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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

A Reduced-Order Model of a Nuclear Power Plant with Thermal Power Dispatch

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations.

08 HYDROGEN↗
DOE OSTI2021

TPCPF: Three-Phase Continuation Power Flow Tool for Voltage Stability Assessment of Distribution Networks With Distributed Energy Resources

This article presents a three-phase unbalanced continuation power flow algorithm for voltage stability assessment of distribution systems with high penetration of distributed energy resources (DERs). Analyzing distribution system voltage stability with DER will allow high penetration of renewable energy necessary for the sustainability goals. The developed algorithm can analyze voltage stability for both the meshed and radial systems and the balanced and unbalanced three-phase distribution systems. The developed tool allows the voltage stability analysis to facilitate the planning, operation, control, and distribution system management. The impact of DER on the voltage stability of several test cases has been analyzed considering constant power (P Q) and regulated-voltage (P V ) modes of operation for DER units. Moreover, different voltage stability case studies are presented to demonstrate the impact of unbalance, load increment, and network topology on the maximum loading capacity. Finally, results using the IEEE 13-node feeder, the 18-bus balanced shipboard system, the 13-node CIGRE benchmark system, and the 136-bus redial distribution feeder demonstrate that the developed continuation power flow tool can efficiently perform voltage stability analysis for active distribution systems.

42 ENGINEERING↗
DOE OSTI2017

Improved characterization of heterogeneous permeability in saline aquifers from transient pressure data during freshwater injection

Managing recharge of freshwater into saline aquifers requires accurate estimation of the heterogeneous permeability field for maximizing injection and recovery efficiency. Here we present a methodology for subsurface characterization in saline aquifers that takes advantage of the density difference between the injected freshwater and the ambient saline groundwater. We combine high-resolution forward modeling of density-driven flow with an efficient Bayesian geostatistical inversion algorithm. In the presence of a density difference between the injected and ambient fluids due to differences in salinity, the pressure field is coupled to the spatial distribution of salinity. This coupling renders the pressure field transient: the time evolution of the salinity distribution controls the density distribution which then leads to a time-evolving pressure distribution. We exploit this coupling between pressure and salinity to obtain an improved characterization of the permeability field without multiple pumping tests or additional salinity measurements. We show that the inversion performance improves with an increase in the mixed convection ratio—the relative importance between viscous forces from injection and buoyancy forces from density difference. Thus, our work shows that measuring transient pressure data at multiple sampling points during freshwater injection into saline aquifers can be an effective strategy for aquifer characterization, key to the successful management of aquifer recharge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Radiation GRMHD Models of Accretion onto Stellar-mass Black Holes. II. Super-Eddington Accretion

We present a comprehensive analysis of super-Eddington black hole accretion simulations that solve the GRMHD equations coupled with angle-discretized radiation transport. The simulations span a range of accretion rates, two black hole spins, and two magnetic field topologies, and include resolution studies as well as comparisons with nonradiative models. Super-Eddington accretion flows consistently develop geometrically thick disks supported by radiation pressure, regardless of magnetic field configuration. Radiation generated in the inner disk drives substantial outflows, forming conical funnel regions that limit photon escape and result in very low radiation efficiency. The accretion flows are highly turbulent, with thermal energy transport dominated by radiation advection rather than diffusion. Angular momentum is primarily carried outward by Maxwell stress, with turbulent Reynolds stress playing a subdominant role. Both strong and weak jets are produced. Strong jets arise from sufficient net vertical magnetic flux and rapid black hole spin, and they can effectively evacuate the funnel, enabling radiation to escape through strong geometric beaming. In contrast, weak jets fail to clear the funnel, which becomes obscured by radiation-driven outflows and leads to distinct observational signatures. Spiral structures are observed in the plunging region, behaving like density waves. These super-Eddington models are applicable to a variety of astronomical systems, including ultraluminous X-ray sources, little red dots, and black hole transients.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

Modeling and Grid Generation of Iced Airfoils

SmaggIce Version 2.0 is a software toolkit for geometric modeling and grid generation for two-dimensional, singleand multi-element, clean and iced airfoils. A previous version of SmaggIce was described in Preparing and Analyzing Iced Airfoils, NASA Tech Briefs, Vol. 28, No. 8 (August 2004), page 32. To recapitulate: Ice shapes make it difficult to generate quality grids around airfoils, yet these grids are essential for predicting ice-induced complex flow. This software efficiently creates high-quality structured grids with tools that are uniquely tailored for various ice shapes. SmaggIce Version 2.0 significantly enhances the previous version primarily by adding the capability to generate grids for multi-element airfoils. This version of the software is an important step in streamlining the aeronautical analysis of ice airfoils using computational fluid dynamics (CFD) tools. The user may prepare the ice shape, define the flow domain, decompose it into blocks, generate grids, modify/divide/merge blocks, and control grid density and smoothness. All these steps may be performed efficiently even for the difficult glaze and rime ice shapes. Providing the means to generate highly controlled grids near rough ice, the software includes the creation of a wrap-around block (called the "viscous sublayer block"), which is a thin, C-type block around the wake line and iced airfoil. For multi-element airfoils, the software makes use of grids that wrap around and fill in the areas between the viscous sub-layer blocks for all elements that make up the airfoil. A scripting feature records the history of interactive steps, which can be edited and replayed later to produce other grids. Using this version of SmaggIce, ice shape handling and grid generation can become a practical engineering process, rather than a laborious research effort.

Vickerman, Mary B.↗
NASA NTRS1994

Numerical study of the effects of icing on viscous flow over wings

An improved hybrid method for computing unsteady compressible viscous flows is presented. This method divides the computational domain into two zones. In the outer zone, the unsteady full-potential equation (FPE) is solved. In the inner zone, the Navier-Stokes equations are solved using a diagonal form of an alternating-direction implicit (ADI) approximate factorization procedure. The two zones are tightly coupled so that steady and unsteady flows may be efficiently solved. Characteristic-based viscous/inviscid interface boundary conditions are employed to avoid spurious reflections at that interface. The resulting CPU times are less than 60 percent of that required for a full-blown Navier-Stokes analysis for steady flow applications and about 60 percent of the Navier-Stokes CPU times for unsteady flows in non-vector processing machines. Applications of the method are presented for a rectangular NACA 0012 wing in low subsonic steady flow at moderate and high angles of attack, and for an F-5 wing in steady and unsteady subsonic and transonic flows. Steady surface pressures are in very good agreement with experimental data and are essentially identical to Navier-Stokes predictions. Density contours show that shocks cross the viscous/inviscid interface smoothly, so that the accuracy of full Navier-Stokes equations can be retained with a significant savings in computational time.

Sankar, L. N.↗
DOE OSTI2026

Quantifying Energy Storage Density Utilization Trends in Redox Flow Battery Chemistries

The transition to new energy resources is driving the need for efficient energy storage. Redox flow batteries (RFBs) are a promising solution due to their flexible design and safer materials, particularly for long-duration storage applications. Despite these advantages, RFB technologies still have considerable room for growth in terms of utilizing all of their available energy storage density. Herein, we assess RFB energy storage density utilization trends for multiple chemistries by accounting for their unique thermodynamic limits, energy efficiencies, and charge capacity constraints. Of the RFBs chemistries analyzed, most still use less than 60% of their theoretical energy storage capacity. The conventional all-vanadium RFB was the closest to its thermodynamic limit available. Moreover, the volumetric footprint and electrolyte cost analyses demonstrate the real-world implications of these inefficiencies. This analysis further indicates that future studies would benefit from clearer reporting of two key quantities: discharge efficiency, which is more directly tied to practically recoverable energy density than round-trip energy efficiency, and state-of-charge or capacity-utilization limits, which govern access to theoretical storage capacity. More consistent reporting of these factors would improve the accuracy and comparability of energy storage utilization analyses across RFB chemistries.

battery energy storage↗
DOE OSTI2017

Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model

Groundwater model structural error is ubiquitous, due to simplification and/or misrepresentation of real aquifer systems. During model calibration, the basic hydrogeological parameters may be adjusted to compensate for structural error. This may result in biased predictions when such calibrated models are used to forecast aquifer responses to new forcing. Here, we investigate the impact of model structural error on calibration and prediction of a real-world groundwater flow model, using a Bayesian method with a data-driven error model to explicitly account for model structural error. The error-explicit Bayesian method jointly infers model parameters and structural error and thereby reduces parameter compensation. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models (based on machine learning techniques) as a substitute for the computationally expensive groundwater model. We demonstrate that with explicit treatment of model structural error, the Bayesian method yields parameter posterior distributions that are substantially different from those derived using classical Bayesian calibration that does not account for model structural error. We also found that the error-explicit Bayesian method gives signficantly more accurate prediction along with reasonable credible intervals. Finally, through variance decomposition, we provide a comprehensive assessment of prediction uncertainty contributed from parameter, model structure, and measurement uncertainty. The results suggest that the error-explicit Bayesian approach provides a solution to real-world modeling applications for which data support the presence of model structural error, yet model deficiency cannot be specifically identified or corrected.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Abstract The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( $$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

36 MATERIALS SCIENCE↗
NASA NTRS1999

En route Spacing Tool: Efficient Conflict-free Spacing to Flow-Restricted Airspace

This paper describes the Air Traffic Management (ATM) problem within the U.S. of flow-restricted en route airspace, an assessment of its impact on airspace users, and a set of near-term tools and procedures to resolve the problem. The FAA is committed, over the next few years, to deploy the first generation of modem ATM decision support tool (DST) technology under the Free-Flight Phase-1 (FFp1) program. The associated en route tools include the User Request Evaluation Tool (URET) and the Traffic Management Advisor (TMA). URET is an initial conflict probe (ICP) capability that assists controllers with the detection and resolution of conflicts in en route airspace. TMA orchestrates arrivals transitioning into high-density terminal airspace by providing controllers with scheduled times of arrival (STA) and delay feedback advisories to assist with STA conformance. However, these FFPl capabilities do not mitigate the en route Miles-In-Trail (MIT) restrictions that are dynamically applied to mitigate airspace congestion. National statistics indicate that en route facilities (Centers) apply Miles-In-Trail (MIT) restrictions for approximately 5000 hours per month. Based on results from this study, an estimated 45,000 flights are impacted by these restrictions each month. Current-day practices for implementing these restrictions result in additional controller workload and an economic impact of which the fuel penalty alone may approach several hundred dollars per flight. To mitigate much of the impact of these restrictions on users and controller workload, a DST and procedures are presented. The DST is based on a simple derivative of FFP1 technology that is designed to introduce a set of simple tools for flow-rate (spacing) conformance and integrate them with conflict-probe capabilities. The tool and associated algorithms are described based on a concept prototype implemented within the CTAS baseline in 1995. A traffic scenario is used to illustrate the controller's use of the tool, and potential display options are presented for future controller evaluation.

Green, S.↗
NASA NTRS2013

Scheduling and Separating Departures Crossing Arrival Flows in Shared Airspace

Flight efficiency and reduction of flight delays are among the primary goals of NextGen. In this paper, we propose a concept of shared airspace where departures fly across arrival flows, provided gaps are available in these flows. We have explored solutions to separate departures temporally from arrival traffic and pre-arranged procedures to support controllers' decisions. We conducted a Human-in-the-Loop simulation and assessed the efficiency and safety of 96 departures from the San Jose airport (SJC) climbing across the arrival airspace of the Oakland and San Francisco arrival flows. In our simulation, the SJC tower had a tool to schedule departures to fly across predicted gaps in the arrival flow. When departures were mistimed and separation could not be ensured, a safe but less efficient route was provided to the departures to fly under the arrival flows. A coordination using a point-out procedure allowed the arrival controller to control the SJC departures right after takeoff. We manipulated the accuracy of departure time (accurate vs. inaccurate) as well as which sector took control of the departures after takeoff (departure vs. arrival sector) in a 2x2 full factorial plan. Results show that coordination time decreased and climb efficiency increased when the arrival sector controlled the aircraft right after takeoff. Also, climb efficiency increased when the departure times were more accurate. Coordination was shown to be a critical component of tactical operations in shared airspace. Although workload, coordination, and safety were judged by controllers as acceptable in the simulation, it appears that in the field, controllers would need improved tools and coordination procedures to support this procedure.

shared airspace↗
DOE OSTI2023

3D Printed Carbon Fiber Reinforced Carbon: An energy efficient alternative to graphite EFAS tooling

Carbon fiber preforms are printed in a Quasi-Isotropic layup, where the fibers are aligned to the X and Y axes, and ±45° in plane. Thermal and electrical conductivity values along the fiber axes are comparable to graphite but are extremely low through the Z axis. Low conductivity (high resistivity) in the axis of current flow enables more efficient Joule heating.

36 MATERIALS SCIENCE↗
NASA NTRS2020

NASA Exploration Systems & Habitation (X-Hab)Academic Innovation Challenge 2020: Microgravity Gas/Liquid Separator for the CO2 Revitalization System Final Report

This report outlines the efforts of UNT X-Hab Team on a project titled, Microgravity Gas/Liquid Separator for the CO2 Revitalization System. The project is executed as a senior design project during academic year 2019-2020. The X-Hab 2020 Academic Innovation Challenge has selected eleven senior design teams from colleges across the US to demonstrate working prototypes for exploration systems and habitation. UNT has been tasked with the creation of a gas-liquid separator for an air revitalization system. Air revitalization technology has been used to support spaceflight by removing CO2 from enclosed systems in order to maintain breathable air. Solid sorbents such as zeolites or lithium hydroxide have been used in the past for these systems but are difficult to handle in microgravity environments and require a large amount of energy. This challenge aims to demonstrate vortex phase separator (VPS) technology for removing H2O from a CO2 stream. However, VPS technology also has the capability of using liquid sorbents for removing CO2 from an air stream. Further research could be done on liquid sorbents, such as liquid amine, in use with VPS systems. This would potentially be an alternative technology in replacing use of solid sorbents in CO2 removal systems. In 2019, NASA proposed a design that uses a gas/liquid contactor to allow for efficient contact between the two fluid phase. This was integrated into an overall CO2 removal system. The subsystems for gas-liquid separation and storage in NASA’s previous models for CO2 removal system could be replaced with a VPS. Innovative vortex separator technology is expected to allow for high throughput flow and highly efficient CO2 removal compared to other gas-liquid separation technologies. VPS relies on centripetal driven buoyancy forces to form a gas-liquid vortex within a fixed, right circular cylinder. The gas stream enters the separator through a tangential nozzle and breaks into very small bubbles (<<1 mm) resulting in a very large contact surface area for interaction with the liquid stream via energy and mass exchange. VPS technology can handle mismatches in inlet and outlet flow rates and system volume changes through the range of liquid thickness held in the separator (i.e., buffering and accumulating capability), and requires low pressure differences (<5-10 in H2O in most cases) for operation. The X-Hab 2020 team leverages these characteristics to investigate VPS technology as an alternative CO2 removal technology.

Alyssa Sarvadi↗
NASA NTRS1989

Application of a Two-dimensional Unsteady Viscous Analysis Code to a Supersonic Throughflow Fan Stage

The Rai ROTOR1 code for two-dimensional, unsteady viscous flow analysis was applied to a supersonic throughflow fan stage design. The axial Mach number for this fan design increases from 2.0 at the inlet to 2.9 at the outlet. The Rai code uses overlapped O- and H-grids that are appropriately packed. The Rai code was run on a Cray XMP computer; then data postprocessing and graphics were performed to obtain detailed insight into the stage flow. The large rotor wakes uniformly traversed the rotor-stator interface and dispersed as they passed through the stator passage. Only weak blade shock losses were computerd, which supports the design goals. High viscous effects caused large blade wakes and a low fan efficiency. Rai code flow predictions were essentially steady for the rotor, and they compared well with Chima rotor viscous code predictions based on a C-grid of similar density.

Steinke, Ronald J.↗
NASA NTRS1981

Implicit calculations of transonic flows using monotone methods

Implicit approximate-factorization algorithms have been developed that use monotone methods for the calculation of steady and unsteady transonic flows governed by the small-disturbance-potential equation. These algorithms use the new Engquist-Osher switch in the type-dependent differencing in place of the standard Murman-Cole switch. The resulting algorithms are more stable; hence, calculations can be done more efficiently. For steady flows, the convergence rate is about 35% faster, and for unsteady flows the allowable time step is about 10 times larger. These improvements are achieved with no increase in computer storage and with only minor modifications in codes that use the Murman-Cole switch. Also an implicit algorithm has been developed for the steady full-potential equation in one-dimension, which uses monotone methods.

Goorjian, P. M.↗
NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗