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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
NASA NTRS2014

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

Ancel, Ersin↗
NASA NTRS2018

Automated Collision Avoidance (ACA) and Automated Return to Course (ARTC) Requirement and Guidance Review: Final

This report's objectives were to review regulatory and guidance documents to identify requirements and considerations for the design and operation of automated systems that perform the functions of automated collision avoidance (ACA) and/or automated return to course (ARTC). The importance of this work is twofold: 1) to help focus efforts addressing new automated collision avoidance and return to course systems requirements and considerations, and 2) in pulling together available requirements from multiple sources, generate a master resource for these automated system requirements and considerations. Reviewed documents include regulations, industry standards and research papers that specifically address ACA and ARTC systems, other automatedl control and guidance systems, ICAO (International Civil Aviation Organization) and Eurocontrol documents.

Automation↗
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.↗
NASA NTRS1997

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

Broderick, Ron↗
NASA NTRS2000

Situation Awareness and Levels of Automation: Empirical Assessment of Levels of Automation in the Commercial Cockpit

This report has been prepared to closeout a NASA grant to Mississippi State University (MSU) for research into situation awareness (SA) and automation in the advanced commercial aircraft cockpit. The grant was divided into two obligations including $60,000 for the period from May 11, 2000 to December 25, 2000. The information presented in this report summarizes work completed through this obligation. It also details work to be completed with the balance of the current obligation and unobligated funds amounting to $50,043, which are to be granted to North Carolina State University for completion of the research project from July 31, 2000 to May 10, 2001. This research was to involve investigation of a broad spectrum of degrees of automation of complex systems on human-machine performance and SA. The work was to empirically assess the effect of theoretical levels of automation (LOAs) described in a taxonomy developed by Endsley & Kaber (1999) on naive and experienced subject performance and SA in simulated flight tasks. The study was to be conducted in the context of a realistic simulation of aircraft flight control. The objective of this work was to identify LOAs that effectively integrate humans and machines under normal operating conditions and failure modes. In general, the work was to provide insight into the design of automation in the commercial aircraft cockpit. Both laboratory and field investigations were to be conducted. At this point in time, a high-fidelity flight simulator of the McDonald Douglas (MD) 11 aircraft has been completed. The simulator integrates a reconfigurable flight simulator developed by the Georgia Institute of Technology and stand-alone simulations of MD-11 autoflight systems developed at MSU. Use of the simulator has been integrated into a study plan for the laboratory research and it is expected that the simulator will also be used in the field study with actual commercial pilots. In addition to the flight simulator, an electronic version of the Situation Awareness Global Assessment Technique (SAGAT) has been completed for measuring commercial pilot SA in flight tasks. The SAGAT is to be used in both the lab and field studies. Finally, the lab study has been designed and subjects have been recruited for participation in experiments. This study will investigate the effects of five levels of automation, described in Endsley & Kaber's (1999) taxonomy and applied to the MD-11 autoflight system, on private pilot performance and SA in basic flight tasks by using the MD-11 simulator. The field study remains to be planned and executed.

Kaber, David B.↗
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↗
DOE OSTI2023

The Convergence of Vehicle Electrification and Automation - Enabling Sustainable Automated Mobility Districts

The transportation industry is entering a new frontier with three major simultaneous transformations: electrification, automation, and on-demand services. All three directly affect the same spaces of vehicle technology, infrastructure, and system operational performance. Further, each area of change adds its own driving forces of complexity to the others' design challenges. This paper explores these transformations, framing the implications for vehicle technology and infrastructure and their impact on operating concepts and strategies, which in turn impact the sustainability of the transportation infrastructure in the built environment. Specifically, this paper examines battery-electric vehicles operating in a fully automated fleet within an "automated mobility district" (AMD) setting with multiple on-demand services. From this examination, the paper assesses the implications for transportation electrification, comparing and contrasting traditional fixed-route services with those of on-demand transit and automated transit networks, including the trade-offs between vehicle range (battery capacity), charging parameters, power availability, and operations predictability with respect to policy and infrastructure configurations. The paper also addresses the configuration and management of boarding and alighting zones (also called pickup and drop-off zones), and the need for complementary infrastructure intelligence at intersections and curbs. Finally, conclusions are drawn with respect to the system integration and analysis needs for AMDs regarding resilience and sustainability of mobility infrastructure.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Analysis of Automated Transit Network Systems with Battery-Electric Vehicles in Automated Mobility Districts: Preprint

The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory's (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a "jurisdiction having authority," a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL's most recent Phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during Phase I, II, and III, with special emphasis on Phase III results with respect to a descriptive example of the proposed SEM when a "digital twin" analytical model is used to simulate the transport fleet's battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗
DOE OSTI2023

Automated Symbolic Upscaling: 2. Model Generation for Extended Applicability Regimes

Abstract In this second part of the two paper series, we detail an algorithmic procedure for systematically implementing the generalized closure form strategy presented in Part 1. This strategy extends the applicability of homogenized models with respect to classical homogenization theory, as demonstrated in Part 1 where upscaled models are rigorously derived in moderately reactive physical regimes. After encoding the algorithm into Symbolica, an automated upscaling framework, we upscale two reactive mass transport problems and numerically validate the resulting nonlinear homogenized models by showing the absolute error estimates predicted by homogenization theory are satisfied. In both problems, nontrivial closure forms and closure problems are automatically formulated using the encoded strategy with no human interaction, nor prior knowledge regarding the closure required for the systems. We hope these demonstrations spark further interest in automated analytical frameworks for multiscale modeling, as such capabilities are invaluable for generating rigorous multiscale models of complex phenomena in porous media.

Pietrzyk, Kyle↗
DOE OSTI2018

PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency

Genome-scale metabolic reconstructions help us to understand and engineer metabolism. Next-generation sequencing technologies are delivering genomes and transcriptomes for an ever-widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k-mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at , which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species.

Flux Balance Analysis↗
NASA NTRS1990

The 'problem' with automation - Inappropriate feedback and interaction, not 'over-automation'

Automation in high-risk industry is often blamed for causing harm and increasing the chance of human error when failures occur. It is proposed that the problem is not the presence of automation, but rather its inappropriate design. The problem is that the operations are performed appropriately under normal conditions, but there is inadequate feedback and interaction with the humans who must control the overall conduct of the task. The problem is that the automation is at an intermediate level of intelligence, powerful enough to take over control which used to be done by people, but not powerful enough to handle all abnormalities. Moreover, its level of intelligence is insufficient to provide the continual, appropriate feedback that occurs naturally among human operators. To solve this problem, the automation should either be made less intelligent or more so, but the current level is quite inappropriate. The overall message is that it is possible to reduce error through appropriate design considerations.

Norman, D. A.↗
NASA NTRS2014

An Extended Case Study Methoology for Investigating Influence of Cultural, Organizational, and Automation Factors on Human-Automation Trust

This paper discusses a case study that examined the influence of cultural, organizational and automation capability upon human trust in, and reliance on, automation. In particular, this paper focuses on the design and application of an extended case study methodology, and on the foundational lessons revealed by it. Experimental test pilots involved in the research and development of the US Air Forces newly developed Automatic Ground Collision Avoidance System served as the context for this examination. An eclectic, multi-pronged approach was designed to conduct this case study, and proved effective in addressing the challenges associated with the cases politically sensitive and military environment. Key results indicate that the system design was in alignment with pilot culture and organizational mission, indicating the potential for appropriate trust development in operational pilots. These include the low-vulnerabilityhigh risk nature of the pilot profession, automation transparency and suspicion, system reputation, and the setup of and communications among organizations involved in the system development.

automation suspicion↗
NASA NTRS2014

Influence of Cultural, Organizational, and Automation Capability on Human Automation Trust: A Case Study of Auto-GCAS Experimental Test Pilots

This paper discusses a case study that examined the influence of cultural, organizational and automation capability upon human trust in, and reliance on, automation. In particular, this paper focuses on the design and application of an extended case study methodology, and on the foundational lessons revealed by it. Experimental test pilots involved in the research and development of the US Air Force's newly developed Automatic Ground Collision Avoidance System served as the context for this examination. An eclectic, multi-pronged approach was designed to conduct this case study, and proved effective in addressing the challenges associated with the case's politically sensitive and military environment. Key results indicate that the system design was in alignment with pilot culture and organizational mission, indicating the potential for appropriate trust development in operational pilots. These include the low-vulnerability/ high risk nature of the pilot profession, automation transparency and suspicion, system reputation, and the setup of and communications among organizations involved in the system development.

trust↗
DOE OSTI2022

The Automated Mobility District Implementation Catalog, 2nd Edition: Safe and Efficient Automated Vehicle Fleet Operations for Public Mobility

This second volume of the Automated Mobility District (AMD) Implementation Catalog series has been prepared in three parts to assist readers in their review and understanding of the technical information herein. The material may be more easily ingested if each of the three parts is read and then contemplated for its ramifications to the specific interest of the reader before continuing to the next part. Further, each part has been prepared with the intent that it could be read independently from the others. Part 1 is Progress of Automated Vehicle R&D for Deployments in Passenger Service; Part 2 is 10 Early Deployment Sites as Prototypes of AMD Implementation; Part 3 is Five Cardinal Principles for AMD Implementation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Multi-component background learning automates signal detection for spectroscopic data

Abstract Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.

Ament, Sebastian E.↗
DOE OSTI2025

Workflow for Process Automation of Soil Gas Results from an Automated Soil Gas-Sampling System for Application in Carbon Storage Projects

Conference presentation at Geoconvention, Calgary, Alberta, Canada, May 12–14, 2025. The Energy & Environmental Research Center (EERC) developed an automated workflow for processing soil gas measurements collected from the automated soil gas-sampling systems deployed across the project site. Raw soil gas measurements are collected from each station every 4 hours and automatically uploaded to a cloud database. The workflow begins by writing code to download the data to a workstation automatically, then the data are published to an online dashboard that visualizes the measurements in time-series plots and a process-based decision-making framework. This automated workflow accelerates the time from data acquisition to decision-making. It supports carbon storage project operators by preparing and delivering a live, standardized dataset for quick analysis and source attribution to provide assurance of containment and overall permit compliance.

02 PETROLEUM↗