Fluid Phenomena Research Articles

Numerical simulation of jet interaction flow field of complex configuration based on hybrid grid method

Abstract The supersonic/hypersonic jet interaction flow field includes many fluid phenomena such as shock waves, separation and reattachment of the boundary layer, expansion wave, Mach disk, shear layer, and so on. The complexity of the jet interaction flow field makes it difficult to simulate accurately, and the structured grid method is generally used in engineering applications. However, the structured grid has many shortcomings when dealing with complex configurations, and the traditional hybrid grid method has the problem of insufficient accuracy. In the paper, a structured/unstructured grid algorithmic coupling numerical simulation method is developed. Firstly, the feasibility and accuracy of this method are verified by numerical simulation of the jet interference flow field of cone-cylinder-skirt shape, along with a comparison with the results of the experiment and structural method. Then, the jet interference flow fields of the rudders and the grid fins configuration are simulated to show the efficiency of the method, and the accuracy is further verified. The results show that the hybrid numerical simulation method can save grid generation time and grid quantity to a certain extent when compared with the structural, showing higher accuracy, which provides an accurate and efficient solution to jet interaction problems with complex configurations.

Free convective flow of Ostwald-de Waele fluid in a Π-shaped cavity with heated strip and lateral wall cooling

This paper studies the free convection of Ostwald-de Waele fluid in a Π-shaped chamber with a Π-shaped heated strip at the lower mid-section. The upper wall and the remaining sections of the lower wall are thermally insulated, while the lateral walls are maintained at a low temperature. The primary objective is to identify the optimal power-law index (n) and the geometric configurations of the strip that yield enhanced heat transfer. Furthermore, the impact of Rayleigh number (Ra) on the thermal performance of the cavity in that optimum condition is to be investigated. The Galerkin finite element approach has been employed to resolve the governing equations and auxiliary conditions. Within the study, the power-law index of the Ostwald-de Waele fluid has been systematically varied (0.6 ≤ n ≤ 1.4). At the same time, the variations in the non-dimensional height (0.1 ≤ ζ ≤ 0.5) and width (0.1 ≤ ε ≤ 0.3) of the heated strip have been explored. The results show improved thermal performance with a growth in Rayleigh number, particularly when n < 1. The average Nussetl number for n = 0.6 at Ra = 106 is approximately 14 % higher than for n = 1.4. The findings further indicate an increment of 35 % in the heat transfer rate when the height of the strip decreases from 0.5 to 0.1. This investigation provides valuable insights into the natural convection phenomena of Ostwald-de Waele fluid in complex geometries, paving the way for advanced thermal management techniques in various engineering applications.

Relativistic fluids in cosmological spacetimes

Abstract We review the status of mathematical research on the dynamical properties of relativistic fluids&amp;#xD;in cosmological spacetimes - both, in the presence of gravitational backreaction as well as the&amp;#xD;evolution on fixed cosmological backgrounds. We focus in particular on the phenomenon of fluid&amp;#xD;stabilization, which describes the taming effect of spacetime expansion on the fluid. While fluids&amp;#xD;are in general known to form shocks from regular initial data, spacetime expansion has been found&amp;#xD;to suppress this behaviour. During the last decade, various rigorous results on this problem have&amp;#xD;been put forward. We review these results, the mathematical methods involved and provide an&amp;#xD;outlook on open questions.

Significant statistical model of heat transfer rate in radiative Carreau tri-hybrid nanofluid with entropy analysis using response surface methodology used in solar aircraft

The recent advancement of technologies focuses on the use of solar-based heat radiation and nanotechnology. The primary source of heat is solar energy, which is obtained by absorbing sunlight. In addition, solar thermal planes, photovoltaic cells, and sun-based hybrid nanofluids all help to harness solar energy. Therefore, main concern of the study is to explored the two-dimensional Engine oil-based Carreau tri-hybrid nanofluid (SiO2 (silica dioxide), MOS2 (Molybdenum disulfide), Fe3O4(black iron oxide)) flow via a stretching sheet within solar wings. The flow phenomena of the non-Newtonian fluid enrich for the effects of radiant energy and the external heat source. By adopting similarity transfiguration, the dimensional partial main flow equations are changed into nonlinear dimensionless ordinary differential equations. The most effective and powerful tool of the semi-analytical method ADM (Adomain Decomposition Method) is utilized to solve the nanofluid flow problem. The uniqueness of the proposed study arises due to the analysis of entropy generation obtained by various irreversibility processes. The graphical illustration of outcome of various governing parameters on velocity, temperature, entropy terms and engineering quantities have been presented and theoretically analyzed. The findings reveal that The Carreau ternary hybrid nanofluids enhance the system's thermal conductivity for optimal temperature regulation in solar aircraft and better heat dissipation. The use of Carreau ternary hybrid nanofluids significantly reduces entropy generation, which implies a more reversible process, enhancing the overall efficiency of the energy conversion systems in solar aircraft. The heat flow of the system is accurately reflected by the chosen variables and their interactions, as demonstrated by the constructed model's strong statistical significance. For real-world applications, this improves the model's predictability and dependability.

Predicting heat transfer Performance in transient flow of CNT nanomaterials with thermal radiation past a heated spinning sphere using an artificial neural network: A machine learning approach

An efficient heat transfer phenomenon using nanofluid have greater challenges in various industries, engineering application the recent trend. Keeping this in present scenario, this study aims to optimize the heat transmission rate in the magnetized flow of nanomaterials through a rotating, spinning sphere. The heat transfer phenomena in the time-dependent fluid are enhanced by the incorporation of nonlinear radiation and a variable heat source. Additionally, the free convective flow is influenced by the effects of thermal buoyancy and a transverse magnetic field. The proposed model along with several factors is standardized through adequate transformation rules. Further, shooting-based Runge-Kutta technique is adopted with the help of built-in MATLAB function bvp4c for the solution of the transformed system. The prime focus of the proposed work is the optimizing heat transfer rate combined with regression analysis using artificial neural network and then it uses Levenberg Marquardt algorithm with well-posed training, testing, and validation data. The error analysis also presented briefly and the variation of characterizing parameters is depicted via graphs. Further, the important outcomes are; the particle concentration of carbon nanotubes contributes to decelerating the velocity profiles, leading to an increase in boundary layer thickness. In contrast, increasing magnetization has the opposite effect. Both nonlinear radiative heat and an additional heat source enhance the heat transfer phenomenon.

Numerical investigation of an axisymmetric, dissipative, and unstable micropolar fluid flow over a stretched Riga plate incorporating mixed convection and chemical reaction

AbstractThe demand to improve the thermal performance of industrial working fluids and materials for better quality in engineering and household devices has spurred numerous studies on non‐Newtonian viscous fluids under various conditions. Researchers have explored different structures and geometries to understand and enhance the thermal propagation of fluid particles. As such, this research aims to investigate the effects of two‐dimensional unsteady boundary layer flow phenomena of a dissipative micropolar fluid over a radially stretching Riga plate when first‐order slip, thermal and solutal boundary conditions, mixed convection, and thermal radiation are all considered. Ordinary differential equations are the governing equations resulting from converting partial differential equations. The boundary layer conditions are altered and numerically solved using the Runge–Kutta fourth‐order shooting approach. Graphs and tables are used to visually analyze the physical attributes of temperature, concentration, velocity distributions, skin friction, Nusselt number, and Sherwood in relation to various factors. The results reveal significant insights into the impact of mixed convection and chemical reactions on the flow characteristics and dimensions. These findings have important implications for various engineering applications, particularly in enhancing industrial systems' heat and mass transfer processes, providing practical knowledge for improving thermal performance in real‐world applications.

Design & automation of a small-scale towing tank for flow visualization

Although the towing tank is a standard piece of equipment used to investigate fluid phenomena, it primarily exists as custom-built hardware that takes up a significant footprint. The size, cost, and custom-built nature have heretofore inhibited the production of this equipment in the authors’ context, an African university. This paper presents a small-scale (1000 mm x 200 mm x 200 mm), low-cost (<$1,000) towing tank made using readily available components and basic digital fabrication tools. Other universities on the continent and beyond can hence create this foundational platform for fluid mechanics-related teaching and research. Leveraging an Arduino microcontroller loaded with the GRBL firmware, G-code is sent from the computer to stepper motors to execute movements in two axes. This allows for automation capabilities, controlled towing speeds, and consistent experimental conditions. Validation tests revealed motion accuracy within 1 %. A glitter-based flow visualization approach to measuring surface phenomena is demonstrated here. Experiments conducted successfully visualized relevant flow characteristics generated by bluff bodies being towed in the tank. As the Reynolds number increased within the operating range, wider wakes and larger, more distinct vortices were generated, as expected. This platform can be replicated widely in institutions that may otherwise forego experimentation in fluid mechanics.

Koopman-inspired data-driven quantification of fluid–structure energy transfers

The linear-time-invariance notion to the Koopman analysis is a recent advance in fluid mechanics [Li et al., “The linear-time-invariance notion to the Koopman analysis: The architecture, pedagogical rendering, and fluid–structure association,” Phys. Fluids 34(12), 125136 (2022c) and Li et al., “The linear-time-invariance notion of the Koopman analysis—Part 2. Dynamic Koopman modes, physics interpretations and phenomenological analysis of the prism wake,” J. Fluid Mech. 959, A15 (2023a)], targeting the long-standing issue of correlating nonlinear excitation and response phenomena in fluid–structure interactions (FSI), or, in the simplified case, flow over rigid obstacles. Continuing the serial research, this work presents a data-driven, Koopman-inspired methodology to decouple nonlinear FSI by establishing cause-and-effect correspondences between structure surface pressure and the flow field. Exploiting unique features of the Koopman operator, the new methodology renders dynamic visualizations of in-sync, fluid–structure-coupled Koopman modes possible, fostering phenomenological analysis and statistical quantifications of FSI energy transfers. Instantaneous contribution contours and densities offer new angles to evaluate pathways of energy amplification and diminution. The methodology enables better descriptions and interpretations of phenomena occurring in the flow and on the boundary (walls) of an FSI domain and readily applies to a broad spectrum of engineering problems given its data-driven nature.

Adsorption of bile salts onto crystalline ritonavir particles under simulated gastrointestinal conditions

The formation of a biomolecular corona on nanoparticles in blood is a well-known phenomenon influencing in vivo performance. Analogous phenomena in other biological fluids, such as the formation of a gastrointestinal (GI) corona, remain under-investigated. The ingestion of medicines leads to the generation of drug particles in the GI fluids. Consequently, an understanding of the behavior of drug particles in the gut requires determination of the adsorption of bile components onto these drug particles. This work aims to elucidate the factors affecting adsorption of bile salts onto ritonavir (RTV) particles. Crystalline RTV particles were incubated with non-micellar or micellar bile salts and bile salt depletion from solution was measured using HPLC and small angle X-ray scattering (SAXS). HPLC results show that bile salts adsorb onto RTV particles, with greater adsorption observed for more hydrophobic bile salts, following the order sodium glycodeoxycholate (SGDC)>sodium taurodeoxycholate (STDC)>sodium glycochenodeoxycholate SGCDC>sodium glycocholate (SGC)>sodium taurocholate (STC). Increasing the ionic strength of the solution led to increased adsorption of STDC, where buffer containing 300 mM NaCl resulted in greater adsorption than 150 mM NaCl. Additionally, micellar bile salts showed greater affinity for RTV particles than unimeric bile salts. In contrast, the extent of bile salt depletion was unaltered by addition of phospholipid to form mixed micelles. SAXS analysis of mixed micelles after incubation with RTV particles confirms depletion of bile salt from the supernatant, evidenced by reduced intensity of the micellar scattering feature. In conclusion, this study investigated factors influencing bile salt adsorption onto drug particles, highlighting the need to consider adsorption of bile components in forming the GI-corona.

A novel variable-coefficient extended Davey–Stewartson system for internal waves in the presence of background flows

We propose a novel variable-coefficient Davey–Stewartson type system for studying internal wave phenomena in finite-depth stratified fluids with background flows, where the upper- and lower-layer fluids possess distinct velocity potentials, and the variable-coefficient terms are primarily controlled by the background flows. This realizes the first application of variable-coefficient DS-type equations in the field of internal waves. Compared to commonly used internal wave models, this system not only describes multiple types of internal waves, such as internal solitary waves, internal breathers, and internal rogue waves, but also aids in analyzing the impact of background flows on internal waves. We provide the influence of different background flow patterns on the dynamic behavior and spatial position of internal waves, which contribute to a deeper understanding of the mechanisms through which background flows influence internal waves. Furthermore, the system is capable of capturing variations in the velocity potentials of the upper and lower layers. We discover a connection between internal waves under the influence of background flows and velocity potentials. Through the variations in velocity potentials within the flow field, the dynamic behaviors of internal waves can be indirectly inferred, their amplitude positions located, and different types of internal waves distinguished. This result may help address the current shortcomings in satellite detection of internal wave dynamics and internal rogue waves.

Phase diagram for nanodroplet impact on solid spheres: From hydrophilic to superhydrophobic surfaces

The impact of droplets on solid surfaces is a crucial fluid phenomenon in the additive industry, biotechnology, and chemistry, where controlling impact dynamics and duration is essential. While extensive research has focused on flat substrates, our understanding of impact dynamics on curved surfaces remains limited. This study seeks to establish phase diagrams for the process of droplet impact on solid spheres and further quantitatively describe the effect of curvature through theoretical analysis. It aims to determine the critical conditions between different impact outcomes and also establish a scaling relationship for the contact time. Here, the post-impact outcome regimes occurring for a wide range of Weber numbers (We) from 1.2 to 173.8, diameter ratio (λ) of solid spheres to nanodroplets from 0.25 to 2, and surface wettability (θ) from 21° to 160°, through the molecular dynamics simulation method (MD) and theoretical analysis. The MD simulations reveal that the phase diagrams of droplet impacts on hydrophilic, hydrophobic, and superhydrophobic spheres differ, with specific distinctions focusing on rebound and three different forms of dripping. Furthermore, a theoretical model based on the principle of energy conservation during impact on superhydrophobic surfaces has been developed to predict the critical conditions between rebound and dripping states, showing good agreement with simulation results. Additionally, a new scaling relationship of contact time for droplet impact on superhydrophobic spherical surfaces has also been established by extending and modifying the existing models, which also agrees well with the simulated results. These insights provide a foundational understanding for designing surface structures.

Impact of single and two successive droplets on a liquid pool

The interaction between droplets and a liquid pool is a widely observed fluid phenomenon with significant relevance to various industrial applications. This study numerically investigates the impact of both a single droplet and two successive droplets on a liquid pool with a fixed thickness. Particular emphasis was focused on the evolution of cavity depth and width during the deformation process. For single droplet impacts, the cavity depth exhibits linear growth with time in the early stage, consistent with predictions based on energy balance. This growth is independent of the Weber number (We) within the explored range of 96&amp;lt;We&amp;lt;345. Similarly, the cavity width shows weak dependence on the Weber number during early development, deviating and reaching a maximum width at later times. The maximum cavity width follows a power-law relationship with the Weber number, with a 0.5 exponent. In the case of successive droplet impacts with small initial separation, cavity depth also evolves linearly with time in the early stage but over an extended period. This prolonged growth is attributed to droplet merging, resulting in an effectively larger merged droplet. However, for successive droplets with large separation, the two linear growth stages exhibit intermittent interruptions due to the second impact occurring at a later time. The variation in cavity width due to different initial spacings between two successive droplets still exhibits similarity until a larger spacing causes a change in the rate of cavity width development.

Various exact solutions of the (4+1)-dimensional Boiti–Leon–Manna–Pempinelli-like equation by using bilinear neural network method

In this paper, Bilinear neural network method is used to investigate the (4+1)-dimensional Boiti–Leon–Manna–Pempinelli-like (BLMP-like) equation, which can be utilized to model wave phenomena in incompressible fluids and in the study of fluid mechanics. On the basis of the generalized bilinear operators, the generalized bilinear equation is formulated. In the bilinear neural network method, a variety of neural network structures, including the single hidden layer and multiple hidden layers neural network are employed to investigate the exact solutions. By considering the special values of the parameters involved, the dynamical behavior of the solution is analyzed graphically. The effective method adopted significantly contributes to the understanding of nonlinear evolution equations in various fields, including plasma physics, mathematical physics, electromagnetism, and fluid dynamics.

Optimization and data mining for shock-induced mixing enhancement inside scramjet using stochastic deep-learning flowfield prediction

One of the significant challenges in supersonic combustion ramjet engines lies in effective mixing of the fuel, primarily due to the high momentum of supersonic inflow at the combustor. Among the various fluid phenomena associated with fuel mixing, it is well recognized that the interaction between the fuel-injection jet plume and oblique shock waves can significantly enhance mixing efficiency. However, the most suitable interaction for optimal mixing enhancement remains yet to be clarified. The present study conducts model-based optimization and data mining for shock-induced mixing enhancement of angled-slot injection in a two-dimensional scramjet combustor. Stochastic deep-learning flowfield prediction has been utilized to enable fast and reliable evaluations of a substantial number of designs. Prediction errors can be estimated without requiring correct data owing to uncertainty quantification techniques. Data mining and sensitivity analysis, coupled with flowfield prediction, have revealed the optimal shock interaction with the fuel jet plume characterized by a pronounced downstream recirculation region. The mechanism that drives mixing enhancement through this recirculation region has been discussed based on the results of optimization and sensitivity analysis. This study has yielded valuable insights for the future design of scramjet injectors. Furthermore, the effectiveness of the model-based design and analysis has been demonstrated through the present study, showcasing its potential for guiding future developments in scramjet technology.

Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning

The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scale-specific models. To bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh–Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics. Training and testing data are generated by conducting mDPD and RP simulations for nonlinear bubble dynamics with initial bubble radii ranging from 0.1 to 1.5 micrometers. The results show that the trained composite neural operator model can accurately predict bubble dynamics across scales, with a 99% predictive accuracy for the time evolution of the bubble radius under varying external pressure while containing correct size-dependent stochastic fluctuations in microscale bubble growth dynamics. The composite neural operator is the first deep learning surrogate for multiscale bubble growth dynamics that can capture correct stochastic fluctuations in microscopic fluid phenomena, which sets a new direction for future research in multiscale fluid dynamics modeling.

Nonlinear flow phenomenon of a power-law non-Newtonian fluid falling down a cylinder surface

In this paper, we present a comprehensive study of the fingering phenomenon of a power-law non-Newtonian fluid falling down a cylinder surface. A theoretical analysis is firstly carried out and the governing equation describing the film thickness is established for the non-Newtonian fluid denoted by a power-law index n. Using the lubrication theory with dimensionless variables, the partial differential equation for the film thickness is derived, and both two-dimensional flow and three-dimensional flow are investigated. The traveling wave characteristic of the two-dimensional flow is displayed by using the finite difference scheme associated with a Newton iteration technique. The effect of changing the radius of the cylinder, precursor-layer thickness and power-law index is then considered through examining the variation of the capillary waves. Furthermore in three-dimensional complex flow, the fingering patterns for different parameters are simulated. The linear stability analysis based on the traveling wave solutions is given to elucidate the influence of different physical parameters, explaining the physical mechanism of nonlinear flow behaviors in two dimension and three dimension. Results from linear stability analysis show that the power-law index reinforces the fingering instability whether the fluid is shear-thinning or shear-thickening. Moreover, the numerical results illustrate that increasing the radii of the cylinder expands the unstable region. The flow profiles for different power-law coefficients are consistent with the result of the linear stability analysis, proving the unstable role of the power-law coefficient.

The variational approach to study the mixed convection boundary layer flow over a permeable Riga plate

AbstractThe physical problem of steady state, laminar, mixed convection (Ri) with double‐diffusive (N) in an electrically low conducting fluid past a semi‐infinite electromagnetic () influenced flat plate with internal uniform heat generation (Q) in the presence of suction/injection (H) by considering viscous dissipation (Ec), thermophoresis (Nt) and thermal diffusion effects (Sr) is mathematically modeled as a simultaneous system of nonlinear partial differential equations. To achieve the solution of the problem numerically, Gyarmati's variational principle known as the “Governing Principle of Dissipative Processes” on the basis of nonequilibrium thermodynamic processes in the theory of continua, is adopted. This research work correlates the phenomenon of fluid around submersibles/space vehicles and provides related insights. To estimate the transportation fluid fields within the boundary layer, the appropriate trial polynomials have been employed, and functionals for the integral variational principle are determined. Next, the Euler–Lagrange equations of the functionals are obtained as a system of polynomial equations involving boundary layer thicknesses of momentum, temperature, and concentration. The expressions of local shear stress, local Nusselt, and local Sherwood numbers have been derived and the effects of various physical factors involved in the problem are explored. A comparison with the previously published results in the literature is provided to confirm the validity of the solution procedure. The results depict that injection () and opposing buoyancy () decrease the skin friction about 38% in sea water and 11% in ionized air when compared to impermeable plate for . The aiding buoyancy () plays a dominant role in heat and mass transfers, respectively, with the massive gradients of 340% and 763% in magnitude for the heavier fluid sea water flow while 47% and 3% for the lighter fluid ionized air flow when . The buoyancy parameters (Ri, N) decrease the heat transfer, but increase the mass transfer.

Neural Networks For Particle Diffusometry In The Presence Of Flow And With Defocused Particles

Diffusion is a natural phenomenon in fluids. Its measurement can be done optically by seeding an otherwise featureless fluid with tracer particles and observing their motion. However, existing algorithms for particle-based diffusion coefficient measurement have multiple failure modes especially when the fluid has a flow or the particles are defocused. We present a method based on Convolutional Neural Networks (CNNs) for predicting diffusion coefficients in the presence of these real-world effects. The CNNs were trained, validated, and tested on crops from four-frame temporally averaged images. The study includes three simulated datasets: Gaussian-shaped particles under no fluid flow condition, Gaussian-shaped particles under an arbitrary flow condition, and defocused particles under no fluid flow condition. The results show that the CNNs have a low Mean Absolute Error (MAE) of 0.12 µm 2 /s between the true and predicted diffusion coefficient values for the dataset with Gaussian-shaped particles under no fluid flow condition. The CNNs have a slightly higher MAE of 0.19 µm^2 /s for Gaussian-shaped particles under arbitrary flow and defocused particles under no fluid flow conditions. The performance of the CNNs was also benchmarked against four conventional algorithms on these simulated datasets. The results show that the conventional algorithms perform better than the CNNs when the particles are assumed to be Gaussian and the fluid has no flow. However, the CNNs outperform conventional methods in the presence of flow or if the particles are defocused. Finally, the outputs of CNNs were compared against the outputs of conventional algorithms on experimental datasets. This provides uncertainty in the range of 0.19 µm^2 /s - 0.47 µm^2 /s, which is slightly larger than the errors obtained from simulated datasets. Hence, the study shows that CNNs can be used to reliably predict diffusion coefficients from complex particle datasets with as little as four frames.

Ultrafast 2D Nanosheet Assembly via Spontaneous Spreading Phenomenon.

In 2D materials, a key engineering challenge is the mass production of large-area thin films without sacrificing their uniform 2D nature and unique properties. Here, it is demonstrated that a simple fluid phenomenon of water/alcohol solvents can become a sophisticated tool for self-assembly and designing organized structures of 2D nanosheets on a water surface. In situ, surface characterizations show that water/alcohol droplets of 2D nanosheets with cationic surfactants exhibit spontaneous spreading of large uniform monolayers within 10s. Facile transfer of the monolayers onto solid or flexible substrates results in high-quality mono- and multilayer films with high coverages (>95%) and homogeneous electronic/optical properties. This spontaneous spreading is quite general and can be applied to various 2D nanosheets, including metal oxides, graphene oxide, h-BN, MoS2, and transition metal carbides, enabling on-demand smart manufacture of large-size (>4 inchϕ) 2D nanofilms and free-standing membranes.

Fluid Dynamics Effects of a Porous Blockage on Laminar Flow in the Interior Subchannels of a 61-Pin Wire-Wrapped Rod Bundle

Flow blockages in Liquid Metal Fast Reactor (LMFR) fuel assemblies that can cause fuel pin cladding failures require further investigation for the development and optimization of these Generation IV reactor designs. The objective of this study was to experimentally evaluate the effects of a porous blockage accident scenario on laminar flow behavior in the interior subchannels of a prototypical 61-pin wire-wrapped rod bundle. The laminar flow condition is considered because of its importance to natural circulation and loss-of-coolant-accident operating scenarios as well as the limited availability of experimental data. The matched-index-of-refraction method was utilized to obtain two-dimension two-component time-resolved particle image velocimetry (TR-PIV) measurements at three planes centered on blocked and neighboring subchannel regions. Time-averaged first-order and second-order statistics, computed by Reynolds decomposition, were visualized including the mean and fluctuating streamwise and spanwise velocity components. Line profiles described the evolution of flow from upstream regions, to separated flow, and recombination downstream, while a modified Galilean decomposition distinguished differences between flow in the blocked measurement plane and flow in its neighboring counterpart region. Spatial-temporal cross-correlations were performed for the streamwise velocity fluctuations to characterize the convection velocity and decay of traveling vortices in the wake of the porous blockage. The isothermal TR-PIV measurements from this study provide high-fidelity experimental data sets for the validation of computational models and numerical studies to characterize complex fluid phenomena in wire-wrapped rod bundles during the potential accident scenario of a porous, interior flow blockage.