Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Programming Framework”

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 1,297 records · Page 72

Analyses of requirements for computer control and data processing experiment subsystems. Volume 2: ATM experiment S-056 image data processing system software development

The IDAPS (Image Data Processing System) is a user-oriented, computer-based, language and control system, which provides a framework or standard for implementing image data processing applications, simplifies set-up of image processing runs so that the system may be used without a working knowledge of computer programming or operation, streamlines operation of the image processing facility, and allows multiple applications to be run in sequence without operator interaction. The control system loads the operators, interprets the input, constructs the necessary parameters for each application, and cells the application. The overlay feature of the IBSYS loader (IBLDR) provides the means of running multiple operators which would otherwise overflow core storage.

Source record↗

Turbulent transport modelling of separating and reattaching shear flows

The improvement of capabilities for computer simulation of turbulent recirculating flows was investigated. Attention has been limited to two dimensional flows and principally to statistically stationary motion. Improvement of turbulence modeling explored the treatment of the near wall sublayer and of the exterior fully turbulent region, working within the framework of turbulence closures requiring the solution of transport equations for the turbulence energy and its dissipation rate. The work on the numerical procedure, based on the Gosman-Pun program TEACH, addressed the problems of incorporating the turbulence model as well as the extension to time dependent flows, the incorporation of a third order approximation of convective transport, and the treatment of non-orthogonal boundaries.

Launder, B. E.↗

Space platform expendables resupply concept definition study. Volume 3: Work breakdown structure and work breakdown structure dictionary

The work breakdown structure (WBS) for the Space Platform Expendables Resupply Concept Definition Study is described. The WBS consists of a list of WBS elements, a dictionary of element definitions, and an element logic diagram. The list and logic diagram identify the interrelationships of the elements. The dictionary defines the types of work that may be represented by or be classified under each specific element. The Space Platform Expendable Resupply WBS was selected mainly to support the program planning, scheduling, and costing performed in the programmatics task (task 3). The WBS is neither a statement-of-work nor a work authorization document. Rather, it is a framework around which to define requirements, plan effort, assign responsibilities, allocate and control resources, and report progress, expenditures, technical performance, and schedule performance. The WBS element definitions are independent of make-or-buy decisions, organizational structure, and activity locations unless exceptions are specifically stated.

Source record↗

Concept of adaptive structures

The concept of adaptive structures is brought up in connection with the need in ultra lightweight structural systems to maintain desired properties and configurations without human intervention when subjected to dynamic, thermal, and other environmental forces. Examples are large antenna structures and flexible robotic structures. In the both cases such adaptivity would allow less massive structural members to be employed under normal loading conditions. During special circumstances when unusually large loads are encountered, temporary stiffening would allow the use of less sturdy structures, resulting in large savings in their cost, and in increasing their mobility and efficiency. Within the framework of a finite-dimensional representation of structural dynamics, the adaptivity can be implemented by the dependence of the stiffness matrix (k) upon the expected load (Q), or expected (programmed) changes in configurations, i.e., (k) = (k(t)) where the dependence upon time is programmed in advance. In order to sustain unexpected loads the adaptive structure can be provided by feedback force control, or by a parametrical stiffness control.

Zak, Michail↗

Polymer Crosslinked 3-D Assemblies of Nanoparticles: Mechanically Strong Lightweight Porous Materials

In analogy to supramolecular assemblies, which are pursued because of properties above and beyond those of the individual molecules, self-standing monolithic three-dimensional assemblies of nanoparticles also have unique properties attributed to their structure. For example, ultra low-density 3-D assemblies of silica nanoparticles, known as silica aerogels, are characterized by large internal void space, high surface area and very low thermal conductivity. Aerogels, however, are also extremely fragile materials, limiting their application to a few specialized environments, e.g., in nuclear reactors as Cerenkov radiation detectors, in space (refer to NASA's Stardust Program) and aboard certain planetary vehicles (thermal insulators on Mars Rovers in 1997 and 2004). The fragility problem is traced to well-defined weak points in the aerogel skeletal framework, the interparticle necks. Using the surface functionality of the nanoparticle building blocks as a focal point, we have directed attachment of a conformal polymer coating over the entire framework, rendering all necks wider. Thus, although the bulk density may increase only by 3x, the mesoporosity (pores in the range 2-50 nm) remains unchanged, while the strength of the material increases by up to 300... Having addressed the fragility problem, aerogels are now robust materials, and a variety of applications, ranging from thermal/acoustic insulators to catalyst supports, to platform for sensors, and dielectrics are all within reach. Our approach employs molecular science to manipulate nanoscopic matter for achieving useful macroscopic properties, and in our view it resides at the core of what defines nanotechnology. In that spirit, this technology is expandable in three directions. Thus, we have already crosslinked successfully amine-modified silica, and we anticipate that more rich chemistry will be realized by been creative with the nanoparticle surface modifiers. On the other hand, although we do not expect orders-of-magnitude increase in strength, nevertheless, by varying the polymer we expect to impart other properties such as hydrophobicity, thermal stability and perhaps electrical conductivity. Besides polymers, crosslinkers will include metals and ceramics (e.g., through POSS precursors). Finally, network morphology directs load dissipation, and of approximately 30 different crosslinked oxide aerogels, vanadia, whose structure is fibrous rather than particulate, yields a much stronger (by 4..) material than silica of the same density. It seems imperative to implement control of network morphology, even through templating.

Leventis, Nicholas↗

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Luxhoj, J. T.↗

Command and Control System Automated Testing

To support the National Aeronautics and Space Administration’s (NASA) Space Launch System (SLS) rocket and the Orion capsule, designed to take humans back to the moon in 2024, Kennedy Space Center (KSC) has developed the Spaceport Command and Control System (SCCS) to monitor and control the launch. Within SCCS, the Launch Control System (LCS) is designed to allow console engineers to control and monitor the status of the launch and flight hardware, as well as issue commands to ground control systems and launch vehicles. The messaging software of LCS is responsible for handling the various data types that can be sent between the hardware and software components of the LCS. Since this system is interacting with numerous devices, controllers, and viewports in real time, the distribution of data across the system must be fast, but also reliable and accurate. To verify the accuracy and reliability of the system, developers on the project have created a set of tests to be performed that covers all operations allowed by the system. Given the extensive Application Programming Interface(API) provided by the messaging software, these unit tests are rather time-consuming and costly (in terms of man-hours) to perform. Therefore, an automated testing framework is used to perform supplemental tests automatically when updates are made to the code base.

Rebecca McFadden↗

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗

Advancing Solar Innovation for Low and Moderate-Income Households (Final Technical Report)

The overarching goal of this project was to deepen the understanding of the social and economic aspects of solar adoption, with a specific eye towards any pathways or barriers that could be identified that directly impact low and moderate-income households. Specific objectives included (1) identifying and assessing significant non-economic barriers that complicate the ability of LMI households to adopt and benefit from solar, even under scenarios with low economic barriers; (2) assessing the differential ability of groups to benefit from solar adoption based on their ability to invest in different solar innovations; (3) explaining why some LMI communities nonetheless experience significantly higher rates of solar adoption and benefit than others; (4) developing a methodological model for generating data addressing key issues to the project; and (5) designing and developing an interactive web portal that aggregates existing datasets, provides researchers trend analysis capabilities, standardizes project protocols for other states and communities; provides the opportunities for researchers using the same protocol in other communities to upload their results; and engages with the general public by providing them the ability to upload their own perspectives and stories of solar technology adoption. Through a common framework for conducting semi-structured field interviews. In partnership with cooperative extension services, in conjunction with survey research and the aggregation of existing solar adoption datasets, this study has identified that currently ,there is a strong values alignment and knowledge access challenge that continues to affect solar adoption. Participants in this study – many of whom are either low or moderate-income and/or live in underserved rural communities do not believe solar is an option where they live or believe solar is appropriate for their lifestyle. This perception challenge is further exacerbated by the absolute dearth of adopters in most communities and a total lack of local marketing for solar energy. What marketing that does exist is geared towards audiences other than those in the participant's community, reinforcing a perception that 22% of survey respondents (n = 1,551) have: that solar energy is not meant for them and their community. Overcoming these serious human challenges pertaining to knowledge and marketing are critical to reducing the customer acquisition costs incurred amongst LMI communities and especially those in rural America where the opportunities solar provides to increase local energy resilience, security, all while engaging in personal stewardship of the planet could align with local values. Community-Based Social Marketing, built in partnership with community leaders and geared towards meeting local visions of ideal life, is a near term opportunity to bridge the gap between local priorities and national market and policy imperatives for solar while moving towards the administration's goals for renewable energy as part of a just and equitable decarbonization program. The tools and methods developed for this study can serve as a template for identifying other specific local challenges for solar adoption in other state, and provide the opportunity for a common data framework for understanding system-wide trends and barriers to achieving uptake of residential solar in communities that may, ultimately, benefit the most from the technology.

14 SOLAR ENERGY↗

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

Prediction of Adverse Events in Time Series Data Using ACCEPT (Adverse Condition and Critical Event Prediction Toolbox)

Abstract:An open-source toolbox called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox) that operates from within the Matlab programming environment is introduced. The toolbox provides an open source, special-purpose functionality that can be used specifically for the prediction or forecasting of adverse events in time series data. It also provides a single, unifying framework in which to compare a variety of combinations of algorithmic approaches addressing this problem. The architectural framework also offers the flexibility for expansion to accommodate the newest, latest popular regression and detection methods for this particular problem, enabling the development of an infrastructure that can act as a proving ground platform for comparing new techniques to the state of the art. As such, its intention is to act as a catalyst in advancing the state of the art in technologies related to this problem. There are a variety of other toolboxes that offer similar capabilities and features, however they are either more suitable as general purpose machine learning toolboxes or are only available commercially or through agreements. ACCEPT fills that niche by offering visibility into a specific approach for addressing the adverse event prediction problem.

programming environments↗

Development Roadmap of an Evolvable and Extensible Multi-Mission Telecom Planning and Analysis Framework

In this paper, we describe the development roadmap and discuss the various challenges of an evolvable and extensible multi-mission telecom planning and analysis framework. Our long-term goal is to develop a set of powerful flexible telecommunications analysis tools that can be easily adapted to different missions while maintain the common Deep Space Communication requirements. The ability of re-using the DSN ground models and the common software utilities in our adaptations has contributed significantly to our development efforts measured in terms of consistency, accuracy, and minimal effort redundancy, which can translate into shorter development time and major cost savings for the individual missions. In our roadmap, we will address the design principles, technical achievements and the associated challenges for following telecom analysis tools (i) Telecom Forecaster Predictor - TFP (ii) Unified Telecom Predictor - UTP (iii) Generalized Telecom Predictor - GTP (iv) Generic TFP (v) Web-based TFP (vi) Application Program Interface - API (vii) Mars Relay Network Planning Tool - MRNPT.

telecommunications↗

FAIR AI models in high energy physics

Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Computer program performs stiffness matrix structural analysis

Computer program generates the stiffness matrix for a particular type of structure from geometrical data, and performs static and normal mode analyses. It requires the structure to be modeled as a stable framework of uniform, weightless members, and joints at which loads are applied and weights are lumped.

Bamford, R.↗

A study of characteristics of intercity transportation systems. Phase 1: Definition of transportation comparison methodology

Decision making in early transportation planning must be responsive to complex value systems representing various policies and objectives. The assessment of alternative transportation concepts during the early initial phases of the system life cycle, when supportive research and technology development activities are defined, requires estimates of transportation, environmental, and socio-economic impacts throughout the system life cycle, which is a period of some 40 or 50 years. A unified methodological framework for comparing intercity passenger and freight transportation systems is described and is extended to include the comparison of long term transportation trends arising from implementation of the various R & D programs. The attributes of existing and future transportation systems are reviewed in order to establish measures for comparison, define value functions, and attribute weightings needed for comparing alternative policy actions for furthering transportation goals. Comparison criteria definitions and an illustrative example are included.

English, J. M.↗

Multidisciplinary Design Technology Development: A Comparative Investigation of Integrated Aerospace Vehicle Design Tools

This research effort is a joint program between the Departments of Aerospace and Mechanical Engineering and the Computer Science and Engineering Department at the University of Notre Dame. The purpose of the project was to develop a framework and systematic methodology to facilitate the application of Multidisciplinary Design Optimization (MDO) to a diverse class of system design problems. For all practical aerospace systems, the design of a systems is a complex sequence of events which integrates the activities of a variety of discipline "experts" and their associated "tools". The development, archiving and exchange of information between these individual experts is central to the design task and it is this information which provides the basis for these experts to make coordinated design decisions (i.e., compromises and trade-offs) - resulting in the final product design. Grant efforts focused on developing and evaluating frameworks for effective design coordination within a MDO environment. Central to these research efforts was the concept that the individual discipline "expert", using the most appropriate "tools" available and the most complete description of the system should be empowered to have the greatest impact on the design decisions and final design. This means that the overall process must be highly interactive and efficiently conducted if the resulting design is to be developed in a manner consistent with cost and time requirements. The methods developed as part of this research effort include; extensions to a sensitivity based Concurrent Subspace Optimization (CSSO) NMO algorithm; the development of a neural network response surface based CSSO-MDO algorithm; and the integration of distributed computing and process scheduling into the MDO environment. This report overviews research efforts in each of these focus. A complete bibliography of research produced with support of this grant is attached.

Renaud, John E.↗

The Umbra Simulation and Integration Framework Applied to Emergency Response Training

The Mine Emergency Response Interactive Training Simulation (MERITS) is intended to prepare personnel to manage an emergency in an underground coal mine. The creation of an effective training environment required realistic emergent behavior in response to simulation events and trainee interventions, exploratory modification of miner behavior rules, realistic physics, and incorporation of legacy code. It also required the ability to add rich media to the simulation without conflicting with normal desktop security settings. Our Umbra Simulation and Integration Framework facilitated agent-based modeling of miners and rescuers and made it possible to work with subject matter experts to quickly adjust behavior through script editing, rather than through lengthy programming and recompilation. Integration of Umbra code with the WebKit browser engine allowed the use of JavaScript-enabled local web pages for media support. This project greatly extended the capabilities of Umbra in support of training simulations and has implications for simulations that combine human behavior, physics, and rich media.

Hamilton, Paul Lawrence↗

All-sky Microwave Radiance Assimilation in The GEOS Atmospheric Reanalysis for The Early 21st Century (GEOS-21C)

The Goddard Earth Observing System (GEOS) is used for weather, climate, and air quality forecasts and producing reanalysis datasets. Its atmospheric data assimilation system (ADAS) is a hybrid–4DEnVar system. Various satellite radiance, atmospheric motion vector (winds), aircraft, and convectional data are assimilated by this system to produce global atmospheric states. Satellite data assimilation within GEOS ADAS is enhanced by considering correlated observational errors among infrared radiance observations and assimilating new microwave radiance data in all-sky conditions since the release of the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). These new microwave radiance data include brightness temperature measurements made by the Global Precipitation Mission (GPM) microwave imager (GMI) and Advanced Microwave Scanning Radiometer 2 / Global Change Observation Mission 1st – Water (AMSR2/GCOM-W). Historical microwave radiance data made by various Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) aboard the Aqua satellite are tested within the all-sky microwave radiance assimilation framework in the preparation of the GEOS enhanced Atmospheric Reanalysis for the early 21st century (GEOS-21C). Assimilation of these data in all-sky conditions enhances use of other satellite data and improves GEOS analysis and forecast. In this talk, we will present the procedures of to assimilate microwave radiance data in all-sky conditions and their impact on atmospheric analysis in the preparation of GEOS-21C.

Jianjun Jin↗