate that this model represents the turbulent wake nearly as accurately as the dynamic Smagorinsky model. Spalart-Allmaras-based DES is also included in the cylinder flow simulation for comparison.

Jee, Sol Keun↗
NASA NTRS2013

Impact of Radiatively Interactive Dust Aerosols in the NASA GEOS-5 Climate Model: Sensitivity to Dust Particle Shape and Refractive Index

We investigate the radiative effects of dust aerosols in the NASA GEOS-5 atmospheric general circulation model. GEOS-5 is improved with the inclusion of a sectional aerosol and cloud microphysics module, the Community Aerosol and Radiation Model for Atmospheres (CARMA). Into CARMA we introduce treatment of the dust and sea salt aerosol lifecycle, including sources, transport evolution, and sinks. The aerosols are radiatively coupled to GEOS-5, and we perform a series of multi-decade AMIP-style simulations in which dust optical properties (spectral refractive index and particle shape distribution) are varied. Optical properties assuming spherical dust particles are from Mie theory, while those for non-spherical shape distributions are drawn from a recently available database for tri-axial ellipsoids. The climatologies of the various simulations generally compare well to data from the MODIS, MISR, and CALIOP space-based sensors, the ground-based AERONET, and surface measurements of dust deposition and concentration. Focusing on the summertime Saharan dust cycle we show significant variability in our simulations resulting from different choices of dust optical properties. Atmospheric heating due to dust enhances surface winds over important Saharan dust sources, and we find a positive feedback where increased dust absorption leads to increased dust emissions. We further find that increased dust absorption leads to a strengthening of the summertime Hadley cell circulation, increasing dust lofting to higher altitudes and strengthening the African Easterly Jet. This leads to a longer atmospheric residence time, higher altitude, and generally more northward transport of dust in simulations with the most absorbing dust optical properties. We find that particle shape, although important for radiance simulations, is a minor effect compared to choices of refractive index, although total atmospheric forcing is enhanced by greater than 10 percent for simulations incorporating a spheroidal shape distribution versus ellipsoidal or spherical shapes.

radiative forcing↗
NASA NTRS2016

A Requirements-Driven Optimization Method for Acoustic Treatment Design

Acoustic treatment designers have long been able to target specific noise sources inside turbofan engines. Facesheet porosity and cavity depth are key design variables of perforate-over-honeycomb liners that determine levels of noise suppression as well as the frequencies at which suppression occurs. Layers of these structures can be combined to create a robust attenuation spectrum that covers a wide range of frequencies. Looking to the future, rapidly-emerging additive manufacturing technologies are enabling new liners with multiple degrees of freedom, and new adaptive liners with variable impedance are showing promise. More than ever, there is greater flexibility and freedom in liner design. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. The subject of this paper is an analytical method that derives the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground.

Aircraft Noise↗
NASA NTRS2017

A Requirements-Driven Optimization Method for Acoustic Liners Using Analytic Derivatives

More than ever, there is flexibility and freedom in acoustic liner design. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. In a previous paper on this subject, a method deriving the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground was described. A simple code-wrapping approach was used to evaluate a community noise objective function for an external optimizer. Gradients were evaluated using a finite difference formula. The subject of this paper is an application of analytic derivatives that supply precise gradients to an optimization process. Analytic derivatives improve the efficiency and accuracy of gradient-based optimization methods and allow consideration of more design variables. In addition, the benefit of variable impedance liners is explored using a multi-objective optimization.

Aircraft Noise↗
NASA NTRS2017

A Requirements-Driven Optimization Method for Acoustic Treatment Design

Acoustic treatment designers are able to attenuate specific noise sources inside turbofan engines. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But, characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. The subject of this paper is an analytic method that derives the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground. Software is developed and used to test and validate the method on a simple, notional source. With experience gained from minimizing noise of the simple source, the software is tested against a realistic hardwall fan source.

aircraft noise↗
DOE OSTI2023

LA100 Equity Strategies. Chapter 9: Equitable Community Solar Access and Benefits

The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. This chapter focuses on community solar as a means to provide equitable access to local solar and storage benefits in Los Angeles. Specifically, NREL identified potential community solar sites that could host 30 kilowatts (kW)1 of solar or more and evaluated economic and equity metrics under various program design options. Analysis included a Baseline scenario (business-as-usual) and an Equity scenario, which modeled program enhancements to increase access and benefits to low-income customers. Both scenarios modeled the economics of solar and storage under the LADWP Feed-in Tariff (FiT) program (LADWP 2023b) and the LADWP Feed-in-Tariff Plus Pilot program (LADWP 2023d) compared to a community solar financial model. This research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.

14 SOLAR ENERGY↗
NASA NTRS2018

New Metadata Capabilities within NASA's Common Metadata Repository (CMR)

This talk will convey the new capabilities and features of the UMM-Variables and the UMM-Services metadata model within the Common Metadata Repository (CMR) and how community engagement through the ESDIS Standards Office (ESO) review shaped the new versions of the models. The Unified Metadata Model (UMM) provides a common metadata model to unify legacy systems (i.e. GCMD (Global Change Master Directory), ECHO (Earth Observing System (EOS) Clearinghouse)) with new systems (i.e. CMR). The rationale and migration process of the Service Entry Resource Format (SERF) to the UMM-S will also be conveyed. The talk will conclude with discussing issues and lessons learned from the review process and how future reviews will be conducted to ensure a more targeted, meaningful review with a faster turn-around time for triage and implementation.

Earth Observing System↗
NASA NTRS2019

Benchmarking CME Arrival Time and Impact: Progress on Metadata, Metrics, and Events

Accurate forecasting of the arrival time and subsequent geomagnetic impacts of coronal mass ejections (CMEs) at Earth is an important objective for space weather forecasting agencies. Recently, the CME Arrival and Impact working team has made significant progress toward defining communit yagreed metrics and validation methods to assess the current state of CME modeling capabilities. This will allow the community to quantify our current capabilities and track progress in models over time. First, it is crucial that the community focuses on the collection of the necessary metadata for transparency and reproducibility of results. Concerning CME arrival and impact we have identified six different metadata types: 3D CME measurement, model description, model input, CME (non)arrival observation, model output data, and metrics and validation methods. Second, the working team has also identified a validation time period, where all events within the following two periods will be considered: 1 January 2011 to 31 December 2012 and January 2015 to 31 December 2015. Those two periods amount to a total of about 100 hit events at Earth and a large amount of misses. Considering a time period will remove any bias in selecting events and the event set will represent a sample set that will not be biased by user selection. Lastly, we have defined the basic metrics and skill scores that the CME Arrival and Impact working team will focus on.

Verbeke, C.↗
DOE OSTI2025

Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning

Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased and could indeed prevent discovery of interesting, diverse, and unconventional peptides with desired nanostructure assembly. Using β sheet formation in pentapeptides as a case study, we used an integrated high-throughput experimental workflow and an artificial intelligence–driven active learning framework to improve prediction accuracy of self-assembly. By focusing on sequences where machine learning (ML) predictions deviate from conventional design strategies, we synthesized and tested 268 pentapeptides, successfully finding 96 forming β sheet assemblies, including unconventional sequences (e.g., ILFSM, LMISI, MITIY, MISIW, and WKIYI) not predicted by traditional methods. Our ML models outperformed conventional β sheet propensity tables, revealing useful chemical design rules. A web interface is provided to facilitate community access to these models. This work highlights the value of ML-driven approaches in overcoming the limitations of current peptide design strategies.

Talluri, Y. Nissi [Indian Inst. of Technology (IIT↗
DOE OSTI2023

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗