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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Perspective on the oil-dominated gas hydrate plugging conceptual picture as applied to transient Shut-In/Restart

Gas hydrate formation is rarely an issue during continuous production conditions. Unplanned and/or extended shut-downs comprise the majority of scenarios where overlapping concern and uncertainty for gas hydrate blockages exist. To date, most experimental studies and theoretical analysis of gas hydrate formation derive from the steady-state conceptual picture. Yet, significant distinctions induced by phase separation and prolonged cooling suggest an altered conceptual picture is required for transient shut-in/restart operations. To address this outstanding issue, this work presents a conceptual picture for the base case of a non-surface active hydrocarbon undergoing cold restart without enacted mitigation efforts. First, phase separation upon shut-in leads to the localization of gas hydrate ingredients due to fluids density differences and pipeline inclination. Cooling to the seafloor temperature, then, induces gas hydrate film formation at the hydrocarbon interface and maximizes subcooling, resulting in rapid and confined gas hydrate growth occurring upon restart. Furthermore, this ultimately leads to plugging dominated by the deposition of porous, low conversion gas hydrate masses. Direct visual observations and results from Raman spectroscopy, hydrate contact angle and film growth, micromechanical force apparatus, rocking cell, and flow loop studies support the mechanisms outlined within each step. Similar to the influence provided by the original Colorado School of Mines steady-state conceptual picture that was introduced about 15 years ago, this transient counterpart builds a foundation that could inspire new directions of hydrate research and analysis in areas where uncertainty abounds.

02 PETROLEUM↗
DOE OSTI2025

Scattering insights into shear-induced scission of rod-like micelles

Understanding the scission of rod-like micelles under mechanical forces is crucial for optimizing their stability and behavior in industrial applications. This study investigates how micelle length, flexibility, and external forces interact, offering insights into the design of micellar systems in processes influenced by mechanical stress. Although significant, direct experimental observations of flow-induced micellar scission using scattering techniques remain scarce. Small angle neutron scattering (SANS) is used to explore the shear response of aqueous cetyltrimethylammonium bromide (CTAB) solutions with sodium nitrate. Rheological tests show shear thinning with no shear banding, ensuring a uniform flow field for reliable interpretation of scattering data. As shear rate increases, the scattering spectra show angular distortion, which is analyzed using spherical harmonic decomposition to characterize flow-induced scission and micelle orientation under shear. Two analysis steps are used: a model-independent spectral eigendecomposition reveals a decrease in micellar length, while regression analysis quantifies the evolution of the length distribution and mean length with shear rate. Additionally, micelle alignment increases with shear, quantified by the orientational distribution function. In conclusion, these findings provide experimental evidence for flow-induced alignment and scission, offering a new framework for understanding shear-induced phenomena in micellar systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Assessing the Optimality of LinDist3Flow for Optimal Tap Selection of Step Voltage Regulators in Unbalanced Distribution Networks

The adoption of distributed energy resources such as photovoltaics (PVs) has increased dramatically during the previous decade. The increased penetration of PVs into distribution networks (DNs) can cause voltage fluctuations that have to be mitigated. One of the key utility assets employed to this end are step-voltage regulators (SVRs). It is desirable to include tap selection of SVRs in optimal power flow (OPF) routines, a task that turns out to be challenging because the resultant OPF problem is nonconvex with added complexities stemming from accurate SVR modeling. While several convex relaxations based on semi-definite programming (SDP) have been presented in the literature for optimal tap selection, SDP based schemes do not scale well and are challenging to implement in large-scale planning or operational frameworks. This paper deals with the optimal tap selection (OPTS) problem for wye-connected SVRs using linear approximations of power flow equations. Specifically, the LinDist3Flow model is adopted and the effective SVR ratio is assumed to be continuous- enabling the formulation of a problem called LinDist3Flow-OPTS, which amounts to a linear program. The scalability and optimality gap of LinDist3Flow-OPTS are evaluated with respect to existing SDP-based and nonlinear programming techniques for optimal tap selection in three standard feeders, namely, the IEEE 13-bus, 123-bus, and 8500-node DNs. For all DNs considered, LinDist3Flow-OPTS achieves an optimality gap of approximately 1% or less while significantly lowering the computational burden.

linear approximations↗
DOE OSTI2022

Development of Explainable Data-Driven Turbulence Models with Application to Liquid Fuel Nuclear Reactors

Liquid fuel nuclear reactors offer innovative possibilities in terms of nuclear reactor designs and passive safety systems. Molten Salts Reactors (MSRs) with a fast spectrum are a particular type of these reactors using liquid fuel. MSFRs often involve large open cavities in their core in which the liquid fuel circulates at a high speed to transport the heat generated by the nuclear reactions into the heat exchangers. This high-speed flow yields a turbulent field with large Reynolds numbers in the reactor core. Since the nuclear power, the neutron precursor’s transport and the thermal exchanges are strongly coupled in the MSFR’s core cavity, having accurate turbulent models for the liquid fuel flow is necessary to avoid introducing significant errors in the numerical simulations of these reactors. Nonetheless, high-accuracy simulations of the turbulent flow field in the reactor cavity of these reactors are usually prohibitively expensive in terms of computational resources, especially when performing multiphysics numerical calculations. Therefore, in this work, we propose a novel method using a modified genetic algorithm to optimize the calculation of the Reynolds Shear Stress Tensor (RST) used for turbulence modeling. The proposed optimization methodology is particularly suitable for advanced liquid fuel reactors such as the MSFRs since it allows the development of high-accuracy but still low-computational-cost turbulence models for the liquid fuel. We demonstrate the applicability of this approach by developing high accuracy Reynolds-Averaged Navier–Stokes (RANS) models (averaged flow error less than 5%) for a low and a large aspect ratio in a Backward-Facing Step (BFS) section particularly challenging for RANS models. The newly developed turbulence models better capture the flow field after the boundary layer tipping, over the extent of the recirculation bubble, and near the boundary layer reattachment region in both BFS configurations. The main reason for these improvements is that the developed models better capture the flow field turbulent anisotropy in the bulk region of the BFS. Then, we illustrate the interest in using this turbulence modeling approach for the case of an MSFR by quantifying the impact of the turbulence modeling on the reactor key parameters.

42 ENGINEERING↗
DOE OSTI2022

Assessing the Optimality of LinDist3Flow for Optimal Tap Selection of Step Voltage Regulators in Unbalanced Distribution Networks: Preprint

The adoption of distributed energy resources such as photovoltaics (PVs) has increased dramatically during the previous decade. The increased penetration of PVs into distribution networks (DNs) can cause voltage fluctuations that have to be mitigated. One of the key utility assets employed to this end are step-voltage regulators (SVRs). It is desirable to include tap selection of SVRs in optimal power flow (OPF) routines, a task that turns out to be challenging because the resultant OPF problem is nonconvex with added complexities stemming from accurate SVR modeling. While several convex relaxations based on semi-definite programming (SDP) have been presented in the literature for optimal tap selection, SDP based schemes do not scale well and are challenging to implement in large-scale planning or operational frameworks. This paper deals with the optimal tap selection (OPTS) problem for wye-connected SVRs using linear approximations of power flow equations. Specifically, the LinDist3Flow model is adopted and the effective SVR ratio is assumed to be continuous–enabling the formulation of a problem called LinDist3Flow-OPTS, which amounts to a linear program. The scalability and optimality gap of LinDist3Flow-OPTS are evaluated with respect to existing SDP-based and nonlinear programming techniques for optimal tap selection in three standard feeders, namely, the IEEE 13-bus, 123-bus, and 8500-node DNs. For all DNs considered, LinDist3Flow-OPTS achieves an optimality gap of approximately 1% or less while significantly lowering the computational burden.

linear approximations↗
DOE OSTI2026

Theoretical background for a fast flow liquid metal divertor experiment

Tokamak Energy has developed a liquid metal experiment featuring a lithium loop that circulates lithium through the HIDRA stellarator at the University of Illinois at Urbana-Champaign. The loop includes a replaceable divertor module, designed to demonstrate fast, steady, open-surface liquid metal flow. The first-generation divertor module was deliberately kept simple—an open-surface chute—to enable modelling validation and establish a benchmark for future design iterations. This paper presents the theoretical foundations of the experimental design. Specifically, we identify module overflooding as the primary challenge and determine the limits of fluid velocity and magnetic field strength necessary to prevent it. The steady-state flow patterns are expected to exhibit relatively slow surface velocities, which do not fully align with the fast-flow concept. Nevertheless, the experiment is designed to achieve controllable, continuous open-surface flow within an operational fusion device, representing a significant step toward the development of liquid metal divertor technology.

Experiment↗
DOE OSTI2020

Experimental demonstration of a 5 kW th granular-flow reactor for solar thermochemical energy storage with aluminum-doped calcium manganite particles

A two-step cycle was discussed for solar thermochemical energy storage based on aluminum-doped calcium manganite reduction/oxidation reactions for direct integration into Air Brayton cycles. The two steps encompassed 1) the storage of concentrated solar direct irradiation via the thermal reduction of aluminum-doped calcium manganite and 2) the delivery of heat to an Air-Brayton cycle via re-oxidation of oxygen-deficient aluminum-doped calcium manganite. The re-oxidized aluminum-doped calcium manganite was fed back to the first step to complete the cycle. A 5 kWth solar thermochemical reactor operating under vacuum was fabricated and tested to examine the first cycle reduction step. Reactor operating conditions and high-flux solar simulator control were tuned for continuous reactor operation with particle temperatures > 1073 K. Continuous operation was achieved using intermittent, dense granular flows. A maximum absorption efficiency of 64.7% was shown, accounting for both sensible and chemical heat storage.

25 ENERGY STORAGE↗
DOE OSTI2020

An Experimental Investigation of Multilayer Flow in a Slide Die Coating Process

Roll-to-roll coating technology has been actively applied not only for conventional film production but also for emerging energy devices such as organic solar cells, organic light emitting diodes, batteries, smart windows, and fuel cells. The devices are multilayer structures requiring several coating and drying steps when implemented in a serial fashion, which increases capital costs and energy consumption. Simultaneous multilayer (ML) coating technologies present an opportunity for cost reductions and improved process efficiency by reducing equipment footprint and the number of process steps. In this talk, we experimentally investigate ML slide die coating flow for the manufacturing of fuel cell membrane electrode assemblies (MEAs). For ML slide coating, understanding fluid properties such as density, surface tension, and viscosity are necessary for stable flows and limiting intermixing of the miscible layers. Here, we utilize a design-of-experiment methodology - Box-Behnken design (BBD) - to correlate ink formulation parameters to fluid properties. Using the ink properties obtained from the BBD, we performed numerical flow simulations to obtain a proper process conditions to guide experimental coating trials. Finally, experimental verification is performed on a ML slide die and a degree of miscibility was observed for various ink formulation conditions.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2023

Inverse aqueous transport modeling for emergency response

ALGE is a three-dimensional, finite-difference aqueous transport model that simulates pollutant fate and transport in lakes, rivers, bays, and estuaries by solving the prognostic equations of mass, momentum, and energy. Its current modeling capabilities include transport of dissolved tracer for a series of predefined basins across the continental United States. Recently, an inverse method (also known as backtracking) has been added to ALGE to provide a possible source of a pollutant should one be detected by a sensor in a body of water and a source is not known. This inverse method is a three step process that uses an algorithm to inverse the flow. We demonstrate the new model’s capabilities through simulating the 2021 Piney Point spill in Tampa Bay, Florida (USA). This involves moving tracer backwards from its detection points, encompassing a potential source area, and applying Bayes’ Theorem and $\frac{𝜒}{𝑄}$ to reduce the area within which the true source could be located.

hydrological modeling↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2020

Structure and Reactivity of Single-Site Vanadium Catalysts Supported on Metal–Organic Frameworks

Single-site catalysts with active species anchored over metal–organic framework (MOF) supports have garnered significant interest in recent years. Catalysts with vanadium oxide (VO x ) species immobilized over Zr-NU-1000 and Hf-MOF-808 (which have nodes containing six Zr (IV) or Hf (IV) ions, respectively) were recently characterized experimentally and were reported to be active for the selective oxidation of benzyl alcohol to benzaldehyde. Here, we report a detailed computational investigation of these VO x -incorporated MOF catalysts (V-MOF) by employing density functional theory. Based on the mode of VO x attachment, various structures are explored, and their relative stabilities and computed IR spectral features are reported. Mechanisms for the selective oxidation reaction are investigated. The analysis of electron flow in the turnover-limiting C–H activation step shows that the hydrogen-atom abstraction process involves a concerted proton-coupled electron-transfer mechanism. Here, the results of this study suggest that in addition to the identity of the metal in the node, the MOF node architecture plays a crucial role in driving the catalytic activities of V-MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Intrinsic rotation modulation by diffusive neutral particles in tokamaks

Abstract The modulated transport model, a model kinetic ion transport equation for the pedestal and scrape-off layer (SOL), is generalized to self-consistently include effects of a single neutral particle species. The neutrals contribute additional transport terms, modifying the v ∥ -dependent orbit-averaged ion diffusivities of the original work and the resulting predicted intrinsic rotation of the ions. After making simplifying assumptions of the neutral transport, in particular taking the continuous transport limit via a short charge-exchange step expansion, we derive relatively simple analytic expressions that capture the diffusive neutral physics. Within the scope of the model’s validity, the neutral-driven intrinsic rotation can compete with the turbulence-driven intrinsic rotation. However, for physically motivated parameters, the neutral-driven intrinsic rotation appears negligible, either on a term-by-term basis or due to a strong cancellation between the neutral-driven momentum diffusion and pinch terms. It appears that a treatment containing finite charge-exchange steps is necessary to capture neutral transport of strong flow momentum into the confined region from the SOL.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Estimating perturbed stress from 3-D borehole displacements induced by fluid injection in fractured or faulted shales

SUMMARY Hydrofracturing stress measurements in fractured and anisotropic shales are notoriously difficult, because opening of existing geological features tends to prevent the creation of a pure hydraulic fracture perpendicular to the least compressive principal stress. Here we show how adding 3-D borehole-displacement measurements while conducting the hydraulic injection test helps to better constrain the principal stress orientations and magnitudes. We developed a 3-D fully coupled hydromechanical numerical model to analyse the displacement, fluid pressure and injection flow-rate data measured during an injection pressure-step-rate test conducted to activate a faulted borehole interval in the Mont Terri Opalinus Clay (Switzerland). We find that injected fluids can only penetrate the fault when it is at or above the Coulomb failure pressure. Borehole displacement orientations are sensitive to a ∼15° variation in the stress–tensor orientation and a 1 MPa stress magnitude variation. Although some dispersion occurs while rupture is propagating along the fault plane ∼4 m away from the borehole, the maximum density of displacement orientations consistently informs about the stress orientation. Thus, an extended injection step-rate approach coupled with an accurate in situ measurement of the borehole wall displacements can be used to better constrain the local stress field perturbations in fractured shales and in heterogeneous rock in general.

Guglielmi, Yves↗
DOE OSTI2023

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

17 WIND ENERGY↗
DOE OSTI2021

Alternative low cost electrodes for hybrid flow batteries

An electrode for use in an all-iron redox flow battery is provided. In one example, the electrode may include a plastic mesh; and a coating on the plastic mesh. The coating may be a hydrophilic coating or a conductive coating and the electrode may have an electrode reaction potential is less than 0.8V. Further, a method of manufacturing a coated plastic mesh electrode for use in an all-iron redox flow battery is provided. In one example method, the steps include fabricating a plastic mesh, treating the plastic mesh by applying a solvent treatment or a plasma treatment or a mechanical abrasion treatment; coating the plastic mesh with a material selected from: carbon inks, metal oxides, and hydrophilic polymers.

Evans, Craig E.↗
DOE OSTI2025

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Uncertainty quantification (UQ) in nuclear reactors for transients is directly linked with safety assessment through the cross-sections uncertainties, provided as a covariance matrix, which are propagated through the reactor system to output of interest pertaining to reactor safety, such as peak temperatures in fuel/clad/coolant. Using a two-step approach, uncertainties are first quantified and propagated from basic input variables (such as reaction cross-sections) to intermediate quantities (such as reactivity feedback coefficients) through lattice level calculations. Uncertainties of intermediate quantities (from the first step) are then propagated through the system transient calculations, in the second step, to obtain uncertainties on reactor safety output parameters of interest. The scope of this work consists of Uncertainty Quantification & Propagation of nuclear data uncertainties that are highly correlated through unprotected transient overpower and unprotected loss of flow to assess their impact on core safety parameters. This two-step approach in the presence of covariance renders the sensitivity analysis very challenging. In fact, usually the sensitivity analysis is restricted to each step, which limits its application since the sensitivities between the system output quantities and the basic input variables are difficult to obtain. Here, in this work, we address this issue by proposing a simple, general methodology to combine the sensitivity indices obtained in each step by assuming the model behavior being linear. For the first step Generalized Perturbation theory based indices are used while in the second step the recently studied Johnson indices. The uncertainty quantification and sensitivity methodologies discussed here are demonstrated on a generic LFR design which is based on the 500 MWth demonstration Lead-cooled fast reactor (DLFR) using oxide fuel, developed by Westinghouse Electric Company (WEC).

42 - ENGINEERING↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2022

Six-electron organic redoxmers for aqueous redox flow batteries

Here we have developed a novel molecular design that enables six-electron redox activity in fused phenazine-based organic scaffolds. Combined electrochemical and spectroscopic tests successfully confirm the two-step 6e - redox mechanism. This work offers an opportunity for achieving energy-dense redox flow batteries, on condition that the solubility and stability issues are addressed.

25 ENERGY STORAGE↗