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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Deep Learning enabled spectral energy conversion for in situ exposure measurements

A detector-specific deep learning (DL) approach is presented for spectra-to-exposure conversion using large-format sodium iodide (NaI(Tl)) detectors deployed for in situ environmental radiation measurements in emergency response scenarios. Accurate determination of exposure from NaI spectra is challenging due to poor energy resolution, partial energy absorption, and the strong sensitivity of traditionally deployed analytical conversion methods to calibrated source geometry and pre-deployment assumptions. Here, to address these limitations, a multi-layer perceptron model was trained on a hybrid in situ /Monte Carlo dataset constructed to span a broad range of photon energies, spatial extents, and realistic deployment variability, representative of general in situ emergency response conditions. The DL model was evaluated against commonly fielded analytical approaches under matched simulation conditions, including a single-factor method, a G-function method, and a modeled pressurized ion chamber (PIC) baseline. This study was intentionally computational in scope to enable controlled, like-for-like comparisons between conversion techniques while minimizing confounding real-world variability. Comparison to the modeled PIC provides contextual benchmarking and is not intended as a field inter-comparison with deployed instruments. Across the evaluated 20 keV to 3 MeV energy range, the DL approach consistently exhibited higher accuracy and reduced variance relative to the analytical methods against a deterministically calculated exposure. This may indicate improved robustness to spectral complexity without reliance on source-, geometric-, or spectral region-specific optimization. While results do not represent real-world validation, the presented work demonstrates that deep learning may effectively learn the nonlinear detector response-to-exposure relationship for asymmetric NaI(Tl) detectors and offers a promising pathway for improving in situ exposure estimation using spectroscopic systems already integrated into initial real-time emergency response operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Impact of undoped substrates on high performance silicon solar cells

Today's highest-efficiency silicon solar cells typically operate near the threshold between low-level and high-level injection. It is not well understood if pushing further into a regime in which the cell operating point is solidly in high-level injection at all times of the day has further benefits for the solar cell performance. From a reliability perspective, cells fabricated on lower doped silicon have a larger breakdown voltage. This advantage can affect the design of modules allowing higher voltages and a relaxation of the number of bypass diodes needed. In this project, we present a comprehensive assessment, both experimental and using simulation, of how bulk resistivity, light intensity, and operation temperature impact the performance of the silicon solar cell. This work incorporates a comprehensive device physics analysis assisted by numerical simulation. The source code is now available under General Public License (GPL3), and to further leverage the findings of this project and outreach people outside of the scientific community, we are working with www.pveducation.org (which receives >1 million visitors a year) to create interactive content using our simulation results. The simulation results indicate that high bulk resistivity wafers (>>10 Ωcm) require bulk Shockley-Read-Hall (SRH) lifetimes in the millisecond range to outperform wafers with standard bulk resistivities (<10 Ωcm). Additionally, above bulk resistivities of 10 Ωcm (the exact value depends on the bulk characteristics of the wafer), the cell efficiency is weakly dependent on the bulk resistivity. As a result, ingot manufactures may have an opportunity to further reduce wafer cost by growing higher resistivity ingots that are more tolerant to resistivity variations. This project is particularly relevant today, as solar cell architectures with improved surface passivation and milliseconds lifetimes wafers are commercially available, leveraging potential benefits of using higher bulk resistivities. Outside of interdigitated back contact (IBC) cell, reported studies on high resistivity silicon (>100 Ωcm) are very limited. To the best of our knowledge, this project provides for the first-time experimental insight on solar cells fabricated on wafers with bulk resistivities up to several thousand Ωcm, delivering a comprehensive vision of their performance under real-world temperature and light intensity operation conditions. We manufactured and characterized solar cells with bulk resistivities in the range of 1 Ωcm to >15k Ωcm. Under standard testing conditions (STC), we measured solar cells efficiencies over 20% over the entire range of bulk resistivities, using our baseline cell processing. To evaluate the cell performance in real-world operation conditions, the solar cells were measured at different temperatures (25-80°C) and at different light intensities (0.1-1 suns). The measurements show that the bulk resistivity does not impact the solar cell response to temperature and light intensity. Similar thermal coefficients (TC) were measured for standard and high bulk resistivities, and they are comparable with the TC values reported in the literature for standard bulk resistivities <10 Ωcm. After light soaking, the solar cell didn’t show signs of light-induced degradation (LID). This result was expected since n-type float zone (FZ) wafers were used in this work, i.e. low traces of boron and low concentration of oxygen (oxygen is typically found in the seed end of Czochralski (CZ) ingots). We measured for high bulk resistivities (>10 Ωcm) extremely high breakdown voltages (>1000V). In conclusion, the insight provided by this project can positively impact the levelized cost of energy (LCOE) of the photovoltaic systems through its effect on cell and ingot manufacturing yield, silicon cell power output, and module reliability.

14 SOLAR ENERGY↗
DOE OSTI2023

EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.

Suryanarayana, Harish↗
DOE OSTI2023

Practical Guide to Chemometric Analysis of Optical Spectroscopic Data

The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS), as well as the univariate Beer’s law method have been described and compared, building students’ understanding by starting with the univariate method and progressing step by step into the multivariate methods. Equations for the production of regression vectors from training set spectral data is described and their use demonstrated for the prediction of constituent concentrations on a separate validation set of spectra. Extreme care is taken to ensure consistency in variable formatting of data matrices. This provides a key foundation to understanding how spectral data are manipulated using these different mathematical approaches for building quantitative regression models. Each method is applied to a real-world data set, and the results are discussed to show students the types of information that can be gleaned from each method. A training set comprised of 20 infrared absorbance spectra containing 3 constituents (benzene, polystyrene, and gasoline) of known composition are used to demonstrate the matrix operations for each regression method. A separate set of 12 real-world napalm samples (containing benzene, polystyrene and gasoline) are used as a validation set to demonstrate the ability to utilize the regression models on an unknown dataset. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supplemental Information file and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The datasets of the infrared spectra are also supplied, allowing users to build and inspect the chemometric models on their own. Finally, the Toolbox includes scripts to assist users in loading their own datasets into MATLAB and performing CLS, PCR, and PLS on their data.

Upper-Division Undergraduate, Analytical Chemistry↗
DOE OSTI2020

Cooperative lane control application for fully connected and automated vehicles at multilane freeways

Advances in communications technology have made the development of connected and automated vehicle (CAV) applications that may potentially replace traditional, gantry operated lane control signals (LCSs) possible. Here, we develop the concept of a prototype CAV-enabled LCS application and provides a preliminary assessment of the potential improvement it offers over traditional LCS. Real-world data obtained from an LCS on I-66 in Northern Virginia was used to calibrate and validate a baseline simulation model. Further, the current lane control scenarios utilized in the Northern Virginia LCS were identified and relevant data resulting from implementation of those scenarios were collected to model the LCS in a simulation environment. The performance of real-world LCS was then compared to a prototype CAV-enabled LCS application developed in this research. The CAV-enabled LCS application consistently outperformed the traditional LCS with increased throughput and speeds. On average, an increase of 18.4%, 9.6% and 12.8% in throughput was observed for three selected scenarios under the best case of a 1 sec headway. Furthermore, the CAV-enabled LCS application was also found to reduce volatility, represented by variation in acceleration and deceleration regimes, by an average of 25.6% and 49.6%. The reduction in turbulence in the traffic stream may indicate potential improved safety with the CAV system.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1990

Stereopsis cueing effects on hover-in-turbulence performance in a simulated rotorcraft

The efficacy of stereopsis cueing in pictorial displays was assessed in a real-time piloted simulation experiment of a rotorcraft precision hover-in-turbulence task. Seven pilots endeavored to maintain a hover by visually aligning a set of inner and outer wickets (major elements of a real-world pictorial display, thus attaining the desired hover position, in a full factorial experimental design. The display conditions examined included the presence or absence of a velocity display element (a velocity head-up display) as well as the stereopsis cueing conditions, which included non-stereo (binoptic or monoscopic - no depth cues other than those provided by a perspective, real-world display), stereo 3-D, and hyper stereo (telestereoscopic). Subjective and objective results indicated that the depth cues provided by the stereo displays enhanced the situational awareness of the pilot and enabled improved hover performance to be achieved. The velocity display element also improved the hover performance, with the best hover performance being achieved with the combined use of stereo and the velocity display element. Pilot control input data revealed that less control action was required to attain the improved hover performance with the stereo displays.

Parrish, Russell V.↗
NASA NTRS2023

Robustness of Observing System Simulation Experiments

Observing System Simulation Experiments (OSSEs) are used to investigate the potential performance of proposed new instruments on numerical weather prediction (NWP). As OSSEs involve a framework in which the atmosphere and observations are all completely simulated, it is necessary to perform validation of the OSSE to ensure that it is sufficiently realistic to provide useful experimental results. A common issue that affects the forecast skill and observation impacts is the tendency of OSSEs to have insufficient model error compared to NWP in the real world. In this work, two versions of the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) NWP OSSE framework are compared, with the newer framework having more simulated forecast model error than the older framework due to changes to the NWP system. The performance of the updated OSSE is validated against corresponding behavior of the same NWP system in the real world in terms of the simulated observations, the analysis increments, forecast error, and observation impacts. OSSE results of analysis and forecast impacts for three proposed new observation systems are also compared between the older and newer frameworks to evaluate the robustness of the OSSE experiments and the role of model error in observation impacts. These three new instruments are the Geostationary eXtended Observations (GeoXO) hyperspectral infrared sounder (GXS), the Midwave Infrared Sounding of Temperature and humidity in a Constellation for Winds (MISTiC Winds) atmospheric motion vectors, and additional Global Navigation Satellite System Radio Occultations (GNSS-RO).

Observing System Simulation Experiments↗
DOE OSTI2025

Deformable phrase level attention: A flexible approach for improving AI based medical coding

Objective: Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel attention mechanism designed to enhance text classification models and ensure appropriate classification of medical concepts in unstructured electronic health records. Materials and Methods: We developed a deformable, phrase-level attention mechanism to identify important lexical word-level and contextual phrase-level information from clinical text documents. We evaluated conventional and transformer-based deep learning models that we extended with our attention mechanism on the extraction of critical cancer information (e.g., site, subsite, laterality, histology, behavior) from 629,908 electronic pathology reports and on the automated medical encoding of 52,722 hospital discharge summaries. Results: Transformer-based models with the deformable, phrase-level attention mechanism achieved the best performance on the extraction of critical cancer information from pathology reports. Conventional- and transformer-based models show similar or better performance than their baseline counterparts on the automated medical encoding of clinical documents. Discussion: The addition of phrase-level information allowed models extended with our proposed method to outperform standard word-level attention. Our method showed favorable properties for the real-world application in terms of model robustness and phenotyping. These results indicate that our method is promising for automated data harmonization for common data models. Conclusion: This work proposes a novel deformable, phrase-level attention mechanism that enhances text classification models in the extraction of medical concepts from clinical text documents. We demonstrate strong performances on two clinical text datasets and showcase real-world deployability of our method.

Automated medical encoding↗
DOE OSTI2021

Reliability and Power Degradation Rates of PERC Modules Using Differentiated Packaging Strategies and Characterization Tools

The reliability, durability and lifetime performance of passivated emitter, rear cell (PERC) modules used in real-world PV power plants is a critical challenge underlying the rapid adoption and bankability of these PERC cells, whose high efficiency help reduce the levelized cost of electricity (LCOE). We propose a degradation-science study of PERC module degradation pathways, benchmarking them relative to known degradation mechanisms and pathways of the incumbent aluminum back surface field (Al-BSF) modules exposed to real-world and accelerated exposure conditions.

14 SOLAR ENERGY↗
DOE OSTI2023

Universal Fourier Attack for Time Series

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Real-Time Ecodriving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programing

Connected and automated vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the ecodriving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multilayer model predictive control (MPC) approach for improving the fuel economy of a 48 V mild-hybrid powertrain in a connected vehicle environment. Approximate dynamic programing (DP) is used to solve the receding horizon optimal control problem, whose terminal cost is approximated with the base policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Automation & Control Systems↗
DOE OSTI2024

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

Integrated human-machine intelligence in space systems

The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.

Boy, Guy A.↗
NASA NTRS2007

Educational Experiences of Embry-Riddle Students through NASA Research Collaboration

NASA's educational programs benefit students while increasing the overall productivity of the organization. The NASA Graduate Student Research Program (GSRP) awards fellowships for graduate study leading to both masters and doctoral degrees in several technical fields, while the Cooperative Education program allows undergraduate and graduate students the chance to gain work experience in the field. The Mission Analysis Branch of the Expendable Launch Vehicles Division at NASA Kennedy Space Center has utilized these two programs with students from Embry-Riddle Aeronautical University to conduct research in modeling and developing a parameter estimation method for spacecraft fuel slosh using simple pendulum analogs. Simple pendulum models are used to understand complicated spacecraft fuel slosh behavior. A robust parameter estimation process will help to identiFy the parameters that will predict the response fairly accurately during the initial stages of design. NASA's Cooperative Education Program trains the next wave of new hires while allowing graduate and undergraduate college students to gain valuable "real-world" work experience. It gives NASA a no risk capability to evaluate the true performance of a prospective new hire without relying solely on a paper resume, while providing the students with a greater hiring potential upon graduation, at NASA or elsewhere. In addition, graduate students serve as mentors for undergrad students and provide a unique learning environment. Providing students with a unique opportunity to work on "real-world" aerospace problems ultimately reinforces their problem solving abilities and their communication skills (in terms of interviewing, resume writing, technical writing, presentation, and peer review) that are vital for the workforce to succeed.

Schlee, Keith↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1976

Visual space perception on a computer graphics night visual attachment

A series of experiments was conducted to compare five psychophysical methods of measuring how people perceive visual space in simulators. Psychologists have used such methods traditionally to measure visual space perception in the real world. Of the five tasks - objective-size judgments, angular-size judgments, shape judgments, slant judgments, and distance judgments - only the angular-size judgment task proved to be of potential use as a measure of simulator realism. In this experiment pilots estimated the relative angular size of triangles placed at various distances along a simulated runway. Estimates made when the display was collimated were closer to real-world performance than estimates made with an uncollimated display.

Palmer, E.↗