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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Software Defined Cyber-Physical Testbed for Analysis of Automated Cyber Responses for Power System Security

As the power grid becomes more interconnected the attack surface increases and determining the causes of anomalies becomes more complex. Automated responses are a mechanism which can provide resilience in a power system by responding to anomalies. An automated response system can make intelligent decisions when paired with an automated health assessment system which includes a human in the loop for making critical decisions. Effective responses can be determined by developing a matrix which considers the likely impacts on resilience if a response is taken. A testbed assists to analyze these responses and determine their effects on system resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

RISMC Industry Application no. 1 (ECCS/LOCA) Core characterization automation: Reference PWR designs for IA no. 1

The U.S. NRC is currently proposing rulemaking '10 CFR 50.46c' to revise the loss-of-coolant-accident (LOCA) and emergency core cooling system (ECCS) acceptance criteria to include the effects of higher burn-up on cladding performance. The key implications of this proposition are that both cladding and ECCS performance need to be considered in a coupled manner. This implies that LOCA methodologies will need to be updated for regulatory purposes. The Risk-Informed Safety Margin Characterization (RISMC) Pathway, as part of the DOE Light Water Reactor Sustainability (LWRS) research and development program initialed several programs at the Idaho National Laboratory (INL) to support the industry by offering potential solutions to this new LOCA/ECCS analysis. One of these programs is the Industry Application no. 1 (IA1). It's goal is to develop an integrated evaluation model called LOCA Toolkit for the US (LOTUS) to revisit how risks are evaluated in a LOCA under 10 CFR 50.46c. The focus of LOTUS is to establish the automation interfaces among the physics disciplines involved, namely the core design automation, the fuels/clad performance, the system analysis, the risk assessment and the core design optimization. The risk-informed margins management approach for IA1 will provide a means of quantifying the impact on the key LOCA analysis figures-of-merit like peak cladding temperature (PCT), equivalent cladding reacted (ECR), etc. of a change in LOCA analysis inputs. The information obtained can then be used for decision-making and margin management. The modular approach for LOTUS will enable plant owners/vendors to further customize the LOTUS framework for use within their established codes and methods. One of the LOTUS modules deals with the core design automation (CD-A). The purpose of this module is to supply the LOCA analysis with initial conditions, i.e. assembly/pin power histories, power shapes, etc. to be employed by the Fuels/Clad Performance (EP) and System Analysis (SA) modules. In the CD-A module, the plant owner/operator will characterize his core design with the tools he has. For the current LOTUS demonstration, the tools used are the already coupled codes PHISICS and RELAP5-3D2,3. Cross section generation will be done using the Studsvik lattice code HELIOS-2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed automated transit network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level is the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles: Preprint

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed Automated Transit Network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level are the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach

Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects’ neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants’ demographic characteristics. Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

neuropsychological↗
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 OSTI2022

Deep Learning‐Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard‐to‐Transfect Cells

Abstract Genome engineering of cells using CRISPR/Cas systems has opened new avenues for pharmacological screening and investigating the molecular mechanisms of disease. A critical step in many such studies is the intracellular delivery of the gene editing machinery and the subsequent manipulation of cells. However, these workflows often involve processes such as bulk electroporation for intracellular delivery and fluorescence activated cell sorting for cell isolation that can be harsh to sensitive cell types such as human‐induced pluripotent stem cells (hiPSCs). This often leads to poor viability and low overall efficacy, requiring the use of large starting samples. In this work, a fully automated version of the nanofountain probe electroporation (NFP‐E) system, a nanopipette‐based single‐cell electroporation method is presented that provides superior cell viability and efficiency compared to traditional methods. The automated system utilizes a deep convolutional network to identify cell locations and a cell‐nanopipette contact algorithm to position the nanopipette over each cell for the application of electroporation pulses. The automated NFP‐E is combined with microconfinement arrays for cell isolation to demonstrate a workflow that can be used for CRISPR/Cas9 gene editing and cell tracking with potential applications in screening studies and isogenic cell line generation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Automated Experiments of Local Non–Linear Behavior in Ferroelectric Materials

An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al 0.93 B 0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al 0.93 B 0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. Furthermore, this technique can also be extended to electron, probe, and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline

Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subset of prior studies that are more likely to be relevant to the current study, usually by comparing anatomic coverage of both the current and prior studies. It is incumbent on the radiologist to review the full text report and/or images from those prior studies, a process that is time-consuming and confers substantial risk of overlooking a relevant prior study or finding. This risk is compounded when patients have dozens or even hundreds of prior imaging studies. Our goal is to assess the feasibility of natural language processing techniques to automatically extract asserted and negated disease entities from free-text radiology reports as a step towards automated report summarization. We compared automatically extracted disease mentions to a gold-standard set of manual annotations for 50 radiology reports from CT abdomen and pelvis examinations. The automated report summarization pipeline found perfect or overlapping partial matches for 86% of the manually annotated disease mentions (sensitivity 0.86, precision 0.66, accuracy 0.59, F1 score 0.74). The performance of the automated pipeline was good, and the overall accuracy was similar to the interobserver agreement between the two manual annotators.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
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 OSTI2022

From fault-detection to automated fault correction: A field study

A fault detection and diagnostics (FDD) tool, as addressed by this study, is a tool that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Although FDD tools can inform operators of building operational faults, currently an action is always required to correct the faults to generate energy savings. Fault auto-correction integrating with commercial FDD technology offerings can close the loop between the passive diagnostics and active control, increase the savings generated by FDD tools, and reduce the reliance on human intervention. This paper presents the field study of seven fault auto-correction algorithms implemented in commercial FDD platforms. Implementation includes software changes in the FDD tools and additional controls hardware or software changes in the BAS that were required to enable the execution of different types of auto-correction algorithms in real buildings. The routines successfully and automatically correct faults and improve the operation of large built-up Heating, Ventilation, and Air Conditioning (HVAC) systems, common in most commercial buildings. The auto-correction algorithms are tested across four buildings and three different building automation systems, following a rigorous procedure to make sure they work properly and do not negatively impact the system and building occupants. Finally, technology benefits, market drivers, and scalability changes are drawn from the implementation effort and test results, to drive future research and industry engagement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Automated thermal extraction-desorption gas chromatography mass spectrometry: A multifunctional tool for comprehensive characterization of polymers and their degradation products

The TED-GC–MS analysis is a two-step method. A sample is first decomposed in a thermogravimetric analyzer (TGA) and the gaseous decomposition products are then trapped on a solid-phase adsorber. Subsequently, the solid-phase adsorber is analyzed with thermal desorption gas chromatography mass spectrometry (TDU-GC–MS). This method is ideally suited for the analysis of polymers and their degradation processes. A new entirely automated system is introduced which enables high sample throughput and reproducible automated fractioned collection of decomposition products. The fractionated collection together with low temperatures reduces the risk of contamination, improves instrumental stability and minimizes maintenance efforts. Through variation of the two main parameters (purge gas flow and heating rate) it is shown how the extraction process can be optimized. By measuring the decomposition products of polyethylene it is demonstrated that compounds with masses of up to 434 Da can be detected. This is achieved despite the low temperature (˜40 °C) of the solid-phase adsorber and the low thermal desorption temperature of 200 °C in the TDU unit. It is now shown that automated TED-GC–MS represents a new flexible multi-functional method for comprehensive polymer analyses. Comparable polymer characterization was previously only achievable through a combination of multiple independent analytical methods. This is demonstrated by three examples focused on practical challenges in materials analysis and identification: The first one is the analysis of wood plastic composites for which the decomposition processes of the polymer and the bio polymer (wood) could be clearly distinguished by fractionated collection using sequential adsorbers. Secondly, a fast quantitative application is shown by determining the weight concentrations of an unknown polyolefin blend through comparison with a reference material. Additionally, the determination of microplastic concentrations in environmental samples is becoming an increasingly important analytical necessity. It is demonstrated that with TED-GC–MS calibration curves showing good linearity for the most important precursors for microplastic, even complex matrix materials (suspended particulate matter) can be successfully analyzed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Three-dimensional (3D) analysis of white etching bands (WEBs) in AISI M50 bearing steel using automated serial sectioning

Dark etching regions (DER) and white etching bands (WEB) form in the subsurface stressed regions of bearing raceways and rolling elements. Also, “butterfly” formations form around non-metallic inclusions and large carbides. The formation of WEBs, DERs and butterfly are dependent on operating conditions, material alloying elements and heat treatment. The DER, WEB and butterfly formations are well studied in two dimensions (2D). The objective of this study was to analyze WEB formation in three dimensions (3D) that will allow better understanding of material transformation after stress cycling. Three rolling contact fatigue (RCF) tested vacuum induction melted vacuum arc re-melted (VIM-VAR) AISI M50 samples (from a bench test, sub-scale test and commercial gas turbine engine) were analyzed using automated serial sectioning to map WEB networks and a butterfly formation in 3D. Automated serial sectioning and 2D optical microstructural data for 3D reconstructions and analysis was done using a fully automated serial sectioning system – Robo-Met.3D®. 3D analysis was performed using Image-Pro Premier 3D. The data set allowed 3D visualization and analysis of the evolution and propagation of WEB and butterfly in M50 bearing steel, including the positive microscopic identification of a ceramic inclusion that was the source of a WEB.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated reactor physics analysis framework of High Flux Isotope Reactor low-enriched uranium silicide dispersion fuel designs

The High Flux Isotope Reactor (HFIR) is a versatile research reactor that provides one of the highest steady-state neutron fluxes of any reactor in the world. The HFIR reactor physics team investigated the conversion of the current 93 wt% highly enriched uranium U 3 O 8 -Al dispersion fuel to a 19.75% low-enriched uranium (LEU) U 3 Si 2 -Al dispersion fuel. The team continuously develops a Python module to streamline the analysis steps required for an LEU core design to ensure reproducible and agile design iteration. The Python module automates the data processing between analysis steps and automates the input perturbation for branch calculations and design changes. The automated framework has proven to significantly increase the efficiency and reproducibility of the reactor physics team to design High Flux Isotope Reactor (HFIR) LEU cores and thoroughly analyze performance metrics, safety metrics, and thermal safety margins. Consequently, the team can now respond rapidly to fuel fabrication engineer and thermal-hydraulic-structural analyst requests. Numerous combinations of LEU fuel designs are explored, of which two LEU fuel designs are presented here in this paper: a low density silicide design, and a high-density silicide design. Results show that both designs meet or exceed safety and performance metrics with exception for minor differences caused by the hardened spectrum from LEU.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
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 OSTI2018

Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan

It has been shown that shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. Here we bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗