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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

REDLY

SAND2025-11838O REDLY provides a framework for designing, training, and validating physics-informed neural networks for power system applications. It trains surrogates for Alternating Current Power Flow prediction and general economic dispatch problems, such as Alternating Current Optimal Power Flow and Direct Current Power Flow. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Blakely, Logan↗
DOE OSTI2017

Inter-plume aerodynamics for gasoline spray collapse

The collapse or merging of individual plumes of direct-injection gasoline injectors is of fundamental importance to engine performance because of its impact on fuel–air mixing. But, the mechanisms of spray collapse are not fully understood and are difficult to predict. The purpose of this work is to study the aerodynamics in the inter-spray region, which can potentially lead to plume collapse. High-speed (100 kHz) particle image velocimetry is applied along a plane between plumes to observe the full temporal evolution of plume interaction and potential collapse, resolved for individual injection events. Supporting information along a line of sight is obtained using simultaneous diffused back illumination and Mie-scatter techniques. Experiments are performed under simulated engine conditions using a symmetric eight-hole injector in a high-temperature, high-pressure vessel at the “Spray G” operating conditions of the engine combustion network. Indicators of plume interaction and collapse include changes in counter-flow recirculation of ambient gas toward the injector along the axis of the injector or in the inter-plume region between plumes. Furthermore, the effect of ambient temperature and gas density on the inter-plume aerodynamics and the subsequent plume collapse are assessed. Increasing ambient temperature or density, with enhanced vaporization and momentum exchange, accelerates the plume interaction. Plume direction progressively shifts toward the injector axis with time, demonstrating that the plume interaction and collapse are inherently transient.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

EGS Collab Experiment 1: Common Discrete Fracture Network

This package includes data and models that support hydraulic fracture stimulation and fluid circulation experiments in the Sanford Underground Research Facility (SURF). A paper by Schwering et al. (2020) describes the deterministic basis for developing a "common" discrete fracture network (CDFN) model of significant natural fractures in EGS Collab Testbed 1 on the 4850-Level of SURF. The ReadMe for this model shows drift, wells, scanlines, fracture data, interpreted fractures, and geophysical visualizations. There is also a summary of the data that was used in this experiment and includes results from reviewing core, televiewer (TV) logs, core-TV depth/feature registration, and from mapping weeps in the 4850-Level drift. The CDFN is intended to be a baseline model of the pre-stimulated testbed (though some observations from stimulation helped inform the model).

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE 4-2492: Design and Implementation of Innovative Stimulation Treatments to Maximize Energy Recovery - Workshop Presentation

This is a presentation on the Design and Implementation of Innovative Stimulation Treatments to Maximize Energy Recovery Efficiency at the Utah FORGE Site project by The University of Texas at Austin, presented by Professor Mukul M. Sharma. The project's objectives were to place fractures uniformly in a horizontal well (improve cluster efficiency) to ensure uniform distribution of flow into all hydraulic fractures, maximize the area of the created fracture network, ensure connectivity of the fractures from the injector to the producer, and optimize fracture size. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Marginal Loss Calculations for the DCOPF

The purpose of this paper is to explain some aspects of including a marginal line loss approximation in the DCOPF. The DCOPF optimizes electric generator dispatch using simplified power flow physics. Since the standard assumptions in the DCOPF include a lossless network, a number of modifications have to be added to the model. Calculating marginal losses allows the DCOPF to optimize the location of power generation, so that generators that are closer to demand centers are relatively cheaper than remote generation. The problem formulations discussed in this paper will simplify many aspects of practical electric dispatch implementations in use today, but will include sufficient detail to demonstrate a few points with regard to the handling of losses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Comparison of characteristic schemes for three-dimensional, steady, isentropic flow.

The accuracy and order of three bicharacteristic schemes based on the numerical method of characteristics for three-dimensional, steady, isentropic, supersonic flow were compared based on numerical calculations. Comparisons were made for source flows and for flows in contoured axisymmetric nozzles. The first two methods were tetrahedral line networks, differing only in the manner in which the cross derivatives were evaluated. The third method was the pentahedral line network proposed by Butler. All three methods were numerically stable and produced efficient and accurate solutions. A discussion of the relative merits and disadvantages of each method is presented.

Cline, M. C.↗
NASA NTRS1978

Symmetric voltage-controlled variable resistance

Feedback network makes resistance of field-effect transistor (FET) same for current flowing in either direction. It combines control voltage with source and load voltages to give symmetric current/voltage characteristics. Since circuit produces same magnitude output voltage for current flowing in either direction, it introduces no offset in presense of altering polarity signals. It is therefore ideal for sensor and effector circuits in servocontrol systems.

Vanelli, J. C.↗
NASA NTRS1978

A finite volume method for calculating transonic potential flow around wings from the pressure minimum integral

Analysis of the pressure minimum integral in the calculation of three-dimensional potential flow around wings makes it possible to use non-rectangular mesh networks for distributing the three-dimensional potential into discrete points. The method is comparatively easily expanded to the treatment of realistic airplane configurations. Shock-pressure affected pressure distributions on any wings are determined with accuracy using this method.

Eberle, A.↗
NASA NTRS1991

Surface wind convergence as a short-term predictor of cloud-to-ground lightning at Kennedy Space Center: A four-year summary and evaluation

Since 1986, USAF forecasters at NASA-Kennedy have had available a surface wind convergence technique for use during periods of convective development. In Florida during the summer, most of the thunderstorm development is forced by boundary layer processes. The basic premise is that the life cycle of convection is reflected in the surface wind field beneath these storms. Therefore the monitoring of the local surface divergence and/or convergence fields can be used to determine timing, location, longevity, and the lightning hazards which accompany these thunderstorms. This study evaluates four years of monitoring thunderstorm development using surface wind convergence, particularly the average over the area. Cloud-to-ground (CG) lightning is related in time and space with surface convergence for 346 days during the summers of 1987 through 1990 over the expanded wind network at KSC. The relationships are subdivided according to low level wind flow and midlevel moisture patterns. Results show a one in three chance of CG lightning when a convergence event is identified. However, when there is no convergence, the chance of CG lightning is negligible.

Watson, Andrew I.↗
DOE OSTI2025

Internship Presentation: Reactor System Facility Modification to Detect Compromised Human Machine Interfaces

This study presents a multi-layered Industrial Control System (ICS)/Operational Technology (OT) security architecture aimed at detecting and mitigating compromised Human Machine Interface (HMI) and Instrumentation & Control (I&C) systems within the Flowing Autoclave System (FAS) at Idaho National Laboratory (INL). The approach combines network security solutions, hash-based algorithms, and blockchain technologies to verify system integrity and provide an immutable record of network activity. This integrated three-pronged strategy enhances the detection of system compromises, enabling preemptive action before significant damage occurs.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Stanford Thermal Earth Model for the Conterminous United States

Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Bypass Flow Resistance in Prismatic Gas-Cooled Nuclear Reactors

Available computational fluid dynamics (CFD) predictions of pressure distributions in the vertical bypass flow between blocks in a prismatic gas-cooled reactor (GCR) have been analyzed to deduce apparent friction factors and loss coefficients for systems and network codes. We performed calculations for vertical gap spacings "s" of 2, 6 and 10 mm, horizontal gaps between the blocks of two mm and two flow rates, giving a range of gap Reynolds numbers Re Dh of about 40 to 5300. Laminar predictions of the fully-developed friction factor f fd were about three to ten per cent lower than the classical infinitely-wide channel In the entry region, the local apparent friction factor was slightly higher than the classic idealized case but the hydraulic entry length L hy was approximately the same. The per cent reduction in flow resistance was greater than the per cent increase in flow area at the vertical corners of the blocks. The standard k-ϵ model was employed for flows expected to be turbulent. Its predictions of f fd and flow resistance were significantly higher than direct numerical simulations for the classic case; the value of L hy was about thirty gap spacings. Initial quantitative information for entry coefficients and loss coefficients for the expansion-contraction junctions between blocks is also presented. Our study demonstrates how CFD predictions can be employed to provide integral quantities needed in systems and network codes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Data Driven Temperature Control of Heat Exchangers

The following poster presents a couple of approaches for temperature control at heat exchanger’s exit. One of the approaches is based on using n-step ahead neural network while the other approach utilizes a combination of control + learning module for temperature control. Designing robust controllers for typical heat exchangers is very challenging primarily because of complexities associated with the synthesis of dynamics of industrial heat exchangers. Even if a dynamics model is synthesized, the accuracy of it would be called into question as there can be lot of unmodeled system dynamics and random noise in input parameters. In many situations, the degree of uncertainty in the model of system being controlled limits the utility of optimal control design. Controller can be manually tuned on the field but it is very difficult to determine manual adjustments that result in overall improvement. These practical challenges call for learning based approaches for temperature control that would result in optimal heat exchanger performance. We utilize the existing mathematical model for air-water system synthesized by Underwood and Crawford for testing our algorithms. Underwood and Crawford developed a model of heating coil by fitting a second-order non-linear equations to measurements of air and water temperature and flow rates obtained from the actual coil. The uncertainties in the model include air and water inlet temperatures and air-flow rate. These uncertainties are modeled as random variables that are sampled from a uniform distribution. The control variable is the water flow rate which is controlled for regulating the air-outlet temperature. Neural networks are frequently used in various literatures for learning time-series. We propose a modified version of neural networks known as n-step ahead neural network that take the state variables and output at time t as input to successfully predict output at time t+n. The variable n is a hyper-parameter which can be optimized based on performance constraints. The n-step ahead neural network was then trained on training data generated from experimental model to high training accuracies. This trained model performed appreciably in its objective to regulate outlet temperature of a heat exchanger. The second approach uses a combination of control and learning module for temperature regulation. The control module used is a proportional controller while the learning module used is steady state neural network. The steady state neural network is used to learn the controller output as a function of random variables and temperature set-point. The proportional controller is then used to compensate for transient behavior of linearized system around various operating points. The algorithm works effectively for temperature regulation as neural network captures the non-linear input-output behavior of closed loop system leaving proportional controller to compensate for steady state errors of a linearized plant. The proposed algorithm’s performances were evaluated against the performance of a PI controller. Further, the robustness of the algorithms were also studied in great detail under small perturbations around tuned parameters of the proposed algorithms.

Sundar, Sreenath↗
DOE OSTI2022

CPES-QSM: A Quantitative Method Towards the Secure Operation of Cyber-Physical Energy Systems

Power systems are evolving into cyber-physical energy systems (CPES) mainly due to the integration of modern communication and Internet-of-Things (IoT) devices. CPES security evaluation is challenging since the physical and cyber layers are often not considered holistically. Existing literature focuses on only optimizing the operation of either the physical or cyber layer while ignoring the interactions between them. This paper proposes a metric, the Cyber-Physical Energy System Quantitative Security Metric (CPES-QSM), that quantifies the interaction between the cyber and physical layers across three domains: electrical, cyber-risk, and network topology. A method for incorporating the proposed cyber-metric into operational decisions is also proposed by formulating a cyber-constrained AC optimal power flow (C-ACOPF) that considers the status of all the CPES layers. The C-ACOPF considers the vulnerabilities of physical and cyber networks by incorporating factors such as voltage stability, contingencies, graph-theory, and IoT cyber risks, while using a multi-criteria decision-making technique. We note that simulation studies are conducted using standard IEEE test systems to evaluate the effectiveness of the proposed metric and the C-ACOPF formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning

This paper considers distribution systems with a high penetration of distributed, renewable generation and addresses the problem of incorporating the associated uncertainty into the optimal operation of these networks. Joint chance constraints, which satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints, leading to a chance-constrained optimal power flow problem. Departing from the computationally-heavy scenario-based approaches or approximations that transform the joint constraint into conservative deterministic constraints, this paper develops a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and accurately and efficiently approximates bounds on the joint chance constraint iteratively. In particular, the proposed framework improves upon the classic methods based on the union bound (or Boole's inequality) by generating a much less conservative set of single chance constraints that also guarantees the satisfaction of the original joint constraint. The proposed framework is evaluated numerically using the IEEE 37-node test feeder, focusing on the problem of voltage regulation in distribution grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2017

Investigation of Vapor Cooling Enhancements for Applications on Large Cryogenic Systems

The need to demonstrate and evaluate the effectiveness of heat interception methods for use on a relevant cryogenic propulsion stage at a system level has been identified. Evolvable Cryogenics (eCryo) Structural Heat Intercept, Insulation and Vibration Evaluation Rig (SHIIVER) will be designed with vehicle specific geometries (SLS Exploration Upper Stage (EUS) as guidance) and will be subjected to simulated space environments. One method of reducing structure-born heat leak being investigated utilizes vapor-based heat interception. Vapor-based heat interception could potentially reduce heat leak into liquid hydrogen propulsion tanks, increasing potential mission length or payload capability. Due to the high number of unknowns associated with the heat transfer mechanism and integration of vapor-based heat interception on a realistic large-scale skirt design, a sub-scale investigation was developed. The sub-project effort is known as the Small-scale Laboratory Investigation of Cooling Enhancements (SLICE). The SLICE aims to study, design, and test sub-scale multiple attachments and flow configuration concepts for vapor-based heat interception of structural skirts. SLICE will focus on understanding the efficiency of the heat transfer mechanism to the boil-off hydrogen vapor by varying the fluid network designs and configurations. Various analyses were completed in MATLAB, Excel VBA, and COMSOL Multiphysics to understand the optimum flow pattern for heat transfer and fluid dynamics. Results from these analyses were used to design and fabricate test article subsections of a large forward skirt with vapor cooling applied. The SLICE testing is currently being performed to collect thermal mechanical performance data on multiple skirt heat removal designs while varying inlet vapor conditions necessary to intercept a specified amount of heat for a given system. Initial results suggest that applying vapor-cooling provides a 50 heat reduction in conductive heat transmission along the skirt to the tank. The information obtained by SLICE will be used by the SHIIVER engineering team to design and implement vapor-based heat removal technology into the SHIIVER forward skirt hardware design.

small scale testing↗