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In this work, we focus on a multi-hole pressure-probe-based flow measurement system for wind tunnel measurements that provides real-time feedback to a robot probe-manipulator, rendering the system autonomous. The system relies on a novel, computationally efficient flow analysis technique that translates the probe's point measurements of velocity and pressure into an updatable mean flow map that is accompanied by an uncertainty metric. The latter provides guidance to the manipulator when planning the optimal probe path. The probe is then guided by the robot in the flow domain until an available time budget has been exhausted, or until the uncertainty metric falls below a prescribed target threshold in the entire flow domain. We assess the capabilities of our new measurement system using computational fluid dynamics data, for which the ground truth is available in the form of a mean flow field. An application in a real wind tunnel setting is provided as well.

Upon interaction with underwater shock waves, bubbles can collapse and produce high-speed liquid jets in the direction of the wave propagation. This work experimentally investigates the impact of laser-induced underwater impulsive shock waves, i.e. shock waves with a short, finite width, of variable peak pressure on bubbles of radii in the range 10–500 $\\mathrm {\\mu }$m. The high-speed visualisations provide new benchmarking of remarkable quality for the validation of numerical simulations and the derivation of scaling laws. The experimental results support scaling laws describing the collapse time and the jet speed of bubbles driven by impulsive shock waves as a function of the impulse provided by the wave. In particular, the collapse time and the jet speed are found to be, respectively, inversely and directly proportional to the time integral of the pressure waveform for bubbles with a collapse time longer than the duration of shock interaction and for shock amplitudes sufficient to trigger a nonlinear bubble collapse. These results provide a criterion for the shock parameters that delimits the jetting and non-jetting behaviour for bubbles having a shock width-to-bubble size ratio smaller than one. Jetting is, however, never observed below a peak pressure value of 14 MPa. This limit, where the pressure becomes insufficient to yield a nonlinear bubble collapse, is likely the result of the time scale of the shock wave passage over the bubble becoming very short with respect to the bubble collapse time scale, resulting in the bubble effectively feeling the shock wave as a spatially uniform change in pressure, and in an (almost) spherical bubble collapse.

Scale analysis of unstable density-driven miscible convection in porous media is performed. The main conclusions for instabilities in the developed (long time scales) regime are that (i) large-scale structures are responsible for the bulk of the production of concentration variance, (ii) variance dissipation is dominated by the small (diffusive) scales and that (iii) both the production and dissipation rates are independent of the Rayleigh number. These findings provide a strong basis for a new modelling approach, namely, large-mode simulation (LMS), for which closure is achieved by replacing the actual diffusivity with an effective one. For validation, LMS results for vertical flow in a homogeneous rectangular domain are compared with direct numerical simulations (DNS). Some of the analysis is based on the derivation and closure of the concentration mean and variance equations, whereby averaging over the ensemble of all possible initial perturbations is considered. While self-similar solutions are obtained for vertical, statistically one-dimensional fingering, triple correlation of concentration and scalar dissipation rate (rate at which the concentration variance decays due to diffusion) have to be modelled in the general case. For this purpose, an ensemble-averaged Darcy modelling (EADM) approach is proposed.

Addition of particles or droplets to turbulent liquid flows or addition of droplets to turbulent gas flows can lead to modulation of turbulence characteristics. Corresponding observations have been reported for very small particle or droplet volume loadings  ${\\Phi }_{v} $ and therefore may be important when simulating such flows. In this work, a modelling framework that accounts for preferential concentration and reproduces isotropic and anisotropic turbulence attenuation effects is presented. The framework is outlined for both Reynolds-averaged Navier–Stokes (RANS) and joint probability density function (p.d.f.) methods. Validations are performed involving a range of particle and flow-field parameters and are based on the direct numerical simulation (DNS) study of Boivin, Simonin & Squires (J. Fluid Mech., vol. 375, 1998, pp. 235–263) dealing with heavy particles suspended in homogeneous isotropic turbulence (Stokes number $\\mathit{St}= O(1{\\unicode{x2013}} 10)$ , particle/fluid density ratio ${\\rho }_{p} / \\rho = 2000$ , ${\\Phi }_{v} = O(1{0}^{- 4} )$ ) and the experimental investigation of Poelma, Westerweel & Ooms (J. Fluid Mech., vol. 589, 2007, pp. 315–351) involving light particles ( $\\mathit{St}= O(0. 1)$ , ${\\rho }_{p} / \\rho \\gtrsim 1$ , ${\\Phi }_{v} = O(1{0}^{- 3} )$ ) settling in grid turbulence. The development in this work is restricted to volume loadings where particle or droplet collisions are negligible.

In the context of flow and transport in porous and fractured media, Darcy-based continuum models, while computationally inexpensive, are of limited use when the scale of interest is of similar size or smaller than the characteristic network connection length. Recently, we have outlined a non-local Darcy model that bridges the gap between network and Darcy-based descriptions. This formulation is able to account for non-local pressure effects that are not accounted for in a classical Darcy description. At the heart of this non-local flow formulation is a conductivity distribution or kernel that is related to the scalar permeability in the classical Darcy law. In this paper, ensembles of flow networks are considered, of which the necessary statistical information is assumed to be known. In order to relate the conductivity distribution with the flow statistics, a stochastic transport model for fluid particles, termed generalized continuous time random walk (g-CTRW), which is a generalization of correlated continuous time random walk, is introduced. Note that similar assumptions as for correlated CTRW are made, i.e., that lengths and velocities of connections between successive nodes along the trajectories can be described by Markov processes. In order to proceed with a theoretical analysis, a Boltzmann equation is presented, which is consistent with the particle time marching algorithm based on g-CTRW. An important outcome of the analysis is an expression relating the joint probability density function of velocity and connection length in the networks with the conductivity kernel. A numerical, stationary flow example demonstrates how the kernel can be extracted. Further, an algorithm is proposed to compute consistent velocity statistics, mean pressure distribution, and spatially varying conductivity kernel in the case of non-stationary flow. This coupled iterative approach is an attempt to consistently compute stochastic flow and transport in large network ensembles.

Monte Carlo (MC) studies of flow in heterogeneous porous formations, in which the log‐conductivity field is multi‐Gaussian, have shown that as the log‐conductivity variance σY2 increases beyond about 0.5, the one‐point velocity probability density functions (PDFs) deviate significantly from Gaussian behavior. The velocity statistics become more complex due to the formation of preferential flow paths, or channels, as σY2 increases. Methods that employ low‐order approximations (e.g., truncated perturbation expansions) are limited to small σY2 and are unable to represent the complex velocity statistics associated with σY2 > 1. Here a stochastic transport model for highly heterogeneous domains (i.e., σY2 > 1) is proposed. In the model, the Lagrangian velocity components of tracer particles are represented using continuous Markovian stochastic processes in time. The Markovian velocity process (MVP) model is described using a set of first‐order ordinary and stochastic differential equations, which are easy to solve using a particle‐based method. Once the MVP model is calibrated based on velocity statistics from MC simulations of the flow (hydraulic head and velocity), the MVP‐based model can be used to describe the evolution of the tracer concentration field accurately and efficiently. Specifically, the MVP model is validated using MC simulations of longitudinal and transverse tracer spreading due to a point‐like injection in the domain. We demonstrate that for σY2 > 1, the ensemble‐averaged tracer cloud remains markedly non‐Gaussian for relatively large travel distances from the point source. The MVP transport model captures this behavior and reproduces the velocity statistics quite accurately.

In the context of stochastic two-phase flow in porous media, we introduce a novel and efficient method to estimate the probability distribution of the wetting saturation field under uncertain rock properties in highly heterogeneous porous systems, where streamline patterns are dominated by permeability heterogeneity, and for slow displacement processes (viscosity ratio close to unity). Our method, referred to as the frozen streamline distribution method (FROST), is based on a physical understanding of the stochastic problem. Indeed, we identify key random fields that guide the wetting saturation variability, namely fluid particle times of flight and injection times. By comparing saturation statistics against full-physics Monte Carlo simulations, we illustrate how this simple, yet accurate FROST method performs under the preliminary approximation of frozen streamlines. Further, we inspect the performance of an accelerated FROST variant that relies on a simplification about injection time statistics. Finally, we introduce how quantiles of saturation can be efficiently computed within the FROST framework, hence leading to robust uncertainty assessment.

We derive analytical expressions for the one-point cumulative distribution function and probability density function of water saturation in the one-dimensional stochastic immiscible two-phase (Buckley–Leverett) problem. The sources of uncertainty are the spatial distributions of porosity and total velocity. The derived distribution functions involve integrals of the input random parameters. Comparisons with standard Monte Carlo simulation demonstrate that the method is applicable for input parameters with large variance and arbitrary correlation lengths. We also show that the proposed method is superior to the low-order statistical moment equations approach. We also outline a streamline-based strategy to extend our distribution-based method to multiple spatial dimensions.

Recently, distribution element trees (DETs) were introduced as an accurate and computationally efficient method for density estimation. In this work, we demonstrate that the DET formulation promotes an easy and inexpensive way to generate random samples similar to a smooth bootstrap. These samples can be generated unconditionally, but also, without further complications, conditionally using available information about certain probability-space components. This article is accompanied by the R codes that were used to produce all simulation results. Supplementary material for this article is available online.