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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.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Propulsion-induced interference effects on jet-lift VTOL aircraft

Recent investigations of propulsion-induced aerodynamics are reviewed. Experimental and analytical studies of the fundamental properties of lifting jet wakes and wind-tunnel investigations of VTOL configuration variables are discussed. The jet-wake flow studies will provide the data base for evaluating theoretical performance prediction methods; while the configuration studies will provide guidance for optimizing airframe/propulsion system integration. The influence of results to date on high-performance VTOL designs emphasizing minimum jet-induced losses and improved thrust vectoring in forward flight (VIFF) capability is illustrated.

Winston, M. M.↗
NASA NTRS1984

Experimental and analytical investigations into airfoil icing

Methods of analyzing and measuring experimentally the accretion of ice and its effect on airfoil aerodynamic performance are presented. An analytical method for predicting water droplet impingement has been developed and shows the influence of airfoil leading edge radius and thickness on droplet impingement to be significant. Rime ice accretions are predicted including time dependent effects and these shapes compare well to experiment. Scaling water droplet impingement and rime ice accretion is discussed. Predictions of the effect of rime ice on airfoil performance are developed based on empirical and analytical methods. Techniques for measuring and simulating ice accretions for wind tunnel tests compare well to actual iced airfoil aerodynamic data. Data showing the effect of ice on measured airfoil performance using real and simulated ice are presented.

Bragg, M. B.↗
NASA NTRS1984

Turbine airfoil gas side heat transfer

Work is currently underway to develop and characterize an analytical approach, based on boundary layer theory, for predicting the effects of leading edge (showerhead) film cooling on downstream gas side heat transfer rates. Parallel to this work, experiments are being conducted to build a relevant data base for present and future methods verification.

Turner, E. R.↗
NASA NTRS1987

A canopy reflectance model based on an analytical solution to the multiple scattering equation

An approximation to the radiative transfer equation for solar radiation in relatively full, homogeneous plant canopies is presented and solved analytically for solar zenith angles less than 60 deg. The model predicts reflectance at any depth in the canopy and in any direction and may be inverted with bidirectional reflectance measurements. The model was fit to data at two sun angles and two wavebands (visible and NIR) to within the assumed errors on the reflectance data. The calculated albedos are insensitive to achievable measurement errors. Some of the parameter values themselves found by the inversion agree reasonably well with independent measurements, but the uncertainties introduced by the data noise are rather large. However, the agreement is good enough to demonstrate that the model is physically realistic.

Camillo, Peter↗
NASA NTRS1996

Further Study of 'Pop-Up' Vortex Generators

The model is a replica of a modified MAX-103 kit aircraft that our Parks College of St. Louis University Student Design Group built and modified from a tail wheel to a tricycle configuration. A model was tested in the Parks College low-speed wind tunnel. I hope to initiate flight-testing upon my second return to. St. Louis. The combined data using wind tunnel, water tunnel, RC, flight-testing and analytical results will be very valuable for assessing the correlation between the different methods of analyses, since at present it is almost impossible to accurately predict flight characteristics from anything but in-situ tests. Unfortunately, political/financial reasons dictated using a generic wing rather than a specific model in the NASA-DFRC water tunnel.

Manor, David↗
NASA NTRS2000

Photogrammetrically Measured Distortions of Composite Structure Microwave Reflectors at -90K

The Microwave Anisotropy Probe (MAP) Observatory, scheduled for a late 2000 launch, is designed to measure temperature fluctuations (anisotropy) and produce a high sensitivity and high spatial resolution (better than 0.3 deg. at 90 GHz.) map of the Cosmic Microwave Background (CMB) radiation over the entire sky between 22 and 90 GHz. MAP utilizes back-to-back composite Gregorian telescopes supported on a composite truss structure to focus the microwave signals into 10 differential microwave receivers. Proper position and shape of the telescope reflectors at the operating temperature of -90 K is a critical element to ensure mission success. We describe the methods and analysis used to validate the in-flight position and shape predictions for the reflectors based on photogrammetric metrology data taken under vacuum with the reflectors at -90 K. Contour maps showing reflector distortion were generated. The resulting reflector distortion data are shown to be crucial to the analytical assessment of the MAP instrument's microwave system in-flight performance.

Mule, Peter↗
NASA NTRS2010

Predicting Failure Progression and Failure Loads in Composite Open-Hole Tension Coupons

Failure types and failure loads in carbon-epoxy [45n/90n/-45n/0n]ms laminate coupons with central circular holes subjected to tensile load are simulated using progressive failure analysis (PFA) methodology. The progressive failure methodology is implemented using VUMAT subroutine within the ABAQUS(TradeMark)/Explicit nonlinear finite element code. The degradation model adopted in the present PFA methodology uses an instantaneous complete stress reduction (COSTR) approach to simulate damage at a material point when failure occurs. In-plane modeling parameters such as element size and shape are held constant in the finite element models, irrespective of laminate thickness and hole size, to predict failure loads and failure progression. Comparison to published test data indicates that this methodology accurately simulates brittle, pull-out and delamination failure types. The sensitivity of the failure progression and the failure load to analytical loading rates and solvers precision is demonstrated.

Arunkumar, Satyanarayana↗
NASA NTRS2017

Microscopic Evaluation of Friction Plug Welds- Correlation to a Processing Analysis

Recently an analysis of dynamic forge load data from the friction plug weld (FPW) process and the corresponding tensile test results showed that good plug welds fit well within an analytically determined processing parameter box. There were, however, some outliers that compromised the predictions. Here the microstructure of the plug weld material is presented in view of the load analysis with the intent of further understanding the FPW process and how it is affected by the grain structure and subsequent mechanical properties.

Rabenberg, Ellen M.↗
DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Roadmap for the Future of Systems Biology in Cancer Research

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field’s promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, in this study, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field.

Modeling & Simulation↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2021

The Cell Utilized Partitioning Model as a Predictive Tool for Optimizing Counter-Current Chromatography Processes

Counter-current chromatography (CCC) is capable of unique elution modes that isolate analytes using the movement of the stationary phase in addition to moving the mobile phase. These modes include elution-extrusion CCC (EECCC) and dual-mode CCC (DM CCC) that are not possible in traditional solid-liquid chromatography systems. Although EECCC and DM CCC are widely used to recover highly retained components, to our knowledge, optimizing the elution process in these modes with predictive models has not been reported. To address this gap, we developed a predictive model for CCC dubbed the Cell Utilized Partitioning (CUP) model. The CUP model accurately predicts the effluents of multicomponent separations in EECCC and DM CCC modes when compared to experimental data. Furthermore, CUP model simulations were extended to investigate the influence of operating and intrinsic parameters on the yield and productivity, and to compare the separation performances of EECCC and DM CCC in various conditions. The results demonstrate that low distribution constants, usually a KD less than 1, and a selectivity > 1.3, under specific flowrate ranges, increase both productivity and yield. From these results, generalized optimization and scaleup guidelines are proposed that can apply to research settings and to industrial processes to maximize preparative CCC performance.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2024

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Insights into co-pyrolysis of polyethylene terephthalate and polyamide 6 mixture through experiments, kinetic modeling and machine learning

The non-isothermal pyrolysis of polyethylene terephthalate (PET), polyamide 6 (PA6), and their mixtures was studied in a thermogravimetric analyzer at different heating rates. Temperature of maximum decomposition (T max ) decreased by 25–45 °C and 35–55 °C for the PET:PA6 mixtures (3:1, 1:1, 1:3) compared to PET and PA6, respectively. The kinetic analysis was initially carried out using isoconversional method. However, the dependency of activation energy on conversion was observed for the co-pyrolysis of PET and PA6 that suggested the occurrence of multi-step reactions in the mixtures. Distributed activation energy model (DAEM) was used in this study to describe the multistep reactions occurring during pyrolysis of PET:PA6 mixtures. Here, in this work, a four-parallel reaction DAEM was developed to describe the pyrolysis kinetics of PET:PA6 mixtures. The apparent mean activation energies (E o ) for PET, PA6, and mixtures varied in the range of 244–255, 140–215, and 138–255 kJ mol –1 , respectively. The mass loss profiles of PET and PA6 mixtures were also modeled using artificial neural network (ANN). Out of 155 ANN models, the best prediction was made by ANN511 with R 2 greater than 0.997 for both test and unseen data. The interaction effects observed through TGA experiments and subsequent kinetic analysis were further assessed in terms of product composition using analytical pyrolysis coupled with gas chromatograph/mass spectrometer (Py-GC/MS). Co-pyrolysis of PET and PA6 resulted in the formation of new aromatic compounds with nitrogen-containing functional groups, which were not detected when PET or PA6 were pyrolyzed individually.

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

Design-time performance modeling of compositional parallel programs

Performance models are powerful instruments for understanding the performance of parallel systems and uncovering their bottlenecks. Already during system design, performance models can help ponder alternative development options. However, creating a performance model – whether theoretically or empirically – for an entire application that does not exist yet is challenging. In this paper, we propose to generate performance models of full programs from performance models of their components using formal composition operators derived from parallel design patterns. As long as the design of the overall system follows such a pattern, its performance model can be predicted with reasonable accuracy without an actual implementation. In conclusion, we demonstrate our approach with design patterns of varying complexity, including pipeline, task pool, and eventually MapReduce, which is representative of a broad class of data-analytics applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

nrelWattileExt (SkySpark Wattile Extension) [SWR-24-73]

The NREL Wattile extension, nrelWattileExt, provides an interface between SkySpark, an energy management and analytics software, and Wattile, an NREL-developed Python package for probabilistic prediction of building energy consumption. Wattile models predict discrete quantiles of the probability distribution of a target quantity (typically energy consumption) using the historical time series data from one or more predictors (typically weather data). Within SkySpark, predictions from Wattile models can be used for measurement & verification of building performance, detection of energy anomalies, and fault detection. Related to: https://github.com/NREL/Wattile

Frank, Stephen↗
NASA NTRS1972

A NASTRAN correlation study for vibrations of a cross-stiffened ship's deck

To evaluate the effectiveness of NASTRAN for predicting the vibration modes of panels with bending-membrane coupling, a cross-stiffened ship deck was analyzed. In correlation with experimental data, one NASTRAN finite element representation gave results slightly more accurate than a previous analytical solution. Computational time was excessively long, due to the Guyan method of reducing the eigenvalue problem. It is recommended that a more efficient method of matrix reduction be implemented for the lumped mass formulation.

Thornton, E. A.↗