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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Comprehensive Analysis of Real-World Accelerometer Data Quality in a Global Smartphone-based Seismic Network

The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. In addition, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.

58 GEOSCIENCES↗
DOE OSTI2024

Real World Deployments

This brochure provides insight into the electrification deployments in Utah including details, benefits and potential next steps.

Zane, Regan↗
DOE OSTI2026

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1987

Route planning in a four-dimensional environment

Robots must be able to function in the real world. The real world involves processes and agents that move independently of the actions of the robot, sometimes in an unpredictable manner. A real-time integrated route planning and spatial representation system for planning routes through dynamic domains is presented. The system will find the safest most efficient route through space-time as described by a set of user defined evaluation functions. Because the route planning algorthims is highly parallel and can run on an SIMD machine in O(p) time (p is the length of a path), the system will find real-time paths through unpredictable domains when used in an incremental mode. Spatial representation, an SIMD algorithm for route planning in a dynamic domain, and results from an implementation on a traditional computer architecture are discussed.

Slack, M. G.↗
NASA NTRS2012

Expedition Earth and Beyond: Using Crew Earth Observation Imagery from the International Space Station to Facilitate Student-Led Authentic Research

Student-led authentic research in the classroom helps motivate students in science, technology, engineering, and mathematics (STEM) related subjects. Classrooms benefit from activities that provide rigor, relevance, and a connection to the real world. Those real world connections are enhanced when they involve meaningful connections with NASA resources and scientists. Using the unique platform of the International Space Station (ISS) and Crew Earth Observation (CEO) imagery, the Expedition Earth and Beyond (EEAB) program provides an exciting way to enable classrooms in grades 5-12 to be active participants in NASA exploration, discovery, and the process of science. EEAB was created by the Astromaterials Research and Exploration Science (ARES) Education Program, at the NASA Johnson Space Center. This Earth and planetary science education program has created a framework enabling students to conduct authentic research about Earth and/or planetary comparisons using the captivating CEO images being taken by astronauts onboard the ISS. The CEO payload has been a science payload onboard the ISS since November 2000. ISS crews are trained in scientific observation of geological, oceanographic, environmental, and meteorological phenomena. Scientists on the ground select and periodically update a series of areas to be photographed as part of the CEO science payload.

Graff, P. V.↗
NASA NTRS2007

United Space Alliance LLC Parachute Refurbishment Facility Model

The Parachute Refurbishment Facility Model was created to reflect the flow of hardware through the facility using anticipated start and delivery times from a project level IV schedule. Distributions for task times were built using historical build data for SFOC work and new data generated for CLV/ARES task times. The model currently processes 633 line items from 14 SFOC builds for flight readiness, 16 SFOC builds returning from flight for defoul, wash, and dry operations, 12 builds for CLV manufacturing operations, and 1 ARES 1X build. Modeling the planned workflow through the PRF is providing a reliable way to predict the capability of the facility as well as the manpower resource need. Creating a real world process allows for real world problems to be identified and potential workarounds to be implemented in a safe, simulated world before taking it to the next step, implementation in the real world.

Esser, Valerie↗
DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗
NASA NTRS2024

Preventing Camera Clipping Through Varied Terrain and Other Planetary Objects

Within the scope of computer visualization systems and technologies, camera objects allow users to navigate three-dimensional (3D) scenes much like a person would navigate the real world. As such, when one seeks to model a scene or environment based on real-world laws, the camera must often follow these same conventions. Accomplishing this in the world of computer renderings presents unique challenges for defining space, spatial relations, depth, distance, and height. Therefore, movement for a camera must be specifically designated and processed by the program in order to achieve accurate movement representations. Although this thought process involves each aspect of camera movement, one specific aspect of camera movement is the primary focus of this report: ensuring that the camera does not clip through an object and appear subsurface. This is important to mimic real-world movements because people in the real world would not go beneath the solid-surface terrain unless they intended to do so. By working with numerical definitions, equations, and variables, a computer can relate an object to its existence in space. Thus, such challenges in camera movements with computationally rendered 3D spaces can be mitigated.

Kristie O'Brien↗
DOE OSTI2023

Degree-preserving graph dynamics: a versatile process to construct random networks

Real-world networks evolve over time via the addition or removal of vertices and edges. In current network evolution models, vertex degree varies or grows arbitrarily. A recently introduced degree-preserving network growth (DPG) family of models preserves vertex degree, resulting in structures significantly different from and more diverse than previous models. Despite its degree preserving property, the DPG model is able to replicate the output of several well-known real-world network growth models. Simulations showed that many real-world networks can also be constructed from small seed graphs via the DPG process. Here, we start the development of a rigorous mathematical theory underlying the DPG family of network growth models. We prove that the degree sequence of the output of some of the well-known, real-world network growth models can be reconstructed via the DPG process, using proper parametrization. We also show that the general problem of deciding whether a simple graph can be obtained via the DPG process from a small seed (DPG feasibility) is, however, NP-complete. In conclusion, it is an intriguing open problem to uncover whether there is a structural reason behind the DPG-constructability of real-world networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗
DOE OSTI2023

Real-Sim Flexible Interface for X-in-the-loop Simulation (FIXS)

Real-Sim is a multi-resolution X-in-the-loop simulation framework to support inclusive testing and evaluation of CAVs before actual implementation in the real world. Real-Sim framework connects different simulation components and integrates various traffic and vehicle simulation tools, virtual environments, and XIL systems through a Flexible Interface for XIL Simulation (FIXS). Connections among different tools are transparent to users and handled by the interface behind the scenes. Co-simulation of various vehicle and traffic simulation tools with different XIL systems can be easily achieved and become a transparent "plug-and-play" process to users.

Shao, Yunli (0000000185287925)↗
DOE OSTI2020

COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility

Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL’s end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordi- nate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs’ local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.

demand response↗
DOE OSTI2023

Assessing the National Off-Cycle Benefits of 2-Layer HVAC Technology Using Dynamometer Testing and a National Simulation Framework

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory's FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls. Validation of component thermal models and engine efficiency ensured accurate thermal dynamics, fuel consumption, and two-layer benefit. The real-world benefit of the two-layer technology was calculated by simulating the validated powertrain model across a representative test matrix comparing performance with and without the two-layer system. Simulation across the test matrix revealed a real-world representative benefit of 0.0835%. Analysis of test matrix results at the regional level revealed the most benefit in cold climates and rural regions. Mean results across cycle length sensitivity simulations revealed a larger real-world benefit of 0.0872%. These benefit values can be considered a more accurate assessment of real-world technology performance. Future work is planned to explore the requisite number of drive cycles to ensure the full technology benefit is captured.

2-layer↗
DOE OSTI2021

Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge

Extremely low power neuromorphic systems are well-suited for deployment to the edge for many applications. In many use cases of neuromorphic computing for control, a spiking neural network is trained off-line using a simulation and then deployed to a neuromorphic system at the edge, where it will operate without ongoing training or learning. However, it may be desirable to continue training or learning at the edge to refine or adapt to the real-world system. In this work, we propose an approach for performing real-time evolutionary optimization for spiking neural networks for neuromorphic deployment at the edge. In particular, we propose a combination of simulation and real-world evaluations, along with feedback from the real-world environment, to train spiking neural networks for continuous deployment to the edge. We show that the real-time evolution at the edge approach achieves comparable performance to an evolution approach that requires constant evaluation in the realworld environment.

Schuman, Catherine↗
NASA NTRS1978

A method motion simulator design based on modeling characteristics of the human operator

A design criteria is obtained to compare two simulators and evaluate their equivalence or credibility. In the subsequent analysis the comparison of two simulators can be considered as the same problem as the comparison of a real world situation and a simulation's representation of this real world situation. The design criteria developed involves modeling of the human operator and defining simple parameters to describe his behavior in the simulator and in the real world situation. In the process of obtaining human operator parameters to define characteristics to evaluate simulators, measures are also obtained on these human operator characteristics which can be used to describe the human as an information processor and controller. First, a study is conducted on the simulator design problem in such a manner that this modeling approach can be used to develop a criteria for the comparison of two simulators.

Repperger, D. W.↗
DOE OSTI2022

Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

adaptive LQR control↗
DOE OSTI2021

Advancing Platooning with ADAS Control Integration and Assessment Test Results

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world driving conditions. On-highway testing is done on a route in Indiana representing operation of long-haul Class 8 trucks in the United States. The road includes low-, medium-, and high-grade segments. The highway test results of a two-truck platooning configuration indicate considerable fuel-saving reduction comparing to the test track data collected under ideal driving conditions. The test data indicate that platooning could lead to increases in fuel consumption during traffic or high-grade portions of the route, causing reduction of the overall fuel saving on the road comparing to test track results. However, integration of ADAS features on the lead truck during on-road tests leads to significant improvement of fuel saving for both trucks in CACC operation.

ADAS↗