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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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DOE OSTI2024

Adaptive Power Flow Approximations With Second-Order Sensitivity Insights

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. In conclusion, this approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Measurements of the Flow in the Vicinity of an Additively Manufactured Turbine Leading-Edge Using X-Ray Particle Tracking Velocimetry

X-ray particle tracking velocimetry (PTV) is performed, for the first time, to measure the velocity field inside a leading-edge of a turbine blade made by laser-additive-manufacturing (LAM) process. The traditional showerhead holes were replaced by a porous matrix in the leading-edge. The flow through such a leading-edge piece cannot be faithfully recreated by traditional prototype testing methods due to the surface roughness and imperfections caused by LAM process. Hence, direct measurement is the only option. However, it is difficult to measure flow inside such pieces with traditional velocimetry measurements due to the existence of metallic walls. Moreover, small internal size and high flow speeds call for a measurement technique with high spatial and temporal resolutions. To address these issues, we performed time-resolved X-ray PTV using the Advanced Photon Source (APS) synchrotron facility at the Argonne National Laboratory (ANL). A hydraulic system was constructed to run water, mixed with seeding particles, through the leading-edge piece. A high-speed camera captured the images of the seeding particles, which were later processed to create particle tracks. The time-averaged velocity field showed distinct pairs of vortices located in front of the porous outlet inside the leading-edge piece. The inlet channel showed reversed flow due to partial obstruction by the porous inlet of the test piece. Such knowledge of the flow field inside a leading-edge of a turbine blade will help us to design better cooling paths leading to higher cooling efficiency and increased life-span of a turbine blade.

42 ENGINEERING↗
DOE OSTI2020

<i>Finite Element Modeling of Biomass Hopper Flow</i>

The hopper is one of the widely used biomass handling devices that channels the bulk biomass from storage to the subsequent handling systems. Although Jenike's approach has been successfully used for hoppers handling grains and other agricultural produces for decades, designing a hopper, ensuring reliable biomass handling is found to be challenging. This study aims to address this engineering problem with alternative constitutive material models concerning the flow behavior of bulk solids. Finite element modeling is an approach that allows for implementing different material models. Underlying constitutive theories of different material models assist in investigating the mechanical behavior of a particulate system, including the flow characteristics of ground biomass. This study demonstrates hopper flow models of two types of biomass, i.e., ground corn stover and Douglas fir, with the Mohr-Coulomb model, which is based on Jenike's approach, modified Cam-Clay model, and Drucker-Prager/Cap model. The modeling results are compared with hopper flow experiments to highlights the advantages and shortfalls of each constitutive biomass flow model.

09 BIOMASS FUELS↗
DOE OSTI2020

Probing interfacial momentum closures in two-phase bubbly flow with machine learning-aided methods

Computational fluid dynamics (CFD) approach has already reached a high level of maturity for single-phase flows, however the development of closure models for two-phase flow requires additional attention. Multiphase CFD (M-CFD) methods resolve the conservation equations for mass, momentum and energy while differing in the approaches and strategies adopted in the physical closure models. The most widely adopted framework for M-CFD is the Eulerian-Eulerian two-fluid approach which assumes that all phases are co-existing inside each computational cell. For each fluid, the full set of conservation equations is solved; therefore, each fluid has a different velocity field. For adiabatic two-phase flow, the mechanisms of the interfacial momentum transfer are modeled by the interfacial forces representing different physical mechanisms. One of the crucial issues in the development and application of two-fluid model is the understanding of the interfacial momentum closures which determines the bubble distribution and migration behaviors. Dedicated experiments are performed to support the physical understanding and drive the closures’ development. However, limitations exist due to the uncertainties in the experimental measurement and the simplified analytical assumptions which have difficulties on representing the complex non-linear flow fields. In this paper, a data-driven approach, Feature Similarity Measurement (FSM), is developed and proposed to resolve the challenges of modeling the interfacial forces closures. Case study is performed with two-phase flow scenarios where the high-fidelity experimental data is available. Within the Eulerian-Eulerian two-fluid framework, only momentum equations for gas and liquid phases are solved and reduced-order interfacial momentum closures are aided with FSM. Predictions of void fraction and velocity fields are analyzed and demonstrate the potential of machine learning-driven interfacial forces closures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Designing Hydropower Flows to Balance Energy and Environmental Needs (HydroWIRES Topic A Final Project Report)

Hydropower is expected to play a new role in the US electricity grid as more variable renewable energy sources like wind and solar come online. Wind and solar generation increase fluctuations in electrical supply increasing the value of flexible generation sources that can quickly ramp generation up and down. The flexible generation hydropower can provide as well as the ancillary services (e.g., frequency and voltage regulation and reserves, black start capability) it provides for balancing and stabilizing the power grid are predicted to be of increased value in these future grid scenarios. Yet, the flexibility of hydropower may come with environmental costs due to up- and down-ramping of hydropower plants (i.e., quickly increasing or decreasing generation flows, respectively) which may strand fish, dewater or scour fish nests, alter habitat, or create unsafe recreational conditions that may be unacceptable to participants in the hydropower regulatory process. These types of environmental impacts are often mitigated through environmental flow requirements that specify minimum or maximum flow releases, or ramp-rates changes allowed at a hydropower facility. While it is not currently known to what degree electrical grid reliability could be affected by environmental flow requirements, gaining a better understanding of these interactions before the grid becomes more deeply decarbonized can help define what policy, regulation, or infrastructure may be needed to support the clean energy transition. As the future grid will rely on hydropower to provide both flexibility and robust environmental protections, the analyses and tools described in this report are centered on making mechanistic linkages between energy and the environment in hydropower systems. This understanding of energy-environment linkages can provide the foundational understanding needed to quantitatively assess the trade-offs between the increased generation flexibility that hydropower will be expected to provide and the environmental impacts of this flexibility. This report seeks to provide an objective foundation for building future science and tools that can be used by a broad spectrum of the hydropower community that is involved in licensing or environmental regulatory proceedings tasked with balancing energy and environmental objectives through flow management.

13 HYDRO ENERGY↗
DOE OSTI2021

Applying Time-Parallelization to Turbulent Flows

Parallelization of the temporal domain is explored for the solution of turbulent flows. Multigrid reduction-in-time (MGRIT) is used to advance the large-scale fluid dynamics in time sequentially on the coarsest space-time grid but propagate the information in time parallel on all other levels. The goal of this process is to accurately and efficiently resolve the coarse-scale turbulence structure and use that to drive the fine-scales of the turbulent flow. The extra forcing from nonlinear multigrid facilitates the coupling and interaction between fine and coarse scales, through which the multiscale nonlinear physics is properly captured. Adaptive mesh refinement is employed to finely resolve only the regions with strong gradients, which provides further computational efficiency. The underlying computational fluid dynamics solver is a fourth-order finite-volume scheme with the standard 4-stage Runge-Kutta method. An advanced approach is devised and implemented to enable MGRIT to solve highly turbulent flows successfully. Furthermore, the method is applied to solve a Taylor-Green vortex problem and a doubleshear-layer turbulent mixing flow. Results are promising, validating that MGRIT with the filtering approach has the potential to efficiently solve general turbulent flows.

Computational Fluid Dynamics↗
DOE OSTI2024

Applying colloidal silica suspensions injection and sequential gelation to block vertical water flow in well annulus: laboratory testing on rheology, gelation, and injection

We evaluated the application of silica suspension injection and sequential gelation to block vertical water flow in the annuli of long-screened wells. First, we studied the viscosity, rheological behavior, and gelation performance of colloidal silica suspensions in batch tests. Then, we tested the injection of silica suspensions and the water flow blocking efficiency of the later formed silica gel in column and bench-scale sandbox experiments. Micron-sized fumed powder silica suspensions and nanosized silica suspensions recovered from geothermal fluids were tested in this work. Fumed silica suspensions showed shear thinning, while nanosized silica suspensions exhibited Newtonian flow behavior. During the gelation process, the nanosized silica suspension changed from a Newtonian fluid to a shear thinning fluid while increasing its overall viscosity. At comparable concentrations, the nanosized silica suspensions have much lower viscosity than that of the fumed silica suspensions. Increases in the Na + concentration and silica particle concentration in these suspensions shortened the gelation time. Silica suspension gelation in sand columns completely blocked the water flow and sustained the injection pressure up to 50 psig (344.7 kPa). A silica suspension was successfully injected into the target zone in the annulus of a bench-scale sandbox mimicking long-screened wells in the field. The silica gel formed in the annulus effectively blocked chemical transport through the gelled zone. Our research reveals that a process using silica suspension injection and sequential gelation technology is promising for blocking the vertical water flow and chemical transport through the filter pack in targeted zones within the annulus of long-screened well systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

High y + Shear-Stress Turbulence Implementation for High Flux Isotope Reactor Narrow Channel Flows

The research objective of this work was to improve the engineering predictions of the turbulence characteristics of flows in curved narrow channels. Such channel flows are commonly encountered in nuclear research and test reactors, with one of them being the high-flux isotope reactor (HFIR). Research reactors bear high heat fluxes, and the proper computing of turbulence is paramount for safe and reliable reactor operation. The study builds on the results of a previous direct numerical simulation of turbulence to inform a well-known Reynolds-averaged Navier–Stokes shear-stress turbulence model and improves its accuracy in simulating parallel channel flows. A new formulation of the loss term in the dissipation conservation equation is suggested. Combined with high wall distance computational grids, the new implementation provides a fast-running flow solution, suitable for engineering purposes. Model generalization for parallel channel flows, in a broader range of frictional Reynolds numbers, is suggested by introducing a new form of the model constants.

CFD↗
DOE OSTI2022

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.

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

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions

Wind profiling by Doppler lidar is common practice and highly useful in a wide range of applications. Airborne Doppler lidar can provide additional insights relative to ground-based systems by allowing for spatially distributed and targeted measurements. Providing a link between theory and measurement, a first large eddy simulation (LES)-based airborne Doppler lidar simulator (ADLS) has been developed. Simulated measurements are conducted based on LES wind fields, considering the coordinate and geometric transformations applicable to real-world measurements. The ADLS provides added value as the input truth used to create the measurements is known exactly, which is nearly impossible in real-world situations. Thus, valuable insight can be gained into measurement system characteristics as well as retrieval strategies. As an example application, airborne Doppler lidar wind profiling is investigated using the ADLS. For commonly used airborne velocity azimuth display (AVAD) techniques, flow homogeneity is assumed throughout the retrieval volume, a condition which is violated in turbulent boundary layer flow. Assuming an ideal measurement system, the ADLS allows to isolate and evaluate the error in wind profiling which occurs due to the violation of the flow homogeneity assumption. Overall, the ADLS demonstrates that wind profiling is possible in turbulent wind field conditions with reasonable errors (root mean squared error of 0.36 m s –1 for wind speed when using a commonly used system setup and retrieval strategy for the conditions investigated). Nevertheless, flow inhomogeneity, e.g., due to boundary layer turbulence, can cause an important contribution to wind profiling error and is non-negligible. Results suggest that airborne Doppler lidar wind profiling at low wind speeds (<5 m s –1 ) can be biased, if conducted in regions of inhomogeneous flow conditions.

17 WIND ENERGY↗
DOE OSTI2022

The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake

In this paper, a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at a low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics. This is achieved by creating so-called observation points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computationally lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to Simulator for Offshore Wind Farm Applications (SOWFA) simulations in three- and nine-turbine cases under static and changing environmental conditions. The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three-turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model-based dynamic wind farm control.

17 WIND ENERGY↗
DOE OSTI2020

Examining the Net Revenue and Downstream Flow Impact Trade-Offs for a Network of Cascading, Small-Scale Hydropower Facilities: Preprint

In this work, we used a price-taker model to investigate the trade-offs between net revenue and downstream flow impacts for a network of small, cascading hydro facilities. The network consisted of 36 facilities, each with a small amount of local storage (between 2 and 45 minutes). Generator sizes ranged between 0.5 and 1 MW, nominal, and the total capacity of the network was 33.5 MW. We used a multi-integer linear programing model to maximize the net revenue of the combined network subject to operating and environmental constraints. Net revenue optimizations relied on historic price data, and dry, typical, and wet years were studied to help ensure robustness. Energy and ancillary service sales were included in the net revenue calculations, and both unit commitment and dispatch simulations were performed. We found that limiting the downstream flows to ±50% of the river’s natural flows had a negligible impact on net revenues (<1% reduction), irrespective of hydrologic conditions, and even when downstream flows were limited to ±5%, net revenues were only impacted by 4%. These outcomes are significant because they demonstrate how an array of small-scale hydropower facilities can be operated to have minimal impact on natural stream flows—addressing a critical environmental concern.

downstream flow↗
DOE OSTI2021

Standing Shock Regulates Sparks in Explosive Flows

Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact electrification and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and < 100 mg of diamond particles. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. The sparks occur below the sharp boundary of a condensation cloud. These results agree closely with the Mach disk shock shape and height in the simulation. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Observation of Hydrodynamic Flows in Imploding Fusion Plasmas on the National Ignition Facility [plus supplemental information]

Inertial confinement fusion implosions designed to have minimal fluid motion at peak compression often show significant linear flows in the laboratory, attributable per simulations to percent-level imbalances in the laser drive illumination symmetry. In this work, we present experimental results which intentionally varied the mode 1 drive imbalance by up to 4% to test hydrodynamic predictions of flows and the resultant imploded core asymmetries and performance, as measured by a combination of DT neutron spectroscopy and high-resolution x-ray core imaging. Neutron yields decrease by up to 50%, and anisotropic neutron Doppler broadening increases by 20%, in agreement with simulations. Furthermore, a tracer jet from the capsule fill-tube perturbation that is entrained by the hot-spot flow confirms the average flow speeds deduced from neutron spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Multiscale Shear Properties and Flow Performance of Milled Woody Biomass

One dominant challenge facing the development of biorefineries is achieving consistent system throughput with highly variant biomass feedstock quality and handling performance. Current handling unit operations are adapted from other sectors (primarily agriculture), where some simplifying assumptions about granular mechanics and flow performance do not translate well to a highly compressible and anisotropic material with nonlinear time- and stress-dependent properties. This work explores the shear and frictional properties of loblolly pine at multiple experimental test apparatus and particle scales to elucidate a property window that defines the shear behavior over a range of material attributes (particle size, size distribution, moisture content, etc.). In general, it was observed that the bulk internal friction and apparent cohesion depend strongly on both the stress state of the sample in granular shear testers and the overall particle size and distribution span. For equipment designed to characterize the quasi-static shear stress failure of bulk materials ranging from 50 to 1,000 ml in test volume, similar test results were observed for finely milled particles (50% passing size of 1.4 mm) with a narrow size distribution (span between 10 and 90% passing size of 0.9 mm), while stress chaining and over-torque issues persisted for the bench-scale test apparatus for larger particle sizes or widely dispersed sample sizes. Measurement of the anisotropic particle–particle friction ranged from coefficients of approximately 0.20 to 0.45 and resulted in significantly higher and more variable friction measurements for larger particle sizes and in perpendicular alignment orientations. To supplement these laboratory-scale properties, this work explores the flow of loblolly pine and Douglas fir through a pilot-scale wedge-shaped hopper and a screw feeder. For the gravity-driven hopper flow, the critical arching distance and mass discharge rate ranged from approximately 10 to 30 mm and 2 to 16 tons/hour, respectively, for both materials, where the arching distance depends strongly on the overall particle size and depends less on the hopper inclination angle. Comparatively, the auger feeder was found to be much more impacted by the size of the particles, where smaller particles had a more consistent and stable flow while consuming less power.

09 BIOMASS FUELS↗
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↗