Physics-based Flow Research Articles

Vortical Structures over a Generic Tailless Chined Forebody–Delta Wing

The flowfield over a model having a chined forebody and a simple delta wing was investigated experimentally using oil flow visualizations and stereoscopic particle image velocimetry at a mean chord-based Reynolds number of 2.3×105, angles of attack of 20 and 30 deg, and yaw angles of 0 and 5 deg. The flowfield over the model exhibited pairs of leading-edge vortices similar to those around a double-delta wing, except for the influence of the fuselage, where the chine vortex followed the curvature. The development and interaction of the two vortices were measured and analyzed. It was found that increasing the angle of attack resulted in wakelike vortices, whereas increasing the yaw angle yielded a wakelike vortex on the windward side and a jetlike vortex on the leeward side. The interaction of the windward side vortex with the physical barrier of the forebody surface was observed to greatly affect its behavior and the interaction downstream. In some configurations, the merged vortex exhibited a breakdown. The flowfield and the vortex dynamics under various conditions are discussed in detail to provide insight into manipulating the flowfield using physics-based flow control.

A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems

Abstract In this present work, mechanical engineering optimization problems are solved by employing a novel optimizer (HFDO-DOBL) based on a physics-based flow direction optimizer (FDO) and dynamic oppositional-based learning. Five real-world engineering problems, viz. planetary gear train, hydrostatic thrust bearing, robot gripper, rolling bearing, and multiple disc clutch brake, are considered. The computational results obtained by HFDO-DOBL are compared with several newly proposed algorithms. The statistical analysis demonstrates the HFDO-DOBL dominance in finding optimal solutions relatively and competitiveness in solving constraint design optimization problems.

Physics-Based Flow Stress Model for Alloy 718

A dislocation density-based model for alloy 718 in the annealed state is proposed in order to accurately describe the deformation behavior of this alloy for a wide range of thermo-mechanical loadings. The model accounts for numerous microstructural mechanisms, including strain hardening, grain size effect, dynamic strain aging (DSA), solid solution strengthening, as well as phonon and electron drag which affects dislocation movements at high strain rates. Two types of recovery mechanisms are also included: recovery due to dislocation glide and recovery associated with cross-slip of screw dislocations. The model is calibrated using experimentally determined stress–strain curves for both low and high strain rates in the order of 10–3 to 103 s−1, and for temperatures in the range 20 °C to 800 °C. The stress–strain data computed with the model are in good agreement with the experimental data. The inclusion of DSA is found to be effective in the combination of temperatures and strain rates corresponding to experimental observations. The solid solution strengthening contribution increases with decreasing temperature and increasing strain rate. The drag effect in the model proves to be significant only for deformation at high strain rate (~ 103 s−1).

Using Bayesian Leave-One-Out and Leave-Future-Out Cross-Validation to Evaluate the Performance of Rate-Time Models to Forecast Production of Tight-Oil Wells

Summary Production forecasting is usually performed by applying a single model from a classical statistical standpoint (point estimation). This approach neglects: (a) model uncertainty and (b) quantification of uncertainty of the model’s estimates. This work evaluates the predictive accuracy of rate-time models to forecast production from tight-oil wells using Bayesian methods. We apply Bayesian leave-one-out (LOO) and leave-future-out (LFO) cross-validation (CV) using an accuracy metric that evaluates the uncertainty of the models’ estimates: the expected log predictive density (elpd). We illustrate the application of the procedure to tight-oil wells of west Texas. This work assesses the predictive accuracy of rate-time models to forecast production of tight-oil wells. We use two empirical models, the Arps hyperbolic and logistic growth models, and two physics-based models: scaled slightly compressible single-phase and scaled two-phase (oil and gas) solutions of the diffusivity equation. First, we perform Bayesian inference to generate probabilistic production forecasts for each model using a Bayesian workflow in which we assess the convergence of the Markov chain Monte Carlo (MCMC) algorithm, calibrate, and evaluate the robustness of the models’ inferences. Second, we evaluate the predictive accuracy of the models using the elpd accuracy metric. This metric provides a measure of out-of-sample predictive performance. We apply two different CV techniques: LOO and LFO. The results of this study are the following. First, we evaluate the predictive performance of the models using the elpd accuracy metric, which accounts for the uncertainty of the models’ estimates assessing distributions instead of point estimates. Second, we perform fast CV calculations using an important sampling technique to evaluate and compare the results of the application of two CV techniques: leave-one-out cross-validation (LOO-CV) and leave-future-out cross-validation (LFO-CV). While the goal of LOO-CV is to evaluate the models’ ability to accurately resemble the structure of the production data, LFO-CV aims to assess the models’ capacity to predict future-time production (honoring the time-dependent structure of the data). Despite the difference in their prediction goals, both methods yield similar results on the set of tight-oil wells under study. The logistic growth model yields the best predictive performance for most of the wells in the data set, followed by the two-phase physics-based flow model. This work shows the application of new tools to evaluate the predictive accuracy of models used to forecast production of tight-oil wells using: (a) an accuracy metric that accounts for the uncertainty of the models’ estimates and (b) fast computation of two CV techniques, LOO-CV and LFO-CV. To our knowledge, the proposed approach is novel and suitable to evaluate and eventually select the rate-time model(s) with the best predictive accuracy of models to forecast hydrocarbon production.

Nonlinear Evolutionary PDE-Based Refinement of Optical Flow

The goal of this paper is to propose two nonlinear variational models for obtaining a refined motion estimation from an image sequence. Both the proposed models can be considered as a part of a generalized framework for an accurate estimation of physics-based flow fields such as rotational and fluid flow. The first model is novel in the sense that it is divided into two phases: the first phase obtains a crude estimate of the optical flow and then the second phase refines this estimate using additional constraints. The correctness of this model is proved using an evolutionary PDE approach. The second model achieves the same refinement as the first model, but in a standard manner, using a single functional. A special feature of our models is that they permit us to provide efficient numerical implementations through the first-order primal-dual Chambolle-Pock scheme. Both the models are compared in the context of accurate estimation of angle by performing an anisotropic regularization of the divergence and curl of the flow respectively. We observe that, although both the models obtain the same level of accuracy, the two-phase model is more efficient. In fact, we empirically demonstrate that the single-phase and the two-phase models have convergence rates of order O(1/N2) and O(1/N) respectively.

Physics-based stitching of multi-FOV PIV measurements for urban wind fields

Particle image velocimetry (PIV) is commonly used to measure multiple field-of-views (FOVs) of the flow in large urban domains, performed in wind tunnels using an array of cameras located at fixed positions or a single camera setup that traverses during the measurements. With respect to the large amount of data produced in this type of measurement, traditional stitching approaches are no longer adequate to efficiently merge the image data. A physics-based flow field stitching method is proposed for stitching flows in urban wind fields, named regional-flow stitching. It matches the flow characteristics within neighboring FOVs of flow using a vorticity-based reference window. The method is based on finding the optimal similarity between a reference window in one FOV and another possible matching window in the other FOV. The matching window rolls in both directions at the interval of two adjacent data points, which determines the stitching resolution. The matching in flow similarity is evaluated using the normalized root-mean-squared error (NRMSE) between reference and possible matching windows, which is evaluated by comparison to a traditional stitching method and further verified using planar and stereo wind tunnel PIV measurement results. A comparison of computational cost with 8 FOVs and approximate 7% surface overlap between FOVs shows that our method is faster, taking about 46% of processing time, than a traditional stitching method. 3D visualization of the merged FOVs is further demonstrated with ParaView. The proposed stitching method may serve as a powerful tool for experimental studies involving large domains of urban wind flow fields.

Application of physics-based flow models in cardiovascular medicine: Current practices and challenges.

Personalized physics-based flow models are becoming increasingly important in cardiovascular medicine. They are a powerful complement to traditional methods of clinical decision-making and offer a wealth of physiological information beyond conventional anatomic viewing using medical imaging data. These models have been used to identify key hemodynamic biomarkers, such as pressure gradient and wall shear stress, which are associated with determining the functional severity of cardiovascular diseases. Importantly, simulation-driven diagnostics can help researchers understand the complex interplay between geometric and fluid dynamic parameters, which can ultimately improve patient outcomes and treatment planning. The possibility to compute and predict diagnostic variables and hemodynamics biomarkers can therefore play a pivotal role in reducing adverse treatment outcomes and accelerate development of novel strategies for cardiovascular disease management.

Production rates and EUR forecasts for interfering parent-parent wells and parent-child wells: Fast analytical solutions and validation with numerical reservoir simulators

Analytical expressions are given to forecast the production decline and estimated ultimate reserves (EUR) for pairs of both parent-parent wells and parent-child wells. The forecasts use physics-based flow rate quantification in elementary flow cells between hydraulic fractures sets. The intrinsic rate in the flow cells for a given shale formation can be obtained and scaled by a type curve specific for its completion and reservoir parameters. The model then can predict how changes in fracture treatment design parameters will affect the performance of new wells in the same target zone. The effect of well down-spacing and the onset of pressure interference on the performance of parent-parent wells can be forecasted with reasonable accuracy. Similarly, the equations can also predict the effect of infill drilling of child wells on the performance of both well types. The method is applied to real world examples from Eagle Ford acreage. The fast analytical model forecasts are benchmarked against the results from an independent numerical reservoir simulator, with satisfactory matches. The validated approach aims to provide a practical analytical tool for the production forecasting of multi-wells in shale formations.

Using a segregated flow model to forecast production of oil, gas, and water in shale oil plays

Application of a segregated-flow assumption to a physics-based flow model simplifies decline analysis for multi-phase fluid production. The segregated flow model assumes that, during production, oil and water can be treated as taking different flow paths from the matrix to the hydrofracture network. This simplifies forecasting production of both the oil and water phases. Subsurface and reservoir property data for horizontal fractured wells are limited. As a consequence, attempts to estimate decline curves and ultimate recoveries utilizing full-physics, multiphase reservoir simulations are poorly constrained. In contrast, robust production forecasts can be made using a simple, physics-based model based on scaling laws.In this study, analysis of production decline has been carried out on 10,000 Permian basin, 12,000 Bakken and Three Forks, and 10,000 Eagle Ford oil wells. Additionally, 5000 Eagle Ford and 1000 Permian shale gas wells were analyzed. The approach enables estimation of the time to boundary-dominated flow and drainage volume for each well. In addition, the ultimate recoveries of oil, gas, and water were estimated. These can be used to compare wells drilled using different technologies or in different formations, plays, and geographic locations.Physics-based, segregated-flow models are broadly applicable to analysis of production from tight oil reservoirs. Empirical observations show that flow can be treated as segregated, where water and oil follow independent paths to the wellbore. Therefore, this model can be used to perform reliable, fast, automated decline analysis for tight oil fields.

Groundwater Remediation through Pump-Treat-Inject Technology Using Optimum Control by Artificial Intelligence (OCAI)

Optimum Control by Artificial Intelligence (OCAI) is presented in this paper as a dynamic decision making algorithm for optimising pumpage schedule to remediate a contaminated aquifer using the Pump, Treat and Inject (PTI) method. OCAI integrates three modules to control contaminants, to reduce runtime and to meet water quality constraints and discharge capacity at the wells. There is no bespoke capability for the strategy presented by the paper, which formulates: (i) Module 1 comprises models of physics-based flow and transport for simulating heads and contamination; (ii) Module 2 serves as the “surrogate” of Module 1 by transforming the simulation outputs of Module 1 into two fast forecasting Sugeno Fuzzy Logic (SFL) models; and (iii) Module 3 is a user-defined unit to implement OCAI, to run Genetic Algorithm (GA) and to interrogate Module 2, where Modules 2 and 1 are pre-processed. The OCAI strategy resolves two barriers: (i) the information created in the past time step is passed on to new time step for an efficient control; and (ii) the ‘hunger’ of GA for function evaluation is met by the fast Module 2 but not by the slow Module 1. The novelty in OCAI includes: an optimum PTI schedule to control contaminants and to remediate contaminated plumes. The results show that the maximum Total Dissolved Matter is reduced from a range of 3500 to 8000 to a range from 1490 to 3450. The results provide “proof-of-concept” for OCAI.

Active attenuation of a trailing vortex inspired by a parabolized stability analysis

Designing effective control for complex three-dimensional flow fields proves to be non-trivial. Often, intuitive control strategies lead to suboptimal control. To navigate the control space, we use a linear parabolized stability analysis to guide the design of a control scheme for a trailing vortex flow field aft of a NACA0012 half-wing at an angle of attack $\unicode[STIX]{x1D6FC}=5^{\circ }$ and a chord-based Reynolds number $Re=1000$ . The stability results show that the unstable mode with the smallest growth rate (fifth wake mode) provides a pathway to excite a vortex instability, whereas the principal unstable mode does not. Inspired by this finding, we perform direct numerical simulations that excite each mode with body forces matching the shape function from the stability analysis. Relative to the uncontrolled case, the controlled flows show increased attenuation of circulation and peak streamwise vorticity, with the fifth-mode-based control set-up outperforming the principal-mode-based set-up. From these results, we conclude that a rudimentary linear stability analysis can provide key insights into the underlying physics and help engineers design effective physics-based flow control strategies.

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT-FFR).

The purpose of this study is to develop and evaluate a functionally personalized boundary condition (BC) model for estimating the fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA) using flow simulation (CT-FFR). The CCTA data of 90 subjects with subsequent invasive FFR in 123 lesions within 21 days (range: 0-83) were retrospectively collected. We developed a functionally personalized BC model accounting specifically for the coronary microvascular resistance dependency on the coronary outlets pressure suggested by several physiological studies. We used the proposed model to estimate the hemodynamic significance of coronary lesions with an open-loop physics-based flow simulation. We generated three-dimensional (3D) coronary tree geometries using automatic software and corrected manually where required. We evaluated the improvement in CT-FFR estimates achieved using a functionally personalized BC model over anatomically personalized BC model using k-fold cross-validation. The functionally personalized BC model slightly improved CT-FFR specificity in determining hemodynamic significance of lesions with intermediate diameter stenosis (30%-70%, N = 72), compared to the anatomically personalized model lesions with invasive FFR measurements as the reference (sensitivity/specificity: 0.882/0.79 vs 0.882/0.763). For the entire set of 123 coronary lesions, the functionally personalized BC model improved only the area under the curve (AUC) but not the sensitivity/specificity in determining the hemodynamic significance of lesions, compared to the anatomically personalized model (AUC: 0.884 vs 0.875, sensitivity/specificity: 0.848/0.805). The functionally personalized BC model has the potential to improve the quality of CT-FFR estimates compared to an anatomically personalized BC model.

Integration of computational stratigraphy models and seismic data for subsurface characterization

Computational stratigraphy provides a method to model depositional and stratigraphic processes on a grain size scale while honoring physics-based flow dynamics. Using rock-physics equations and assumptions, these computational stratigraphy models can be converted to rock properties such as velocity and density. The rock-physics properties are in turn used to generate synthetic seismic data, which allow studying the seismic expression of stratigraphic features on different scales. These models and their application to existing hydrocarbon reservoirs can provide new insight into connectivity and other characteristics of subsurface reservoirs that are not provided by traditional reservoir-modeling approaches.

Statistical Analysis of Uncertainties in Deterministic Computational Modeling – Application to Composite Process Resin Infusion Flow Model

Deterministic physics-based flow modeling provides an effective way to simulate and understand the resin flow infusion process in liquid composite molding processes and its variants. These are effective to provide optimal injection time and locations prior to gelation for given process parameters of resin viscosity and preform permeability. However, there could be significant variations in these two parameters during actual manufacturing. This paper presents simulation-based statistical analysis of uncertainties of these process parameters involved in the resin flow infusion. Two key process parameters, viscosity and permeability, and their statistical variations are examined individually and subsequently in combination for their impact on the associated injection time. Values from statistical probability distribution of the process parameters were employed to find the solution space for this engineering application through deterministic physics-based process flow modeling simulations. A bivariate confidence envelope was developed using the appropriate Cumulative Density Function for a 95% probability of successfully completing resin infusion prior to physical resin gelation time. A logistic regression model for the influence of resin viscosity and permeability on the binary response of successful resin infusion is presented and conforms well to the sensitivity analysis inferences.

Groundwater remediation design using physics-based flow, transport, and optimization technologies

BackgroundThe purpose of this work was to demonstrate an approach to groundwater remedial design that is automated, cost-effective, and broadly applicable to contaminated aquifers in different geologic settings. The approach integrates modeling and optimization for use as a decision support framework for the optimal design of groundwater remediation systems employing pump and treat and re-injection technologies. The technology resulting from the implementation of the methodology, which we call Physics-Based Management Optimization (PBMO), integrates physics-based groundwater flow and transport models, management science, and nonlinear optimization tools to provide stakeholders with practical, optimized well placement locations and flow rates for remediating contaminated groundwater at complex sites.ResultsThe algorithm implementation, verification, and effectiveness testing was conducted using groundwater conditions at the Umatilla Chemical Depot in Umatilla, Oregon, as a case study. This site was the subject of a government-sponsored remedial optimization study. Our methodology identified the optimal solution 40 times faster than other methods, did not fail to perform when the physics-based models failed to converge, and did not require human intervention during the solution search, in contrast to the other methods. The integration of the PBMO and Lipschitz Global Optimization (LGO) methods with standalone physically based models provides an approach that is applicable to a wide range of hydrogeological flow and transport settings.ConclusionsThe global optimization based solutions obtained from this study were similar to those found by others, providing method verification. Automation of the optimal search strategy combined with the reliability to overcome inherent difficulties of non-convergence when using physics models in optimization promotes its usefulness. The application of our methodology to the Umatilla case study site represents a rigorous testing of our optimization methodology for handling groundwater remediation problems.

Virtual Rheoscopic Fluids for Flow Visualization

Physics-based flow visualization techniques seek to mimic laboratory flow visualization methods with virtual analogues. In this work we describe the rendering of a virtual rheoscopic fluid to produce images with results strikingly similar to laboratory experiments with real-world rheoscopic fluids using products such as Kalliroscope. These fluid additives consist of microscopic, anisotropic particles which, when suspended in the flow, align with both the flow velocity and the local shear to produce high-quality depictions of complex flow structures. Our virtual rheoscopic fluid is produced by defining a closed-form formula for the orientation of shear layers in the flow and using this orientation to volume render the flow as a material with anisotropic reflectance and transparency. Examples are presented for natural convection, thermocapillary convection, and Taylor-Couette flow simulations. The latter agree well with photographs of experimental results of Taylor-Couette flows from the literature.

Physics-based flow estimation of fluids

We present a physics-based method to compute the optical flow of a fluid. In most situations, gray level changes in an image do not provide sufficient information to completely ascertain optical flow, necessitating the use of a supplementary constraint. For this, the smoothness constraint is often employed. This constraint is, however, general and does not express well a priori knowledge of a specific object. We therefore propose a method in which physical equations describing the object are used as supplementary constraints. In this way, more accurate flow estimation can be achieved. The physical model employed is a combination of the continuity equation and Navier–Stokes’ equations. After describing how we integrate these equations into fluid flow estimation, we demonstrate the effectiveness of the proposed method by presenting experimental results of its application to simulated and real Karman flows.