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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Discoveries in Blast-Driven Turbulence of Astrophysical Relevance

The fluid mixing caused by variable-density instabilities is important in a wide variety of scenarios from ocean mixing and astrophysical phenomena to nuclear fusion techniques and atomic weapons. This thesis explores the mixing resulting from a specific instability known as the Blast Driven Instability (BDI). This work investigates the variable density mixing in an explosively driven environment due to the fluid instabilities at the material interfaces. Specifically, diverging Richtmyer-Meshkov (impulsive-acceleration environment) and Rayleigh-Taylor (variable-acceleration environment) instabilities (present in supernova and inertial confinement fusion) are studied using advanced high-speed diagnostics in carefully designed laboratory experiments. The BDI morphology is presented through a time development of Mie scattering images, and steps through the parameter space (varying density ratio and driver speed), highlighting the development of the structures that form during mixing. A scaling criterion is used to relate the two systems of vastly different spatiotemporal scales. Velocity fields in the BDI have been captured for the first time using the high temporal resolution PIV technique. Subsequent analysis of the dynamics of the instability from the velocity fields illustrates the distribution of kinetic energy, the transition to turbulence, and the characteristic growth of the instability are discussed. This study furthers understanding of how blast-driven instability pertains to supernova and inertial confinement fusion science. The morphology of the BDI has been characterized for the first time. This work steps through the parameter space covered in Mie scattering experiments, and how the different parameters contribute to development of structures and mixing. It also examines a scaling of the Atwood number for expanding predictive capabilities to other experimental conditions and simulations. The first collection of velocity fields acquired for the BDI are recorded, and subsequent analysis evaluating the distribution of kinetic energy throughout space and time for two density ratios from the overall parameter space, as well as the transition to turbulence, estimated from a Reynolds number calculated based on momentum mixing are all presented. This information is useful in advancing the development of models to predict physics of high energy density applications where experiments are not always readily available. This research has successfully demonstrated understanding for the time criteria defining regimes where the shock driven (Richtmyer-Meshkov instability) and the buoyancy driven (Rayleigh-Taylor instability) dominates through a parametric study of density variation (Atwood number) and driver speed (Mach number). All this furthers understanding of how the BDI pertains to SN and ICF.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Development of a Deep Learning Model for Predicting the Drag Coefficients of Spherical and Non-Spherical Particles,

There is yet to be a well-established drag model for non-spherical particles required in a particle-laden flow that could cover a wide range of sphericities. This talk will explore the development of a general drag model for non-spherical particles by applying deep learning using available experimental data available in the literature. The integration of several raw experimental measurements from different sources and research directions allows the training of robust Artificial Intelligence and Machine Learning (ML) models. Neural networks are an ML approach inspired by the inner biological workings of the brain. This work aims to develop a Deep Neural Network (DNN) that predicts drag coefficient values with the ability to adapt appropriately to unseen data. Given the limited number of data points available and the variance found within the data collected from various sources, challenges may arise when looking to train the model. Our study tests and implements various model regularization techniques and assesses different loss and activation functions for the proposed DNN. The proposed model considers a broader range of features other than sphericity and Reynold number. These features include density ratio, solid volume fraction, lengthwise and crosswise sphericity, and more. Furthermore, we present the features that play a significant role in predicting different drag coefficients through feature importance. Within the investigated parameter ranges in this study, the following conclusions can be achieved and summarized below: • An improved drag coefficient model can be developed by considering more features such as, aspect ratio, lengthwise sphericity, crosswise sphericity, and density ratio. • DNN model can predict better results compared to traditional methods using MAE metric. • The proposed model addresses data challenges such as limited data and extreme data points through expanded feature-set and regularization. • Three major features that mostly affect the drag coefficient were identified from a feature importance analysis.

Presa-Reyes, Maria↗
DOE OSTI2023

Optimal white-noise stochastic forcing for linear models of turbulent channel flow

In the present study an optimisation problem is formulated to determine the forcing of an eddy-viscosity-based linearised Navier–Stokes model in channel flow at $Re_\tau \approx 5200$ ( $Re_\tau$ is the friction Reynolds number), where the forcing is white-in-time and spatially decorrelated. The objective functional is prescribed such that the forcing drives a response to best match a set of velocity spectra from direct numerical simulation (DNS), as well as remaining sufficiently smooth. Strong quantitative agreement is obtained between the velocity spectra from the linear model with optimal forcing and from DNS, but only qualitative agreement between the Reynolds shear stress co-spectra from the model and DNS. The forcing spectra exhibit a level of self-similarity, associated with the primary peak in the velocity spectra, but they also reveal a non-negligible amount of energy spent in phenomenologically mimicking the non-self-similar part of the velocity spectra associated with energy cascade. By exploiting linearity, the effect of the individual forcing components is assessed and the contributions from the Orr mechanism and the lift-up effect are also identified. Finally, the effect of the strength of the eddy viscosity on the optimisation performance is investigated. The inclusion of the eddy viscosity diffusion operator is shown to be essential in modelling of the near-wall features, while still allowing the forcing of the self-similar primary peak. In particular, reducing the strength of the eddy viscosity results in a considerable increase in the near-wall forcing of wall-parallel components.

Mechanics↗
DOE OSTI2022

Experimental measurements of fluid flow in an 84-pin hexagonal rod bundle with spacer grid for a gas-cooled fast modular reactor

A 50 MW e helium-cooled fast modular reactor (FMR) is under development, and as part of the Department of Energy Integrated Research Project (IRP), Texas A&M University is conducting the thermal-hydraulic characterization of its baseline core configuration. Here, we experimentally investigated the axial and cross flow fields characteristics in the hexagonal fuel rod bundle composed of 84 rods, a central rod, and spacer grids at a Reynolds number of 12,000. A fully transparent experimental facility resembling of one unit of the fuel assembly was constructed. Time-resolved particle image velocimetry (TR-PIV) measurements were performed to characterize hydraulic behavior downstream the spacer grid. Velocity measurements were conducted in the axial and radial direction of the rod bundle. From the PIV velocity vector fields, the full-field flow statistics were computed for the mean velocity, vorticity, and Reynolds stresses. An analysis of the energy decay downstream of the spacer grid was conducted with calculations of secondary-flow intensities, turbulent kinetic energy, power spectrum, and spatial-temporal velocity cross correlations. The vorticity field was obtained and pairs of counter-rotating vortexes were identified using a 3D reconstruction of several measurement planes. The data from this experimental campaign will be used to validate numerical models based on Computational Fluid Dynamics and other codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

High-Fidelity CFD Assessments of Flow Resistance in a 61-Pin Wire-Wrapped Assembly with Partially Blocked Channels

The examination of thermal-hydraulic behaviors in wire-wrapped rod bundles continues to be an active area of research. The sodium fast reactor, a prominent candidate in next-generation nuclear designs, utilizes a hexagonal configuration of wire-wrapped fuel pins. Here, the potential for channel blockage within this compact arrangement poses a significant safety challenge, spurring a number of recent experimental and computational investigations to evaluate its impact on flow and heat transfer. The present work aims to benchmark the high-fidelity NekRS computational fluid dynamics (CFD) solver in predicting the pressure drops associated with substantial blockages, using available experimental data as a reference. A 61-pin wire-wrapped fuel assembly with two flow blockage configurations has been simulated and investigated at a range of low to moderate Reynolds numbers (487 ≤ Re ≤ 14 600). The NekRS solver demonstrates an exponential reduction of spatial discretization error with increasing polynomial order. The high level of agreement between the numerical results and measured data confirms the accuracy and consistency of the present numerical approach. This benchmark study establishes the capability of NekRS to perform reliable hydrodynamic simulations for sodium fast reactor applications and supports its use in design, licensing, and safety analyses.

CFD Benchmarking↗
DOE OSTI2023

Helical Fins for Concentrated Solar Receivers: Design Optimization and Entropy Analysis

Concentrated solar power (CSP) with thermal energy storage (TES) has the potential to achieve grid parity. This can be realized by operating CSP systems at temperatures above 700 °C with high-efficiency sCO 2 power cycles. However, operating CSP systems at such temperatures poses several challenges, among which the design of solar receivers to accommodate increased thermal loads is critical. To this end, this work explores and optimizes various swirl-inducing internal fin designs for solar receiver tubes. These fin designs not only improve the thermal performance of receiver tubes but also levelize temperature unevenness caused by non-uniform thermal loading. In this work, the geometric parameters of the fin designs are optimized to maximize the Nusselt number with a constraint on the friction factor. This optimization, however, is computationally intensive, requiring hundreds of simulation calls to computational fluid dynamics (CFD) models. Here, to circumvent this problem, surrogate models are used to approximate the simulation outputs needed during the optimization. In addition, this study also examines the fin designs from an entropy generation perspective. To this end, the entropy contributions from thermal and viscous effects are quantitatively compared while varying the operational Reynolds number.

14 SOLAR ENERGY↗
DOE OSTI2021

Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear stochastic boundary value problems described by partial differential equations (PDEs) for which a stochastic computational model (SCM) is available and which depend on a vector-valued random control parameter. The cost of a single numerical evaluation of this SCM is assumed to be such that only a limited number of points can be computed for constructing the training dataset (small data). Each point of the training dataset is made up of realizations from a vector-valued stochastic process (the stochastic solution) and the associated random control parameter on which it depends. The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter. These learned realizations are generated so as to minimize the vector-valued random residual of the PDE in the mean-square sense. Appropriate novel methods are developed to solve this challenging problem. Three applications are presented. The first one is a simple uncertain nonlinear dynamical system with a nonstationary stochastic excitation. The second one concerns the 2D nonlinear unsteady Navier–Stokes equations for incompressible flows in which the Reynolds number is the random control parameter. Here, the last one deals with the nonlinear dynamics of a 3D elastic structure with uncertainties. The results obtained make it possible to validate the PLoM constrained by stochastic PDE but also provide further validation of the PLoM without constraint.

Machine learning↗
DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗
DOE OSTI2025

Using 3D Measurements from MRI to Improve VVUQ

Magnetic resonance velocimetry (MRV) is a diagnostic that can measure 3D, three-component turbulent velocity fields in arbitrarily complex flow configurations. The approach uses magnetic resonance imaging (MRI), which is a system commonly available in radiology departments or medical research centers. MRV acquires signal from hydrogen protons in water channel flows. Despite the obvious utility of the measurements for investigating a variety of flows, comparisons with computational fluid dynamic (CFD) simulations largely retain traditional metrics, comparing velocity profiles and planes at discrete regions within the flow rather than using the 3D nature of the measured field. In this effort, MRV was conducted in a simple water channel with six spanwise-centered periodic obstacles roughly shaped as cubes. At a fully turbulent Reynolds number of 15,000, the channel includes two flows: a main streamwise flow and, in the wake of the second obstacle, an injected flow oriented perpendicular to the streamwise flow. The flow geometry includes partial obstacles on the side walls and is inherently 3D because of the interaction of the two streams and the wake features, making the flow challenging to completely measure with traditional optical techniques. Aside from line profiles and planar comparisons, two 3D metrics are used to compare the data with a steady Reynolds-averaged Navier–Stokes (RANS) simulation result. A brief discussion about the comparison is provided, including comments about uncertainty.

Benson, Mike [ORNL] (ORCID:000000023210116X)↗
DOE OSTI2021

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗
DOE OSTI2020

General Correlations for Laminar Flow Friction Loss and Heat Transfer in Plain Rectangular Plate-Fin Cores

In this paper, new generalized correlations for predicting the average fanning friction factor f and average Nusselt number Nu for laminar flow in plain plate-fin compact cores of rectangular cross section are presented. These are based on extended experimental data, as well as three-dimensional computational simulations, obtained for a broad range of fin density and geometrical attributes. The results indicate that while the fully developed forced convection scales only with the interfin channel cross-sectional ratio α (fin spacing by fin height), the entrance region hydrodynamic and thermal performance is additionally a function of the fin-core length L, flow Reynolds number Re, and fluid Prandtl number Pr. The developing flow and convection is further shown to scale as: (fRe)~(L/d h Re) 1/2 , and Nu ~(L/d h Re) 1/2 Pr 1/3 Φ(α), where f, Re, and Nu are all based on the hydraulic diameter dh of the interfin flow channel. Generalized correlations for both (fRe) and Nu are developed by the corresponding scaling of the forced convection behavior and asymptotic matching of the entrance or developing flow (short fin-core flow length) and the fully developed flow (large fin-core flow length) region performance. Finally, the predictions from these correlations are found to be within ±15% of all available experimental data for air, water, and glycol (0.71 ≤ Pr ≤ 10), and fin cores with 0 < α ≤ 1.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Qualification and Commissioning of Helium Flow Loop Experiment for Blanket Design Measurements

Sufficient cooling of plasma-facing materials remains an outstanding challenge in the design of fusion reactor blankets in commercial power demonstration plants. Due to its chemical inertness and low neutron interaction cross section, pressurized helium is a candidate coolant fluid for such systems; however, helium has a small thermal mass compared to liquid coolants, potentially reducing heat removal performance. To address this need, a number of heat transfer enhancements have been proposed to improve the cooling efficiency of such components, thereby decreasing pumping power needs and improving overall plant efficiency. Toward this end, a helium flow loop experiment (HFLE) has been designed and commissioned to test advanced passive heat transfer enhancements in unit-cell test sections, providing necessary data for model validation and subsequent system design. The HFLE is designed to provide flow of pressurized (up to 4 MPa) helium at flow rates up to 80 g/s, enabling heat transfer and pressure drop measurements in test pieces at Reynolds numbers in excess of 180 000. To explore the effects of novel and complex heat transfer enhancements, test sections are produced via additive manufacturing, providing geometries not typically obtainable by conventional machining. Here in this work, we present results from HFLE commissioning and the initial thermal-hydraulic tests of an additively manufactured rifled-rib test section. Results are compared to smooth pipe correlations, and plans are described for future HFLE measurements. These preliminary experiments indicate the utility of the HFLE for heat transfer enhancement testing and simulation validation activities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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 OSTI2023

Strongly Heated Turbulent Flow in a Channel with Pin Fins

Large-eddy simulations (LES) were performed to study the turbulent flow in a channel of height H with a staggered array of pin fins with diameter D = H/2 as a function of heating loads that are relevant to the cooling of turbine blades and vanes. The following three heating loads were investigated—wall-to-coolant temperatures of Tw/Tc = 1.01, 2.0, and 4.0 - where the Reynolds number at the channel inlet was 10,000 and the back pressure at the channel outlet was 1 bar. For the LES, two different subgrid-scale models—the dynamic kinetic energy model (DKEM) and the wall-adapting local eddy-viscosity model (WALE)—were examined and compared. This study was validated by comparing with data from direct numerical simulation and experimental measurements. The results obtained show high heating loads to create wall jets next to all heated surfaces that significantly alter the structure of the turbulent flow. Results generated on effects of heat loads on the mean and fluctuating components of velocity and temperature, turbulent kinetic energy, the anisotropy of the Reynolds stresses, and velocity-temperature correlations can be used to improve existing RANS models.

42 ENGINEERING↗
DOE OSTI2020

Analysis of Laminar Convective Heat Transfer Over Structured Non-Wetting Surfaces

Structured non-wetting surfaces provide alternating no-slip and partial slip boundary conditions to the fluid flow which, in turn, affects the convective heat transfer performance over the surfaces. In this work, an analytical model is developed for the interfacial Nusselt number, the overall Nusselt number and a thermal hydraulic performance factor for fluid flow in a cylinder patterned with structured non-wetting surfaces, for the two cases of uniform wall heat flux and uniform wall temperature. In addition, by considering the stability of the Cassie state of wettability and its transition to the Wenzel state for flow over superhydrophobic surfaces, the present model overcomes certain limitations of the previously reported studies in the literature. Based on the analytical formulations and the stability constraints, the present paper provides optimum design maps for tailoring structured non-wetting surfaces for maximizing convective heat transfer and the combined thermal-hydraulic performance in applications. Use of the design maps on example cases is also discussed. It is shown that the use of structured non-wetting surfaces is most effective for low Reynolds numbers and/or small cylinder radius.

42 ENGINEERING↗
DOE OSTI2021

Scaling of Turbulent Viscosity and Resistivity: Extracting a Scale-dependent Turbulent Magnetic Prandtl Number

Turbulent viscosity νt and resistivity ηt are perhaps the simplest models for turbulent transport of angular momentum and magnetic fields, respectively. The associated turbulent magnetic Prandtl number Pr t ≡ ν t /η t has been well recognized to determine the final magnetic configuration of accretion disks. Here, we present an approach to determining these "effective transport" coefficients acting at different length scales using coarse-graining and recent results on decoupled kinetic and magnetic energy cascades. By analyzing the kinetic and magnetic energy cascades from a suite of high-resolution simulations, we show that our definitions of ν t , η t , and Pr t have power-law scalings in the "decoupled range." We observe that Pr t ≈ 1–2 at the smallest inertial-inductive scales, increasing to ≈5 at the largest scales. Furthermore, based on physical considerations, our analysis suggests that Pr t has to become scale independent and of order unity in the decoupled range at sufficiently high Reynolds numbers (or grid resolution) and that the power-law scaling exponents of velocity and magnetic spectra become equal. In addition to implications for astrophysical systems, the scale-dependent turbulent transport coefficients offer a guide for large-eddy simulation modeling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multifidelity Approach to Sensitivity Estimation in Large-Eddy Simulation

A novel approach to compute affordable approximate sensitivities in a large-eddy simulation (LES) is proposed and assessed. The approach is based on solving a Reynolds-averaged Navier–Stokes (RANS) problem that has been linearized around the mean LES solution, with closure modeling required for the linearized changes in turbulent Reynolds stresses. In the present study, the closure modeling is based on a linearized algebraic turbulence model. The method is assessed for the flow over a NACA0012 airfoil at a fixed angle of attack, with the Reynolds number as the variable parameter. The results show that, provided an accurate linearized closure model, the method predicts the correct sensitivity of the skin friction coefficient and the mean velocity field at the cost of a linearized RANS, which provides an important proof-of-concept for this approach. Here, the linearized algebraic turbulence model with standard model coefficients produces a reasonably accurate sensitivity, but the results also suggest that accuracy could be gained from recalibrating the model coefficients for this new use of the model.

42 ENGINEERING↗
DOE OSTI2024

Direct numerical simulation of open-channel flow over a heterogeneous particle bed at low relative submergence

In this article, this study investigates turbulent open-channel flows over beds of irregularly arranged particles, using direct numerical simulations at a friction Reynolds number of Re T = 300⁠. Two distinct cases are examined: a polydisperse bed (P800) composed of multiple layers of randomly distributed spheres of varying sizes, and a monodisperse bed (M1015) formed by a random distribution of uniform sized spheres, with a bottommost single layer of varied-sized particles to introduce realistic randomness. Our investigation unveils a rich network of low- and high-speed streaks within the flow field, exhibiting distinctive behaviors in different bed configurations. The P800 case presents a poorly organized flow pattern induced by the varied particle sizes and arrangements, while the M1015 case shows a more regular flow pattern, marked by larger streaks. We also observe that total wall shear stress is substantially influenced by surface roughness-induced drag, extending beyond the effects documented in existing studies of open-channel flows. The present study reveals intricate secondary flow patterns over irregular particle beds. Large-scale circulations are discerned around particle crests in the P800 case and localized circulations with increased turbulence in the M1015 case. Furthermore, analysis of Reynolds stress tensor components indicates that roughness disrupts coherent turbulent eddies, consequently mitigating peak stress. We quantify correlations between drag force and local fluid velocity fluctuations. Notably, a larger deviation in drag is observed in the P800 case compared to M1015, accentuating the influence of particle size and distribution on fluid–particle interactions.

42 ENGINEERING↗