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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Will the Measurement Robots Take Our Jobs? An Update on the State of Automated M&V for Energy Efficiency Programs

Trustworthy savings calculations are critical to convincing regulators of both the cost-effectiveness of energy efficiency program investments and their ability to defer supply-side capital investments. Today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of energy efficiency programs. They also require time-consuming data acquisition. A spectrum of savings calculation approaches is used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data. The rising availability of “smart” meters and devices that report near-real time data, combined with new analytical approaches to quantifying savings, offers potential to conduct M&V more quickly and at lower cost, with comparable or improved accuracy. Commercial energy management and information systems (EMIS) technologies are beginning to offer M&V capabilities, and program administrators want to understand how they might assist programs in quickly and accurately measuring energy savings. This paper presents the results of recent testing of the ability to use automation to streamline some parts of M&V. Here in this paper, we detail metrics to assess the performance of these new M&V approaches, and a framework to compute the metrics. We also discuss the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. Finally we discuss the potential evolution of M&V and early results of pilots currently underway to incorporate M&V automation into ratepayer-funded programs and professional implementation and evaluation practice.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Safety Impacts of Automated Vehicles in Mixed Traffic

Previous research have shown that Automated Vehicles (AV) have the potential to improve transportation thanks to the possibility to perform some control functions without driver input (1). A question that remains is what are the safety impacts of the interaction between human-driven vehicles with different levels of automation at a particular intersection?The main objective of this paper is to build the behavioral framework to simulate different levels of automation and to study the safety impact of these levels under different penetration scenarios. Real-life connected vehicle data were incorporated to revise the Wiedemann car-following model and simulate the behavior of human-driven vehicles, level 3 and level 5 AVs.

Ramin, Arvin↗
DOE OSTI2019

Evaluating Safety with Automated Vehicles at Signalized Intersections: Application of Adaptive Cruise Control in Mixed Traffic

Automated Vehicles (AV) can potentially improve the performance of transportation system by reducing human errors and enhancing mobility and safety. In lower levels of automation, humans are still controlling the vehicle and receiving some advisory information regarding their surrounding environment. This paper investigates the safety impact of low and high levels of AVs, and their interaction with conventional human-driven vehicles at intersections. In order to enhance our understanding of the future interactions between conventional vehicles with AVs, we developed a framework to simulate the mixed traffic environment. Different market penetration scenarios are simulated using VENTOS (VEhicular NeTwork Open Simulator) software. A modified Wiedemann car-following model was calibrated to represent the behavior of human drivers of both conventional and low-level AVs, while an Adaptive Cruise Control model was used to represent high-level AVs. Notably, this study investigates purely the automation improvement in the system. We modified the acceleration and deceleration regimes of the Wiedemann model and tuned it by harnessing real-world connected vehicle data. Next, the simulation was calibrated utilizing two measures of driving volatility to ensure it closely represents the volatility measures of the real-world data. To evaluate the safety performance of a representative intersection under different scenarios, the number of longitudinal conflicts and driving volatility are used as surrogate safety indicators. The results reveal that increase in market penetration rate of LAVs and HAVs substantially improves intersection safety performance by reducing the number of conflicts and driving volatility.

Arvin, Ramin↗
DOE OSTI2024

Improving and Automating Building Model Data Exchange

There are many instances throughout a project’s lifecycle where there arises a need for quick and accurate risk assessment of building designs. For example, an unexpected design change during construction may necessitate structural engineers to perform a seismic risk assessment on analytical models of the updated building design using high fidelity structural analysis software, such as ANSYS or Abaqus. However, the efficiency of such workflows often depends upon the interoperability of architectural design software and structural analysis software. When the quality of this interoperability is lacking or even non-existent, the efficiency of virtual engineering workflows is hampered, which increases project costs. A McGraw Hill industry survey of professional users of Building Information Modeling (BIM) technologies found that there is high demand for BIM interoperability for structural analysis, but that the value/difficulty ratio is currently too low for practical use. There have been efforts by the academic community to facilitate model data exchange between the architectural design and structural analysis domains, but such solutions have not been widely adopted by industry, face technical challenges, and oftentimes are limited in applicability for users of various BIM software. Therefore, INL is developing capabilities to improve, automate, and generalize model data exchange between architectural BIM software (e.g., Revit) and structural analysis software (e.g., SAP2000, ANSYS). The goal is to help expedite and automate as much of the pre-processing step for creating analytical models in finite element analysis software as reasonably as possible. Such a "BIM-to-FEA" conversion tool should provide direct benefit to end-users through accuracy, automation, quick turn-around, and wide applicability. To generalize the application of this BIM-to-FEA conversion tool and increase its useability among the many different commercial BIM software currently used by industry, the program is being developed with the concept of openBIM. OpenBIM is the application of non-proprietary, open data standards that allow for BIM model data exchange in a format that is accessible, retainable, and useable for all users. The most widely used open, non-proprietary data exchange format for BIM is the Industry Foundation Classes (IFC) schema. IFC is developed by buildingSMART international and is ISO certified (ISO 16739-1:2018). The BIM-to-FEA conversion tool is being developed for compatibility with typical commercial building designs of steel framed structures. The tool is currently capable of importing architectural BIM data of framed building structures, recognizing and extracting the aspects of the model that are required for structural analysis, adjusting the connectivity of frame members, and finally exporting to an analytical model stored in the IFC format. The exported IFC analytical model can then be imported into various openBIM compliant software, such as SAP2000. Such capabilities have already been tested on commercial software, as shown above, and continue to be improved. Work is underway to test the conversion on various commercial BIM software, develop a user-friendly interface, incorporate the program into the broader DeepLynx data warehouse project being developed by INL, and to eventually open-source the tool for the benefit of the community. Future development of the tool envisions the ability for efficient iterative risk assessment of generative building designs, all within a workflow utilizing open-source tools. One such open-source tool will be MOOSE, an advanced finite element analysis tool developed at INL. The conversion tool will also branch out from typical commercial building designs and will aim to incorporate nuclear construction. The aim will be to convert both structural and non-structural components of nuclear facilities, such as curved concrete containment structures and piping systems, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2020

Use of Shared Automated Vehicles for First-Mile Last-Mile Service: Micro-Simulation of Rail-Transit Connections in Austin, Texas

Shared fleets of fully automated vehicles (SAVs) coupled with real-time ride-sharing to and from transit stations are of interest to cities and nations in delivering more sustainable transportation systems. By providing first-mile last-mile (FMLM) connections to key transit stations, SAVs can replace walk-to-transit, drive-to-transit, and drive-only trips. Using the SUMO (Simulation of Urban MObility) toolkit, this paper examines mode splits, wait times, and other system features by micro-simulating two fleets of SAVs providing an FMLM ride-sharing service to 10% of central Austin’s trip-makers near five light-rail transit stations. These trips either start or end within two geofenced areas (called automated mobility districts [AMDs]), and travel time and wait time feedbacks affect mode choices. With rail service headways of 15?min, and 15 SAVs serving FMLM connections to and from each AMD, simulations predict that 3.7% of the person-trip-making will shift from driving alone to transit use in a 3?mi?×?6?mi central Austin area. During a 3-h morning peak, 30 SAVs serve about 10 person-trips each (to or from the stations), with 3.4?min average wait time for SAVs, and an average vehicle occupancy of 0.74 persons (per SAV mile-traveled), as a result of empty SAV driving between riders. Sensitivity analysis of transit headways (from 5 to 20?min) and fleet sizes (from 5 to 20 vehicles in each AMD) shows an increase in FMLM mode share with more frequent transit service and larger fleet size, but total travel time served as the biggest determinant in trip-makers’ mode share.

47 OTHER INSTRUMENTATION↗
NASA NTRS2010

SCDU Testbed Automated In-Situ Alignment, Data Acquisition and Analysis

In the course of fulfilling its mandate, the Spectral Calibration Development Unit (SCDU) testbed for SIM-Lite produces copious amounts of raw data. To effectively spend time attempting to understand the science driving the data, the team devised computerized automations to limit the time spent bringing the testbed to a healthy state and commanding it, and instead focus on analyzing the processed results. We developed a multi-layered scripting language that emphasized the scientific experiments we conducted, which drastically shortened our experiment scripts, improved their readability, and all-but-eliminated testbed operator errors. In addition to scientific experiment functions, we also developed a set of automated alignments that bring the testbed up to a well-aligned state with little more than the push of a button. These scripts were written in the scripting language, and in Matlab via an interface library, allowing all members of the team to augment the existing scripting language with complex analysis scripts. To keep track of these results, we created an easily-parseable state log in which we logged both the state of the testbed and relevant metadata. Finally, we designed a distributed processing system that allowed us to farm lengthy analyses to a collection of client computers which reported their results in a central log. Since these logs were parseable, we wrote query scripts that gave us an effortless way to compare results collected under different conditions. This paper serves as a case-study, detailing the motivating requirements for the decisions we made and explaining the implementation process.

Automation↗
NASA NTRS2019

Command and Control System Automated Testing

The Kennedy Space Center (KSC) has developed its own Command and Control System for the launch of the Space Launch System (SLS) and Orion capsule. The Command and Control System (CCS) is used by console engineers for the launch and system checkout of aerospace vehicles. The CCS allows console engineers to read data from the flight hardware on the launch pad and from the ground control systems and allows console engineers to issue commands, like opening a valve, to the flight hardware and ground control systems. The CCS needs to interact with thousands of devices and hardware controllers for the spacecraft and ground systems, receive data from these devices, distribute the data to console engineers in real-time, and allow console engineers to issue commands to manipulate hardware on the launch pad. The system needs to be robust, fault tolerant, responsive, and fast. In order to keep up with the pace of development of the CCS, a Test Automation System (TAS) is needed to validate the integrity of the system as a whole along with its individual components. Automated tests allow for faster development time, since tests can be ran through a Continuous Integration system and allow developers to check their code faster. Currently, the different modules, classes, and functions that make up the CCS are tested at the unit level, and the system level, with all the modules working together. My project was to implement a system for the data protocol layer of the Command Control System to be tested as a complete functional unit, with all of its classes and functions working together, but independent of the other modules of the CCS.

Automated TestingTest Automation↗
NASA NTRSDate not supplied

NAS Automation Enabled Pilot (AEP): AEP-1 Study

This presentation describes work perform under the Advanced Air Mobility project - Automated Flight and Contingency Management subproject on developing industry representative powered lift aircraft and aircraft automation capabilities for the purpose of developing evaluation methods and Means of Compliance.

powered-lift aircraft↗
DOE OSTI2022

Automated Mobility Platforms: A Framework for Versatile, Energy-Efficient Urban Transportation for the 21st Century: Preprint

This paper presents automated mobility platforms (AMPs) as a framework to deliver high-quality urban mobility. AMPs leverage advances in sensing technology developed from automated roadway vehicles to provide opportunities for efficient movement of people and goods in dense urban environments, campuses, and large facilities like airports. In this paper, we layout the concept of AMPs and how they can serve the mobility needs of the population by providing service at the intersection of micromobility (services such as electric scooters or bikes), personal rapid transit, and moving walkways. Several key attributes and justification for the AMPs concept are described, including improvements to accessibility (inherent in design) covering both socioeconomic and disability equity, safety, energy efficiency, cost-effectiveness, urban land and space management, and scalability. We summarize the results from an ongoing national laboratory-sponsored, faculty-supported, student-led investigation into AMPs that includes stakeholder engagement and technical feasibility assessment.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Automated Image Segmentation and Processing Pipeline Applied to X–Ray Computed Tomography Studies of Pitting Corrosion in Aluminum Wires

Understanding pitting corrosion is critical, yet its kinetics and morphology remain challenging to study from X-ray computed tomography (XCT) due to manual segmentation barriers. To address this, an automated pipeline leveraging deep learning for efficient large-scale XCT analysis is developed, revealing new corrosion insights. The pipeline enables pit segmentation, 3D reconstruction, statistical characterization, and a topological transformation for visualization. Here, the pipeline is applied to 87 648 XCT images capturing commercial purity aluminum (1100 Al) wire exposed to sodium chloride (NaCl) salt particles over a period of 122 h. The pipeline achieves complete feature extraction and statistical quantification across the entire XCT dataset, leveraging distributed computing environment for high efficiency. Global growth kinetics such as high-level stepwise sigmoidal volume loss patterns and granular individual pit developments are both captured for 36 detected pits. By combining automation, computer vision, and extensive XCT datasets, this research accelerates precise corrosion assessment to enable materials science discoveries at scale.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Automated Bacterial Identification and Morphological Feature Analysis in Low‐Dose Cryo‐EM Using YOLOv11

Bacteria rapidly adapt to environmental cues through morphological and ultrastructural changes that correlate with physiology and behavior. Cryogenic transmission electron microscopy (cryo‐TEM) can capture these phenotypic changes in near‐native, vitrified states, but manual analysis of low‐dose micrographs is labor intensive and limits throughput. Here, we present an end‐to‐end workflow that combines low‐dose cryo‐TEM imaging with a YOLOv11‐based instance‐segmentation model to automatically identify bacteria and quantify key structural features directly from the micrographs. This workflow enables (i) robust bacterial localization and counting from low‐magnification atlas/montage images, (ii) automated measurements of cell‐envelope (outer–inner membrane) thickness and anisotropy from higher‐magnification views, and (iii) detection and quantification of bacteria–flagella interactions, including overlap length and curvature metrics for interacting versus noninteracting flagella. Using Pantoea sp. YR343 grown under distinct media conditions, we show that the automated measurements agree with manual annotations while substantially reducing analysis time. Together, these tools provide a practical framework for scalable bacterial identification and quantitative phenotyping in low‐dose cryo‐TEM datasets and establish a foundation for extending cryo‐TEM image analysis toward higher‐throughput studies of microbial heterogeneity and biointerfaces.

YOLOv11↗
DOE OSTI2023

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties**

Abstract The study presents an ab‐initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF‐vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first‐principles‐based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange‐correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT‐constrained microkinetic model optimization for other heterogeneously catalyzed systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Automated chemical resonance generation and structure filtration for kinetic modeling

This study discusses efficient and automated methods for constructing a set of representative resonance structures for arbitrary chemical species, including radicals and biradicals, consisting of the elements H, C, O, N, and S. Determining the representative reactive structures of chemical species is crucial for identification of reactive sites and consequently applying the correct reaction templates to generate the set of important reactions during automated chemical kinetic model generation. We describe a fundamental set of resonance pathway types, accounting for simple resonating structures, as well as global approaches for polycyclic aromatic species. Automatically discovering potential localized structures along with filtration to identify the representative structures was shown to be robust and relatively fast. The algorithms discussed here were recently implemented in the Reaction Mechanism Generator (RMG) software. The final structures proposed by this method were found to be in reasonable agreement with quantum chemical computation results of localized structure contributions to the resonance hybrid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automated Laboratory Kilogram-Scale Graphene Production from Coal

The flash Joule heating (FJH) method converts many carbon feedstocks into graphene in milliseconds to seconds using an electrical pulse. This opens an opportunity for processing low or negative value resources, such as coal and plastic waste, into high value graphene. Here, in this work, a lab-scale automation FJH system that allows the synthesis of 1.1 kg of turbostratic flash graphene from coal-based metallurgical coke (MC) in 1.5 h is demonstrated. The process is based on the automated conversion of 5.7 g of MC per batch using an electrical pulse width modulation system to conduct the bottom-up upcycle of MC into flash graphene. This study then compare this method to two other scalable graphene synthesis techniques by both a life cycle assessment and a technoeconomic assessment.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Toolpath generation for automated wind turbine blade finishing operations

Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading-edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5-m blade section, and the results were analyzed in terms of operation speed and accuracy. Finally, future work is discussed to improve the performance of the system.

17 WIND ENERGY↗