Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network solutions”

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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Parallel Implementation of Power System Dynamic Simulation

Dynamic simulation of power system transient stability is important for planning, monitoring, operation, and control of electrical power systems. However, modeling the system dynamics and network involves the computationally intensive time-domain solution of numerous differential and algebraic equations (DAE). This results in a transient stability implementation that may not maintain the real-time constraints of an online security assessment. This paper presents a parallel implementation of the dynamic simulation on a high-performance computing (HPC) platform using parallel simulation algorithms and computation architectures. It enables the simulation to run even faster than real time, enabling the “look-ahead” capability of upcoming stability problems in the power grid.

dynamic simulation↗
DOE OSTI2019

skyline

skyline is a header-only C++ library for the direct solution of linear algebraic equations that are stored in skyline format. It is intended for medium-sized sparse problems with moderate bandwidth. This solution approach has historically been used for pressure network solvers and in some finite element codes. It was developed to replace legacy solver code included in the EnergyPlus building energy modeling software package, but is not limited to use by EnergyPlus.

DeGraw, Jason↗
DOE OSTI2016

Resilience and Robustness in Long-Term Planning of the National Energy and Transportation System

The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases for any developed nation today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting and converting energy for electric and transportation systems are numerous. Addressing costs, sustainability and resilience of electric and transportation needs requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. Yet, the advent of electrically driven transportation, including cars, trucks and trains, creates potential interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modelling capability to perform long-term electric and transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow modelling with a multi-objective solution method. We describe and compare it to the state of the art in energy planning models. An example is presented to illustrate important features of this new approach.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Prediction of Total Dissolved Gas Exchange at Hydropower Dams

Total dissolved gas (TDG) supersaturation in waters released at hydropower dams can cause gas bubble trauma in fisheries resulting in physical injuries and eyeball protrusion that can lead to mortality. Elevated TDG pressures in hydropower releases are generally caused by the entrainment of air in spillway releases and the subsequent exchange of atmospheric gasses into solution during passage through the stilling basin. The network of dams throughout the Columbia River Basin (CRB) are managed for irrigation, hydropower production, flood control, navigation, and fish passage that frequently result in both voluntary and involuntary spillway releases. These dam operations are constrained by state and federal water quality standards for TDG saturation which balance the benefits of spillway operations designed for Endangered Species Act (ESA)-listed fisheries versus the degradation to water quality as defined by TDG saturation. In the 1970s, the United States Environmental Protection Agency (USEPA), under the federal Clean Water Act (Section 303(d)), established a criterion not to exceed the TDG saturation level of 110% in order to protect freshwater and marine aquatic life. The states of Washington and Oregon have adopted special water quality standards for TDG saturation in the tailrace and forebays of hydropower facilities on the Columbia and Snake Rivers where spillway operations support fish passage objectives. The physical processes that affect TDG exchange at hydropower facilities have been studied throughout the CRB in site-specific studies and routine water quality monitoring programs. These data have been used to quantify the relationship between project operations, structural properties, and TDG exchange. These data have also been used to develop predictive models of TDG exchange to support real-time TDG management decisions. These empirically based predictive models have been developed for specific projects and account for both the fate of spillway and powerhouse flows in the tailrace channel and resultant exchange in route to the next downstream dam. Currently, there exists a need to summarize the general finding from operational and structural TDG abatement programs conducted throughout the CRB and for the development of a generalized prediction model that pools data collected at multiple projects with similar structural attributes. A generalized TDG exchange model can be tuned to specific projects and coupled with water regulation models to allow the formulation of optimal daily water regulation schedules subject to water quality constraints for TDG supersaturation. A generalized TDG exchange model can also be applied to other hydropower dams that affect TDG pressures in tailraces and can be used to develop alternative operational and structural measures to minimize TDG generation. It is proposed to develop a methodology for predicting TDG levels downstream of hydropower facilities with similar structural properties as a function of a set of variables that affect TDG exchange; such as tailwater depth, spill discharge and pattern, project head, and entrainment of powerhouse releases. TDG data from hydropower facilities located throughout the northwest region of the United States will be used to identify relationships between TDG exchange and relevant dependent variables. Data analysis and regression techniques will be used to develop predictive TDG exchange expressions for various structural categories.

13 HYDRO ENERGY↗
DOE OSTI2017

Cyber Security Concerns for Sharing Distributed Antenna Systems

Wireless access to data is a challenge for environments with poor radio frequency coverage such as nuclear power plants. An 802.11 Wi-Fi data network is often not a cost-effective solution not a cost-effective solution due to the requirement for extensive antenna outlays in large nuclear facilities and poor signal penetration due to thick concrete walls. Combining commercial Long-Term Evolution (LTE) with an existing distributed antenna system’s (DAS) resources and infrastructure is an attractive solution due to and lower cost of implementation and likely coverage increases. A 2017 technical report by EPRI has demonstrated that multiple systems such as Land Mobile Radio (LMR) and LTE can be operated using the same DAS and by sharing the leaky coaxial cabling and indoor antenna systems (Reference 1). Since the radio frequency (RF) bands used by the two wireless systems are specified to be non-overlapping, it is believed that the systems are isolated from each other. No extensive analysis has been conducted to determine whether RF signals from one system sharing antennas and coaxial cable can influence another. This paper investigates the assumption of isolation between the two wireless systems and provides recommendations for future needed work to identify specific vulnerabilities and potential mitigations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Understanding machine learning approaches for partial differential equations

Recent works in computational physics have been successful in the super-resolution of numerical solutions of partial differential equations (PDEs) via neural networks. In this paper we explore the possibilities and limitations of the data-driven discretization approach implemented by Bar-Sinai et. al., while contrasting it with a simpler yet weaker approach utilizing the nangs Python library. The results demonstrate what neural network parameters optimize accuracy and performance, as well as what conditions on PDEs are necessary to maintain convergence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Learning Solutions for a Stable Grid Recovery

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Prototype Software to Demonstrate a Data Catalog for Hanford Environmental Datasets

Ensuring that data on long-term environmental remediation at the Hanford Site is high-quality, traceable, and easily accessible is an ongoing challenge, complicated by decades of data collection, multiple contractors maintaining data sources, and the wide range of data types. A centralized data catalog, known as the Hanford Environmental Information and Data Index (HEIDI), has been under development as part of the Hanford Environmental Data Management (HEDM) program to address these challenges. HEIDI fulfills a critical need to bring together a wide range of data types and sizes from multiple authoritative data sources, while documenting the data pedigree and quality information (i.e., traceable to the data source/originator). This document describes additional development and maturation of the HEIDI prototype. Key accomplishments included deploying the catalog software, Esri Geoportal Server, on a server accessible to Hanford Local Area Network users, conducting cybersecurity evaluations, investigating integrated authentication solutions, and conducting functional testing of the catalog prototype. The server-based deployment enabled targeted feedback, leading to enhancements including improved accessibility features and an expanded metadata schema. Specifications for the server-based deployment of the prototype catalog and the HEIDI metadata schema are provided in this document to support subsequent HEIDI deployment by the U.S. Department of Energy Richland Operations Office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Launch Alaska Transportation and Energy Accelerator (LATEA)

The Launch Alaska Transportation and Energy Accelerator (LATEA), funded through the U.S. Department of Energy Office of Technology Commercialization’s Energy Program for Innovation Clusters (EPIC),advanced deployment of innovative and efficient transportation and energy technology in Alaska from October 2021 through June 2025. The project was designed to leverage Launch Alaska’s accelerator model to identify, recruit, and support transportation technology companies with novel solutions to market needs while building the stakeholder networks, demonstration opportunities, and institutional capacity necessary to accelerate commercialization in one of the most challenging operating environments in the United States.

08 HYDROGEN↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1996

On the Retrieval of Lightning Radio Sources from Time-of-Arrival Data

We examine the problem of retrieving three-dimensional lightning locations from radio frequency Time-Of-Arrival (TOA) measurements. Arbitrary antenna locations are considered. By judiciously differencing measurements that are related to the location of the antennas and their excitation times, the problem is converted from the initial spherical nonlinear form to a system of linear equations. In the linear formalism, the source location and time-of-occurrence is viewed geometrically as an intersection of hyperplanes in the four-dimensional Minkowski space (x,y,z,t). The linear equations are solved to obtain explicit analytic expressions for the location and time variables. Retrieval errors are not interpreted with conventional Geometrical Dilution of Precision (GDOP) arguments as discussed by Holmes and Reedy (1951), but with more recent inversion analyses considered by Twomey (1977). Measurement errors are propagated analytically so that the specific effect of these errors on the solution is clarified. The sensitivity of the solution on the number of antennas used, antenna network geometry, source position, and measurement differencing schemes are discussed in terms of the eigenvalues of the linear system.

Koshak, William J.↗
NASA NTRS2005

A Parallel Trade Study Architecture for Design Optimization of Complex Systems

Design of a successful product requires evaluating many design alternatives in a limited design cycle time. This can be achieved through leveraging design space exploration tools and available computing resources on the network. This paper presents a parallel trade study architecture to integrate trade study clients and computing resources on a network using Web services. The parallel trade study solution is demonstrated to accelerate design of experiments, genetic algorithm optimization, and a cost as an independent variable (CAIV) study for a space system application.

Kim, Hongman↗
NASA NTRS2000

Cascade Optimization Strategy with Neural Network and Regression Approximations Demonstrated on a Preliminary Aircraft Engine Design

A preliminary aircraft engine design methodology is being developed that utilizes a cascade optimization strategy together with neural network and regression approximation methods. The cascade strategy employs different optimization algorithms in a specified sequence. The neural network and regression methods are used to approximate solutions obtained from the NASA Engine Performance Program (NEPP), which implements engine thermodynamic cycle and performance analysis models. The new methodology is proving to be more robust and computationally efficient than the conventional optimization approach of using a single optimization algorithm with direct reanalysis. The methodology has been demonstrated on a preliminary design problem for a novel subsonic turbofan engine concept that incorporates a wave rotor as a cycle-topping device. Computations of maximum thrust were obtained for a specific design point in the engine mission profile. The results (depicted in the figure) show a significant improvement in the maximum thrust obtained using the new methodology in comparison to benchmark solutions obtained using NEPP in a manual design mode.

Hopkins, Dale A.↗
NASA NTRS2012

Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms

Wireless sensors connected in a local network offer revolutionary exploration capabilities, but the current solutions do not work in extreme environments of low temperatures (200K) and low to moderate radiation levels (<50 krad). These sensors (temperature, radiation, infrared, etc.) would need to operate outside the spacecraft/ lander and be totally independent of power from the spacecraft/lander. Flash memory field-programmable gate arrays (FPGAs) are being used as the main signal processing and protocol generation platform in a new receiver. Flash-based FPGAs have been shown to have at least 100 reduced standby power and 10 reduction operating power when compared to normal SRAM-based FPGA technology.

Sheldon, Douglas J.↗
NASA NTRSDate not supplied

Flight Dynamics and Navigation Performance of the BioSentinel Mission

BioSentinel was one of the ten 6U CubeSats launched by the Space Launch System (SLS) as part of the Artemis-I mission on November 16th, 2022. The spacecraft was deployed after the trans-lunar injection imparted by the upper stage of SLS, and performed a lunar flyby that provided the necessary energy to escape the Earth-Moon system into heliocentric space. The BioSentinel mission required flight dynamics support prior to flight to assess the deployment conditions and the trajectory design, as well as navigation support during flight, to provide orbit determination solutions for the mission and the Deep Space Network (DSN). This paper discusses the BioSentinel mission flight dynamics campaign, from mission design to the challenges and lessons learned encountered during flight.

Astrodynamics↗
NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗
DOE OSTI2015

Security-Constrained Unit Commitment with Linearized AC Optimal Power Flow.

We propose a mathematical programming-based approach to optimize the security-constrained unit commitment problem with a full AC transmission network representation. Our approach is based on our previously introduced successive linear programming (SLP) approach to solving the non-linear, nonconvex AC optimal power flow (ACOPF) problem. By linearizing the ACOPF, we are able to leverage powerful commercial mixed-integer solvers to iteratively optimize the combined unit commitment plus ACOPF model. We demonstrate our approach on six-bus, IEEE RTS-96, and IEEE 118-bus test systems. We perform a comparative analysis of the relative impacts of singlebus, DC, and AC transmission network models on the unit commitment and dispatch solutions and their associated costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Method and system for training a visual prosthesis

A method for training a visual prosthesis includes presenting a non-visual reference stimulus corresponding to a reference image to a visual prosthesis patient. Training data sets are generated by presenting a series of stimulation patterns to the patient through the visual prosthesis. Each stimulation pattern in the series is determined at least in part on a received user perception input and a fitness function optimization algorithm. The presented stimulation patterns and the user perception inputs are stored and presented to a neural network off-line to determine a vision solution.

47 OTHER INSTRUMENTATION↗