NIMBLE. A data management and visualization framework for large scale engineering and science VVUQ applications
Explore the source record for details and available documents.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
SEARCH · Search NASA
Results for “engineering data”
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.
Advanced engine study program
A design and analysis study was conducted to provide advanced engine descriptions and parametric data for space transfer vehicles. The study was based on an advanced oxygen/hydrogen engine in the 7,500 to 50,000 lbf thrust range. Emphasis was placed on defining requirements for high-performance engines capable of achieving reliable and versatile operation in a space environment. Four variations on the expander cycle were compared, and the advantages and disadvantages of each were assessed. Parametric weight, envelope, and performance data were generated over a range of 7,500 to 50,000 lb thrust and a wide range of chamber pressure and nozzle expansion ratio.
Masters, A. I.↗
Data management and processing workflow for the Materials Physics and Engineering group beamlines at the Advanced Photon Source
The ability to store, organize, process and distribute experimental data effectively, efficiently and securely is particularly important for large user facilities like the Advanced Photon Source. In this article, the deployment of the APS Data Management System (DM) at the 1-ID and 6-BM beamlines of the APS is described. These two beamlines support a wide range of experimental techniques and generate data at relatively high rates, making them ideal candidates to illustrate the deployment and customization of the DM system and its tools. Using several usage examples at these beamlines, various capabilities of the DM system are described.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Altitude Cooling Investigation of the R-2800-21 Engine in the P-47G Airplane. IV - Engine Cooling-Air Pressure Distribution
A study of the data obtained in a flight investigation of an R-2800-21 engine in a P-47G airplane was made to determine the effect of the flight variables on the engine cooling-air pressure distribution. The investigation consisted of level flights at altitudes from 5000 to 35,000 feet for the normal range of engine and airplane operation. The data showed that the average engine front pressures ranged from 0.73 to 0.82 of the impact pressure (velocity head). The average engine rear pressures ranged from 0.50 to 0.55 of the impact pressure for closed cowl flaps and from 0.10 to 0.20 for full-open cowl flaps. In general, the highest front pressures were obtained at the bottom of the engine. The rear pressures for the rear-row cylinders were .lower and the pressure drops correspondingly higher than for the front-row cylinders. The rear-pressure distribution was materially affected by cowl-flap position in that the differences between the rear pressures of the front-row and rear-row cylinders markedly increased as the cowl flaps were opened. For full-open cowl flaps, the pressure drops across the rear-row cylinders were in the order of 0.2 of the impact pressure greater than across the front-row cylinders. Propeller speed and altitude had little effect on the -coolingair pressure distribution, Increase in angle of inclination of the thrust axis decreased the front ?pressures for the cylinders at the top of the engine and increased them for the cylinders at the bottom of the engine. As more auxiliary air was taken from the engine cowling, the front pressures and, to a lesser extent, the rear pressures for the cylinders at the bottom of the engine decreased. No correlation existed between the cooling-air pressure-drop distribution and the cylinder-temperature distribution.
Kaufman, Samuel J.↗
Development and evaluation of a skeletal mechanism for EHN additized gasoline mixtures in large Eddy simulations of HCCI combustion
Advanced Low Temperature Combustion modes, such as the Sandia proposed Additive-Mixing Fuel Injection (AMFI), can unlock significant potential to boost fuel conversion efficiency and ultimately improve the energy conversion of internal combustion engines. This is a novel improved combustion process that is enabled by supplying small (<5%) variable amounts of autoignition improver to the fuel to enhance the engine operation and control. Common, diesel-fuel ignition-quality enhancing additive, 2-ethylexyl nitrate (EHN), is doped into gasoline to enable Sandia LTGC + AMFI combustion. This manuscript focuses on the development of a reduced sub-mechanism for EHN chemical kinetics at engine relevant conditions that is implemented into a skeletal mechanism for chemical kinetic studies of gasoline surrogate fuels. The mechanism validation utilized zero-dimensional numerical simulations and comparison to shock tube ignition-delay data of pure and EHN-doped n-heptane. Additional validation is presented with Homogeneous Charge Compression-Ignition (HCCI) engine data of pure and EHN-doped research-grade E10 gasoline. Then, the mechanism was deployed in a 3-D computational fluid dynamics (CFD) using Large Eddy Simulations (LES) to model the HCCI engine experiments of 0.4% vol EHN additized E10 gasoline at several equivalence ratios. Simulations showed a very good performance of the mechanism, and the model accurately reproduced (a) the ignition point, (b) combustion phasing, (c) combustion duration, and (d) the peak of the heat release rates of the engine experiments. The results show that EHN promotes Low-Temperature Heat Release, ultimately driving the gasoline to autoignite at thermodynamic conditions where the fuel would not otherwise ignite. Overall, this work demonstrates a viable reduced chemical-kinetic mechanism for EHN and shows that it can be combined with a skeletal gasoline mechanism for CFD-LES analysis of well-mixed LTGC that matches well with experimental results. The CFD-LES analysis also shows the spatial distribution of EHN-fuel interactions that control the autoignition throughout the combustion chamber.
Guleria, Gaurav↗
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations
Here, this work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterative method, a lower-fidelity model, or a projection-based reduced-order model, for example. The proposed statistical model comprises the sum of a deterministic regression-function model and a stochastic noise model. The method constructs the regression-function model by applying regression techniques from machine learning (e.g., support vector regression, artificial neural networks) to map features (i.e., error indicators such as sampled elements of the residual) to a prediction of the approximate-solution error. The method constructs the noise model as a mean-zero Gaussian random variable whose variance is computed as the sample variance of the approximate-solution error on a test set; this variance can be interpreted as the epistemic uncertainty introduced by the approximate solution. This work considers a wide range of feature-engineering methods, data-set-construction techniques, and regression techniques that aim to ensure that (1) the features are cheaply computable, (2) the noise model exhibits low variance (i.e., low epistemic uncertainty introduced), and (3) the regression model generalizes to independent test data. Finally, numerical experiments performed on several computational-mechanics problems and types of approximate solutions demonstrate the ability of the method to generate statistical models of the error that satisfy these criteria and significantly outperform more commonly adopted approaches for error modeling.
97 MATHEMATICS AND COMPUTING↗
Multi-scale modeling of microstructure dependent intergranular brittle fracture using a quantitative phase-field based method
In this study, the fracture behavior of brittle materials is strongly influenced by their underlying microstructure that needs explicit consideration for accurate prediction of fracture properties and the associated scatter. In this work, a hierarchical multi-scale approach is pursued to model microstructure sensitive brittle fracture. A quantitative phase-field based fracture model is utilized to capture the complex crack growth behavior in the microstructure and the related parameters are calibrated from lower length scale atomistic simulations instead of engineering scale experimental data. The workability of this approach is demonstrated by performing porosity dependent intergranular fracture simulations in UO 2 and comparing the predictions with experiments.
36 MATERIALS SCIENCE↗
A foundation model for non-destructive defect identification from vibrational spectra
Defects are ubiquitous in solids and strongly influence materials’ functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here, we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet employs a tailored attention mechanism to identify up to six distinct defect elements at concentrations ranging from 0.2% to 25%. The model generalizes well to unseen crystals across 56 elements and can be fine-tuned on experimental data. Validation using inelastic scattering measurements of SiGe alloys and MgB 2 superconductor demonstrates its accuracy and transferability. Furthermore, our work establishes vibrational spectroscopy as a viable, non-destructive probe for bulk point defect quantification, and highlights the promise of foundation models in data-driven defect engineering.
artificial intelligence↗
The human dimensions of energy use in buildings: A review
The “human dimensions” of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This work presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building's life cycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights. Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners; enhanced comfort conditions and productivity for building occupants; more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
A hidden cysteine in Fis1 targeted to prevent excessive mitochondrial fission and dysfunction under oxidative stress
Fis1-mediated mitochondrial localization of Drp1 and excessive mitochondrial fission occur in human pathologies associated with oxidative stress. However, it is not known how Fis1 detects oxidative stress and what structural changes in Fis1 enable mitochondrial recruitment of Drp1. We find that conformational change involving α1 helix in Fis1 exposes its only cysteine, Cys41. In the presence of oxidative stress, the exposed Cys41 in activated Fis1 forms a disulfide bridge and the Fis1 covalent homodimers cause increased mitochondrial fission through increased Drp1 recruitment to mitochondria. Our discovery of a small molecule, SP11, that binds only to activated Fis1 by engaging Cys41, and data from genetically engineered cell lines lacking Cys41 strongly suggest a role of Fis1 homodimerization in Drp1 recruitment to mitochondria and excessive mitochondrial fission. The structure of activated Fis1-SP11 complex further confirms these insights related to Cys41 being the sensor for oxidative stress. Importantly, SP11 preserves mitochondrial integrity and function in cells during oxidative stress and thus may serve as a candidate molecule for the development of treatment for diseases with underlying Fis1-mediated mitochondrial fragmentation and dysfunction.
59 BASIC BIOLOGICAL SCIENCES↗
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from health care to industrial engineering, especially where data cannot be easily exchanged due to sensitive information or privacy laws. However, recent research has demonstrated that FL protocols can be easily compromised by active reconstruction attacks executed by dishonest servers. These attacks involve the malicious modification of global model parameters, allowing the server to obtain a verbatim copy of users' private data by inverting their gradient updates. Tackling this class of attack remains a crucial challenge due to the strong threat model. In this paper, we propose a defense mechanism, namely OASIS, based on image augmentation that effectively counteracts active reconstruction attacks while preserving model performance. We first uncover the core principle of gradient inversion that enables these attacks and theoretically identify the main conditions by which the defense can be robust regardless of the attack strategies. We then construct our defense with image augmentation showing that it can undermine the attack principle. Comprehensive evaluations demonstrate the efficacy of the defense mechanism highlighting its feasibility as a solution.
deep neural networks↗
Flow Mechanisms and Diffusion Combustion of Turbulent Jets
The problem of flow and combustion of turbulent jets of fuel gas in the external medium of an oxidant (air) is solved with regard to the existence of the actual boundary of the turbulent flow region of a jet. Based on the ideas of the friction force of the external flow acting on the boundary of a jet, the entrainment equation for the external medium is derived that closes the system of equations of motion of turbulent jets. The physical meaning of the dissipation rate of the turbulent energy of a jet is interpreted as the work of the friction force. To describe the combustion kinetics, the limit of instantaneous reactions corresponding to the diffusion combustion mode is used. Calculations of the effective reaction rates for reactants and the volumes occupied by them are based on the representation of a turbulent medium as an aggregation of independent turbulent particles—vortices—whose random contacts lead to the mixing and combustion of reacting substances [31]. The concomitant phenomena of flow and combustion are analyzed, including radiation effects. In particular, it is shown that the apparent increase in the combustion temperature with increasing Reynolds number is in fact attributed to the relative decrease of thermal radiation losses. Qualitative agreement is obtained between the results of the theoretical calculations of the length of a combustion torch and experimental data.
42 ENGINEERING↗
You Never Know Who is Going to Walk Through the Door – The Interesting Life of Radiochemistry [Slides]
Radiochemistry is central to many aspects of nuclear science and engineering. Interpretation of data is just as important as the measurements themselves. Problem solving is key! A new problem will always be just around the corner.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Improving the Monitoring, Verification, and Accounting of CO 2 Sequestered in Geologic Systems with Multicomponent Seismic Technology and Rock Physics Modeling
Research done in this study showed that P-SV seismic data provide better spatial resolution of geologic targets at our Appalachian Basin study area than do P-P data. This finding is important because the latter data (P-P) are the principal seismic data used to evaluate rock systems considered for CO 2 sequestration. The increase in P-SV 1 resolution over P-P resolution was particularly significant, with P-SV 1 wavelengths being approximately 40-percent shorter than P-P wavelengths. CO 2 sequestration projects across the Appalachian Basin should take advantage of the increased resolution provided by converted-shear seismic modes relative to P-wave seismic data.
54 ENVIRONMENTAL SCIENCES↗
Experimental Investigation of Turbine Vane Heat Transfer for Alternative Fuels
The focus of this program was to experimentally investigate advanced gas turbine cooling schemes and the effects of and factors that contribute to surface deposition from particulate matter found in coal syngas exhaust flows on turbine airfoil heat transfer and film cooling, as well as to characterize surface roughness and determine the effects of surface deposition on turbine components. The program was a comprehensive, multi-disciplinary collaborative effort between aero-thermal and materials faculty researchers and the Department of Energy, National Energy Technology Laboratory (NETL). The primary technical objectives of the program were to evaluate the effects of combustion of syngas fuels on heat transfer to turbine vanes and blades in land-based power generation gas turbine engines. The primary questions to be answered by this investigation were; What are the factors that contribute to particulate deposition on film cooled gas turbine components? An experimental program was performed in a high-temperature and pressure combustion rig at the DOE NETL; What is the effect of coal syngas combustion and surface deposition on turbine airfoil film cooling? Deposition of particulate matter from the combustion gases can block film cooling holes, decreasing the flow of the film coolant and the film cooling effectiveness; How does surface deposition from coal syngas combustion affect turbine surface roughness? Increased surface roughness can increase aerodynamic losses and result in decreased turbine hot section efficiency, increasing engine fuel consumption to maintain desired power output. Convective heat transfer is also greatly affected by the surface roughness of the airfoil surface; Is there any significant effect of surface deposition or erosion on integrity of turbine airfoil thermal barrier coatings (TBC) and do surface deposits react with the TBC in any way to decrease its thermal insulating capability? Spallation and erosion of TBC is a persistent problem in modern turbine engines; and What advancements in film cooling hole geometry and design can increase effectiveness of film cooling in turbines burning high-hydrogen coal syngas due to the higher heat loads and mass flow rates of the core flow? Experimental and numerical investigations of advanced cooling geometries that can improve resistance to surface deposition were performed. The answers to these questions were investigated through experimental measurements of turbine blade surface temperature and coolant coverage (via infrared camera images and thermocouples) and time-varying surface roughness in the NETL high-pressure combustion rig with accelerated, simulated surface deposition and advanced cooling hole concepts, coupled with detailed materials analysis and characterization using conventional methods of Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), X-Ray Diffraction (XRD), 3-D Surface Topography (using a 3-D stylus profilometer). Detailed surface temperatures and cooling effectiveness could not be measured due to issues with the NETL infrared camera system. In collaboration with faculty startup funding from the principal investigator, experimental and numerical investigations were performed of an advanced film cooling hole geometry, the anti-vortex hole (AVH), focusing on improving cooling effectiveness and decreasing the counter-rotating vortex of conventional cooling holes which can entrain mainstream particulate matter to the surface. The potential benefit of this program is in gaining a fundamental understanding of how the use of alternative fuels will effect the operation of modern gas turbine engines, providing valuable data for more effective cooling designs for future turbine systems utilizing alternative fuels.
10 SYNTHETIC FUELS↗
2014 Navajo Nation Energy and Water Consumption
The Navajo Nation is the home of the largest land-based Indian reservation in the U.S., covering more than twenty-seven thousand square miles. The land in the southwestern U.S. holds an abundance of natural resources, which are intimately integrated in the history, economy, and growth of the Navajo tribe. This report aims to wholly visualize the Navajo Nation’s resources and energy and water consumption using quantitative data and systems engineering analysis. The energy and water flow chart visualizations provide structured information for tribal leaders, policymakers, and educators around energy and water system discussions, technology development opportunities, and policy decisions. The analysis of both energy and water is a first step to visualizing the interconnectedness and complexities of the energy-water-food nexus of the nation. The goal of this energy analysis was to first estimate coal resource consumption because of the considerable impact coal has on the Navajo economy, recently as much as $26 million per year in coal royalties.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
ATOM - Accelerating therapeutics through opportunities in medicine
Create a new paradigm of drug discovery that would reduce the time from an identified drug target to clinical candidate from the current ~6 years to just 12 months. ATOM will develop, test, and validate a multidisciplinary approach to drug discovery in which modern science, technology and engineering, supercomputing, simulations, data science, and artificial intelligence are highly integrated into a single drug-discovery platform that can ultimately be shared with the drug development community at-large.
60 APPLIED LIFE SCIENCES↗
Frontiers of Materials Research: A Decadal Survey
Modern materials science builds on knowledge from physics, chemistry, biology, mathematics, computer and data science, and engineering sciences to enable us to understand, control, and expand the material world. Although it is anchored in inquiry-based fundamental science, materials research is strongly focused on discovering and producing reliable and economically viable materials, from super alloys to polymer composites, that are used in a vast array of products essential to today’s societies and economies. Frontiers of Materials Research: A Decadal Survey is aimed at documenting the status and promising future directions of materials research in the United States in the context of similar efforts worldwide. This third decadal survey in materials research reviews the progress and achievements in materials research and changes in the materials research landscape over the last decade; research opportunities for investment for the period 2020-2030; impacts that materials research has had and is expected to have on emerging technologies, national needs, and science; and challenges the enterprise may face over the next decade.
36 MATERIALS SCIENCE↗