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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission 2004. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and patter recognition to radically increase science return by enabling intelligent downlink selection and autnomous retargeting. In this paper we will discuss how these AI technologies are synergistically integrated in multi-layer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
DOE OSTI2022

Control of Low-Inertia Power Systems

Electric power systems are undergoing an unprecedented transition from fossil fuel–based power plants to low-inertia systems that rely predominantly on power electronics and renewable energy resources. This article reviews the resulting control challenges and modeling fallacies, at both the device and system level, and focuses on novel aspects or classical concepts that need to be revised in light of the transition to low-inertia systems. To this end, we survey the literature on modeling of low-inertia systems, review research on the control of grid-connected power converters, and discuss the frequency dynamics of low-inertia systems. Moreover, we discuss system-level services from a control perspective. Overall, we conclude that the system-theoretic mindset is essential to bridge different research communities and understand the complex interactions of power electronics, electric machines, and their controls in large-scale low-inertia power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Physics Coupled Machine Learning Applications for Geological Carbon Storage

This is the conference paper accompanying a poster presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. In this paper, a physics-based method, CRM is coupled with the advanced artificial intelligence (AI)/machine learning (ML) models in virtual learning environment (VLE) for three-dimension details of reservoir responses and evaluations for a comprehensive understanding for CCS field operations and reservoir managements.

Liu, Guoxiang↗
NASA NTRS2003

The autonomous sciencecraft constellations

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE AI technolo↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecruft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE virtual spa↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular inefficiency that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based metering of internal departures, or TMA scheduling. Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot be easily fitted into the overhead stream. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. Depending on how the double delay is defined between 0.3 percent and 0.8 percent of arrivals at EWR experienced double delays in this period. However, this represents between 21 percent and 62 percent of all internal departures in GDP and TMA scheduling. A deep dive into the data reveals that two causes of high internal departure scheduling delays are upstream flights making up time between their estimated departure clearance times (EDCTs) and entry into time based metering, which undermines the sequencing and spacing underlying the flight EDCTs, and high demand on TMA, when TMA airborne metering delays are high. Data mining methods (currently) including logistic regression, support vector machines and K-nearest neighbors are used to predict the occurrence of double delays and high internal departure scheduling delays with accuracies up to 0.68. So far, key indicators of double delay and high internal departure scheduling delay are TMA virtual runway queue size, and the degree to which estimated runway demand based on TMA estimated times of arrival has changed relative to the estimated runway demand based on EDCTs. However, more analysis is needed to confirm this.

traffic management advisor↗
NASA NTRSDate not supplied

Design of a Lunar Plume-Surface Interaction Measurement System

Plume-surface interactions between a rocket plume and the lunar surface will be studied in-situ during two of NASA’s upcoming Commercial Lunar Payload Services Program missions. The payload, Stereo Cameras for Lunar Plume-Surface Studies (SCALPSS), will employ a multiple-camera photogrammetry system to obtain stereo images of the lunar regolith before, during, and after significant erosion and subsequent crater formation. The evaluation of measurement system capabilities and design process of the payload is informed by computational fluid dynamics predictions, accuracy modeling based on experimental data, camera simulation, and lander design, all of which are combined in the Virtual Diagnostic Interface. The second SCALPSS mission, traveling to the moon on Firefly Aerospace’s Blue Ghost lander in early 2023, aims to build on the design and complexity of the first payload, which is launching in early 2022 on Intuitive Machines’ Nova-C lander. The Blue Ghost SCALPSS mission will include two additional cameras and a total of three different lens focal lengths which will target specific points in the vehicle’s descent to obtain quantitative and accurate 3D reconstruction of the lunar surface both prior to and after crater formation.

photogrammetry↗
DOE OSTI2024

Overview of SMART Initiative

The objective of the SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to show how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations in three main areas: real-time visualization, virtual learning, and real-time forecasting. This presentation reports the status of SMART initiative for demonstrating: (a) virtual learning during the pre-injection permitting phase, and (b) ML-assisted operational decision making and visualization.

Siriwardane, Hema↗
NASA NTRS1990

An Auger electron spectroscopy study of surface-preparation contaminants

There are many cleaning techniques that are presently being employed for surface preparation of materials that are subsequently exposed to ultrahigh vacuum (UHV). Unfortunately, there are virtually no comparative measurements which establish the residual contaminant level of each method. In this report, eleven different cleaning methods, ranging from only detergent cleaning to electrochemical polishing, were applied to identical samples of 347 stainless steel. Two surface conditions, a standard machined surface and a mechanically polished surface, were studied. Auger electron spectroscopy (AES) within a UHV environment was then used to detect the types of contaminants and the magnitudes found on the sample surfaces. It was found that the electrochemical polishing gave the least contaminated surface of all metals studied and that mechanically polished surfaces were significantly cleaner than the as-machined surfaces for any given cleaning method. Furthermore, it was also found that the residual contaminations left by methanol, ethanol, isopropyl alcohol, acetone, and freon finishing rinses are almost the same.

Wu, D.↗
DOE OSTI2019

Augmented Reality Rendering of Computed Tomography 3-D Density Data

Overall Objectives: Purchase equipment and Software required to develop the technology; Import CT Data for viewing on an AR device; Develop the tools necessary to manipulate the data in AR. With Augmented and Virtual Reality technology becoming more common and affordable everyday, new visualization techniques need to be developed that can leverage this technology to visualize data in new and useful ways. Computed Tomography (CT) scans, are the result of a powerful X-ray machine taking multiple images of the same object. These images represent slices of the original object and have extremely high resolution. Because of the high resolution, CT scanning is often used in the medical field but is becoming increasingly predominate in diverse industrial fields as well. This is an example of a CT scan that shows an object with multiple items inside. These items are represented by different colors in gray scale to indicate different densities. This is also from the data set used for the duration of the project. The data sets usually contain hundreds of slides that looks like just like this. Visualizing these large data sets can be difficult. The general approach for this seedling was to identify the devices and software that are currently in use for data visualization and evaluate them for use in the project. After that a plan was made to convert the data into a format viewable in AR. Tools were then developed to manipulate that data. The project was a success, but there is plenty of room to advance the technology and create better visualizations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Solving the Orszag–Tang vortex magnetohydrodynamics problem with physics-constrained convolutional neural networks

We study the 2D Orszag–Tang vortex magnetohydrodynamics (MHD) problem through the use of physics-constrained convolutional neural networks (PCNNs) for forecasting the density, ρ, and the magnetic field, B, as well as the prediction of B given the velocity field v of the fluid. In addition to translation equivariance from the convolutional architecture, other physics constraints were embedded: absence of magnetic monopoles, non-negativity of ρ, use of only relevant variables, and the periodic boundary conditions of the problem. The use of only relevant variables and the hard constraint of non-negative ρ were found to facilitate learning greatly. The divergenceless condition ∇·B=0 was implemented as a hard constraint up to machine precision through the use of a magnetic potential to define B=∇×A. Residual networks and data augmentation were also used to improve performance. This allowed for some of the residual models to function as surrogate models and provide reasonably accurate simulations. For the prediction task, the PCNNs were evaluated against a physics-informed neural network, which had the ideal MHD induction equation as a soft constraint. Several models were able to generate highly accurate fields, which are visually almost indistinguishable and have low mean squared error. Only methods with built-in hard constraints produced physical fields with ∇·B=0. The use of PCNNs for MHD has the potential to produce physically consistent real-time simulations to serve as virtual diagnostics in cases where inferences must be made with limited observables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

The Microwave-Heated Bumpy Torus: A Concept for Fusion Energy

In the last 3 decades, giant steps in technology and simulations capability have been made for the benefit of all magnetic fusion concepts. This is especially true for virtually a one-of-a-kind device, a microwave-driven bumpy-torus magnetic-fusion-energy system. In the 1980s, the prototype experiment was technology limited, and it was judged based on unrealistic expectations from an incomplete understanding of the machine performance. For those experiments, the machine did not achieve the theoretical stability threshold to demonstrate good plasma confinement. This paper examines the premise that the experimental platform was underpowered, which prevented acceptance of its value for fusion. In this paper, the experimental plasma scaling data with power are reviewed. If, with the advancements of technology and simulations capability of the last 30 years, stable operation can be proven, a much improved device competitive with tokamak and stellarator performance may be possible. The advantages of this concept include steady-state operation, gentle shut down, modular construction for fabrication and maintenance, and the natural diversion of impurities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1983

On Why It Is Impossible to Prove that the BDX90 Dispatcher Implements a Time-sharing System

The Software Implemented Fault Tolerance SIFT system, is written in PASCAL except for about a page of machine code. The SIFT system implements a small time sharing system in which PASCAL programs for separate application tasks are executed according to a schedule with real time constraints. The PASCAL language has no provision for handling the notion of an interrupt such as the B930 clock interrupt. The PASCAL language also lacks the notion of running a PASCAL subroutine for a given amount of time, suspending it, saving away the suspension, and later activating the suspension. Machine code was used to overcome these inadequacies of PASCAL. Code which handles clock interrupts and suspends processes is called a dispatcher. The time sharing/virtual machine idea is completely destroyed by the reconfiguration task. After termination of the reconfiguration task, the tasks run by the dispatcher have no relation to those run before reconfiguration. It is impossible to view the dispatcher as a time-sharing system implementing virtual BDX930s running concurrently when one process can wipe out the others.

Boyer, R. S.↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
DOE OSTI2019

Binder Jet Additive Multi-Material Manufacturing for the Transformational Challenge Reactor

Additive manufacturing (AM) is a process in which material is deposited in a layer-by-layer fashion, thereby building up a geometry. This contrasts with subtractive manufacturing in which material is removed from bulk feedstock to create a final geometry. Binder jet AM (BJAM) is a specific process in the AM field in which a binder, or glue, is selectively deposited on a bed of spread powder feedstock and cured. Binder is deposited with an inkjet printhead, which allows for high-resolution shaping of the powder layers. Compared with other AM processes, BJAM is fast, efficient, and scalable. Further, BJAM has one of the largest material selections of the powder AM processes as it can process ceramics, plastics, metals, and virtually any powdered material. Thus, the opportunity exists to leverage the versatility and productivity of BJAM to enable multi-material printing, the next generation of AM technology. The aim of this research is the design and manufacture of a purpose-built BJAM machine that can process multiple materials in its normal operation. This binder jet additive multi-material manufacturing (BJAM 3 ) machine will be able to produce parts that have multiple materials intra- and inter-layer. An example of this would be encapsulating metal spheres inside of a ceramic cube or vice versa. This BJAM 3 process adds another step of freedom to designing and manufacturing complex parts. The milestone for this work is to provide details on the technical progress made to date on the development of multi-material binder jetting and to facilitate simultaneous additive manufacturing of at least two different powder feedstocks.

36 MATERIALS SCIENCE↗