Search NASA⌕ Search

SEARCH · Search NASA

Results for “data driven”

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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

GES DISC Datalist Enables Easy Data Selection for Natural Phenomena Studies

In order to investigate and assess natural hazards such as tropical storms, winter storms, volcanic eruptions, floods, and drought in a timely manner, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been developing an efficient data search and access service. Called Datalist, this service enables users to acquire their data of interest all at once, with minimum effort. A Datalistis a virtual collection of predefined or user-defined data variables from one or more archived data sets. Datalistsare more than just data. Datalistseffectively provide users with a sophisticated integrated data and services package, including metadata, citation, documentation, visualization, and data-specific services (e.g., subset and OPeNDAP), all available from one-stop shopping. The predefined Datalists, created by the experienced GES DISC science support team, should save a significant amount of time that users would otherwise have to spend. The Datalistservice is an extension of the new GES DISC website, which is completely data-driven. A Datalist, also known as data bundle, is treated just as any other data set. Being a virtual collection, a Datalistrequires no extra storage space.

Earth events↗
NASA NTRSDate not supplied

Usability of Pre-Flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗
NASA NTRSDate not supplied

Usability of Pre-flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
DOE OSTI2025

A Simple Data-Centric Methodology for Producible Geothermal Well Determinations: Preprint

The Bureau of Land Management (BLM) has traditionally lacked a standardized methodology for determining if a newly drilled geothermal well is "producible," a designation essential for deciding whether a lease should be "held by production." This is a straightforward problem to solve in oil and gas: Demonstrate that a well is economically viable, meaning it produces sufficient oil or gas to exceed direct operating costs and lease-related expenses, such as rentals or minimum royalties. In geothermal, the problem is more complex: Geothermal wells are tightly coupled with the downstream infrastructure - specifically, the power plant, which is often not designed until well after a lease is deemed as "held by production." Although this designation is critical for advancing geothermal power plant development on BLM-managed lands, current geothermal well assessments often rely on ad hoc approaches that can be complex, operator-biased, and heavy in assumptions related to economic viability. To address this, we have developed two complementary methodologies: a minimum power requirement-based approach and a productivity index (PI)-based approach. These methods leverage key flow test data - pressure, temperature, flow rate, and specific enthalpy - to provide reliable and standardized producible well determinations. The minimum power requirement-based approach evaluates wells against specific power output thresholds informed by reservoir experts and the associated temperature requirements. The PI-based approach assesses well productivity using widely accepted reservoir engineering metrics, proposing a threshold of 2.5 kg/s/bar. Both methods are data-driven and grounded in empirical production data from operational geothermal wells, avoiding uncertain economic assumptions while maintaining decision-making accuracy. Wells falling below key performance thresholds (i.e., PI, specific power) are deemed non-producible. These methodologies aim to streamline BLM's decision-making process, reduce nontechnical barriers to geothermal energy adoption, and enable regulatory expansion into states lacking geothermal expertise. Preliminary results indicate clear trends and thresholds in production data that provide actionable insights for evaluating well producibility. Validation using well completion report (WCR) data is ongoing, with promising results demonstrating the potential for these standardized methodologies to impact geothermal development significantly.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analyzing Residential Charging Demand for Light-Duty Electric Vehicles in Colorado: Preprint

The past decade has witnessed the rapid adoption of electric vehicles (EVs). The momentum is expected to continue with strong support from the government and industry. Rapid EV adoption brings significant charging demand to the power grid, causing additional stress and non-negligible risks to the already-aging power grid. To help power grid operators understand the impacts of EV home charging on the grid and identify risk factors, this study presents a data-driven residential charging demand analysis for light-duty vehicles. This study considers two real-world grid service regions in Colorado and merges multiple data sources and state-of-the-art tools that characterize EV adoption projections, vehicle travel patterns, seasonal variations, residential charging accessibility, ambient temperature impact, EV charging behaviors, grid utility customers, vehicle registration, and household-level EV charging demand distribution. We characterize potential residential charging demand in 2030 for two regions within the state of Colorado: Boulder and Aurora. We project that EVs will be 26% of the light-duty vehicle population in Boulder and 16% in Aurora. Charging demand is characterized for ten power grid feeders (five for each study region). Across the ten feeders, peak total EV charging powers during wintertime range from less than 1 MW to more than 4 MW.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Scalable Data Management, Analysis, and Visualization (SDAV) Institute

Analytics-driven data layout optimizations and compression (NCSU): We have developed solutions that address challenges related to scientific data indexing, compression, and storage layout problems. Moreover, in addition to focusing on mainstream uniform mesh data, we have also addressed Adaptive Mesh Refinement (AMR) data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Domain Specific Language Support for Exascale. Final Project Report

The project developed a domain specific translator enable legacy MPI source code to tolerate communication delays, which are increasing over time due to technological factors. The translator performs source-to-source translation that incorporates semantic information into the translation process. The output of the translator is a C program runs as a data driven program, and uses an existing run time to overlap communication automatically

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Model certainty in cellular network-driven processes with missing data

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (ders). to address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines taylor series expansion knowledge with a data-driven regression technique. the proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified taylor expansion. to mitigate the approximation loss that surges due to the der integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. comparative analysis in the 13-bus and 123-bus ieee unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

data-driven↗
DOE OSTI2026

Movement Models to Predict Low‐Altitude Flight of Soaring Birds Using Look‐Ahead Environmental Factors

Advances in fine-scale movement modeling of soaring birds can aid efforts to understand and resolve the impacts of anthropogenic activities on such birds. Soaring birds often rely on underlying terrain and low-altitude updrafts to govern their flights at rotor-swept altitudes (≤ 200 m above ground level), which puts them at risk of collision with wind turbines. We developed a data-driven Markov model at 1-s resolution that predicts the fine-scale flight behavior of golden eagles (Aquila chrysaetos) as a function of ecological covariates at the current location as well as those within an eagle's line of sight. We only considered ecological covariates that are readily available in real-time (ground elevation and wind conditions). Latent factors (age, sex, species, behavioral intent, migratory status) were intentionally left out of the model. We calibrated the model using golden eagle telemetry data collected in two different ecoregions of the United States. Given a starting location, the calibrated model simulates multiple stochastic 3D paths to produce a time-explicit and spatially explicit risk map of turbine collisions. We discovered an empirical relation between the rate of change of heading and the orographic updraft conditions within an eagle's line of sight. Our model performed most effectively when predicting predominantly-soaring flights at rotor-swept altitudes during wind conditions in which turbines are likely to be operational. The calibrated model could be used in concert with automated eagle detection and turbine curtailment technologies. Specifically, once an eagle is detected by those systems, our model could then provide accurate predictions of turbines the eagle is likely to interact with in the near term.

17 WIND ENERGY↗
DOE OSTI2019

FAST: Using Federal Fleet Data for Decision-Making

This presentation provides an overview of the information currently being collected by the US Department of Energy, US General Services Administration, and US Energy Information Administration about the federal government's fleet of motor vehicles. The recent transition to collecting this information at the individual vehicle level provides new ways of visualizing the fleet and its operation and of using the information to support new forms of data-driven decision-making about the fleet. This presentation provides three examples of the types of analysis and decision-making this dataset supports.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Autonomous Inverter Controls for Resilient and Secure Grid Operation: Vector Control Design for Grid Forming

The project addresses both fundamental and practical challenges of GFM/GFL inverter control for the power grids with high inverter based resources (IBRs) penetration. A data- driven modeling technique is applied to accurately model dynamics of PWM inverters, including electromagnetic-transient (EMT). Systematic and integrative designs of grid- forming (GFM) and grid-following (GFL) primary controls are developed to guarantee system performance under either normal or abnormal operating conditions without violating constraints. This modeling and control framework provides black-start capability in case of an outage without relying on rotating generators, and its secondary control is also shown to enhance resilience against cyber-physical attacks.

14 SOLAR ENERGY↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗