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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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The objective of this project was to develop a web-based application (Aurora Envision) that uses sophisticated image recognition and computer vision techniques to perform the following task: given widely available satellite and aerial imagery, with associated geo-coordinates, and proximous weather files, accurately identify the solar potential of a building’s roof.
During the preliminary or conceptual design phase of an aircraft engine, the turbomachinery designer has a need to estimate the effects of a large number of design parameters such as flow size, stage count, blade count, radial position, etc. on the weight and efficiency of a turbomachine. Computer codes are invariably used to perform this task however, such codes are often very old, written in outdated languages with arcane input files, and rarely adaptable to new architectures or unconventional layouts. Given the need to perform these kinds of preliminary design trades, a modern 2-D turbomachinery design and analysis code has been written using the Numerical Propulsion System Simulation (NPSS) framework. This paper discusses the development of the governing equations and the structure of the primary objects used in OTAC.
Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT 2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.
The theoretical basis and architecture of the conceptual design tool NDARC (NASA Design and Analysis of Rotorcraft) are described. The principal tasks of NDARC are to design (or size) a rotorcraft to satisfy specified design conditions and missions, and then analyze the performance of the aircraft for a set of off-design missions and point operating conditions. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. For each component, attributes such as performance, drag, and weight can be calculated. The aircraft attributes are obtained from the sum of the component attributes. NDARC provides a capability to model general rotorcraft configurations, and estimate the performance and attributes of advanced rotor concepts. The software has been implemented with low-fidelity models, typical of the conceptual design environment. Incorporation of higher-fidelity models will be possible, as the architecture of the code accommodates configuration flexibility, a hierarchy of models, and ultimately multidisciplinary design, analysis and optimization.
The International Atomic Energy Agency (IAEA) relies upon laboratory analysis of environmental samples (typically referred to as “swipes”) collected during on-site inspections of safeguarded facilities to support the detection and deterrence of undeclared activities. Unfortunately, chemical processing and assay of the samples is slow and expensive. A rapid, effective, and simple extraction process and analysis method is needed to provide certified results with improved timeliness at reduced costs (principally in the form of reduced labor), while maintaining or improving sensitivity and efficacy. To address these safeguard needs the Pacific Northwest National Laboratory (PNNL) explored and demonstrated improved methods for environmental sample (ES) analysis. Improvements for both bulk and particle analysis were explored. To facilitate continuity and adoption, the new sampling materials and processing methods will be compatible with existing IAEA protocols for ES analysis. PNNL collaborated with Oak Ridge National Laboratory (ORNL), which performed independent validation of the new bulk analysis methods and compared performance to traditional IAEA’s Network of Analytical Laboratories (NWAL) protocol. ORNL efforts are reported separately. This report describes PNNL’s FY 2016 progress, which was focused on analytical application supporting environmental monitoring of uranium enrichment plants and nuclear fuel processing. In the future the technology could be applied to other safeguard applications and analytes related to fuel manufacturing, reprocessing, etc. PNNL’s FY 2016 efforts were broken into two tasks and a summary of progress, accomplishments and highlights are provided below. Principal progress and accomplishments on Task 1, Optimize Materials and Methods for ICP-MS Environmental Sample Analysis, are listed below. • Completed initial procedure for rapid uranium extraction from ES swipes based upon carbonate-peroxide chemistry (delivered to ORNL for evaluation). • Explored improvements to carbonate-peroxide rapid uranium extraction chemistry. • Evaluated new sampling materials and methods (in collaboration with ORNL). • Demonstrated successful ES extractions from standard and novel swipes for a wide range uranium compounds of interest including UO 2 F 2 and UO 2 (NO 3 ) 2 , U 3 O 8 and uranium ore concentrate. • Completed initial discussions with commercial suppliers of PTFE swipe materials. • Submitted one manuscript for publication. Two additional drafts are being prepared. Principal progress and accomplishments on Task 2, Optimize Materials and Methods for Direct SIMS Environmental Sample Analysis, are listed below. • Designed a SIMS swipe sample holder that retrofits into existing equipment and provides simple, effective, and rapid mounting of ES samples for direct assay while enabling automation and laboratory integration. • Identified preferred conductive sampling materials with better performance characteristics. • Ran samples on the new PNNL NWAL equivalent Cameca 1280 SIMS system. • Obtained excellent agreement between isotopic ratios for certified materials and direct SIMS assay of very low levels of LEU and HEU UO 2 F 2 particles on carbon fiber sampling material. Sample activities range from 1 to 500 CPM (uranium mass on sample is dependent upon specific isotope ratio but is frequently in the subnanogram range). • Found that the presence of the UF molecular ions, as measured by SIMS, provides chemical information about the particle that is separate from the uranium isotopics and strongly suggests that those particles originated from an UF6 enrichment activity. • Submitted one manuscript for publication. Another manuscript is in preparation.
A set of imaging techniques based on Fuzzy Reasoning (FR) approach was built for NASA at Kennedy Space Center (KSC) to perform complex real-time visual-related safety prototype tasks, such as detection and tracking of moving Foreign Objects Debris (FOD) during the NASA Space Shuttle liftoff and visual anomaly detection on slidewires used in the emergency egress system for Space Shuttle at the launch pad. The system has also proved its prospective in enhancing X-ray images used to screen hard-covered items leading to a better visualization. The system capability was used as well during the imaging analysis of the Space Shuttle Columbia accident. These FR-based imaging techniques include novel proprietary adaptive image segmentation, image edge extraction, and image enhancement. Probabilistic Neural Network (PNN) scheme available from NeuroShell(TM) Classifier and optimized via Genetic Algorithm (GA) was also used along with this set of novel imaging techniques to add powerful learning and image classification capabilities. Prototype applications built using these techniques have received NASA Space Awards, including a Board Action Award, and are currently being filed for patents by NASA; they are being offered for commercialization through the Research Triangle Institute (RTI), an internationally recognized corporation in scientific research and technology development. Companies from different fields, including security, medical, text digitalization, and aerospace, are currently in the process of licensing these technologies from NASA.
As one of main tasks for FY17 CASL-THM activity, Evaluation study on applicability of the CASL baseline boiling model for 5x5 DNB application is conducted and the predictive capability of the DNB analysis is reported here. While the baseline CASL-boiling model (GEN- 1A) approach has been successfully implemented and validated with a single pipe application in the previous year’s task, the extended DNB validation for realistic sub-channels with detailed spacer grid configurations are tasked in FY17. The focus area of the current study is to demonstrate the robustness and feasibility of the CASL baseline boiling model for DNB performance in a full 5x5 fuel bundle application. A quantitative evaluation of the DNB predictive capability is performed by comparing with corresponding experimental measurements (i.e. reference for the model validation). The reference data are provided from the Westinghouse Electricity Company (WEC). Two different grid configurations tested here include Non-Mixing Vane Grid (NMVG), and Mixing Vane Grid (MVG). Thorough validation studies with two sub-channel configurations are performed at a wide range of realistic PWR operational conditions.
Summary Miscanthus spp. are promising lignocellulosic energy crops, but cell wall recalcitrance to deconstruction still hinders their widespread use as bioenergy and biomaterial feedstocks. Identification of cell wall characteristics desirable for biorefining applications is crucial for lignocellulosic biomass improvement. However, the task of scoring biomass quality is often complicated by the lack of a reference for a given feedstock. A multidimensional cell wall analysis was performed to generate a reference profile for leaf and stem biomass from several miscanthus genotypes harvested at three developmentally distinct time points. A comprehensive suite of 155 monoclonal antibodies was used to monitor changes in distribution, structure and extractability of noncellulosic cell wall matrix glycans. Glycan microarrays complemented with immunohistochemistry elucidated the nature of compositional variation, and in situ distribution of carbohydrate epitopes. Key observations demonstrated that there are crucial differences in miscanthus cell wall glycomes, which may impact biomass amenability to deconstruction. For the first time, variations in miscanthus cell wall glycan components were comprehensively characterized across different harvests, organs and genotypes, to generate a representative reference profile for miscanthus cell wall biomass. Ultimately, this portrait of the miscanthus cell wall will help to steer breeding and genetic engineering strategies for the development of superior energy crops.
Past studies indicate that kitchen ventilation that minimally complies with California’s Residential Building Code is inadequate at controlling combustion pollutants from natural gas burners and particulate matter produced during cooking. Effectiveness is further limited by misperceptions that kitchen ventilation is infrequently needed. This project developed the technical basis for updating kitchen ventilation requirements to protect health in new California homes, especially in smaller homes common among low-income renters. Tasks included (1) a field study of ventilation equipment performance and indoor air quality in 23 low-income apartments at four sites; (2) analysis of range hood use related to cooking time and household parameters using data from 54 houses and 17 low-income apartments; (3)a measurement-based study to quantify performance of over-the-range microwave ovens with integrated exhaust fans;and (4) pollutant exposure simulations to inform capture efficiency standards.The field study found operational deficiencies with mechanical ventilation systems in a substantial fraction of low-income apartments that affected performance, resulting in higher exposures to pollutants generated indoors. Using gas cooking burners produced high short-term and weekly time-averaged nitrogen dioxide in apartments.Range hoods were used more frequently with cooking in houses (36percent) than apartments (28percent); use increased with overall cooking frequency in a home and with duration of cooktop but not oven events; actual use was correlated to, but lower than,self-reported use;and use was more frequent in houses when cooking generated any fine particulate matter (PM2.5)or when high PM2.5resulted from cooking in apartments. Performance of over-the-range microwaves with integrated exhaust fans was similar to that of range hoods of comparable price. Simulation analysis found that performance standards need to be updated to ensure that kitchen exhaust ventilation adequately protects for substantial cooking in new California residences.
This report presents the developments and results of MindSynchro project as part of DOE OE FOA 1861. DOE and Pacific Northwest National Laboratory (PNNL) have made available to FOA awardees datasets containing years of real historical data recorded from various phasor measurement units (PMUs) which are installed in three large US interconnections: Texas (IC A), Western (IC B), and Eastern (IC C). The main goal of the project, which was successfully achieved, was to develop methods for detection and identification of events which are relevant for power grid operation. Tasks performed for achieving the project goals included data exploration and pre-processing, the development and application of physics-based features, data analysis and labeling based on unsupervised learning approaches, training and testing of DSSL models for classification of events which are relevant for power grid operation, and deployment of solutions to cloud environments. The methods developed in the project can potentially provide relevant benefits to power grid asset owners/operators in general in terms of situational awareness. Two main types of outcomes can be provided by these tools: Identification of specific relevant power grid event types: Semi-supervised ML methods developed in the project can adequately employ not only the relatively scarce labeled data but also the large amount of available unlabeled data to train models for detection of specific event types. Such methods enable the application of trained models for the detection of events in a population of PMUs much larger than that associated to the labeled events. Support in data labeling / label validation: Labels are critical for training of models for identification of specific types of events. However, labeling large amounts of data is a manual and tedious process. This means that such process is error prone and is not scalable. Methods developed in the project, based on ensembles of clustering models, have been successfully employed for turning manual labeling into a scalable process. Accurate identification of specific relevant events can provide the operators with immediate situational awareness that could otherwise require hours or days of analysis from domain experts. We envision that such methods could be initially employed in support of post-mortem analysis of events and, as confidence is gained, they could be employed for online/real-time support, providing, among other benefits, insights for avoiding major events which could happen due to a combination of smaller ones. On the longer term, related methods could potentially be employed to improve protection and control.
Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.
The objective of this project is to conduct a comprehensive investigation to develop and demonstrate a practical, reliable, and cost-effective integrated carbon dioxide (CO 2 ) capture and biofixation process for algae production. This process utilizes a chemical hindered ammonium solution (NH 4 OH) as both a capture reagent and an algae nutrient. The membrane absorber employed in the system ensured minimal ammonia (NH 3 ) emissions in the treated flue gas. Additionally, distributed solar-energy powered strippers located near the bioreactor modules facilitate solvent regeneration and enable just-in-time delivery of CO 2 and NH 3 to the algae, thereby minimizing the pH swing for enhanced productivity. The proposed membrane CO 2 absorber and solar-powered stripper is designed and seamlessly integrated with the existing 0.1 MWth bench-scale CO 2 capture system (CCS) and open raceway ponds (ORPs) at the University of Kentucky Center for Applied Energy (UK CAER) campus. The entire integrated process has undergone construction, operation, testing, and analysis. Furthermore, technology has been evaluated with a techno-economic analysis (TEA), technology gap analysis (TGA), life-cycle analysis (LCA), and a technology maturation plan (TMP). This project was performed within two budget periods, 54 months in duration. There are fourteen Project Tasks, twelve Milestones and four Success Criteria.
Guidelines and constraints procedures for LAGEOS assembly, operation, and design performance are given. Special attention was given to thermal, optical, and dynamic analysis and testing. The operation procedures illustrate the interrelation and sequence of tasks in a flow diagram. The diagram also includes quality assurance functions for verification of operation tasks.
A video system for remote manipulation in space was studied. Analysis of basic manipulation tasks and visual requirements; simulation testing to determine the effects of task, scene parameter, and type of video system on manipulation performance; and video system selection and specification are described.
Information theoretic analysis and subjective paired-comparison and task ranking techniques were employed in order to scale the workload of 20 communications-related tasks frequently performed by the captain and first officer of transport category aircraft. Tasks were drawn from taped conversations between aircraft and air traffic controllers (ATC). Twenty crewmembers performed subjective message comparisons and task rankings on the basis of workload. Information theoretic results indicated a broad range of task difficulty levels, and substantial differences between captain and first officer workload levels. Preliminary subjective data tended to corroborate these results. A hybrid scale reflecting the results of both the analytical and the subjective techniques is currently being developed. The findings will be used to select representative sets of communications for use in high fidelity simulation.
TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper.
The proton exchange membrane fuel cell (PEMFC) is one of the most promising electrochemical power sources for space and electric vehicle applications. The wide spectrum of R&D activities on PEMFC's, carried out in our Center from 1988 to date, is as follows (1) Electrode Kinetic and Electrocatalysis of Oxygen Reduction; (2) Optimization of Structures of Electrodes and of Membrane and Electrode Assemblies; (3) Selection and Evaluation of Advanced Proton Conducting Membranes and of Operating Conditions to Attain High Energy Efficiency; (4) Modeling Analysis of Fuel Cell Performance and of Thermal and Water Management; and (5) Engineering Design and Development of Multicell Stacks. The accomplishments on these tasks may be summarized as follows: (1) A microelectrode technique was developed to determine the electrode kinetic parameters for the fuel cell reactions and mass transport parameters for the H2 and O2 reactants in the proton conducting membrane. (2) High energy efficiencies and high power densities were demonstrated in PEMFCs with low platinum loading electrodes (0.4 mg/cm(exp 2) or less), advanced membranes and optimized structures of membrane and electrode assemblies, as well as operating conditions. (3) The modeling analyses revealed methods to minimize mass transport limitations, particularly with air as the cathodic reactant; and for efficient thermal and water management. (4) Work is in progress to develop multi-kilowatt stacks with the electrodes containing low platinum loadings.