Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Predictive Data analytics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

The Fast Scattering Code (FSC): Validation Studies and Program Guidelines

The Fast Scattering Code (FSC) is a frequency domain noise prediction program developed at the NASA Langley Research Center (LaRC) to simulate the acoustic field produced by the interaction of known, time harmonic incident sound with bodies of arbitrary shape and surface impedance immersed in a potential flow. The code uses the equivalent source method (ESM) to solve an exterior 3-D Helmholtz boundary value problem (BVP) by expanding the scattered acoustic pressure field into a series of point sources distributed on a fictitious surface placed inside the actual scatterer. This work provides additional code validation studies and illustrates the range of code parameters that produce accurate results with minimal computational costs. Systematic noise prediction studies are presented in which monopole generated incident sound is scattered by simple geometric shapes - spheres (acoustically hard and soft surfaces), oblate spheroids, flat disk, and flat plates with various edge topologies. Comparisons between FSC simulations and analytical results and experimental data are presented.

Tinetti, Ana F.↗
NASA NTRS1984

Chemoviscosity modeling for thermosetting resins - I

A new analytical model for chemoviscosity variation during cure of thermosetting resins was developed. This model is derived by modifying the widely used WLF (Williams-Landel-Ferry) Theory in polymer rheology. Major assumptions involved are that the rate of reaction is diffusion controlled and is linearly inversely proportional to the viscosity of the medium over the entire cure cycle. The resultant first order nonlinear differential equation is solved numerically, and the model predictions compare favorably with experimental data of EPON 828/Agent U obtained on a Rheometrics System 4 Rheometer. The model describes chemoviscosity up to a range of six orders of magnitude under isothermal curing conditions. The extremely non-linear chemoviscosity profile for a dynamic heating cure cycle is predicted as well. The model is also shown to predict changes of glass transition temperature for the thermosetting resin during cure. The physical significance of this prediction is unclear at the present time, however, and further research is required. From the chemoviscosity simulation point of view, the technique of establishing an analytical model as described here is easily applied to any thermosetting resin. The model thus obtained is used in real-time process controls for fabricating composite materials.

Hou, T. H.↗
NASA NTRS1995

1025:1 Area Ratio Nozzle Evaluated at High Combustion Chamber Pressure

A recently completed experimental test program obtained performance data on an optimally contoured nozzle with an exit-to-throat area ratio of 1025:1 and on a truncated version of this nozzle with an area ratio of 440:1. The nozzles were tested with gaseous hydrogen and liquid oxygen propellants at combustion chamber pressures of 12.4 to 16.5 mPa (1800 to 2400 psia). Testing was conducted in the altitude test capsule at the NASA Lewis Research Center's Rocket Engine Test Facility (RETF), and results were compared with analytical performance predictions. This testing builds on previous work with this nozzle at Lewis, where testing was completed at nominal chamber pressure of 350 psia. High-area-ratio nozzles have long been sought as a means to increase the performance of spacebased rocket engines. However, as the area ratio increases, the physical size and weight of the nozzle also increase. As a result, engine and vehicle designers must make tradeoffs between nozzle size and performance enhancement. Until this test program, very little experimental data existed on the performance of the high-area-ratio nozzles used in rocket engine designs. The computer codes being used by rocket engine designers rely on data extrapolated from tests of low-area-ratio nozzles, and these extrapolations do not always provide the accuracy needed for a reliable design assessment. Therefore, we conducted this high-area-ratio nozzle testing program to provide performance data for use in rocket engine design and analysis computer codes. The nozzle had a nominal 2.54-cm- (1-in.-) diameter throat, an exit diameter of 81.3-cm (32.0-in.) at an exit-to-throat area ratio of 1025, and a length of 128.6 cm (50.6 in.). Testing was conducted in an altitude test capsule to simulate the static pressure at altitude by vacuum pumping. Data such as propellant mass flow, oxidizer-to-fuel mixture, and thrust were measured. These measurements were then used to calculate performance factors such as the thrust coefficient, the characteristic exhaust velocity efficiency, and the vacuum specific impulse. In addition, the nozzle temperature was measured to calculate the amount of heat transferred from the combustion gases to the nozzle.

Source record↗
DOE OSTI2023

Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. With O(N 3 ) scaling with the number of electrons N, DMC has the potential to be a reference method for larger systems that are not accessible to more traditional methods such as CCSD(T). Assessing the accuracy of DMC for smaller molecules becomes the stepping stone in making the method a reference for larger systems. We show that when coupled with quantum machine learning (QML)-based surrogate methods, the computational burden can be alleviated such that quantum Monte Carlo (QMC) shows clear potential to undergird the formation of high-quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: the fixed-node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons-set-based QML (AQML) models. Numerical evidence presented includes converged DMC results for over 1000 small organic molecules with up to five heavy atoms used as amons and 50 medium-sized organic molecules with nine heavy atoms to validate the AQML predictions. Finally, numerical evidence collected for Δ-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.

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

Experimental Set-up for the Study of Chemical Fractionation and Aerosol Dynamics

Experimental investigations of chemical fractionation/phase partition and aerosol dynamics in high-temperature environment were reviewed and discussed. Particle composition and size distribution are the key data for aerosol dynamics predictive model and simulation. However, there were no particle size distribution available from the previous works. Nucleation, condensation, and coagulation are three key physical processes driving the fractionation or phase partition thereby the aerosol dynamics. A new approach was proposed to experimentally observe fractionation/phase partition and provide data for validating the predictive model for aerosol dynamics in the simulation of bomb debris formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2012

Web-Based Predictive Analytics to Improve Patient Flow in the Emergency Department

The Emergency Department (ED) simulation project was established to demonstrate how requirements-driven analysis and process simulation can help improve the quality of patient care for the Veterans Health Administration's (VHA) Veterans Affairs Medical Centers (VAMC). This project developed a web-based simulation prototype of patient flow in EDs, validated the performance of the simulation against operational data, and documented IT requirements for the ED simulation.

Buckler, David L.↗
DOE OSTI2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction

Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure–property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1996

Technique for Extension of Small Antenna Array Mutual-Coupling Data to Larger Antenna Arrays

A technique is presented whereby the mutual interaction between a small number of elements in a planar array can be interpolated and extrapolated to accurately predict the combined interactions in a much larger array of many elements. An approximate series expression is developed, based upon knowledge of the analytical characteristic behavior of the mutual admittance between small aperture antenna elements in a conducting ground plane. This expression is utilized to analytically extend known values for a few spacings and orientations to other element configurations, thus eliminating the need to numerically integrate a large number of highly oscillating and slowly converging functions. This paper shows that the technique can predict very accurately the mutual coupling between elements in a very large planar array with a knowledge of the self-admittance of an isolated element and the coupling between only two-elements arranged in eight different pair combinations. These eight pair combinations do not necessarily have to correspond to pairs in the large array, although all of the individual elements must be identical.

Bailey, M. C.↗
NASA NTRS2011

Solar Dynamics Observatory On-Orbit Jitter Testing, Analysis, and Mitigation Plans

The Solar Dynamics Observatory (SDO) was designed to understand the Sun and the Sun s influence on Earth. SDO was launched on February 11, 2010 carrying three scientific instruments: the Atmospheric Imaging Assembly (AIA), the Helioseismic and Magnetic Imager (HMI), and the Extreme Ultraviolet Variability Experiment (EVE). Both AIA and HMI are sensitive to high frequency pointing perturbations and have sub-arcsecond level line-of-sight (LOS) jitter requirements. Extensive modeling and analysis efforts were directed in estimating the amount of jitter disturbing the science instruments. To verify the disturbance models and to validate the jitter performance prior to launch, many jitter-critical components and subassemblies were tested either by the mechanism vendors or at the NASA Goddard Space Flight Center (GSFC). Although detailed analysis and assembly level tests were performed to obtain good jitter predictions, there were still several sources of uncertainties in the system. The structural finite element model did not have all the modes correlated to test data at high frequencies (greater than 50 Hz). The performance of the instrument stabilization system was not known exactly but was expected to be close to the analytical model. A true disturbance-to-LOS observatory level test was not available due to the tight schedule of the flight spacecraft, the cost in time and manpower, difficulties in creating gravity negation systems, and risks of damaging flight hardware. To protect the observatory jitter performance against model uncertainties, the SDO jitter team devised several on-orbit jitter reduction plans in addition to reserve margins on analysis results. Since some of these plans severely restricted the capabilities of several spacecraft components (e.g. wheels and High Gain Antennas), the SDO team performed on-orbit jitter tests to determine which jitter reduction plans, if any, were necessary to satisfy science LOS jitter requirements. The SDO on-orbit jitter tests were designed to satisfy the following four objectives: 1. Determine the acceptable reaction wheel operational speed range during Science Mode. 2. Determine HGA algorithm jitter parameters (number of stagger steps and enable/disable no-steprequests). 3. Determine acceptable EVE instrument filter wheels spin rates. 4. Determine if AIA instrument filter wheels excite the first AIA telescope structural mode. This paper provides detailed information on the SDO wheel jitter test plan, shows on-orbit jitter measurements and how ground predictions compare to those measurements, and describes the final jitter mitigation plan executed on SDO.

Liu, Kuo-Chia (Alice)↗
DOE OSTI2026

UCB-GLOBES: An open-access mass spectral database of identified and unidentified atmospheric organic compounds

Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.

Mass spectrometry↗
DOE OSTI2023

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. In conclusion, these results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Physics-based modeling and data analytics [Slides]

This presentation contains a summary of ongoing work within the physics-based modeling and data analytics work package within the Nuclear Materials Discovery and Qualification initiative (NMDQi). Topics include work on MOOSE-based crystal plasticity, molecular dynamics modeling of recombination in metals and alloys, the MOOSE Stochastic Tools Module, and machine learning and atomistic modeling to predict thermo-kinetic properties of nuclear structural materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1979

TF34 engine compression system computer study

The stability of the fan and the compressor components was examined individually using linearized and time dependent, one dimensional stability analysis techniques. The stability of the fan core integrated compression system was investigated using a two dimensional compression system model. The analytical equations on which this model was based satisfied the mass, axial momentum, radial momentum, and energy conservation equations for flow through a finite control volume. The results gave an accurate simulation of the flow through the compression system. The speed lines of the components were reproduced; the points of instability were accurately predicted; the locations where the instability was initiated in the fan and the core were indicated; and the variation of the bypass ratio during flow throttling was calculated. The validity of the analytical techniques was then established by comparing these results with test data and with results obtained from the steady state cycle deck.

Hosny, W. M.↗
NASA NTRS1986

Plasma interactions and surface/material effects

A discussion on plasma interactions and surface/material effects is summarized. The key issues in this area were: (1) the lack of data on the material properties of common spacecraft surface materials; (2) lack of understanding of the contamination and decontamination processes; and (3) insufficient analytical tools to model synergistic phenomena related to plasma interactions. Without an adequate database of material properties, accurate system performance predictions cannot be made. The interdisciplinary nature of the surface-plasma interactions area makes it difficult to plan and maintain a coherent theoretical and experimental program. The shuttle glow phenomenon is an excellent example of an unanticipated, complex interaction involving synergism between surface and plasma effects. Building an adequate technology base for understanding and predicting surface-plasma interactions will require the coordinated efforts of engineers, chemists, and physicists. An interdisciplinary R and D program should be organized to deal with similar problems that the space systems of the 21st century may encounter.

Mandel, M.↗
NASA NTRS1993

Numerical and experimental investigation of the bending response of thin-walled composite cylinders

A numerical and experimental investigation of the bending behavior of six eight-ply graphite-epoxy circular cylinders is presented. Bending is induced by applying a known end-rotation to each end of the cylinders, analogous to a beam in bending. The cylinders have a nominal radius of 6 inches, a length-to-radius ratio of 2 and 5, and a radius-to-thickness ratio of approximately 160. A (+/- 45/0/90)S quasi-isotropic layup and two orthotropic layups, (+/- 45/0 sub 2)S and (+/- 45/90 sub 2)S, are studied. A geometrically nonlinear special-purpose analysis, based on Donnell's nonlinear shell equations, is developed to study the prebuckling responses and gain insight into the effects of non-ideal boundary conditions and initial geometric imperfections. A geometrically nonlinear finite element analysis is utilized to compare with the prebuckling solutions of the special-purpose analysis and to study the buckling and post buckling responses of both geometrically perfect and imperfect cylinders. The imperfect cylinder geometries are represented by an analytical approximation of the measured shape imperfections. Extensive experimental data are obtained from quasi-static tests of the cylinders using a test fixture specifically designed for the present investigation. A description of the test fixture is included. The experimental data are compared to predictions for both perfect and imperfect cylinder geometries. Prebuckling results are presented in the form of displacement and strain profiles. Buckling end-rotations, moments, and strains are reported, and predicted mode shapes are presented. Observed and predicted moment vs. end-rotation relations, deflection patterns, and strain profiles are illustrated for the post buckling responses. It is found that a geometrically nonlinear boundary layer behavior characterizes the prebuckling responses. The boundary layer behavior is sensitive to laminate orthotropy, cylinder geometry, initial geometric imperfections, applied end-rotation, and non-ideal boundary conditions. Buckling end-rotations, strains, and moments are influenced by laminate orthotropy and initial geometric imperfections. Measured buckling results correlate well with predictions for the geometrically imperfect specimens. The postbuckling analyses predict equilibrium paths with a number of scallop-shaped branches that correspond to unique deflection patterns. The observed postbuckling deflection patterns and measured strain profiles show striking similarities to the predictions in some cases. Ultimate failure of the cylinders is attributed to an interlaminar shear failure mode along the nodal lines of the postbuckling deflection patterns.

Fuchs, J. P.↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Analytical solution for the cumulative wake of wind turbines in wind farms

This paper solves an approximate form of conservation of mass and momentum for a turbine in a wind farm array. The solution is a fairly simple explicit relationship that predicts the streamwise velocity distribution within a wind farm with an arbitrary layout. As this model is obtained by solving flow-governing equations directly for a turbine that is subject to upwind turbine wakes, no ad hoc superposition technique is needed to predict wind farm flows. A suite of large-eddy simulations (LES) of wind farm arrays is used to examine self-similarity as well as validity of the so-called conservation of momentum deficit for turbine wakes in wind farms. The simulations are performed with and without the presence of some specific turbines in the wind farm. This allows us to systematically study some of the assumptions made to develop the analytical model. A modified version of the conservation of momentum deficit is also proposed to provide slightly better results at short downwind distances, as well as in the far wake of turbines deep inside a wind farm. Model predictions are validated against the LES data for turbines in both full-wake and partial-wake conditions. While our results highlight the limitation in capturing the flow speed-up between adjacent turbine columns, the model is overall able to acceptably predict flow distributions for a moderately sized wind farm. Finally, the paper employs the new model to provide insights on the accuracy of common wake superposition methods.

17 WIND ENERGY↗
DOE OSTI2022

FY2022 Progress on Imbibition Testing in Containment Science

Estimation of two-phase fluid flow properties is important to understand and predict water and gas movement through the vadose zone for agricultural, hydrogeological, and engineering applications, such as for vapor-phase contaminant transport and/or containment of noble gases in the subsurface. In this second progress report of FY22, we present two ongoing activities related to imbibition testing on volcanic rock samples. We present the development of a new analytical solution predicting the temperature response observed during imbibition into dry samples, as discussed in our previous first progress report for FY22. We also illustrate the use of a multi-modal capillary pressure distribution to simulate both early- and late-time imbibition data collected on tuff core that can exhibit multiple pore types. These FY22 imbibition tests were conducted for an extended period (i.e., far beyond the time required for the wetting front to reach the top of the sample), which is necessary for parameter estimation and characterization of two different pore types within the samples.

42 ENGINEERING↗