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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Automating Microbial Directed Evolution For Bioengineering Applications

From a micro-biology perspective, directed evolution is a technique that uses controlled environmental pressures to select for a desired phenotype. Directed evolution has the distinct advantage over rational design of not needing extensive knowledge of the genome or pathways associated with a microorganism to induce phenotypes. However, there are currently limitations to the applicability of this technique including being time-consuming, error-prone, and dependent on existing assays that may lack selectivity for the given phenotype. The AADEC (Autonomous Adaptive Directed Evolution Chamber) system is a proof-of-concept instrument to automate and improve the technique such that directed evolution can be used more effectively as a general bioengineering tool. A series of tests using the automated system and comparable by-hand survival assay measurements have been carried out using UV-C radiation and Escherichia coli cultures in order to demonstrate the advantages of the AADEC versus traditional implementations of directed evolution such as random mutagenesis. AADEC uses UV-C exposure as both a source of environmental stress and mutagenesis, so in order to evaluate the UV-C tolerance obtained from the cultures, a manual UV-C exposure survival assay was developed alongside the device to compare the survival fractions at a fixed dosage. This survival assay involves exposing E. coli to UV-C radiation using a custom-designed exposure hood to control the flux and dose. Surviving cells are counted then transferred to the next iteration and so on for several iterations to calculate the survival fractions for each exposure iteration.This survival assay primarily serves as a baseline for the AADEC device, allowing quantification of the differences between the AADEC system over the manual approach. The primary data of comparison is survival fractions; this is obtained by optical density and plate counts in the manual assay and by optical density growth curve fits pre- and post-exposure in the automated case. This data can then be compiled to calculate trends over the iterations to characterize increasing UV-C resistance of the E.coli strains. The observed trends are statistically indistinguishable through several iterations from both sources.

Bioengineering↗
NASA NTRS2020

An Enhanced Autonomy Approach to Automated Trajectory Negotiation

Improved real-time decision-making capabilities in both civil transport avionics and airline dispatcher workstations are helping to improve commercial aviation operations in many ways. One emerging capability is the automated derivation of alternate flight plans that yield both flight cost savings and smoother workflow. This emerging capability may be complemented with automated trajectory negotiation capabilities. Here we explore such automated trajectory negotiation capabilities, potential benefit mechanisms, and the required tools, procedures, and architectures.

Trajectory↗
NASA NTRS1997

ARTSN: An Automated Real-Time Spacecraft Navigation System

ARTSN, the Automated Real-Time Spacecraft Navigation system is a real time system for the automated navigation of interplanetary spacecraft. The system processes the real time DSN radio metric data flow and generates updated spacecraft solutions and the associated covariance. ARTSN is designed modularly with the user interface input and output separated from the data analysis modules. Designed for distributed application, the system provided, for the first time, an automated method of processing radio metric navigation data for interplanetary spacecraft.

ARTSN↗
NASA NTRS2021

Vertiport Automation Software Architecture and Requirements

The purpose of Vertiport Automation Software Architecture and Requirements was developed to mature the Vertiport Automation System (VAS) described in the High-Density Automated Vertiport ConOps, into a set of artifacts that enable the development of a VAS prototype. The scope of document was to develop: (1) A VAS Software Architecture diagram that logically organizes VAS functionality into specific software components; (2) VAS Software Trade Study identifying existing commercial products and NASA research that can be used in part or with modifications to fulfill VAS functionality. (3) A set of VAS Functional Requirements which decompose the VAS concept into software functionality, including any interfaces and data flows needed; (4) A set of VAS Test Approaches describing a methodology for verification of the VAS Functional Requirements; and (5) An application user experience (UX) design study describing the general features needed in a VAS user interface.

Advanced Air Mobility↗
NASA NTRS2022

Automation & Autonomy Standards and Guidelines for Space Vehicles

The NASA Office of the Chief Health and Medical Officer (OCHMO) requested HRP conduct a survey of industry and government standards and guidelines for the interaction of automation/autonomy with humans. Guidance for verification testing of human-automation designs was also requested. A rapid response was requested so that the deliverables could be provided as part of a future NASA solicitation for automated systems for HLS. The project began with a broad review and analysis of prior NASA work in this area, as well as external standards related to human interaction with automation/autonomy. The team reviewed approximately 200 documents, and interviewed multiple industry and government subject matter experts (SMEs). “Gold standard” documents were selected for focus and key categories were identified. A cross-walk of standards was created, and common standards and guidelines were selected and rephrased (where necessary) to be information-rich, concise, and usable. Verification guidelines and examples were also developed. The final report includes an introduction to automation and autonomy, selected standards and guidelines, verification method guidance, and research gaps. In addition to the report, the team delivered a list of candidate standards for autonomous vehicles, as well as a list of candidate standards for a future NASA-STD-3001 update on automation/autonomy. The presentation will describe the rapid response project approach and methods, examples of standards and guidelines identified and delivered to OCHMO, and research needed to develop additional automation/autonomy design guidance where gaps currently exist.

automation↗
NASA NTRSDate not supplied

Evaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload

Technological advances have increased the automation of Uncrewed Aerial Vehicles, allowing human operators to manage multiple vehicles at a high-level without the need to understand low-level system behaviors. Previous laboratory studies have explored the relationship between reliability, trust, use of automation and the effects of number of vehicles under supervision on subjective workload. Due to the limitations resulting from the COVID-19 pandemic, in-person laboratory studies are not always possible. Therefore, this work aimed to investigate if remote data collection alternatives, such as Amazon’s Mechanical Turk, can provide comparative results as those obtained in laboratory settings. A study was conducted in the context of small drone operations. As expected, higher reliability led to higher trust ratings and the inclusion of more vehicles led to higher workload. In contrast, reliability unexpectedly had no effect on intention to use the automation. Though these results were encouraging, several limitations were identified.

trust↗
NASA NTRSDate not supplied

Evaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload

Introduction - Paradigm shift from one operator supervising a single vehicle, to an operator supervising multiple highly automated vehicles (One-to-Many) - One-to-Many application - Search and Rescue - Foraging - Military ops - Etc. - Calibrated trust in automation enables human operators to effectively manage highly automated vehicles - Past studies show that trust mediates relationship between reliability and dependence (Chancey et al., 2017; Chancey et al., 2015) - Future studies needed to further understand relationship

trust↗
NASA NTRSDate not supplied

Evaluation of Novel eVTOL Aircraft Automation Concepts

As new electric propulsion technologies have matured, many new electric propulsion Vertical Takeoff and Landing (eVTOL) aircraft concepts have been proposed. These new aircraft concepts enable the creation of new aviation markets including Urban Air Mobility (UAM) but also pose new challenges in safety assurance. One early challenge is the evaluation of the diverse aircraft configurations and accompanying advanced control systems and automation designed to aid controllability. This paper describes a research activity to propose candidate means of evaluation for the aircraft concepts and automation in the context of UAM operations. The research activity proposes the adaptation of an evaluation design standard used by the military for advanced rotorcraft along with proposed descriptions and definitions to support evaluation of diverse automated concepts in the civilian eVTOL community.

AAM↗
NASA NTRS2023

Automated Fiber Placement Through Thickness Defect Stacking Optimization

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.

AFP↗
DOE OSTI2024

In‐mold rheology and automated process control for injection molding of recycled polypropylene

Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Krantz, Joshua↗
DOE OSTI2024

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

nmRanalysis: An Open-Source Web Application for Semi-automated NMR Metabolite Profiling

Though data acquisition and initial signal pre-processing of nuclear magnetic resonance (NMR) spectra have achieved high degrees of automation, downstream processing - specifically the profiling of spectra - has bottlenecked the overall NMR analysis workflow. Several efforts have been made to mitigate this bottleneck, but these solutions often trade an increase in automation for limitations elsewhere. Here, in this technical note, we introduce nmRanalysis, a user-friendly web-application that integrates the strengths of existing profiling tools for a more automated profiling workflow. nmRa-nalysis additionally incorporates novel features, including a machine-learning-driven recommender system for me-tabolite identification, further increasing the utility of nmRanalysis over the individual tools that it incorporates.

Flores, Javier E. [Pacific Northwest National Labo↗
DOE OSTI2025

Catalight─An Open-Source Automated Photocatalytic Reactor Package Illustrated through Plasmonic Acetylene Hydrogenation

An open-source and modular Python package, Catalight, is developed and demonstrated to automate (photo)catalysis measurements. (Photo)catalysis experiments require studying several parameters to evaluate performance, including the temperature, gas flow rate and composition, illumination power, and spectral profile. Catalight orchestrates measurements over this complicated parameter space and systematically stores, analyzes, and visualizes the results. To showcase the capabilities of Catalight, we perform an automated apparent activation barrier measurement of acetylene hydrogenation over a plasmonic AuPd catalyst on an Al 2 O 3 support, simultaneously varying laser power, wavelength, and temperature in a multiday experiment controlled by a simple Python script. Our chemical results unexpectedly show an increased activation barrier upon light excitation, contrary to previous findings for other plasmonic reactions and catalysts. We show that the reaction rate order with respect to both acetylene and hydrogen remains unchanged upon illumination, suggesting that molecular surface coverage is not changed by light. By analyzing the inhomogeneity of the laser-induced heating, we attribute these results to a partial photothermal effect combined with a photochemical/hot electron-driven mechanism. In conclusion, our findings highlight the capabilities of a new experiment automation tool; explore the photocatalytic mechanism for an industrially relevant reaction; and identify systematic sources of error in canonical photocatalysis experimental procedures.

Catalysts↗
DOE OSTI2026

Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning

Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2025

Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. Furthermore, these innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Traffic Smoothing Using Explicit Local Controllers: Experimental Evidence for Dissipating Stop-and-go Waves with a Single Automated Vehicle in Dense Traffic

This article presents experimental evidence of the ability of a single automated vehicle acting as a controller to effectively dissipate stop-and-go waves in real traffic. The automated vehicle succeeded in stabilizing the speed profile by reducing oscillations in time and speed variations between vehicles during rush hour on I-24 in the Nashville area. Here, we detail the control design, deployment and results obtained in this experiment, conducted as part of the CIRCLES consortium’s “MegaVanderTest” 2022, which involved a total of 100 automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: DAE Solvers and Automation

Existing electromagnetic transient (EMT) simulation tools face challenges in accelerating EMT simulations, especially for very large-scale power networks. To tackle this issue, next generation EMT simulation tools such as RE-INTEGRATE EMT are being researched upon. Such tools should be equipped with automation capabilities and advanced numerical differential-algebraic equation (DAE) solvers. In this paper, the DAE solvers incorporated within the RE-INTEGRATE EMT simulation tool are discussed. In particular, a modified ODEINT-based DAE solver and the ARKODE solver from SUN-DIALS are leveraged within RE-INTEGRATE EMT. In addition, the automation implemented within RE-INTEGRATE EMT to automate the DAE generation (replacing the need of manual discretization and assembling DAEs) is discussed. Different use cases were implemented using the RE-INTEGRATE EMT tool and were validated with respect to baseline simulations.

Marthi, Phani Ratna Vanamali [ORNL] (ORCID:0000000↗