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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Denitrification and the challenge of scaling microsite knowledge to the globe

Here, our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Catalytic N 2 -to-NH 3 (or -N 2 H 4 ) Conversion by Well-Defined Molecular Coordination Complexes

Nitrogen fixation, the six-electron/six-proton reduction of N 2 , to give NH 3 , is one of the most challenging and important chemical transformations. Notwithstanding the barriers associated with this reaction, significant progress has been made in developing molecular complexes that reduce N 2 into its bioavailable form, NH 3 . This progress is driven by the dual aims of better understanding biological nitrogenases and improving upon industrial nitrogen fixation. In this review, we highlight both mechanistic understanding of nitrogen fixation that has been developed, as well as advances in yields, efficiencies, and rates that make molecular alternatives to nitrogen fixation increasingly appealing. We begin with a historical discussion of N 2 functionalization chemistry that traverses a timeline of events leading up to the discovery of the first bona fide molecular catalyst system and follow with a comprehensive overview of d-block compounds that have been targeted as catalysts up to and including 2019. Furthermore, we end with a summary of lessons learned from this significant research effort and last offer a discussion of key remaining challenges in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum optical classifier with superexponential speedup

Abstract Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $${{\mathcal{O}}}(1)$$ O ( 1 ) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever-increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article, we introduce the Delta framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, Delta allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describes the modular and expandable software architecture of Delta and presents performance benchmarks of individual components as well as of an example workflow. Focusing on a streaming analysis workflow where electron cyclotron emission imaging (ECEi) data is measured at KSTAR on the National Energy Research Scientific Computing Center's (NERSC's) supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. In NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes them in under 5 min. We further discuss how Delta uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. Here, by providing session leaders and physics operators, results of higher-order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Autotuning of Double-Dot Devices In Situ with Machine Learning

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. Here, we demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Evolving Multi-hazard Machine Learning Modeling for Advanced Risk-Informed Infrastructure Resilience Assessment

The socioeconomic impacts of pipeline incidents have escalated over the past three decades, revealing the limitation of traditional risk modeling methods when applied to extensive pipeline networks. This research aims to develop machine learning (ML) models that effectively identify, rank, and predict the diverse hazards and socioeconomic consequences associated with pipeline incidents. Utilizing historical data on pipeline incidents alongside weather and oceanographic data from the 1980s onward, the Houston metropolitan area serves as a testbed for the proposed methodologies. The research segments the combined datasets into three consecutive periods, demonstrating the efficacy of the updated model in predicting future events, particularly concerning precipitation rate data. Despite the challenges posed by a relatively limited dataset, local-level ML modeling offers valuable insights into the spatial and temporal dynamics of multiple hazards that contribute to pipeline incidents. These findings hold significant implications for future research, particularly in understanding and mitigating risks in various locations across the Gulf Coast and other coastal regions.

42 ENGINEERING↗
NASA NTRS1989

Prototype part task trainer: A remote manipulator system simulator

The Part Task Trainer program (PTT) is a kinematic simulation of the Remote Manipulator System (RMS) for the orbiter. The purpose of the PTT is to supply a low cost man-in-the-loop simulator, allowing the student to learn operational procedures which then can be used in the more expensive full scale simulators. PTT will allow the crew members to work on their arm operation skills without the need for other people running the simulation. The controlling algorithms for the arm were coded out of the Functional Subsystem Requirements Document to ensure realistic operation of the simulation. Relying on the hardware of the workstation to provide fast refresh rates for full shaded images allows the simulation to be run on small low cost stand alone work stations, removing the need to be tied into a multi-million dollar computer for the simulation. PTT will allow the student to make errors which in full scale mock up simulators might cause failures or damage hardware. On the screen the user is shown a graphical representation of the RMS control panel in the aft cockpit of the orbiter, along with a main view window and up to six trunion and guide windows. The dials drawn on the panel may be turned to select the desired mode of operation. The inputs controlling the arm are read from a chair with a Translational Hand Controller (THC) and a Rotational Hand Controller (RHC) attached to it.

Shores, David↗
NASA NTRS2005

Dynamics Modeling and Simulation of Large Transport Airplanes in Upset Conditions

As part of NASA's Aviation Safety and Security Program, research has been in progress to develop aerodynamic modeling methods for simulations that accurately predict the flight dynamics characteristics of large transport airplanes in upset conditions. The motivation for this research stems from the recognition that simulation is a vital tool for addressing loss-of-control accidents, including applications to pilot training, accident reconstruction, and advanced control system analysis. The ultimate goal of this effort is to contribute to the reduction of the fatal accident rate due to loss-of-control. Research activities have involved accident analyses, wind tunnel testing, and piloted simulation. Results have shown that significant improvements in simulation fidelity for upset conditions, compared to current training simulations, can be achieved using state-of-the-art wind tunnel testing and aerodynamic modeling methods. This paper provides a summary of research completed to date and includes discussion on key technical results, lessons learned, and future research needs.

Foster, John V.↗
NASA NTRSDate not supplied

Assessing Long-Term Health (LTH) Outcomes in Astronauts

NASA has linked potential Long-Term Health (LTH) risks of astronauts to their spaceflight experience, including the effects of space radiation, microgravity, and stressors such as isolation, sleep deprivation, and stress. Therefore, assessing the LTH outcomes for astronauts exposed to these hazards becomes a critical investigation in ensuring any deleterious effects are minimal. Prior research conducted into LTH outcomes of astronauts has not revealed increases in non-accidental mortality rates when compared to the U.S. general population, analog healthy cohorts, and professional athletes. One study did report an increased incidence of non-fatal cardiovascular disease events (Hazard Ratio=2.41, 95% Confidence Interval 1.26 to 4.63). Additional evidence indicates that astronauts may have increases in incidences of melanoma, prostate, and hematologic cancers, though the former is consistent with rates observed among aircraft pilots and the latter two are likely caused by detection bias due to increased screening on former astronauts.

long term health↗
NASA NTRS1991

The Space Flight Operations Center first phase - Lessons learned

This paper is a retrospective look at the Space Flight Operations Center Project; a large, complex, multimission ground data system which is currently under development at the Jet Propulsion Laboratory (JPL), Pasadena. The Project was established in February 1984 and will be completed in April 1992 when the Magellan, Voyager, Ulysses, Galileo and Mars Observer Projects will be fully supported. The first phase of this project covers the development of a subset of multimission Baseline subsystems, the development of the mission-specific adaptations to this Baseline set for the Magellan Project, the development of a Magellan-specific high rate processor together with the delivery of these subsystems to operations. This first phase became operational in January 1989 in readiness for the Magellan launch in May 1989.

Gainsborough, A. J.↗
DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗
DOE OSTI2023

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations

This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. Finally, while direct ab initio simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Hall, Lawrence O.↗
NASA NTRS1993

Emergency medical service (EMS): A unique flight environment

The EMS flight environment is unique in today's aviation. The pilots must respond quickly to emergency events and often fly to landing zones where they have never been before . The time from initially receiving a call to being airborne can be as little as two to three minutes. Often the EMS pilot is the only aviation professional on site, they have no operations people or other pilots to aid them in making decisons. Further, since they are often flying to accident scenes, not airports, there is often complete weather and condition information. Therefore, the initial decision that the pilot must make, accepting or declining a flight, can become very difficult. The accident rate of EMS helicopters has been relatively high over the past years. NASA-Ames research center has taken several steps in an attempt to aid EMS pilots in their decision making and situational awareness. A preflight risk assessment system (SAFE) was developed to aid pilots in their decision making, and was tested at an EMS service. The resutls of the study were promising and a second version incorporating the lessons learned is under development. A second line of research was the development of a low cost electronic chart display (ECD). This is a digital map display to help pilots maintain geographical orientation. Another thrust was undertaken in conjunction with the Aviation Safety Reporting System (ASRS). This involved publicizing the ASRS to EMS pilots and personnel, and calling each of the reporters back to gather additional information. This paper will discuss these efforts and how they may positively impact the safety of EMS operations.

Shively, R. Jay↗
NASA NTRS2024

Flight Certification Approach for NASA's Space Launch System

NASA’s Space Launch System (SLS) vehicle was successfully launched on November 16, 2022, initiating the Artemis I mission to send the uncrewed Orion spacecraft to the Moon and further into space than any human-rated spacecraft had previously flown. Flight certification of the launch vehicle was part of the systems engineering and integration (SE&I) effort conducted by NASA to successfully certify the mission and begin a new era of human space exploration of the Moon and eventually Mars. This paper describes the SLS Program flight certification approach including the organization, processes, and reporting. This approach used traditional NASA SE&I milestone reviews and lessons learned from the Space Shuttle Program, as well as unique methods to certify the complex system, procedures, and personnel readiness. The approach utilized by the SLS Program to conduct flight certification for the Artemis I mission can be extended to address flight certification for other complex programs.

flight certification↗
DOE OSTI2023

Predicting future well performance for environmental remediation design using deep learning

Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

54 ENVIRONMENTAL SCIENCES↗