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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Overview of NLR Automated Mobility District Implementation Research - Phases I, II, and III. The Convergence of Automation, Electrification, and On-Demand Services: Enabling Resilient Automated Mobility Districts

A research program by the National Renewable Energy Laboratory has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings, referred to as Automated Mobility Districts (AMDs). An AMD emphasizes the deployment of automated vehicles (AV) passenger transport services within a dense urban setting and other major activity centers with intense passenger origin-destination demand patterns, such as those found in large business districts, airports, and university and medical campuses. Phase I and II surveyed 10 early deployment sites and subsequently collected and evaluated the lessons learned from these early deployment sites, with particular attention to fleet operations, impacts of service reliability, and vehicle technology evolution as the field of companies was being progressively winnowed by the challenges of full automation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Optimized anion exchange column isolation of zirconium-89 (89Zr) from yttrium cyclotron target: Method development and implementation on an automated fluidic platform

Zirconium-89 (89Zr), produced by the (p,n) reaction from naturally monoisotopic yttrium (natY), is a promising positron emitting isotope for immunoPET imaging. Its long half-life of 78.4 h is sufficient for evaluating slow physiological processes. A prototype automated fluidic system, coupled to on-line and in-line detectors, has been constructed to facilitate development of new 89Zr purification methodologies. The highly reproducible reagent delivery platform and near-real time monitoring of column effluents allows for efficient method optimization. The separation of Zr from dissolved Y metal targets was evaluated using several anion exchange resins. Each resin was evaluated against its ability to quantitatively capture Zr from a load solution that is high in dissolved Y. The most appropriate anion exchange resin for this application was identified, and the separation method was optimized. The method is capable of a high Y decontamination factor (>105) and has been shown to separate Fe, an abundant contaminant in Y foils, from the 89Zr elution fraction. Finally, the performance of the method was evaluated using cyclotron bombarded Y foil targets. The separation method was shown to achieve >95% recovery of the 89Zr present in the foils. The 89Zr eluent, however, was in a chemical matrix not immediately conducive to labeling onto proteins. The main intent of this study was to develop a tandem column 89Zr purification process, wherein the anion exchange column method described here is the first separation in a dual-column purification process.

Zirconium-89, ImmunoPET, medical isotope, automati↗
DOE OSTI2018

DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs

The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys. The catalogs have diverse applications, such as investigating signatures of fundamental physics in cosmological observables, understanding the effect of systematic uncertainties on measured signals and testing mitigation strategies for reducing these uncertainties, aiding analysis pipeline development and testing, and survey strategy optimization. The list of applications is growing with improvements in the quality of the catalogs and the details that they can provide. Given the importance of simulated catalogs, it is critical to provide rigorous validation protocols that enable both catalog providers and users to assess the quality of the catalogs in a straightforward and comprehensive way. For this purpose, we have developed the DESCQA framework for the Large Synoptic Survey Telescope Dark Energy Science Collaboration as well as for the broader community. The goal of DESCQA is to enable the inspection, validation, and comparison of an inhomogeneous set of synthetic catalogs via the provision of a common interface within an automated framework. Here in this paper, we present the design concept and first implementation of DESCQA. In order to establish and demonstrate its full functionality we use a set of interim catalogs and validation tests. We highlight several important aspects, both technical and scientific, that require thoughtful consideration when designing a validation framework, including validation metrics and how these metrics impose requirements on the synthetic sky catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Interconnection Standard Grid-Support Function Evaluations Using an Automated Hardware-in-the-Loop Testbed

Grid operators are increasingly turning to advanced grid-support functions in distributed energy resources (DER) to assist with distribution circuit voltage regulation, bulk system frequency control, and power system protection. The U.S. DER certification standard, Underwriters Laboratories (UL) 1741, was revised in September 2016 to add test procedures for multiple grid-support functions. Sandia National Laboratories, SunSpec Alliance, and growing community of collaborators have undertaken a multiyear effort to create an open-source system validation platform (SVP) that automates DER interconnection and interoperability test procedures by communicating with grid simulators, photovoltaic (PV) simulators, data acquisition systems, and interoperable equipment under test. However, the power hardware required for generating the test conditions may be untenable for many organizations. In this paper, we discuss development of the SVP testing capabilities for UL 1741 tests utilizing a controller hardware-in-the-loop testbed that precludes the need for power hardware using a 34.5 kW Austrian Institute of Technology smart grid controller. Finally, analysis of normal ramp rate, soft start ramp rate, specified power factor, volt-VAr, and frequency-watt advanced grid functions, and the effectiveness of the UL 1741 test protocols are included.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Safe Operations at Roadway Junctions - Design Principles from Automated Guideway Transit

Herein this paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone "interlocking" at each junction location deconflicts railway traffic, affirming safe passage. The analogous AV protection safety subsystem is defined as fail-safe equipment that monitors roadway intersections and junctions, communicates traffic signal status, perceives and communicates alerts and signals to AV connected vehicles concerning potential unsafe conditions, and performs related primary safety functions. Conclusions are drawn that the AV protection roadway intersection functions must be performed by local roadside equipment dedicated to protecting each roadway intersection and junction. Further, it is concluded that the communications technology connecting the infrastructure with the vehicle to perform this vital, fail-safe protection should meet specific functional and performance criteria.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Vehicle Automation Benefits and Challenges for Passenger Transport System Beyond Automated Driving

The National Renewable Energy Laboratory has been researching the implementation of fully automated passenger transport systems to be operated within dense urban settings, referred to as Automated Mobility Districts, based on roadway vehicle automation as opposed to track or train-based automation. This research, now in its third phase, is presently addressing the full spectrum of benefits and challenges of full automation of passenger transport systems with respect to fleet electrification and the associated multimodal, large fleet operational management, benefits beyond simply automating the driving tasks. The focal points summarized in the paper and presentation address the benefit-analysis of fleet automation to address added complexities imposed on the multi-fleet operational management when there is a simultaneous implementation of an on-demand service mode that connects with and optimizes the effectiveness of legacy transit systems and new sub-regional autonomous vehicle fleets, with specific emphasis on enhanced ability to better meet peak ridership demand. The research also begins to address challenges in operations arising from lack of personnel present to handle unexpected customer and system needs. Combined, this research articulates vehicle automation benefits and challenges beyond simply automating the driving tasks, addressing additional operational benefits automation provides to address the added complexities imposed by electrification and on-demand modes of operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning

Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Optimizing Fleet Operations in Automated Mobility Districts: Serving On-Demand Mobility with Automated Electric Shuttles

On-demand transportation services have seen a dramatic rise in the past decade, thanks to technology. Connected and automated vehicle (CAV) technology holds potential for a major transformation in the on-demand mobility services landscape.The timeline for fully automated vehicles (AVs) to reach the critical market share is still uncertain. In the short term, many cities in the United States and abroad are testing low-speed automated electric shuttles (AES) as a shared on-demand mobility service in geo-fenced regions. An automated mobility district (AMD) is a campus-sized implementation of connected/automated vehicle technology to realize the full benefits of a fully electric automated mobility service within a confined region or district. Building on this concept, an AMD toolkit is under development at the National Renewable Energy Laboratory (NREL) to inform a campus-or district-sized implementation in which automated electric shuttles (AES). This research extends the functionality of the AMD toolkit by developing a mathematical program to optimize the operations of an AES fleet in an on-demand service configuration.

25 ENERGY STORAGE↗
DOE OSTI2018

Will Vehicle Automation Accelerate or Decelerate Electrification: Modeling Demand for Automated Electric Vehicles

Automation may induce more travel demand, posing a threat to energy security and environmental quality unless the induced travel demand is served with more efficient or clean fuel technologies. This study attempts to analyze whether automation may promote or discourage market acceptance of plug-in electric vehicles. A consumer choice model called MA3T-MC is developed to cover automated vehicles by expanding the existing MA3T model that focuses only on fuel type choices. Key findings include: 1) automation may increase sales shares of gasoline conventional vehicles and decrease shares of efficient vehicles including HEVs and PHEVs, due to greater energy cost savings for less efficient vehicles. 2) Automation may increase market shares of BEVs, due to the “free” range extension from the efficiency improvement by automation. 3) consumer heterogeneity is important in quantifying the national impact of automation on sales shares by fuel types and the total energy use.

Lin, Zhenhong↗
DOE OSTI2024

JOINT APPOINTEE: Evolution of ferroelectric properties in SmxBi1-xFeO3 via automated Piezoresponse Force Microscopy across combinatorial spread libraries

Combinatorial spread libraries offer a innovative approach to explore the evolution of material properties over broad concentration, temperature, and growth parameter spaces. However, traditional limitation of this approach is the requirement for the read-out of functional properties across the library. Here we develop automated Piezoresponse Force Microscopy (PFM) for the exploration of combinatorial spread libraries and demonstrate its application in the SmxBi1-xFeO3 system with the ferroelectric-antiferroelectric morphotropic phase boundary. This approach relies on the synergy of the quantitative nature of PFM and the implementation of automated experiments that allow PFM-based sampling over macroscopic samples. The concentration dependence of pertinent ferroelectric parameters has been determined and used to develop the mathematical framework based on Ginzburg-Landau theory describing the evolution of these properties across the concentration space. We pose that a combination of automated scanning probe microscope and combinatorial spread library approach will emerge as an efficient research paradigm to close the characterization gap in the high-throughput materials discovery. We make the data sets open to the community and hope that this will stimulate other efforts to interpret and understand the physics of these systems.

Automated Microscopy, Combinatorial Library, Ferro↗
DOE OSTI2019

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated 'Drive Me' project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Data Trend Shifts Induced by Method of Concentration for Trace Metals in Seawater: Automated Online Preconcentration vs Borohydride Reductive Coprecipitation of Nearshore Seawater Samples for Analysis of Ni, Cu, Zn, Cd, and Pb via ICP-MS

This research compares performance, reproducibility, and detection limits of ambient seawater analysis for trace metals using both borohydride reductive co-precipitation and an automated chelation column (seaFAST™ 2) co-preconcentration for matrix interferent elimination on total and dissolved grab samples in nearshore to marine waters, over a broad concentration range, prior to ICP-MS injection. A move to an online preconcentration method both minimizes sample preparation, and eliminates correction errors when accounting for trace impurities in precipitated samples, induced via reagents. The reproducability of the online preconcentration method described, coupled with low blanks and method detection limits (MDLs), demonstrates an effective move to an automated procedure using ethylenediaminetriacetic and iminodiacetate acid chelation exchange resin and multi-analyte determination via ICP-MS for total and dissolved Ni, Cu, Zn, Cd, and Pb in marine water samples. Average CASS-5 recoveries using the online preconcentration method (n=9) were 109±7%, 104±5%, 103±7%, 101±3%, and 86±8% respectively. The MDLs obtained from the automated method for Ni, Cu, Zn, Cd and Pb were 3.3, 1.8, 13.5, 4, and 10 times lower, respectively, than for the Borohydride method. There were statistically significant differences between the methods for CASS-5 recoveries of Ni, Cu, Zn (p < 0.0001), and Pb (p = 0.0024). Comparison of methods gave high concordance (rC = 0.90) between methods for total and dissolved Ni, Cu, Zn, and Pb, and total Cd.

seaFAST, borohydride, seawater, Ethylenediaminetet↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
DOE OSTI2018

Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

An Automated Ultraclean Ion Exchange Separation Method for the Determinations of 232Th and 238U in Copper using Inductively Coupled Plasma Mass Spectrometry

This work presents a novel automated analytical method developed for high throughput ultrasensitive determinations of Th and U in copper using inductively coupled plasma mass spectrometry (ICP-MS). The method is based on the use of ultra clean sample preparation procedures including the use of a fully automated off-line chromatography system for the extraction and pre-concentration of the analytes prior to ICP-MS analysis. The separation system is equipped with a single reusable chromatographic column that, with the rigorously clean procedures developed herein, provided a low carryover, low background, and reproducible automated separation method. Isotope dilution methods were used for quantitation. Method Detection Limits (MDLs) of 3.7 and 9.4 fg·g-1 were obtained for the quantitation of 232Th and 238U in copper, respectively, corresponding to activities of 0.0148 and 0.116 microBq·kg-1 for 232Th and 238U, respectively. The analytical method provided high and reproducible tracer recoveries. The use of an automated system significantly reduced the extremely tedious active work time required for the chemist (by ca. 80%) relative to manually performing the separation. This method could be adapted to other critical sample matrices requiring the utmost in high throughput clean chemistry procedures for automated ultrasensitive analyses.

Arnquist, Isaac J.↗
DOE OSTI2018

Tandem column isolation of zirconium-89 from cyclotron bombarded yttrium targets using an automated fluidic platform: Anion exchange to hydroxamate resin columns

The development of a tandem column purification method for the production of high-purity 89Zr is presented. The primary column was a macroporous strongly basic anion exchange resin on styrene divinylbenzene co-polymer. The secondary column, with an internal volume of ~27 µL, was packed with hydroxamate resin. A condition of inverted selectivity was developed, whereby the 89Zr eluent from the primary column is synonymous with the load condition on the secondary column. The ability to transfer 89Zr from one column to the next allows two sequential column clean-up methods to be performed prior to the final elution of the 89Zr. This approach assures delivery of high purity 89Zr product and assures a 89Zr product that is eluted in a substantially smaller volume than is possible when using the traditionally-employed single hydroxamate resin column method. The tandem column purification process has been implemented into a prototype automated fluidic system. The system is configured with on-line gamma detection so column effluents can be monitored in near-real time. The automated method was tested using seven cyclotron bombarded Y foil targets. It was found that 95.1±1.3% of the 89Zr present in the foils was recovered in the secondary column elution fraction. Furthermore, elution peak analysis of several 89Zr elution profile radiochromatograms made possible the determination of 89Zr recovery as a function of volume; a 89Zr product volume that contains 90% of the mean secondary column elution peak can be obtained in 0.29±0.06 mL (representing 86±5% of the 89Zr activity in the target). This product volume represents a significant increase in isotope product concentration over the predominant method used in the field. In addition to the reduced 89Zr product elution volume, titrations of the 89Zr product with deferoxamine mesylate salt across two preparatory methods resulted in mean effective specific activity (ESA) values of 279 and 340 TBq·mmole-1 and mean bindable metals concentrations ([MB]) of 13.5 and 16.7 nmole·g-1. These ESA and [MB] values infer that the 89Zr product resulting from this tandem column isolation method has the highest purity reported to date.

immunoPET, zirconium-89, laboratory automation, de↗