Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Smart computing”

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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Towards An Autonomous sUAS Operating in UTM TCL4+ and STEReO Fire Scenario

This study presents two sUAS payload point designs for enabling autonomous BVLOS flight in UTM TCL4+ urban environments and STEReO fire responses. The payload components for the first payload include an onboard computer and a 360 LIDAR. The second payload, which is a continuation of the first, adds a range finder altimeter, downward-facing monocular camera, forward-facing thermal and visible light dual camera, vehicle-to-vehicle radio modem, and Li-ion smart battery. The components are mounted on enclosed structural frames that were designed in-house. Placing the autonomy components onboard leverages the advantages of sUAS over manned aircraft such as low-cost, quick response time, and increased scalability. Autonomous capabilities include embedded processing, SLAM, object and fire detection, and V2V communication. Data processing is conducted onboard the aircraft to eliminate the dependency on a groundstation downlink. The first iteration of the payload is tested in both simulation and flight tests while the continuing iteration is in development.

Autonomous UAS↗
DOE OSTI2020

Extending XACC for Quantum Optimal Control

Quantum computing vendors are beginning to open up application programming interfaces for direct pulse-level quantum control. With this, programmers can begin to describe quantum kernels of execution via sequences of arbitrary pulse shapes. This opens new avenues of research and development with regards to smart quantum compilation routines that enable direct translation of higher-level digital assembly representations to these native pulse instructions. In this work, we present an extension to the XACC system-level quantum-classical software framework that directly enables this compilation lowering phase via user-specified quantum optimal control techniques. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences that are optimal with respect to the backend system dynamics. Our work is modular and extensible, enabling third party optimal control techniques and strategies in both C++ and Python. We demonstrate this extension with familiar gradient-based methods like gradient ascent pulse engineering (GRAPE), gradient optimization of analytic controls (GOAT), and Krotov's method. Our work serves as a foundational component of future quantum-classical compiler designs that lower high-level programmatic representations to low-level machine instructions.

Nguyen, Thien↗
DOE OSTI2020

Automated pipeline framework for processing of large-scale building energy time series data

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗
NASA NTRS1994

Structural Probability Concepts Adapted to Electrical Engineering

Through the use of equivalent variable analogies, the authors demonstrate how an electrical subsystem can be modeled by an equivalent structural subsystem. This allows the electrical subsystem to be probabilistically analyzed by using available structural reliability computer codes such as NESSUS. With the ability to analyze the electrical subsystem probabilistically, we can evaluate the reliability of systems that include both structural and electrical subsystems. Common examples of such systems are a structural subsystem integrated with a health-monitoring subsystem, and smart structures. Since these systems have electrical subsystems that directly affect the operation of the overall system, probabilistically analyzing them could lead to improved reliability and reduced costs. The direct effect of the electrical subsystem on the structural subsystem is of secondary order and is not considered in the scope of this work.

Steinberg, Eric P.↗
NASA NTRS2010

Lessons Learned in Building the Ares Projects

Since being established in 2005, the Ares Projects at Marshall Space Flight Center have been making steady progress designing, building, testing, and flying the next generation of exploration launch vehicles. Ares is committed to rebuilding crucial capabilities from the Apollo era that made the first human flights to the Moon possible, as well as incorporating the latest in computer technology and changes in management philosophy. One example of an Apollo-era practice has been giving NASA overall authority over vehicle integration activities, giving civil service engineers hands-on experience in developing rocket hardware. This knowledge and experience help make the agency a "smart buyer" of products and services. More modern practices have been added to the management tool belt to improve efficiency, cost effectiveness, and institutional knowledge, including knowledge management/capture to gain better insight into design and decision making; earned value management, where Ares won a NASA award for its practice and implementation; designing for operability; and Lean Six Sigma applications to identify and eliminate wasted time and effort. While it is important to learn technical lessons like how to fly and control unique rockets like the Ares I-X flight test vehicle, the Ares management team also has been learning important lessons about how to manage large, long-term projects.

Sumrall, John Phil↗
DOE OSTI2024

Blockchain-based decentralized computing

An exemplary blockchain-based decentralized computing system and method are disclosed for industrial analytics applications. The exemplary system and method leverage blockchain technology to deliver and execute privacy-preserving decentralized predictive analytics, machine learning, and optimization operations for various industrial applications using a set of self-contained analytics block smart contracts that can be readily utilized and in analytics applications to deploy across multiple sites.

Ramanan, Paritosh P.↗
DOE OSTI2023

Ab initio study of tungsten-based alloys under fusion power-plant conditions

Tungsten (W) is considered a leading candidate for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. However, tungsten also presents a very low fracture toughness, primarily associated with intergranular failure and bulk plasticity, limiting its applications. In recent years, several families of tungsten-based alloys have been explored to overcome the aforementioned limitations of pure tungsten. These include tungsten-based high-entropy alloys (W-HEAs) and tungsten-based Self-passivating Metal Alloys with Reduced Thermo-oxidation or “SMART alloys” (W-SAs). Given their proximity to the plasma, it is crucial to understand how the exposure of these candidate plasma-facing materials (PFMs) to the neutron fluxes expected in fusion reactors impacts their material behavior over time. In this work, we present a computational approach that combines inventory codes and first-principles DFT electronic structure calculations to understand the behavior of transmuting tungsten-based PFMs. In particular, we calculate the changes in the chemical composition, production uncertainties, the elastic and ductility properties, and the density of states for five tungsten-based PFMs when exposed to EU-DEMO fusion first wall conditions for ten years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Data-driven based coordinated smart inverter control for distributed energy resources

Smart inverters (SI) for distributed energy resources (DER) are becoming popular since they have the ability to stabilize as well as restore the voltage and frequency of power systems. Aiming at establishing the mathematical models combined with SI control methods, multiple optimization methods are developed. However, the computational complexity of solving such a mathematical model with various uncertainties limits the real-time application of the SI control. To conquer this challenge, a data-driven-based SI control approach is developed to achieve coordinated control in the high penetration DER system. First, an optimization problem for maximizing the active power generation and minimizing the power loss is designed using the Volt/VAR control. To reduce the time consumption, the recurrent neural network (RNN) is proposed to model the relationship between the uncertainties and control actions during the offline site. The RNN with different sub-structures such as the long short-term memory cell and gated recurrent unit cell are included to enrich the diversity of features. In the last stage, different experiment comparisons, including multiple uncertainties maps and stateof- art machine learning methods, are conducted to verify the effectiveness of the proposed method based on the IEEE 123 bus power system. The results demonstrate that the proposed method can effectively achieve a rapid and coordinated control with a lower error rate.

Qiu, Wei↗
DOE OSTI2020

Ibis Networks/WattIQ (IN2 Final Report)

Ibis Networks is a full-stack cleantech company that provides plug-level energy monitoring and control to solve energy and asset management problems for the enterprise. During DATES – DATES, an NREL research team worked with the Ibis team to validate their product, the InteliSocket. The Ibis InteliSocket is a pass-through plug-load energy monitor and controller that is designed to reduce energy wasted by common 120 V plug-in devices in commercial office buildings, such as computer peripherals, conference room AV equipment, and break-room appliances. The system can shut off supply power to these end uses via remote control, manual switches, pre-set schedules, or automated control algorithms. The scope of this IN2 project was the development and refinement of “smart” learning behavior algorithms (LBAs), which could help installation processes and dramatically expand the sockets’ capabilities and energy-saving potential by suggesting suitable control schedules that are based on monitored use patterns. While Ibis has the analytical and software expertise for algorithm development, the lack of test data, both in a controlled laboratory setting and in real-life deployment scenarios, represented a key barrier toward commercialization of the product. Assistance through the IN2 program provided an opportunity to conduct the needed “trial and error” algorithm development. The project included baseline field-data collection, laboratory testing, and field validation components, all of which were conducted at the NREL campus between April 2017 and July 2019.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Recommendations for Trust and Encryption in DER Interoperability Standards

Recently developed Distributed Energy Resource (DER) interoperability standards include communication and cyber security requirements. In 2018, the US national interconnection standard, IEEE 1547, was revised to require DER to include a Sun Spec Modbus, IEEE 2030.5 (Smart Energy Profile, SEP 2.0), or IEEE 1815 (DNP3) communication interface but does not include any normative overarching cybersecurity requirements. IEEE 2030.5 and associated implementation requirements for California, known as the California Smart Inverter Profile (CSIP), prescribe the greatest security features - including encryption, authentication, and key management requirements. Sun Spec Modbus and IEEE 1815 security requirements are not as comprehensive, leading to implementation questions throughout the industry. Further, while the security features in IEEE 2030.5 are commonly used in computing platforms, there are still questions of how well the technologies will scale in highly-distributed, computationally-limited inverter environments. In this paper, (a) the elements of IEEE 2030.5 encryption, authentication, and key management guidelines are analyzed, (b) potential scalability gaps are identified, and (c) alternative technologies are explored for possible inclusion in DER interoperability or cyber security standards.

42 ENGINEERING↗
DOE OSTI2019

Implementing the Yubikey at Fermilab [Slides]

Fermilab, America's particle physics and accelerator laboratory, is an open science facility. Fermilab started limited use of DOE issued PIV PIV-I cards for elevated access to services as part of the 2016 DOE mandate. With FIPS 140 140-2 validated Yubikeys now available Fermilab has begun a much broader implementation using the Yubikey as a PIV -I Smart Card, not only to replace the DOE issued cards, but to expand the usage to more users and services as well as network access.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Comparison of Angle of Attack Measurements for Wind Tunnel Testing

Two optical systems capable of measuring model attitude and deformation were compared to inertial devices employed to acquire wind tunnel model angle of attack measurements during the sting mounted full span 30% geometric scale flexible configuration of the Northrop Grumman Unmanned Combat Air Vehicle (UCAV) installed in the NASA Langley Transonic Dynamics Tunnel (TDT). The overall purpose of the test at TDT was to evaluate smart materials and structures adaptive wing technology. The optical techniques that were compared to inertial devices employed to measure angle of attack for this test were: (1) an Optotrak (registered) system, an optical system consisting of two sensors, each containing a pair of orthogonally oriented linear arrays to compute spatial positions of a set of active markers; and (2) Video Model Deformation (VMD) system, providing a single view of passive targets using a constrained photogrammetric solution whose primary function was to measure wing and control surface deformations. The Optotrak system was installed for this test for the first time at TDT in order to assess the usefulness of the system for future static and dynamic deformation measurements.

Jones, Thomas, W.↗
NASA NTRS2014

NASA Wearable Technology CLUSTER 2013-2014 Report

Wearable technology has the potential to revolutionize the way humans interact with one another, with information, and with the electronic systems that surround them. This change can already be seen in the dramatic increase in the availability and use of wearable health and activity monitors. These devices continuously monitor the wearer using on-­‐body sensors and wireless communication. They provide feedback that can be used to improve physical health and performance. Smart watches and head mounted displays are also receiving a great deal of commercial attention, providing immediate access to information via graphical displays, as well as additional sensing features. For the purposes of the Wearable Technology CLUSTER, wearable technology is broadly defined as any electronic sensing, human interfaces, computing, or communication that is mounted on the body. Current commercially available wearable devices primarily house electronics in rigid packaging to provide protection from flexing, moisture, and other contaminants. NASA mentors are interested in this approach, but are also interested in direct integration of electronics into clothing to enable more comfortable systems. For human spaceflight, wearable technology holds a great deal of promise for significantly improving safety, efficiency, autonomy, and research capacity for the crew in space and support personnel on the ground. Specific capabilities of interest include: Continuous biomedical monitoring for research and detection of health problems. Environmental monitoring for individual exposure assessments and alarms. Activity monitoring for responsive robotics and environments. Multi-modal caution and warning using tactile, auditory, and visual alarms. Wireless, hands-free, on-demand voice communication. Mobile, on-demand access to space vehicle and robotic displays and controls. Many technical challenges must be overcome to realize these wearable technology applications. For example, to make a wearable device that is both functional and comfortable for long duration wear, developers must strive to reduce electronic mass and volume while also addressing constraints imposed by the body attachment method. Depending on the application, the device must be placed in a location that the user can see and reach, and that provides the appropriate access to air and the wearer's skin. Limited power is available from body-­‐worn batteries and heat must be managed to prevent discomfort. If the clothing is to be washed, there are additional durability and washability hurdles that traditional electronics are not designed to address. Finally, each specific capability has unique technical challenges that will likely require unique solutions. In addition to the technical challenges, development of wearable devices is made more difficult by the diversity of skills required and the historic lack of collaboration across domains. Wearable technology development requires expertise in textiles engineering, apparel design, software and computer engineering, electronic design and manufacturing, human factors engineering, and application-­‐specific fields such as acoustics, medical devices, and sensing. Knowledge from each of these domains must be integrated to create functional and comfortable devices. For this reason, the diversity of knowledge and experience represented in the Wearable Technology is critical to overcoming the fundamental challenges in the field.

Simon, Cory↗
DOE OSTI2022

PV Hosting Capacity Estimation: Experiences with Scalable Framework; Preprint

Hosting capacity is an indication of the amount of photovoltaics (PV) can be hosted in a distribution system. This paper presents a framework for estimating distributed PV hosting capacity at scale. We first analyze the key challenges of performing relevant large scale simulation including computational and modeling challenges. Then, we develop two python-based software tools in order to conduct repeatable distribution analyses: Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations, and JADE for parallelizing jobs on HPC clusters. A case study of hosting capacity estimation for SMART-DS SFO 2000+ synthetic feeders is used to demonstrate the capability of the developed framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, through which the overall upgrade cost can be better planned in order to integrate more PV in the future.

distributed energy resources↗
DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗