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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Pneumatic Proboscis Heat-Flow Probe

Heat flow is a fundamental property of a planet, and provides significant constraints on the abundance of radiogenic isotopes, the thermal evolution and differentiation history, and the mechanical properties of the lithosphere. Heat-flow measurements are also essential in achieving at least four of the goals set out by the National Research Council for future lunar exploration. The heat-flow probe therefore directly addresses the goal of the Lunar Geophysical Network, which is to understand the interior structure and composition of the Moon. A key challenge for heat flow measurement is to install thermal sensors to the depths approximately equal to 3 m that are not influenced by the diurnal, annual, and longer-term fluctuations of the surface thermal environment. In addition, once deployed, the heat flow probe should cause little disturbance to the thermal regime of the surrounding regolith. A heat-flow probe system was developed that has two novel features: (1) it utilizes a pneumatic (gas) approach, excavates a hole by lofting the lunar soil out of the hole, and (2) deploys the heat flow probe, which utilizes a coiled up tape as a thermal probe to reach greater than 3-meter depth. The system is a game-changer for small lunar landers as it exhibits extremely low mass, volume, and simple deployment. The pneumatic system takes advantage of the helium gas used for pressurizing liquid propellant of the lander. Normally, helium is vented once the lander is on the surface, but it can be utilized for powering pneumatic systems. Should sufficient helium not be available, a simple gas delivery system may be taken specifically for the heat flow probe. Either way, the pneumatic heat flow probe system would be much lighter than other systems that entirely rely on the electrical power of the lander.

Zacny, Kris↗
DOE OSTI2025

Sensitivity-based voltage constraints for optimal power flow in low-voltage distribution feeders

The optimal power flow (OPF) problem for distribution systems can include network details down to the low-voltage (LV) points of interconnection of individual customers. This paper addresses the implementation of voltage magnitude constraints, and sets forth a practicable approach for capturing the effects on voltage from the switching behavior of loads (e.g., heat pumps, air conditioners, water heaters, or pool pumps) and from the variability of renewable generation (e.g., rooftop solar). The proposed method adjusts the OPF voltage constraints based on forecasts of load and generation upper and lower bounds, in conjunction with sensitivity factors derived from the power flow equations. An illustrative OPF formulation is also provided, which incorporates transformer models that include core loss. We demonstrate that accurate modeling of these LV network components is critical to avoid voltage violations at customer points of interconnection. Furthermore, the ideas are validated through numerical case studies on a realistic distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Physics-informed neural networks (PINNs) for fluid mechanics: a review

Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implement them using physics-informed neural networks (PINNs). Here, we demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows, supersonic flows, and biomedical flows.

42 ENGINEERING↗
DOE OSTI2020

Anomalous Transport in Three-Dimensional Discrete Fracture Networks: Interplay Between Aperture Heterogeneity and Injection Modes

In this work, we study how the interplay between fracture aperture heterogeneity and tracer injection mode controls fluid flow and tracer transport in three-dimensional (3D) discrete fracture networks (DFNs). The direct 3-D DFN simulations show that tracer injection mode has substantial effects on tracer spreading across all levels of aperture heterogeneity. The key controlling factor for effective transport is the initial Lagrangian velocity distribution, which is determined by the interplay between injection mode and aperture heterogeneity. The fundamental difference between initial Lagrangian velocity distribution and domain-scale Eulerian velocity distribution plays a vital role in determining anomalous transport. We effectively capture the observed anomalous transport using an upscaled transport model that incorporates initial velocity distribution, stationary velocity distribution, velocity correlation length, and average advective tortuosity. With the upscaled transport model, we accurately capture the evolution of Lagrangian velocity distribution and predict longitudinal spreading in 3-D DFN.

58 GEOSCIENCES↗
DOE OSTI2018

So ware-Defined Network Solutions for Science Scenarios: Performance Testing Framework and Measurements

High-performance scientific work flows utilize supercomputers, scientific instruments, and large storage systems. Their executions require fast setup of a small number of dedicated network connections across the geographically distributed facility sites. We present Software-Defined Network (SDN) solutions consisting of site daemons that use dpctl, Floodlight, ONOS, or OpenDaylight controllers to set up these connections. The development of these SDN solutions could be quite disruptive to the infrastructure, while requiring a close coordination among multiple sites; in addition, the large number of possible controller and device combinations to investigate could make the infrastructure unavailable to regular users for extended periods of time. In response, we develop a Virtual Science Network Environment (VSNE) using virtual machines, Mininet, and custom scripts that support the development, testing, and evaluation of SDN solutions, without the constraints and expenses of multi-site physical infrastructures; furthermore, the chosen solutions can be directly transferred to production deployments. By complementing VSNE with a physical testbed, we conduct targeted performance tests of various SDN solutions to help choose the best candidates. In addition, we propose a switching response method to assess the setup times and throughput performances of different SDN solutions, and present experimental results that show their advantages and limitations.

Settlemyer, Bradley↗
DOE OSTI2019

Accelerated Voltage Regulation in Multi-Phase Distribution Networks Based on Hierarchical Distributed Algorithm

Here, we introduce a hierarchical distributed algorithm to solve optimal power flow (OPF) problems that aim at dispatching controllable distributed energy resources (DERs) for voltage regulation at minimum cost. The proposed algorithm highlights unprecedented scalability to large multi-phase distribution networks by jointly exploring the tree/subtrees structure of a large radial distribution network and the structure of the linearized distribution power flow (LinDistFlow) model to derive a hierarchical, distributed implementation of the primal-dual gradient algorithm that solves OPF. The proposed implementation significantly reduces the computation loads compared to the centrally coordinated implementation of the same primal-dual algorithm without compromising optimality. Numerical findings on a 4,521-node test feeder show that the designed algorithm achieves more than 10-fold acceleration in the speed of convergence compared to the centrally coordinated primal-dual algorithm through reducing and distributing computational loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Description of a MIL-STD-1553B Data Bus Ada Driver for the LeRC EPS Testbed

This document describes the software designed to provide communication between control computers in the NASA Lewis Research Center Electrical Power System Testbed using MIL-STD-1553B. The software drivers are coded in the Ada programming language and were developed on a MSDOS-based computer workstation. The Electrical Power System (EPS) Testbed is a reduced-scale prototype space station electrical power system. The power system manages and distributes electrical power from the sources (batteries or photovoltaic arrays) to the end-user loads. The electrical system primary operates at 120 volts DC, and the secondary system operates at 28 volts DC. The devices which direct the flow of electrical power are controlled by a network of six control computers. Data and control messages are passed between the computers using the MIL-STD-1553B network. One of the computers, the Power Management Controller (PMC), controls the primary power distribution and another, the Load Management Controller (LMC), controls the secondary power distribution. Each of these computers communicates with two other computers which act as subsidiary controllers. These subsidiary controllers are, in turn, connected to the devices which directly control the flow of electrical power.

Mackin, Michael A.↗
NASA NTRS1998

Emulating the visual receptive-field properties of MST neurons with a template model of heading estimation

We have proposed previously a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to those of neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. These detectors, arranged within cortical-like maps, were designed to extract self-translation (heading) and self-rotation, as well as the scene layout (relative distances) ahead of a moving observer. We then postulated that heading from optic flow is directly encoded by individual neurons acting as heading detectors within the medial superior temporal (MST) area. Others have questioned whether individual MST neurons can perform this function because some of their receptive-field properties seem inconsistent with this role. To resolve this issue, we systematically compared MST responses with those of detectors from two different configurations of the model under matched stimulus conditions. We found that the characteristic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support self-motion estimation via a direct encoding of heading and that the template model provides an explicit set of testable hypotheses that can guide future exploration of MST and adjacent areas within the superior temporal sulcus.

NASA Discipline Neuroscience↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2018

Statistical methods for network surveillance

The term network surveillance is defined in general terms and illustrated with many examples. Statistical methodologies that can be used as tools for network surveillance are discussed. Details for 3 illustrative examples that address network security, surveillance for data network failures, and surveillance of email traffic flows are presented. Some open areas of research are identified.

Jeske, Daniel R.↗
DOE OSTI2022

Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Pyomo.GDP: an ecosystem for logic based modeling and optimization development

We present three core principles for engineering-oriented integrated modeling and optimization tool sets—intuitive modeling contexts, systematic computer-aided reformulations, and flexible solution strategies—and describe how new developments in Pyomo.GDP for Generalized Disjunctive Programming (GDP) advance this vision. We describe a new logical expression system implementation for Pyomo.GDP allowing for a more intuitive description of logical propositions. The logical expression system supports automated reformulation of these logical constraints to linear constraints. We also describe two new logic-based global optimization solver implementations built on Pyomo.GDP that exploit logical structure to avoid “zero-flow” numerical difficulties that arise in nonlinear network design problems when nodes or streams disappear. These new solvers also demonstrate the capability to link to external libraries for expanded functionality within an integrated implementation. We present these new solvers in the context of a flexible array of solution paths available to GDP models. Finally, we present results on a new library of GDP models demonstrating the value of multiple solution approaches.

42 ENGINEERING↗
DOE OSTI2018

An exploration of pressure dynamics using differential equations defined on a fractal geometry

Pressure tests have been used to understand the behavior of naturally fractured reservoirs (NFRs) by establishing parameters such as permeability, connectivity of fractures, system compressibility, fracture network parameters, and others associated with the physical properties of the reservoir. The complex fracture network of a NFR produces anomalous fluid flow, which is shown by the pressure behavior at the well. A plausible model to explain this behavior is to assume that it arises from a fractal fracture system. However, to estimate the parameters that define this fractal fracture system, fluid flow models based on the modification of Darcy’s law are used. On the other hand, in this work, a fractal structure with known fractal dimension: Sierpinski gasket (SG) is proposed. The objective of this work is to solve numerically the diffusion equation on SG, and establish a comparison with flow models used in the oil industry to investigate the fractality assumption. This study is focused on the qualitative behavior of the solution of the diffusion equation defined on a SG supported by the Kigami’s theory of differential equations on fractals.

42 ENGINEERING↗
DOE OSTI2017

Shales at all scales: Exploring coupled processes in mudrocks

Fine-grained sedimentary rocks – namely mudrocks, including their laminated fissile variety — shales – make up about two thirds of all sedimentary rocks in the Earth's crust and a quarter of the continental land mass. Additionally, organic-rich shales and mudstones are the source rocks and reservoirs for conventional and unconventional hydrocarbon resources. Mudrocks are relied upon as natural barriers for geological carbon storage and nuclear waste disposal. Consideration of mudrock multi-scale physics and multi-scale spatial and temporal behavior is vital to address emergent phenomena in shale formations perturbed by engineering activities. Unique physical characteristics of shales arise as a result of their layered and highly heterogeneous and anisotropic nature, low permeability fabric, compositional complexity, and nano-scale confined chemical environments. Barriers of lexicon among geoscientists and engineers impede the development and use of conceptual models for the coupled thermal-hydraulic-mechanical-chemical-biological (THMCB) processes in mudrock formations. This manuscript reviews the THMCB process couplings, resulting emergent behavior, and key modeling approaches. We identify future research priorities, in particular fundamental knowledge gaps in understanding the phase behavior under nano-scale confinement, coupled chemo-mechanical effects on fractures, the interplay between physical and chemical processes and their rates, and issues of non-linearity and heterogeneity. We develop recommendations for future research and integrating multi-disciplinary conceptual models for the coupled multi-scale multi-physics behavior of mudrocks. Lastly, consistent conceptual models across disciplines are essential for predicting emergent processes in the subsurface, such as self-focusing of flow, time-dependent deformation (creep), fracture network development, and wellbore stability.

58 GEOSCIENCES↗