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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. In conclusion, the crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

42 ENGINEERING↗
DOE OSTI2023

Accelerated alloy discovery using synthetic data generation and data mining

The search for new alloys with improved properties is never ending with infinite combinations and amounts of alloying elements in the alloy. Advancements in machine learning have made navigating this enormous search space feasible. However, training the machine learning models and tuning their hyper-parameters to make accurate predictions can be time-consuming and often require high-performance computing resources. Furthermore, the quality of the predictions depend on the availability of sufficient training data. Here, we present a generic approach to accelerate alloy discovery by coupling high throughput CALPHAD calculations, synthetic data generation, and data mining. Finally, as a demonstration of the approach, we design super bainitic steels that form bainite at 200 C in lower transformation times.

36 MATERIALS SCIENCE↗
NASA NTRS1991

A rule-based software test data generator

Rule-based software test data generation is proposed as an alternative to either path/predicate analysis or random data generation. A prototype rule-based test data generator for Ada programs is constructed and compared to a random test data generator. Four Ada procedures are used in the comparison. Approximately 2000 rule-based test cases and 100,000 randomly generated test cases are automatically generated and executed. The success of the two methods is compared using standard coverage metrics. Simple statistical tests showing that even the primitive rule-based test data generation prototype is significantly better than random data generation are performed. This result demonstrates that rule-based test data generation is feasible and shows great promise in assisting test engineers, especially when the rule base is developed further.

Deason, William H.↗
DOE OSTI2024

EMT data generation

The integration of inverter-based resources (IBRs) in power systems is accelerating, bringing with it significant benefits such as reduced greenhouse gas emissions, improved grid resilience, and increased energy independence. Despite these advantages, the widespread adoption of IBRs introduces several challenges, including issues related to grid stability, increased operational complexity, and the need for updated regulatory frameworks. To address these challenges, IEEE released Standard 2800 in 2022, which sets forth the necessary interconnection capabilities and performance criteria for IBRs connected to transmission and sub-transmission systems. This standard outlines the performance requirements to ensure the reliable integration of IBRs into the bulk power system. Furthermore, in 2023, the North American Electric Reliability Corporation (NERC) published a reliability guideline for electromagnetic transient (EMT) modeling of BPS-connected IBRs. This guideline provides recommendations for developing EMT model requirements, performing model quality checks, and implementing verification practices specifically for EMT models representing BPS-connected inverter-based resources in reliability studies conducted by transmission planners and planning coordinators. These standards and guidelines have a profound impact on EMT studies for transmission networks, influencing system stability analyses, grid recovery and resynchronization processes, fault ride-through evaluations, protection and coordination strategies, advanced control methodologies, and the inclusion of IBRs in transient models of transmission networks. As a result, the generation of EMT data is crucial for conducting various transient-based studies to understand the impact of IBRs. EMT data generation use cases serve as the basis for scenarios in event detection and identification use cases, providing comprehensive details about EMT data generation for transmission grids with inverter-based resources. These use cases supply sufficient training and validation datasets for subsequent EMT analysis algorithms.

Xia, Qianxue↗
DOE OSTI2017

PLEXOS Input Data Generator

The PLEXOS Input Data Generator (PIDG) is a tool that enables PLEXOS users to better version their data, automate data processing, collaborate in developing inputs, and transfer data between different production cost modeling and other power systems analysis software. PIDG can process data that is in a generalized format from multiple input sources, including CSV files, PostgreSQL databases, and PSS/E .raw files and write it to an Excel file that can be imported into PLEXOS with only limited manual intervention.

Source record↗
DOE OSTI2017

PLEXOS Input Data Generator

The PLEXOS Input Data Generator (PIDG) is a tool that enables PLEXOS users to better version their data, automate data processing, collaborate in developing inputs, and transfer data between different production cost modeling and other power systems analysis software. PIDG can process data that is in a generalized format from multiple input sources, including CSV files, PostgreSQL databases, and PSS/E .raw files and write it to an Excel file that can be imported into PLEXOS with only limited manual intervention.

Source record↗
DOE OSTI2012

Communication systems, transceivers, and methods for generating data based on channel characteristics

Examples of methods for generating data based on a communications channel are described. In one such example, a processing unit may generate a first vector representation based in part on at least two characteristics of a communications channel. A constellation having at least two dimensions may be addressed with the first vector representation to identify a first symbol associated with the first vector representation. The constellation represents a plurality of regions, each region associated with a respective symbol. The symbol may be used to generate data, which may stored in an electronic storage medium and used as a cryptographic key or a spreading code or hopping sequence in a modulation technique.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Efficient Data Generation and Publication as a Test Tool

A tool to facilitate the generation and publication of test data was created to test the individual components of a command and control system designed to launch spacecraft. Specifically, this tool was built to ensure messages are properly passed between system components. The tool can also be used to test whether the appropriate groups have access (read/write privileges) to the correct messages. The messages passed between system components take the form of unique identifiers with associated values. These identifiers are alphanumeric strings that identify the type of message and the additional parameters that are contained within the message. The values that are passed with the message depend on the identifier. The data generation tool allows for the efficient creation and publication of these messages. A configuration file can be used to set the parameters of the tool and also specify which messages to pass.

command↗
DOE OSTI2011

Understanding Diurnal Patterns in Wind Power Generation Data

Integrating wind energy on the power grid is a challenging task given the intermittent nature of these resources. When the percentage of wind energy was small, control room operators did not have a major problem in scheduling wind resources. However, as this percentage has increased, it is clear that the operators will need more accurate forecasts, as well as any additional information they can exploit, to make better-informed scheduling decisions. In this paper, we investigate diurnal patterns observed in wind-power time series data. Using actual wind generation data from two sites, we try to identify these patterns, understand them better, and determine if it is possible to use weather conditions in the vicinity of the wind farms to predict the pattern for a day. Such analysis could provide insights useful in scheduling wind resources on days with inaccurate forecasts.

17 WIND ENERGY↗
NASA NTRS1977

An automated procedure for calculating system matrices from perturbation data generated by an EAI Pacer and 100 hybrid computer system

Techniques are presented for determining the elements of the A, B, C, and D state variable matrices for systems simulated on an EAI Pacer 100 hybrid computer. An automated procedure systematically generates disturbance data necessary to linearize the simulation model and stores these data on a floppy disk. A separate digital program verifies this data, calculates the elements of the system matrices, and prints these matrices appropriately labeled. The partial derivatives forming the elements of the state variable matrices are approximated by finite difference calculations.

Milner, E. J.↗
NASA NTRS1977

An automated data generator for NASTRAN

A modeling system based on construction-in-context of geometry and physical properties data is adapted for NASTRAN finite element data generation. This system was originally developed for generating three-dimensional finite data for composite structures in PATCHES-III using parametric cubic models. These finite line, surface and volume models constructed using simple data directives will be subdivided into NASTRAN finite elements and their supporting data in the new data generator. Any geometry can be constructed as illustrated by a compressor fan blade with camber, twist and changing airfoil section and the shape then subdivided using a uniform or non-uniform mesh. Special emphasis is given to the adaption of construction-in-context to composite material property modeling. A PMAT series of directives is described for any one, two or three-dimensionally reinforced composite that allows the phase properties, as well as composite properties, to be generated.

Stanton, E. L.↗
NASA NTRS1988

Electronic data generation and display system

The Electronic Data Generation and Display System (EDGADS) is a field tested paperless technical manual system. The authoring provides subject matter experts the option of developing procedureware from digital or hardcopy inputs of technical information from text, graphics, pictures, and recorded media (video, audio, etc.). The display system provides multi-window presentations of graphics, pictures, animations, and action sequences with text and audio overlays on high resolution color CRT and monochrome portable displays. The database management system allows direct access via hierarchical menus, keyword name, ID number, voice command or touch of a screen pictoral of the item (ICON). It contains operations and maintenance technical information at three levels of intelligence for a total system.

Wetekamm, Jules↗
NASA NTRS1973

NASTRAN data generation of helicopter fuselages using interactive graphics

The development and implementation of a preprocessor system for the finite element analysis of helicopter fuselages is described. The system utilizes interactive graphics for the generation, display, and editing of NASTRAN data for fuselage models. It is operated from an IBM 2250 cathode ray tube (CRT) console driven by an IBM 370/145 computer. Real time interaction plus automatic data generation reduces the nominal 6 to 10 week time for manual generation and checking of data to a few days. The interactive graphics system consists of a series of satellite programs operated from a central NASTRAN Systems Monitor. Fuselage structural models including the outer shell and internal structure may be rapidly generated. All numbering systems are automatically assigned. Hard copy plots of the model labeled with GRID or elements ID's are also available. General purpose programs for displaying and editing NASTRAN data are included in the system. Utilization of the NASTRAN interactive graphics system has made possible the multiple finite element analysis of complex helicopter fuselage structures within design schedules.

Sainsbury-Carter, J. B.↗
DOE OSTI2018

Synthetic data generators for the evaluation of biosurveillance outbreak detection algorithms

The research and development of new algorithmic and statistical methods of outbreak detection is an ongoing research priority in the field of biosurveillance. The early detection of emergent disease outbreaks is crucial for effective treatment and mitigation. New detection methods must be compared to established approaches for proper evaluation. This comparison requires biosurveillance test data that accurately reflects the complexity of the real-world data it will be applied to. While the test and evaluation of new detection methods is best performed on real data, it is often impractical to obtain such data as it is either proprietary or limited in scope. Thus, scientists must turn to synthetic data generation to provide enough data to properly eval- uate new detection methodologies. This paper evaluates three such synthetic data sources: The WSARE dataset, the Noufilay equation-based approach, and the Project Mimic data generator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Models for synthetic data generation

The software includes a suite of probabilistic statistical/machine learning models that can generate discrete synthetic data. Each model is trained on a set of real (private) data and then it can be used to generate synthetic but statistically similar data. Once ready, the model can generate as many samples as we want. Finally, in addition to the actual models, the software includes code to process data, evaluate results (based on cross validation), and produce reports.

De Oliveira Sales, Ana Paula↗