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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Application of Domain Knowledge to Software Quality Assurance

This work focused on capturing, using, and evolving a qualitative decision support structure across the life cycle of a project. The particular application of this study was towards business process reengineering and the representation of the business process in a set of Business Rules (BR). In this work, we defined a decision model which captured the qualitative decision deliberation process. It represented arguments both for and against proposed alternatives to a problem. It was felt that the subjective nature of many critical business policy decisions required a qualitative modeling approach similar to that of Lee and Mylopoulos. While previous work was limited almost exclusively to the decision capture phase, which occurs early in the project life cycle, we investigated the use of such a model during the later stages as well. One of our significant developments was the use of the decision model during the operational phase of a project. By operational phase, we mean the phase in which the system or set of policies which were earlier decided are deployed and put into practice. By making the decision model available to operational decision makers, they would have access to the arguments pro and con for a variety of actions and can thus make a more informed decision which balances the often conflicting criteria by which the value of action is measured. We also developed the concept of a 'monitored decision' in which metrics of performance were identified during the decision making process and used to evaluate the quality of that decision. It is important to monitor those decision which seem at highest risk of not meeting their stated objectives. Operational decisions are also potentially high risk decisions. Finally, we investigated the use of performance metrics for monitored decisions and audit logs of operational decisions in order to feed an evolutionary phase of the the life cycle. During evolution, decisions are revisisted, assumptions verified or refuted, and possible reassessments resulting in new policy are made. In this regard we implemented a machine learning algorithm which automatically defined business rules based on expert assessment of the quality of operational decisions as recorded during deployment.

Wild, Christian W.↗
DOE OSTI2023

Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

In the last decades, the Arctic climate has changed dramatically, with the loss of multiyear sea ice one of the clearest consequences. These changes have occurred on relatively rapid timescales, and both accurate short-term Arctic prediction (e.g., 10 days to three months) and climate projection of future Arctic scenarios present ongoing challenges. Here we describe a representation of the Arctic ocean and sea ice in a ultrahigh resolution simulation in which the horizontal grid mesh reduces from 8 km at the equator to 2 km at the poles (UH8to2) for the years 2017-2020. We find the simulation reproduces observed distributions of seasonal sea-ice thickness and concentration realistically, although concentration is biased low in the spring and summer and low biases in thickness are found in the central and eastern basins in the fall. Volume, fresh water, and heat transports through key passages are realistic, lying within observationally determined ranges. Climatological comparisons reveal that the UH8to2 Atlantic Water is shallower, warmer, and saltier than the World Ocean Atlas 2018 climatology for 2005-2017 in the eastern basin. Our analysis suggests that these biases, combined with a lack of stratification in the upper 100 m of the simulated ocean, contribute to the winter biases in modeled sea ice thickness. This relationship between biases in the sea ice and ocean points to a potential positive feedback within the model, illuminating challenges for long term model predictive power in a changing Arctic climate.

58 GEOSCIENCES↗
DOE OSTI2022

Assessing Two Approaches for Enhancing the Range of Simulated Scales in the E3SMv1 and the Impact on the Character of Hourly US Precipitation

Abstract Improving the representation of precipitation in Earth system models is essential for understanding and projecting water cycle changes across scales. Progress has been hampered by persistent deficiencies in representing precipitation frequency, intensity, and timing in current models. Here, we analyze simulated US precipitation in the low‐resolution (LR) configuration of the Energy Exascale Earth System Model (E3SMv1) and assess the effect of two approaches to enhance the range of explicitly resolved scales: high‐resolution (HR) and multiscale modeling framework (MMF), which incur similar computational expense. Both E3SMv1‐MMF and E3SMv1‐HR capture more intense and less frequent precipitation on hourly and daily timescales relative to E3SMv1‐LR. E3SMv1‐HR improves the intensity over the Eastern and Northwestern US during winter, while E3SMv1‐MMF improves the intensity over the Eastern US and summer diurnal timing over the Central US. These results indicate that both methods may be needed to improve simulations of different storm types, seasons, and regions.

58 GEOSCIENCES↗
DOE OSTI2023

Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator

In this work, we present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convolutional neural network-based virtual 6D phase space diagnostic of charged particle beams in the HiRES ultrafast electron diffraction (UED) compact particle accelerator with uncertainty quantification. Our method utilizes model-independent adaptive feedback to tune a low dimensional 2D latent space representation of ~1 million dimensional objects which are the 15 unique 2D projections (x, y),...,(z, p z ) of the 6D phase space (x, y, z, p x , p y , p z ) of the charged particle beams. We demonstrate our method with numerical studies of short electron bunches utilizing experimentally measured UED input beam distributions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Binary Driller

Binary Driller (BD) is a visualization tool which uses the data produced from the Troglodyte tool developed on the Deep Learning Malware project. Binary Driller performs function matching using the provided function embeddings (function representations), then displays the matches for each function in a layout that mimics the location of each function within the binary.

Cutsha, Michael↗
DOE OSTI2022

Learning Optimal Aerodynamic Designs

This project created a framework for efficient, accurate, and scalable deep neural network representations of design optimization problem solutions. The inputs to these DNN representations are the vector of design requirement parameters, the outputs are the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team addressed the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using a Reynolds-Average Navier Stokes model to govern the CFD-based aerodynamic shape optimization. The inverse design map for such problems is very complex and high-dimensional, involving inputs and outputs on the order of 1000s. To approximate this inverse design map, the team developed algorithms to construct parsimonious DNN architectures, which automatically identify low-dimensional manifolds in which design requirements affect optimal shape parameters, and trained these architectures with multifidelity optimization methods. The resulting methodology accurately and automatically designs optimal aerodynamic lifting surfaces with very high accuracy (99%) at interactive speeds, of the order of milliseconds, resulting in factors of one million or more speedup relative to CFD-based design optimization.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Electro-impulse deicing - Structural dynamic studies, icing tunnel tests and applications

This paper contains a discussion of the comparison of analytical and experimental results of the dynamic response of a flat rectangular plate subjected to electro-impulse type deicing forces. Early attempts in this correlation have been hampered by the complex leading edge geometries of the airfoils tested to date. The lack of a suitable analytical model for a typical leading edge structure has prompted these preliminary investigations of simple geometries, beginning with a flat plate and to be followed by a semi-cylindrical leading edge representation. The comparisons reported herein are thus limited to rectangular plate models. Project plans and icing tunnel results are given.

Bernhart, W. D.↗
NASA NTRS1997

Functional specializations in human cerebral cortex analyzed using the Visible Man surface-based atlas

We used surface-based representations to analyze functional specializations in the human cerebral cortex. A computerized reconstruction of the cortical surface of the Visible Man digital atlas was generated and transformed to the Talairach coordinate system. This surface was also flattened and used to establish a surface-based coordinate system that respects the topology of the cortical sheet. The linkage between two-dimensional and three-dimensional representations allows the locations of published neuroimaging activation foci to be stereotaxically projected onto the Visible Man cortical flat map. An analysis of two activation studies related to the hearing and reading of music and of words illustrates how this approach permits the systematic estimation of the degree of functional segregation and of potential functional overlap for different aspects of sensory processing.

Non-NASA Center↗
NASA NTRS2012

Orion Script Generator

NASA Engineering's Orion Script Generator (OSG) is a program designed to run on Exploration Flight Test One Software. The script generator creates a SuperScript file that, when run, accepts the filename for a listing of Compact Unique Identifiers (CUIs). These CUIs will correspond to different variables on the Orion spacecraft, such as the temperature of a component X, the active or inactive status of another component Y, and so on. OSG will use a linked database to retrieve the value for each CUI, such as "100 05," "True," and so on. Finally, OSG writes SuperScript code to display each of these variables before outputting the ssi file that allows recipients to view a graphical representation of Orion Flight Test One's status through these variables. This project's main challenge was creating flexible software that accepts and transfers many types of data, from Boolean (true or false) values to "Unsigned Long Long'' values (any number from 0 to 18,446,744,073,709,551,615). We also needed to allow bit manipulation for each variable, requiring us to program functions that could convert any of the multiple types of data into binary code. Throughout the project, we explored different methods to optimize the speed of working with the CUI database and long binary numbers. For example, the program handled extended binary numbers much more efficiently when we stored them as collections of Boolean values (true or false representing 1 or 0) instead of as collections of character strings or numbers. We also strove to make OSG as user-friendly and accommodating of different needs as possible its default behavior is to display a current CUI's maximum value and minimum value with three to five intermediate values in between, all in descending order. Fortunately, users can also add other input on the same lines as each CUI name to request different high values, low values, display options (ascending, sine, and so on), and interval sizes for generating intermediate values. Developing input validation took up quite a bit of time, but OSG's flexibility in the end was worth it.

Dooling, Robert J.↗
NASA NTRSDate not supplied

Digital Lunar Exploration Sites (DLES)

It has been almost 50 years since humans last set foot on the Moon. With NASA’s Artemis program, the United States and its international and commercial partners are embarking on a new endeavor to explore the lunar surface. Before we return, we will have simulated every aspect of these future missions. Many of these models and simulations (M&S) will rely on well-known and commonly-used technologies, some of which trace their origins back to the Apollo program. However, M&S has advanced significantly, as have the underlying computational capabilities. As a result, we are able to model many more aspects of the Artemis vehicles and support systems with significantly improved detail and confidence. Accurately and efficiently modeling the lunar environment will be critical to simulating the Artemis elements and mission activities. This includes characterizing and modeling lunar topography, smaller craters, exposed surface rocks, lunar regolith, surface lighting, and ambient thermal environment. These are all necessary for understanding fundamental behaviors and performance of vehicles and support systems in the lunar environment and are often determining factors in the selection of exploration sites and defining mission profiles. The Astromaterials Research and Exploration Sciences (ARES) team in the Exploration Integration and Science Directorate at NASA’s Johnson Space Center (JSC) and the NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch at JSC are developing and maintaining the Digital Lunar Exploration Sites (DLES) data, documentation, and software packages. This data is being fed directly into a diverse collection of graphics and simulation environments, where it is used to construct the closest known truth for numerous potential Lunar South Pole landing sites. DLES is available to projects across NASA and particularly the Artemis program to support coordinated digital representations of the lunar environment. This paper describes the fundamental need for DLES, the science data sets that are going into DLES, some of the processes used to integrate this data into DLES products, the basic products that constitute DLES, and some examples of DLES in use.

Lunar↗
NASA NTRS2012

Understanding Differences in Upper Stratospheric Ozone Response to Changes in Chlorine and Temperature as Computed Using CCMVal Models

Projections of future ozone levels are made using models that couple a general circulation model with a representation of atmospheric photochemical processes, allowing interactions among photochemical processes, radiation, and dynamics. Such models are known as chemistry and climate models (CCMs). Although developed from common principles and subject to the same boundary conditions, simulated ozone time series vary for projections of changes in ozone depleting substances (ODSs) and greenhouse gases. In the upper stratosphere photochemical processes control ozone level, and ozone increases as ODSs decrease and temperature decreases due to greenhouse gas increase. Simulations agree broadly but there are quantitative differences in the sensitivity of ozone to chlorine and to temperature. We obtain insight into these differences in sensitivity by examining the relationship between the upper stratosphere annual cycle of ozone and temperature as produced by a suite of models. All simulations conform to expectation in that ozone is less sensitive to temperature when chlorine levels are highest because chlorine catalyzed loss is nearly independent of temperature. Differences in sensitivity are traced to differences in simulated temperature, ozone and reactive nitrogen when chlorine levels are close to background. This work shows that differences in the importance of specific processes underlie differences in simulated sensitivity of ozone to composition change. This suggests a) the multi-model mean is not a best estimate of the sensitivity of upper ozone to changes in ODSs and temperature; b) the spread of values is not an appropriate measure of uncertainty.

Douglass, A. R.↗
DOE OSTI2021

Multi-scale assessment of a grassland productivity model

Grasslands provide many important ecosystem services globally, and projecting grassland productivity in the coming decades will provide valuable information to land managers. Productivity models can be well calibrated at local scales but generally have some maximum spatial scale in which they perform well. Here we evaluate a grassland productivity model to find the optimal spatial scale for parameterization and thus for subsequently applying it in future productivity projections for North America. We also evaluated the model on new vegetation types to ascertain its potential generality. We find the model most suitable when incorporating only grasslands, as opposed to also including agriculture and shrublands, and only in the Great Plains and eastern temperate forest ecoregions of North America. The model was not well suited to grasslands in North American deserts or northwest forest ecoregions. It also performed poorly in agriculture vegetation, likely due to management activities, and shrubland vegetation, likely because the model lacks representation of deep water pools. This work allows us to perform long-term projections in areas where model performance has been verified, with gaps filled in by future modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improving Predictability of Mixed-Phase Clouds and Aerosol Interactions in the Community Earth System Model (CESM) with ARM Measurements

The objective of this project is to improve the simulation and predictability of mixed-phase clouds and aerosol interactions in the Community Earth System Model (CESM) through comparisons with the DOE ARM observations. There are four major goals of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Develop a long-term multi-sensor mixed-phase cloud dataset; (3) Test the performance of improved ice microphysics in CESM-CAM5 with the ARM data; and (4) Examine mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds that include ice nucleation, ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process, feedbacks between cloud microphysics, dynamics and radiation, and provides links to aerosol properties in the Community Earth System Model (CESM)-Community Atmosphere Model version 5 (CAM5); (2) Developed a long-term multi-sensor mixed-phase cloud dataset. The mixed-phase cloud retrieval algorithms are improved by refining the phase determinations and ice number concentration retrievals. A multi-year mixed-phase cloud observation dataset (1999-2004; October 2013-February 2017) is regenerated at the ARM Barrow site with the improved algorithms. The dataset has been used in the global model evaluation to improve the model parameterizations; (3) Tested the performance of improved ice microphysics in CESM-CAM5 with the ARM data. CESM-CAM5 is run in the DOE Cloud-Associated Parameterizations Testbed (CAPT) and in the single-column model (SCM) mode to facilitate comparison with ARM observations. Multi-year CAPT simulations of seasonal variations of cloud microphysical properties, cloud liquid water path (LWP) and ice water path (IWP), cloud longwave and shortwave forcings, cloud occurrence frequency are evaluated against the ARM multi-sensor mixed-phase cloud retrievals; and (4) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5 that include the impacts of different ice nucleation mechanisms, sensitivity to dust IN concentration, treatment of the WBF process on mixed-phase cloud properties. Aerosol effect on mixed-phase clouds through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) is investigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Benchmarking soil moisture and its relationship to ecohydrologic variables in Earth System Models

Soil moisture (SM) is a key regulator of ecosystem biogeophysics, influencing plant water relations and land-atmosphere energy exchanges. We evaluate the representation of SM in 16 Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) using the International Land Model Benchmarking (ILAMB) framework, focusing on surface (0–5, 0–10 cm) and rootzone (0–100 cm) depths, as well as key ecohydrological variables like gross primary productivity (GPP), leaf area index (LAI), and evapotranspiration (ET), and their coupling. Models are benchmarked against multiple observational and assimilated datasets to assess both state variables and cross-variable relationships. Surface SM is generally well represented (r > 0.87), while rootzone SM variability is systematically overestimated (normalized standard deviation > 1). ET shows strong agreement with observations (r > 0.9), whereas GPP and LAI exhibit larger inter-model spread. Skill in individual variables does not guarantee realistic SM–ecohydrology coupling, which varies strongly across models and depends on the reference dataset. Köppen-based regional analyses reveal strong regime dependence, with several models performing well in Tropical and Temperate regions but degrading in Continental (high-latitude) zones. Across both global and regional benchmarks, models cluster by land surface framework, indicating that structural choices in soil hydrology and soil–plant coupling exert a first-order control on performance. These results provide process-relevant benchmarks and suggest that improving the representation of vertical soil structure, rooting depth distributions, and soil–plant hydraulic coupling will be central to advancing soil moisture realism in next-generation Earth system models.

CMIP6↗
NASA NTRS2009

Designing Struts for the Low-Fidelity Orion Cockpit Mockup

The objective of the project was to design and construct nine struts to be installed in the low-fidelity Orion cockpit mockup (Rev F; located at NASA s Johnson Space Center in Houston, TX) as simplified representations of the existing flight designed struts designed by engineers at Lockheed Martin (the primary contractor of the Orion). The project design included: researching the existing flight designs, brainstorming design upgrades, developing three unrelated three-dimensional (3D) strut designs using Pro/Engineer Wildfire 3.0, choosing the best fit design, locating materials and their sources, implementing the chosen design, and making design modifications. The project resulted in making simple modifications to the existing struts used in the last Orion cockpit mockup. The project is relevant to NASA, because upgrades to the low-fidelity Orion cockpit mockup progresses NASA s goals of developing and testing a new spacecraft, conducting the spacecraft's first crewed mission by 2015, returning to the moon by 2020, and exploring Mars and other planets in the future.

Lucienne, Runa A.↗
DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica

Abstract. The ice sheet model intercomparison project for CMIP6 (ISMIP6) effort brings together the ice sheet and climate modeling communities to gain understanding of the ice sheet contribution to sea level rise. ISMIP6 conducts stand-alone ice sheet experiments that use space- and time-varying forcing derived from atmosphere–ocean coupled global climate models (AOGCMs) to reflect plausible trajectories for climate projections. The goal of this study is to recommend a subset of CMIP5 AOGCMs (three core and three targeted) to produce forcing for ISMIP6 stand-alone ice sheet simulations, based on (i) their representation of current climate near Antarctica and Greenland relative to observations and (ii) their ability to sample a diversity of projected atmosphere and ocean changes over the 21st century. The selection is performed separately for Greenland and Antarctica. Model evaluation over the historical period focuses on variables used to generate ice sheet forcing. For stage (i), we combine metrics of atmosphere and surface ocean state (annual- and seasonal-mean variables over large spatial domains) with metrics of time-mean subsurface ocean temperature biases averaged over sectors of the continental shelf. For stage (ii), we maximize the diversity of climate projections among the best-performing models. Model selection is also constrained by technical limitations, such as availability of required data from RCP2.6 and RCP8.5 projections. The selected top three CMIP5 climate models are CCSM4, MIROC-ESM-CHEM, and NorESM1-M for Antarctica and HadGEM2-ES, MIROC5, and NorESM1-M for Greenland. This model selection was designed specifically for ISMIP6 but can be adapted for other applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Use of Remote Sensing and In-Situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the ACME Model

The overachieving goal of the whole CMDV-MCS project is to improve understanding of warm season continental convection and to develop treatments of convection and microphysics capable of representing mesoscale convective systems (MCSs) features in large-scale models. Our tasks for this project contributing to the overachieving goal include: (1) Improve the ice nucleation formulation for MG2 and P3 cloud microphysics schemes; (2) Improve the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM; and (3) Test the performance of improved ice microphysics in E3SM with observation data. In this project, we have (1) Improved the ice nucleation parameterization for MG2 and P3 in E3SM by implementing two advanced empirical parameterizations with connection to aerosols. The two deterministic heterogeneous ice nucleation parameterizations (i.e., DeMott et al., 2015; Niemand et al., 2012) were merged with the MG2 and P3 cloud microphysics schemes in E3SM. Long-term simulations were conducted to examine the impacts of these new parameterizations on simulated cloud properties; (2) Improved the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM. We evaluated the double Gaussian PDF of vertical velocity simulated by the Cloud Layers Unified By Binormals (CLUBB) and the sub-column vertical velocity sampled from the Subgrid Importance Latin Hypercube Sampler (SILHS) in E3SM. We introduced the vertical velocity variance induced by topographic gravity waves for ice nucleation and droplet activation; and (3) Tested the performance of improved ice microphysics in E3SM with observation data. We tested the new treatments of ice nucleation in the single column model (SCM) mode for the stratiform mixed-phase clouds observed during 9-10 October 2004 in the DOE ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) and for the convective clouds observed on 20 May 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E). Modeled ice nucleating particles (INPs) concentrations were compared against observations collected around the globe.

54 ENVIRONMENTAL SCIENCES↗