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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Recent advances in transonic computational aeroelasticity

A transonic unsteady aerodynamic and aeroelasticity code called CAP-TSD was developed for application to realistic aircraft configurations. The code permits the calculation of steady and unsteady flows about complete aircraft configurations for aeroelastic analysis in the flutter critical transonic speed range. The CAP-TSD code uses a time accurate approximate factorization algorithm for solution of the unsteady transonic small disturbance potential equation. An overview is given of the CAP-TSD code development effort and results are presented which demonstrate various capabilities of the code. Calculations are presented for several configurations including the General Dynamics 1/9 scale F-16 aircraft model and the ONERA M6 wing. Calculations are also presented from a flutter analysis of a 45 deg sweptback wing which agrees well with the experimental data. Descriptions are presented of the CAP-TSD code and algorithm details along with results and comparisons which demonstrate these recent developments in transonic computational aeroelasticity.

Batina, John T.↗
DOE OSTI2020

In situ , in vivo , and in operando imaging and spectroscopy of liquids using microfluidics in vacuum

This review offers a succinct overview of the development of a vacuum-compatible microfluidic reactor system for analysis at the liquid vacuum interface (SALVI), and its diverse applications in in situ, in vivo, and in operando imaging of liquid surfaces as well as the air-liquid (a-l), liquid-liquid (l-l), and solid-liquid (s-l) interfaces in the past decade. SALVI is one of the first microfluidics-based reactors that has enabled direct analysis of volatile liquids in vacuum surface tools such as scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Its integration into ambient and vacuum spectroscopy and microscopy is illustrated. Several applications are highlighted including (1) imaging nanoparticles in liquid using in situ SEM; (2) mapping the evolving l-l interface using in situ x-ray absorption spectroscopy and ToF-SIMS; (3) following complex a-l interfacial oxidation reaction products using in situ ToF-SIMS; (4) capturing biological interfaces of cells and microbes via in vivo multimodal and correlative imaging; and (5) monitoring the dynamic solid electrode and liquid electrolyte interface using in operando molecular imaging. Finally, outlook and recommendations are presented. Besides showing the holistic information volume obtained by real-time multiplexed imaging, this review intends to convey the importance of tool development in revolutionizing surface and interface analysis using vacuum platforms previously limited to solid surfaces. Microfluidics is manifested to be not limited to ambient conditions in many examples in this review. Moreover, fundamental interfacial phenomena underpinning mass and charge transfer can now be pursued in real time via innovated chemical imaging and spectroscopy.

42 ENGINEERING↗
NASA NTRS1977

Computerized X-ray reconstruction tomography in stereometric analysis of cardiovascular dynamics

A computerized technique is proposed for obtaining cross-sectional images of the dynamic spatial distribution of X-ray attenuation covering the entire anatomic extent of the thorax and its contents in living dogs with a resolution of 1 mm and at time intervals of 1/60 sec. Use is made of an X-ray imaging chain which is a new high-performance video-fluoroscopic system, unique in its design and construction and called SSDSR for single source dynamic spatial reconstructor. This dynamic spatial reconstruction system is shown to provide the temporally and spatially coherent multiple cross sections required to obtain the full three-dimensional anatomic and simultaneous hemodynamic information necessary for detailed quantitative analyses of regional cardiopulmonary and vascular functions in both basic investigations of animals and clinical diagnostic applications to patients. Numerous photographs supplement the text.

Robb, R. A.↗
NASA NTRS1990

Comparison of SAGE II ozone measurements and ozone soundings at Uccle (Belgium) during the period February 1985 to January 1986

The ozone profiles obtained from 24 balloon soundings at Uccle (50 deg 48 min N, 4 deg 21 min E) made with electrochemical ozonesondes were used as correlative data for SAGE II ozone profiles retrieved within a distance of at most 600 km from Uccle. The agreement between the two data sets is in general quite good, especially for profiles nearly coincident in time and space, and during periods of little dynamic activity over the area considered. The percent difference between the ozone column density of the mean balloon and SAGE profile is 4.4 percent (-3.3) percent in the altitude region between 10 and 26 km. From a statistical analysis it appears that there is a small but meaningful difference between the mean profiles at the level of the ozone maximum and around the 30-km level. An error analysis of both data sets give similar results, leading to the conclusion that these differences are instrumentally induced. However, differences between the mean profiles in the lower stratosphere are probably real and due to the high ozone variability in time and space in that altitude region.

De Muer, D.↗
DOE OSTI2016

Improving Mixed-phase Cloud Parameterization in Climate Model with the ACRF Measurements

Mixed-phase cloud microphysical and dynamical processes are still poorly understood, and their representation in GCMs is a major source of uncertainties in overall cloud feedback in GCMs. Thus improving mixed-phase cloud parameterizations in climate models is critical to reducing the climate forecast uncertainties. This study aims at providing improved knowledge of mixed-phase cloud properties from the long-term ACRF observations and improving mixed-phase clouds simulations in the NCAR Community Atmosphere Model version 5 (CAM5). The key accomplishments are: 1) An improved retrieval algorithm was developed to provide liquid droplet concentration for drizzling or mixed-phase stratiform clouds. 2) A new ice concentration retrieval algorithm for stratiform mixed-phase clouds was developed. 3) A strong seasonal aerosol impact on ice generation in Arctic mixed-phase clouds was identified, which is mainly attributed to the high dust occurrence during the spring season. 4) A suite of multi-senor algorithms was applied to long-term ARM observations at the Barrow site to provide a complete dataset (LWC and effective radius profile for liquid phase, and IWC, Dge profiles and ice concentration for ice phase) to characterize Arctic stratiform mixed-phase clouds. This multi-year stratiform mixed-phase cloud dataset provides necessary information to study related processes, evaluate model stratiform mixed-phase cloud simulations, and improve model stratiform mixed-phase cloud parameterization. 5). A new in situ data analysis method was developed to quantify liquid mass partition in convective mixed-phase clouds. For the first time, we reliably compared liquid mass partitions in stratiform and convective mixed-phase clouds. Due to the different dynamics in stratiform and convective mixed-phase clouds, the temperature dependencies of liquid mass partitions are significantly different due to much higher ice concentrations in convective mixed phase clouds. 6) Systematic evaluations of mixed-phase cloud simulations by CAM5 were performed. Measurement results indicate that ice concentrations control stratiform mixed-phase cloud properties. The improvement of ice concentration parameterization in the CAM5 was done in close collaboration with Dr. Xiaohong Liu, PNNL (now at University of Wyoming).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRS1995

Nonlinear viscoelastic characterization of polymer materials using a dynamic-mechanical methodology

Polymer materials retrieved from LDEF exhibit nonlinear constitutive behavior; thus the authors present a method to characterize nonlinear viscoelastic behavior using measurements from dynamic (oscillatory) mechanical tests. Frequency-derived measurements are transformed into time-domain properties providing the capability to predict long term material performance without a lengthy experimentation program. Results are presented for thin-film high-performance polymer materials used in the fabrication of high-altitude scientific balloons. Predictions based upon a linear test and analysis approach are shown to deteriorate for moderate to high stress levels expected for extended applications. Tests verify that nonlinear viscoelastic response is induced by large stresses. Hence, an approach is developed in which the stress-dependent behavior is examined in a manner analogous to modeling temperature-dependent behavior with time-temperature correspondence and superposition principles. The development leads to time-stress correspondence and superposition of measurements obtained through dynamic mechanical tests. Predictions of material behavior using measurements based upon linear and nonlinear approaches are compared with experimental results obtained from traditional creep tests. Excellent agreement is shown for the nonlinear model.

Strganac, Thomas W.↗
DOE OSTI2022

Perspectives on Microfluidics for the Study of Asphaltenes in Upstream Hydrocarbon Production: A Minireview

The utilization of microfluidics has generated deep insights into asphaltene precipitation mechanisms and oil–water emulsion stabilization. Agglomeration and precipitation of asphaltenes can cause flow assurance problems during the extraction and transportation of crude oil. Change in temperature, pressure, reservoir conditions, and solvents can change the local environment, leading to asphaltene precipitation. Understanding asphaltene properties and precipitation pathways becomes critical in devising mitigation methods, demulsifiers, and suitable conditions during hydrocarbon processing. Microfluidics has helped in high throughput measurement studies, understanding critical processing conditions, fast demulsifier screening, and the effect of solvent concentration on deposition, generating useful information for utilization at the point of resource extraction facilitating improved resource management. It has become possible to capture the porous, complex nature of reservoir formations and the interaction of chemicals during precipitation through integrated analytics and visualization studies available only through microfluidics. The use of droplet microfluidics, with optical microscopy and high-speed imaging to study the oil–water interface, has resulted in greater understanding of the role of asphaltenes in interfacial properties and emulsion stabilization. Here, this minireview highlights the crucial aspects of microfluidics that have been used to understand physicochemical behavior and dynamics of asphaltene deposition. Some of the unique devices have been presented focusing on the key elements of microfluidics design, fabrication, and analysis, as the insight obtained from microfluidics strongly depends on the device design and the controllability of the experimental parameters. Successful implementation of microfluidics for efficient and controlled experiments, short analysis time scales and rapid screening, and generation of high-quality, reliable data that convey asphaltene deposition issues and interface behavior in emulsions shows the importance of microsystems for advancing knowledge in hydrocarbon production and processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Modeling Isothermal Reduction of Iron Ore Pellet Using Finite Element Analysis Method: Experiments & Validation

Iron ore pellet reduction experiments were performed with pure hydrogen (H2) and mixtures with carbon monoxide (CO) at different ratios. For direct reduction processes that switch dynamically between reformed natural gas and hydrogen as the reductant, it is important to understand the effects of the transition on the oxide reduction kinetics to optimize the residence time of iron ore pellets in a shaft reactor. Hence, the reduction rates were studied by varying experimental parameters such as the temperature (800, 850 & 900 °C), reactant gas flow rate (100, 150 & 200 cm3/min), pellet size and composition of the reactant gas mixture. The rate of reduction was observed to increase with an increase in temperature and reactant gas flow rate, but it decreased with an increase in pellet size. SEM greyscale analysis was performed to analyze the porosity and phase composition of partially reduced pellets. The porosity of the pellets was observed to increase from 0.3 for unreacted pellet to 0.42 for a completely reduced pellet. Energy-dispersive X-ray spectroscopy (EDAX) analysis was performed to identify the phases observed in the SEM images. The fraction of iron phase was observed to increase from the shell region of the pellet to the core region with an increase in the degree of reduction. A 2D-axisymmetric numerical model was developed on COMSOL Multiphysics, and it was validated using the conversion (X) vs. time curves obtained from each experiment. The model was able to accurately predict the total time needed for the complete conversion of a single iron ore pellet for multiple experiments. Effects of changes in the porosity and tortuosity of the pellet on the model were also studied and the rate of reduction was observed to be sensitive to changes in both porosity and tortuosity. The SEM analysis and the model results show that tortuosity is higher for pellets reduced with H2 than for pellets reduced with H2-CO gas mixtures.

08 HYDROGEN↗
NASA NTRS1996

[Two-phase Interfaces in Weak External Fields]

Our aim has been that of understanding from first principles the behavior of two-phase interfaces in the absence of gravitational constraints. This is fundamental to our ability to deal with the fluid structures that abound in the real biological, chemical, and physical world. A substantial effort was mounted to determine how familiar hydrodynamic concepts have to be modified and interpreted to make them appropriate to the multi-level structure alluded to above. This was primarily in the context of the microscopic symmetric pressure tensor, which was, for the first time, expressed in the invaluable density functional format, and the used to follow the predictions of popular microscopic models of the energetics of interfacial systems. In the course of these investigations, the previous murky relation between pressure tensor and thermodynamics was completely clarified. The process of extending thermodynamic information to interfacial dynamics was initiated along two paths. One was from the viewpoint of an inertialess lattice gas, resulting in the surprising conclusion that at this level, all transport is governed by precisely the thermodynamic free energy, albeit with a non-trivial effective particle mobility. The other aimed at understanding the fashion in which slow macroscopic motions, accounted for by a time-varying microscopic energy, generate effective hydrodynamic parameters. By examining a solvable model system, it was found that all current procedures for doing so are deficient, and suitable alleviation suggested. The major effect of this project was to set the stage for the analysis of the substantial dynamical regimes in which extensive equilibrium information provides the dominant background. This produces a smooth junction to the models of Araki and Munakata, Giacomin and Lebowitz, and Oxtoby. It is also crucial to our understanding of the complex interfacial equilibrium configurations required for intermediate stages of two-phase separation, for which the "phase-field" techniques we have been developing are uniquely effective. And it puts us within striking range of the physical experiments that can provide incisive tests of the theoretical framework.

Percus, J. K.↗
DOE OSTI2019

Effects of Inflow Spatiotemporal Discretization on Wake Meandering and Turbine Structural Response using FAST.Farm

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm with minimal computational expense. Previous studies have shown similarities and differences in wind turbine performance between FAST.Farm and high-fidelity large-eddy simulations (LES) using both LES precursor-generated inflow and TurbSim-generated synthetic inflow. While conservative resolutions have been used to date, no formal spatial or temporal discretization study has been performed for the FAST.Farm model wind domains. This work aims to study the effects of varying the spatial and temporal discretization of the wind domains on wake meandering (low-resolution domain) and turbine structural response (high-resolution domains) and resulting wake and load calculations. The purpose of this study is to establish convergence criteria and recommendations for discretization values in terms of rotor diameter (D, expressed in meters) that will maximize computational efficiency. To ensure a percent error of ≤ 1% in standard deviation of wake center position relative to the finest included resolution, a low-resolution time step of 0.024D s, 0.016D s, and 0.0079D s and a low-resolution spatial discretization of 0.079D m, 0.16D m, and 0.24D m is recommended for 8 m/s, 12 m/s, and 18 m/s mean ambient wind speed at hub height, respectively. These guidelines are likely applicable to other implementations of the dynamic wake meandering model. To ensure a percent error of ≤ 1% in standard deviation of all considered structural response outputs, a high-resolution time step that captures the highest influential excitation and natural frequencies in the system and a high-resolution spatial discretization comparable to the maximum airfoil cord length is recommended. Lastly, these guidelines are likely applicable to any aeroelastic analysis.

17 WIND ENERGY↗
DOE OSTI2021

Comparative Analysis of ML Techniques for Data-Driven Anomaly Detection, Classification and Localization in Distribution System

High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗
DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit

Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure feasibility of the solutions. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a process and its controller in a low-order representation. In contrast to previous works that have employed nonlinear SBMs, this paper proposes linear SBMs, developed using time-series analysis, to facilitate online scheduling computations. Using a year-long industrial dataset, we nd that compact linear SBMs are suitable approximations over typical scheduling horizons, but that their accuracies are unpredictable over time. We introduce a strategy for online updating of the SBMs, based on Kalman ltering schemes for online parameter estimation. The approach greatly improves the accuracy of SBM predictions and will enable the use of linear SBM-based demand-response scheduling in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.

Clustering↗