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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Fractal dimension analysis reveals skeletal muscle disorganization in mdx mice

Duchenne Muscular Dystrophy (DMD) is characterized by muscle extracellular matrix disorganization due to the increased collagen deposition leading to fibrosis that significantly exacerbates disease progression. Fractal dimension analysis is a method that quantifies tissue/cellular disorganization and characterizes complex structures. The first objective of the present study was use fractal analysis to evaluate extracellular matrix disorganization in mdx mice soleus muscle. Next, we mimic a hyper-proliferation of fibrogenic cells by co-culturing NIH3T3 fibroblasts and C2C12 myoblasts to test whether fibroblasts induce disorganization in myoblast arrangement. Here, we show mdx presented high skeletal muscle disorganization as revealed by fractal analysis. Similarly, this method revealed that myoblasts co-cultured with fibroblast also presented cellular arrangement disorganization. We also reanalyzed skeletal muscle microarrays transcriptomic data from mdx and DMD patients that revealed transcripts related to extracellular matrix organization. This analysis also identified Osteoglycin, which was validated as a potential regulator of ECM organization in mdx dystrophic muscles. Our results demonstrate that fractal dimension is useful tool for the analysis of skeletal muscle disorganization in DMD and also reveal a fibroblast-myoblast cross-talk that contributes to “in vitro” myoblast disarrangement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. Here, in this article, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Surface-Height Determination of Crevassed Glaciers-Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

Glacial acceleration is a main source of uncertainty in sea-level-change assessment. Measurement of ice-surface heights with a spatial and temporal resolution that not only allows elevation-change calculation, but also captures ice-surface morphology and its changes is required to aid in investigations of the geophysical processes associated with glacial acceleration.The Advanced Topographic Laser Altimeter System aboard NASAs future ICESat-2 Mission (launch 2017) will implement multibeam micropulse photon-counting lidar altimetry aimed at measuring ice-surface heights at 0.7-m along-track spacing. The instrument is designed to resolve spatial and temporal variability of rapidly changing glaciers and ice sheets and the Arctic sea ice. The new technology requires the development of a new mathematical algorithm for the retrieval of height information.We introduce the density-dimension algorithm (DDA) that utilizes the radial basis function to calculate a weighted density as a form of data aggregation in the photon cloud and considers density an additional dimension as an aid in auto-adaptive threshold determination. The auto-adaptive capability of the algorithm is necessary to separate returns from noise and signal photons under changing environmental conditions. The algorithm is evaluated using data collected with an ICESat-2 simulator instrument, the Slope Imaging Multi-polarization Photon-counting Lidar, over the heavily crevassed Giesecke Braer in Northwestern Greenland in summer 2015. Results demonstrate that ICESat-2 may be expected to provide ice-surface height measurements over crevassed glaciers and other complex ice surfaces. The DDA is generally applicable for the analysis of airborne and spaceborne micropulse photon-counting lidar data over complex and simple surfaces.

Herzfeld, Ute C.↗
DOE OSTI2018

Mechanism of Ti-Catalyzed Oxidative Nitrene Transfer in [2+2+1] Pyrrole Synthesis from Alkynes and Azobenzene

A combined computational and experimental study on the mechanism of Ti-catalyzed formal [2 + 2 + 1] pyrrole synthesis from alkynes and aryl diazenes is reported. This reaction proceeds through a formally TiII/TiIV redox catalytic cycle as determined by natural bond orbital (NBO) and intrinsic bond orbital (IBO) analysis. Kinetic analysis of the reaction of internal alkynes with azobenzene reveals a complex equilibrium involving Ti=NPh monomer/dimer equilibrium and Ti=NPh + alkyne [2 + 2] cycloaddition equilibrium along with azobenzene and pyridine inhibition equilibria prior to rate-determining second alkyne insertion. Computations support this kinetic analysis, provide insights into the structure of the active species in catalysis and the roles of solvent, and provide a new mechanism for regeneration of the Ti imido catalyst via disproportionation. Reductive elimination from a 6-membered azatitanacyclohexadiene species to generate pyrrole-bound Ti π is surprisingly facile and occurs through a unique electrocyclic reductive elimination pathway similar to a Nazarov cyclization. The resulting Ti π species are stabilized through backbonding into the $π*$ of the pyrrole framework, although solvent effects also significantly stabilize free Ti π species that are required for pyrrole loss and catalytic turnover. Further computational and kinetic analysis reveals that in complex reactions with unysmmetric alkynes the resulting pyrrole regioselectivity is driven primarily by steric effects for terminal alkynes and inductive effects for internal alkynes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A novel framework for hosting capacity analysis with spatio-temporal probabilistic voltage sensitivity analysis

Smart grids are envisioned to accommodate high penetration of distributed photovoltaic (PV) generation, which may cause adverse grid impacts in terms of voltage violations. Therefore, PV Hosting capacity is being used as a planning tool to determine the maximum PV installation capacity that causes the first voltage violation and above which would require infrastructure upgrades. Additionally, traditional methods of Hosting capacity analysis are scenario based and computationally complex as they rely on iterative load flow algorithms that require investigating a large number of scenarios for accurate assessment of PV impacts. Therefore, this paper presents a computationally efficient analytical approach to compute the probability distribution of voltage change due to random behavior of randomly located multiple distributed PVs. The proposed approach is based on Spatio-temporal probabilistic voltage sensitivity analysis that exploits both spatial and temporal uncertainties associated with PV injections. Thereafter, the derived distribution is used to quantify voltage violations for various PV penetration levels and subsequently determine the hosting capacity of the system without the need to examine large number of scenarios. Results of the proposed framework are validated via conventional load flow based simulation approach on the IEEE 37 and IEEE 123 node test systems.

42 ENGINEERING↗
NASA NTRS1994

Towards an integral computer environment supporting system operations analysis and conceptual design

VITROCISET has in house developed a prototype tool named System Dynamic Analysis Environment (SDAE) to support system engineering activities in the initial definition phase of a complex space system. The SDAE goal is to provide powerful means for the definition, analysis, and trade-off of operations and design concepts for the space and ground elements involved in a mission. For this purpose SDAE implements a dedicated modeling methodology based on the integration of different modern (static and dynamic) analysis and simulation techniques. The resulting 'system model' is capable of representing all the operational, functional, and behavioral aspects of the system elements which are part of a mission. The execution of customized model simulations enables: the validation of selected concepts with respect to mission requirements; the in-depth investigation of mission specific operational and/or architectural aspects; and the early assessment of performances required by the system elements to cope with mission constraints and objectives. Due to its characteristics, SDAE is particularly tailored for nonconventional or highly complex systems, which require a great analysis effort in their early definition stages. SDAE runs under PC-Windows and is currently used by VITROCISET system engineering group. This paper describes the SDAE main features, showing some tool output examples.

Barro, E.↗
NASA NTRS1984

Fundamental differences between optimization code test problems in engineering applications

The purpose here is to suggest that there is at least one fundamental difference between the problems used for testing optimization codes and the problems that engineers often need to solve; in particular, the level of precision that can be practically achieved in the numerical evaluation of the objective function, derivatives, and constraints. This difference affects the performance of optimization codes, as illustrated by two examples. Two classes of optimization problem were defined. Class One functions and constraints can be evaluated to a high precision that depends primarily on the word length of the computer. Class Two functions and/or constraints can only be evaluated to a moderate or a low level of precision for economic or modeling reasons, regardless of the computer word length. Optimization codes have not been adequately tested on Class Two problems. There are very few Class Two test problems in the literature, while there are literally hundreds of Class One test problems. The relative performance of two codes may be markedly different for Class One and Class Two problems. Less sophisticated direct search type codes may be less likely to be confused or to waste many function evaluations on Class Two problems. The analysis accuracy and minimization performance are related in a complex way that probably varies from code to code. On a problem where the analysis precision was varied over a range, the simple Hooke and Jeeves code was more efficient at low precision while the Powell code was more efficient at high precision.

Eason, E. D.↗
DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Sensitivity Analysis of Multidisciplinary Rotorcraft Simulations

A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. An unstructured sensitivity-enabled Navier-Stokes solver, FUN3D, and a nonlinear flexible multibody dynamics solver, DYMORE, are coupled to predict the aerodynamic loads and structural responses of helicopter rotor blades. A discretely-consistent adjoint-based sensitivity analysis available in FUN3D provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute DYMORE structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Numerical results verify accuracy of the FUN3D/DYMORE system by conducting simulations for a benchmark rotorcraft test model and comparing solutions with established analyses and experimental data. Complex-variable implementation of sensitivity analysis of DYMORE and the coupled FUN3D/DYMORE system is verified by comparing with real-valued analysis and sensitivities. Correctness of adjoint formulations for FUN3D/DYMORE interfaces is verified by comparing adjoint-based and complex-variable sensitivities. Finally, sensitivities of the lift and drag functions obtained by complex-variable FUN3D/DYMORE simulations are compared with sensitivities computed by the multidisciplinary sensitivity analysis, which couples adjoint-based flow and grid sensitivities of FUN3D and FUN3D/DYMORE interfaces with complex-variable sensitivities of DYMORE structural responses.

Wang, Li↗
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Aeropropulsion 1987. Session 2: Aeropropulsion Structures Research

Aeropropulsion systems present unique problems to the structural engineer. The extremes in operating temperatures, rotational effects, and behaviors of advanced material systems combine into complexities that require advances in many scientific disciplines involved in structural analysis and design procedures. This session provides an overview of the complexities of aeropropulsion structures and the theoretical, computational, and experimental research conducted to achieve the needed advances.

Source record↗
NASA NTRS1987

Aeropropulsion structures

The structural engineer is faced with unique problems when dealing with aeropropulsion systems. He is faced with extremes in operating temperatures, rotational effects, and behaviors of advanced material systems which combine into complexities that require advances in many scientific disciplines involved in structural analysis and design procedures. This presentation provides an overview of the complexities of aeropropulsion structures and the theoretical, computational, and experimental research conducted to achieve the needed advances.

Nichols, Lester D.↗
NASA NTRS1990

Aeropropulsion structures

Aeropropulsion systems present unique problems to the structural engineer. The extremes in operating temperatures, rotational effects, and behaviors of advance material systems combine into complexities that require advances in many scientific disciplines involved in structural analysis and design procedures. This paper provides an overview of the complexities of aeropropulsion structures and the theoretical, computational, and experimental research conducted to achieve the needed advances.

Nichols, Lester D.↗
NASA NTRS2002

Aeroheating Thermal Analysis Methods for Aerobraking Mars Missions

Mars missions often employ aerobraking upon arrival at Mars as a low-mass method to gradually reduce the orbit period from a high-altitude, highly elliptical insertion orbit to the final science orbit. Two recent missions that made use of aerobraking were Mars Global Surveyor (MGS) and Mars Odyssey. Both spacecraft had solar arrays as the main aerobraking surface area. Aerobraking produces a high heat load on the solar arrays, which have a large surface area exposed to the airflow and relatively low mass. To accurately model the complex behavior during aerobraking, the thermal analysis must be tightly coupled to the flight mechanics, aerodynamics, and atmospheric modeling efforts being performed during operations. To properly represent the temperatures prior to and during the drag pass, the model must include the orbital solar and planetary heat fluxes. The correlation of the thermal model to flight data allows a validation of the modeling process, as well as information on what processes dominate the thermal behavior. This paper describes the thermal modeling method that was developed for this purpose, as well as correlation for two flight missions, and a discussion of improvements to the methodology.

Amundsen, Ruth M.↗
DOE OSTI2022

Biorefinery upgrading of herbaceous biomass to renewable hydrocarbon fuels, Part 1: Process modeling and mass balance analysis

Process simulation has long been a well-established tool to track key operational, design, and mass and energy balance metrics for pre-commercial technologies such as advanced lignocellulosic biofuels. While tools such as this are well-documented in the public literature around 2nd-generation cellulosic ethanol technologies (which have been scaled up to commercial deployment to some degree over the past decade), such models and analysis information remain more sparse for more complex biorefinery pathways focused on producing drop-in hydrocarbon fuels and blend-stocks, particularly regarding information required to support air emissions or other environmental analysis. In this work, we summarize key details for an established "design case" modeling the conversion of herbaceous lignocellulosic biomass into a renewable diesel hydrocarbon blend-stock based on a representative lipid pathway from oleaginous yeast. The process is based on a biochemical deconstruction and upgrading approach utilizing deacetylation and dilute acid pretreatment, followed by enzymatic hydrolysis, fermentation, and catalytic upgrading of hydrolysate sugars to fuels. We provide key mass and energy balance outputs from the process models, with accompanying stream tables and component-level flowrates. A total of 12 model scenarios are presented spanning two feedstocks, three biorefinery scales, and two processing approaches for the lignin/residual solids waste streams. This "Part 1" manuscript presents the resulting impacts across the 12 cases on fuel yields and key output streams, focused here on direct biorefinery air emissions for selected components including CO 2 as well as sulfur (SOx) and nitrogen oxides (NOx). In the context of cleaner production, the latter focus on selected biorefinery air emission outputs establishes an initial baseline estimate and accompanying framework of the model cases, upon which an accompanying "Part 2" study will build to refine the values for these and other air pollutants across these scenarios, also considering mitigation options to comply with applicable regulatory standards. We also highlight further optimization opportunities based on potential tradeoffs identified here between air emissions versus life-cycle greenhouse gas profiles attributed to the disposition of lignin/residual solids.

09 BIOMASS FUELS↗
NASA NTRS2018

Data, Meet Compute: NASA's Cumulus Ingest Architecture

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PBs of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. In addition to the massive increase in data collected, researchers and application developers want more and faster access - enabling complex visualizations, long time-series analysis, and cross dataset research without needing to copy and manage massive amounts of data locally. NASA has looked to the cloud to address these needs, building its Cumulus system to manage the ingest of diverse data in a wide variety of formats into the cloud. In this talk, we look at what Cumulus is from a high level and then take a deep dive into how it manages complexity and versioning associated with multiple AWS Lambda and ECS microservices communicating through AWS Step Functions across several disparate installations

EOSDIS↗
NASA NTRS2008

Formation of Clustered DNA Damage after High-LET Irradiation: A Review

Radiation can cause as well as cure cancer. The risk of developing radiation-induced cancer has traditionally been estimated from cancer incidence among survivors of the atomic bombs in Hiroshima and Nagasaki. These data provide the best estimate of human cancer risk over the dose range for low linear energy transfer (LET) radiations, such as X- or gamma-rays. The situation of estimating the real biological effects becomes even more difficult in the case of high LET particles encountered in space or as the result of domestic exposure to particles from radon gas emitters or other radioactive emitters like uranium-238. Complex DNA damage, i.e., the signature of high-LET radiations comprises by closely spaced DNA lesions forming a cluster of DNA damage. The two basic groups of complex DNA damage are double strand breaks (DSBs) and non-DSB oxidative clustered DNA lesions (OCDL). Theoretical analysis and experimental evidence suggest there is increased complexity and severity of complex DNA damage with increasing LET (linear energy transfer) and a high mutagenic or carcinogenic potential. Data available on the formation of clustered DNA damage (DSBs and OCDL) by high-LET radiations are often controversial suggesting a variable response to dose and type of radiation. The chemical nature and cellular repair mechanisms of complex DNA damage have been much less characterized than those of isolated DNA lesions like an oxidized base or a single strand break especially in the case of high-LET radiation. This review will focus on the induction of clustered DNA damage by high-LET radiations presenting the earlier and recent relative data.

Hada, Megumi↗
DOE OSTI2023

FORCE Integration with DRAFT and IDAES

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to produce multiple commodities and improve economic viability under uncertain market conditions. Technical and economic analysis of IES requires modeling of complex processes with software models having enough fidelity to capture real-world dynamics while still being capable of running using reasonable computing resources. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Recent developments for the FORCE toolset have centered on strengthening the flexibility and modularity to link with external models and other available software to enhance IES simulations. Adding versatility to the FORCE toolset improves capability resulting in better techno-economic simulation of nuclear and IES components (both in potential higher fidelity and accuracy to expected performance after deployment). It also helps leverage existing work in the IES field and improve efficiency in national code development. This report focuses on two such endeavors: integration of the Dynamic Reliability Analysis Framework Tool (DRAFT) and the Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) software packages into FORCE.

97 MATHEMATICS AND COMPUTING↗