Search NASA⌕ Search

SEARCH · Search NASA

Results for “model set up”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Template-directed synthesis of oligonucleotides under eutectic conditions

One of the most important sets of model prebiotic experiments consists of reactions that synthesize complementary oligonucleotides from preformed templates under nonenzymatic conditions. Most of these experiments are conducted at 4 degrees C using 0.01-0.1 M concentrations of activated nucleotide monomer and template (monomer equivalent). In an attempt to extend the conditions under which this type of reaction can occur, we have concentrated the reactants by freezing at -18 degrees C, which is close to the NaCl-H2O eutectic at -21 degrees C. The results from this set of experiments suggest that successful syntheses can occur with poly(C) concentrations as low at 5 x 10(-4) M and 2MeImpG concentrations at 10(-3) M. It was also anticipated that this mechanism might allow the previously unsuccessful poly(A)-directed synthesis of oligo(U)s to occur. However, no template effect was seen with the poly(A) and ImpU system. The failure of these conditions to allow template-directed synthesis of oligo(U)s supports the previously proposed idea that pyrimidines may not have been part of the earliest genetic material. Because of the low concentrations of monomer and template that would be expected from prebiotic syntheses, this lower temperature could be considered a more plausible geologic setting for template-directed synthesis than the standard reaction conditions.

NASA Discipline Number 52-20↗
DOE OSTI2025

Structure of Odd-A Ag Isotopes Studied via Algebraic Approaches

The structure of the odd-A silver isotopes Ag 103–115 is discussed within the frame of the interacting boson–fermion model (IBFM). An overview of their key properties is presented, with a particular attention paid to the “ J -1 anomaly”, represented by an abnormal ordering of the lowest 7/2 + and 9/2 + states. By examining previously published data and newly performed calculations, it is demonstrated that the experimentally known level schemes and electromagnetic properties of Ag 103–115 can be reproduced well within IBFM-1 by using a consistent set of model parameters. The contribution of different single-particle orbitals to the structure of the lowest-lying excited nuclear states in Ag 103–115 is discussed. Given that the J-1 anomaly brings down the 7/2 + level from the j −3 multiplet to energies, which can be thermally populated in hot stellar environments, the importance of low-lying excited states in odd-A silver isotopes for astrophysical processes is outlined.

IBFM-1↗
NASA NTRS2006

NASA'S SERVIR Gulf of Mexico Project: The Gulf of Mexico Regional Collaborative (GoMRC)

The Gulf of Mexico Regional Collaborative (GoMRC) is a NASA-funded project that has as its goal to develop an integrated, working, prototype IT infrastructure for Earth science data, knowledge and models for the five Gulf U.S. states and Mexico, and to demonstrate its ability to help decision-makers better understand critical Gulf-scale issues. Within this preview, the mission of this project is to provide cross cutting solution network and rapid prototyping capability for the Gulf of Mexico region, in order to demonstrate substantial, collaborative, multi-agency research and transitional capabilities using unique NASA data sets and models to address regional problems. SERVIR Mesoamerica is seen as an excellent existing framework that can be used to integrate observational and GIs data bases, provide a sensor web interface, visualization and interactive analysis tools, archival functions, data dissemination and product generation within a Rapid Prototyping concept to assist decision-makers in better understanding Gulf-scale environmental issues.

Quattrochi, Dale A.↗
NASA NTRS2014

Distributed Prognostics based on Structural Model Decomposition

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

centrifugal pump↗
NASA NTRS2016

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

knowledge base↗
NASA NTRSDate not supplied

Uncovering Hazards Using a Multi-Objective Optimization to Explore the Faulty State-Space

Considering resilience when designing complex engineered systems is crucial to ensure the system is safe under unexpected hazardous scenarios. Traditional risk-based approaches, such as Failure Modes and Effects Analysis (FMEA) are useful for designing the system to mitigate hazardous scenarios that can be identified by the designer, but often require experience or prior knowledge of system failures to generate. More recently, researchers have developed simulation tools that enable the designer to model large sets of hazardous scenarios (driven by both internal faults and external factors) through simulation. While these tools enable a wider scope of fault modes to be evaluated (e.g., by injecting combined set of fault modes or injecting modes at different times), the resulting assessments (like FMEA) still require knowledge of the specific modes to be evaluated. However, failure to analyze a wide variety of fault scenarios can lead to an incomplete picture of the system resilience, especially to "surprise events'' which may be difficult for the designer to identify and predict beforehand. To overcome this challenge, previous work developed a fault sampling approach for resilience simulations which would procedurally-generate a wide variety of potential faults by systematically perturbing the health states of the system. While the resulting fault modes generated covered a much larger space hazards than would be otherwise considered (and identified many unique failure trajectories which would not have otherwise been identified), it also significantly increased the computational cost of the analysis and resulted in the simulation and analysis of a large set of essentially duplicate scenarios. Additionally, as the number of dimensions in the faulty state-space increases, the full elaboration of possible modes becomes computationally infeasible, justifying the use of a more targeted search. To resolve this limitation, this work proposes the use of a multiobjective optimization algorithm to search the health state space for potential fault modes that are both (1) hazardous and (2) unique. To solve this type of problem, this work proposes the use of a cooperative co-evolutionary algorithm. To demonstrate this approach, it will be applied to a model of an autonomous rover which uses line markings to navigate, focusing on potential hazards in the drive system which could cause the rover to crash. To determine the merit of the approach, it will further be compared with the previously-presented range elaboration approach and a random mode generation approach on the basis of computational efficiency and found modes.

Resilience↗
DOE OSTI2025

Impact of Orographic Drag Schemes on East Asia Rainfall

Current generation of climate models often has significant biases in mountainous regions where the gradient of elevation is steep, and the terrain is complex. Potential reasons for these biases include under‐representation of orographic drag process in climate models. In this study, we assess the impact of orographic drag on East Asia rainfall by comparing the impact of a new orographic drag scheme that considers 3D orographic anisotropy (3D‐oro) with a 2‐D scheme in a general circulation model. Two sets of simulations (medium‐range and seasonal forecast) are carried out for the comparison and validation against observation. It is shown that through local/remote forcing of the drag in the mountainous regions, the 3D‐oro alleviates part of the excessive rainfall in west Tibetan Plateau and parts of insufficient rainfall in Southeast China by about 25%∼50% in the January/winter forecasts; it alleviates about 25%∼50% of the rainfall bias in part of south Tibetan Plateau and of East Asia in the July/summer forecasts. The results suggest the importance of improved orographic drag process and its impact in climate modeling for those regions that are prone to significant impact of hydroclimate events.

orographic anisotropy↗
NASA NTRS2015

Spatiotemporal Coherence of Mean and Extreme Aerosol Particle Events over Eastern North America as Observed from Satellite

Quantifying spatiotemporal scales of coherence and variability of aerosol particle properties provides (i) metrics for evaluating observational data sets and model simulations, (ii) inferences regarding dominant causes of variability, and (iii) information that is necessary to determine their climate forcing and air quality impacts. Accordingly, analyses of columnar aerosol optical depth (AOD) and Ångstrom exponent (AE) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites, and AErosol RObotic NETwork (AERONET) stations are used to evaluate the spatially averaged (Level-3) MODIS products, and to characterize the spatiotemporal scales of variability (and coherence) of aerosol particles over eastern North America (2000-2013). Results show a high degree of consistency in AOD retrievals from the different data sets but larger discrepancies in AE estimates, highest mean AOD and lowest day-to-day variability during the summer, and largest scales of spatial coherence in summer and fall. Consistent with the scales of spatial coherence from MODIS data, in both data sets power spectra indicate AOD variability is manifest primarily on synoptic and annual time scales. Conversely, AE variability in MODIS data is primarily focused on seasonal, semiannual, and annual time scales, and there is an additional mode of AE variability at approx. 30 days in the AERONET measurements. The frequency of cooccurrence of extreme AOD values (>local 90th percentile) decreases to below 50% at approx. 150 km from a central grid cell, but is above that expected by random chance over almost all of eastern North America, indicating supra-regional scale extreme events.

MODIS↗
NASA NTRS1991

Precise orbit computation and sea surface modeling

The research project described below is part of a long-term program at Delft University of Technology aiming at the application of European Remote Sensing satellite (ERS-1) and TOPEX/POSEIDON altimeter measurements for geophysical purposes. This program started in 1980 with the processing of Seasat laser range and altimeter height measurements and concentrates today on the analysis of Geosat altimeter data. The objectives of the TOPEX/POSEIDON research project are the tracking of the satellite by the Dutch mobile laser tracking system MTLRS-2, the computation of precise TOPEX/POSEIDON orbits, the analysis of the spatial and temporal distribution of the orbit errors, the improvement of ERS-1 orbits through the information obtained from the altimeter crossover difference residuals for crossing ERS-1 and TOPEX/POSEIDON tracks, the combination of ERS-1 and TOPEX/POSEIDON altimeter data into a single high-precision data set, and the application of this data set to model the sea surface. The latter application will focus on the determination of detailed regional mean sea surfaces, sea surface variability, ocean topography, and ocean currents in the North Atlantic, the North Sea, the seas around Indonesia, the West Pacific, and the oceans around South Africa.

Wakker, Karel F.↗
NASA NTRS2010

Functional Fault Modeling Conventions and Practices for Real-Time Fault Isolation

The purpose of this paper is to present the conventions, best practices, and processes that were established based on the prototype development of a Functional Fault Model (FFM) for a Cryogenic System that would be used for real-time Fault Isolation in a Fault Detection, Isolation, and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using a suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FFMs were created offline but would eventually be used by a real-time reasoner to isolate faults in a Cryogenic System. Through their development and review, a set of modeling conventions and best practices were established. The prototype FFM development also provided a pathfinder for future FFM development processes. This paper documents the rationale and considerations for robust FFMs that can easily be transitioned to a real-time operating environment.

Ferrell, Bob↗
NASA NTRS1993

A formulation of convection for stellar structure and evolution calculations without the mixing-length theory approximations. II - Application to Alpha Centauri A and B

We have constructed a series of models of Alpha Centauri A and Alpha Centauri B for the purposes of testing the effects of convection modeling both by means of the mixing-length theory (MLT), and by means of parameterization of energy fluxes based upon numerical simulations of turbulent compressible convection. We demonstrate that while MLT, through its adjustable parameter alpha, can be used to match any given values of luminosities and radii, our treatment of convection, which lacks any adjustable parameters, makes specific predictions of stellar radii. Since the predicted radii of the Alpha Centauri system fall within the errors of the observed radii, our treatment of convection is applicable to other stars in the H-R diagram in addition to the sun. A second set of models is constructed using MLT, adjusting alpha to yield not the 'measured' radii but, instead, the radii predictions of our revised treatment of convection. We conclude by assessing the appropriateness of using a single value of alpha to model a wide variety of stars.

Lydon, Thomas J.↗
NASA NTRS1991

An evaluation of the Tsyganenko magnetic field model

A data set of more than 22,000 vector averages of the magnetosphere magnetic field over 0.5 R(E) regions is used to evaluate Tsyganenko's 1982 and 1987 magnetospheric magnetic field models. The magnetic field predicted by the model in various regions is compared to observations to find systematic discrepancies which future models might address. While agreement is generally good, discrepancies are noted which include: (1) a lack of adequate field line stretching in the tail and ring current regions; (2) an inability to predict weak enough fields in the polar cusps; and (3) a deficiency of Kp as a predictor of the field configuration.

Fairfield, D. H.↗
DOE OSTI2026

A Comprehensive Comparison of Methods for Evaluating Dispatch of Long-Duration Energy Storage in Power Systems Models

Long-duration energy storage (LDES) could play a pivotal role in the transformation of electricity grids with high shares of variable renewable energy (VRE) such as solar and wind. However, the weather-dependent nature of VRE introduces challenges for grid balancing and stability, which LDES - along with short-duration energy storage (SDES) - can help address. However, modeling LDES in production cost models (PCMs) is particularly challenging due to the need for high temporal resolution over extended optimization windows while preserving chronology, which ensures the alignment of energy storage operations with VRE generation over multi-day periods. This report compares traditional dispatch methods with advanced LDES dispatch strategies, such as the extended horizon approach, across different PCM platforms and examines tradeoffs and scalability. The comparison reveals that the traditional 1-day optimization horizon within the PCM leads to inefficient utilization of LDES. In contrast, extending the optimization horizon as much as possible significantly reduces curtailment and improves storage dispatch, especially in renewable-dense systems. There is also promise in using state-of-charge or end volume targets set by an external model, however this requires an additional modeling set and generally increases computational burden. This paper presents a comparison of these various methods in a number of power systems, showing algorithms initially in small test systems and scaling up to large, country-wide simulations. Overall, the research presents the trade-offs of various computational methods and illustrates how LDES may play an essential role in power systems of the future.

14 SOLAR ENERGY↗
NASA NTRS1981

Modeling of photodegradation in solar cell modules of substrate and superstrate design made with ethylene-vinyl acetate as pottant material

The rates of hydrogen abstraction by peroxy radicals were proven to be too slow for significant oxidation of the alkane substrate to be important. The numerical procedure, independent of our particular data base was verified by reproducing concentration time profiles for a model reaction set describing the cesium flare system in the upper atmosphere. Simulation was identical to that given in the literature. Experimental verification of the data base is to be attempted by weatherometry studies in the coming year. Work on the new diagnostic techniques was completed. The adapted automated viscometer was demonstrated to be an efficient and reliable tool for routine measurements of viscosity (molecular weight) changes in solid samples after batch solutions have been made up. The laser photolysis GC method for monitoring extremely low levels of oxidation in polymers proved to be impractical because the yields of carbon monoxide were too low for quantification. Much progress was made with the computer model. The reaction matrix was completely revised, resulting in a new scheme of 31 reactions and time, lifetimes in excess of ten years. The results to date lead us to some tentative observations.

Somersall, A. C.↗
DOE OSTI2025

Search for a Dark Higgs Boson Produced in Association with Inelastic Dark Matter at the Belle II Experiment

Inelastic dark matter models that have two dark matter particles and a massive dark photon can reproduce the observed relic dark matter density without violating cosmological limits. The mass splitting between the two dark matter particles 𝜒 1 and 𝜒 2 , with 𝑚⁡(𝜒 2 ) >𝑚⁡(𝜒 1 ), is induced by a dark Higgs field and a corresponding dark Higgs boson ℎ′. We present a search for dark matter in events with two vertices, at least one of which must be displaced from the interaction region, and missing energy. Using a 365 fb −1 data sample collected at Belle II, which operates at the SuperKEKB 𝑒 + ⁢𝑒 − collider, we observe no evidence for a signal. We set upper limits on the product of the production cross section 𝜎⁡(𝑒 + ⁢𝑒 − → ℎ′⁡𝜒 1 ⁢𝜒 2 ), and the product of branching fractions ℬ⁡(𝜒 2 → 𝜒 1⁢ 𝑒 + ⁢𝑒 − ) ×ℬ⁡(ℎ′ → 𝑥 + ⁢𝑥 − ), where 𝑥 + ⁢𝑥 − indicates 𝜇 + ⁢𝜇 − , 𝜋 + ⁢𝜋 − , or 𝐾 + ⁢𝐾 − , as functions of ℎ′ mass and lifetime at the level of 10 −1 fb. We set model-dependent upper limits on the dark Higgs mixing angle at the level of 10 −5 and on the dark photon kinetic mixing parameter at the level of 10 −3 . This is the first search for dark Higgs bosons in association with inelastic dark matter.

dark matter↗
NASA NTRS2020

Data Science for Weather Impacts on Crop Yield

Private businesses in sectors, such as food, energy, and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season-averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.

Data mining, food security, weather impacts↗
NASA NTRS1998

Simulation of Etching Profiles Using Level Sets

Using plasma discharges to etch trenches and via holes in substrates is an important process in semiconductor manufacturing. Ion enhanced etching involves both neutral fluxes, which are isotropic, and ion fluxes, which are anisotropic. The angular distributions for the ions determines the degree of vertical etch, while the amount of the neutral fluxes determines the etch rate. We have developed a 2D profile evolution simulation which uses level set methods to model the plasma-substrate interface. Using level sets instead of traditional string models avoids the use of complicated delooping algorithms. The simulation calculates the etch rate based on the fluxes and distribution functions of both ions and neutrals. We will present etching profiles of Si substrates in low pressure (10s mTorr) Ar/Cl2 discharges for a variety of incident ion angular distributions. Both ion and neutral re-emission fluxes are included in the calculation of the etch rate, and their contributions to the total etch profile will be demonstrated. In addition, we will show RIE lag effects as a function of different trench aspect ratios. (For sample profiles, please see http://www.ipt.arc.nasa.gov/hwangfig1.html)

Hwang, Helen↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗