Computational Fluid Dynamics Model Research Articles

Prognostic value of morphology and hemodynamics in moyamoya disease for long-term outcomes and disease progression.

To explore the relationship between morphological and hemodynamic parameters, baseline characteristics, and long-term outcomes in patients with moyamoya disease (MMD) using a computational fluid dynamics model. We retrospectively reviewed 129 patients at Beijing Tiantan hospital between July 2020 and December 2021. Perioperative clinical variables and Suzuki stage were recorded. Logistic regression analysis was employed to identify the risk factors for unfavorable long-term outcomes. The association between morphological, CT perfusion parameters, hemodynamic parameters and the Suzuki stage, clinical variables of MMD was also analyzed. Patients with high relative Wall Shear Stress (rWSS) were older and had more cases with higher Suzuki stage and worse follow-up mRS scores (p < 0.05). High rWSS at the terminal ICA and diabetes mellitus were identified as independent predictors of unfavorable long-term outcomes [OR = 3.039(1.191-7.754), p = 0.020; OR = 3.164(1.141-8.723), p = 0.027, respectively]. ROC analysis demonstrated that predictive models incorporating rWSS improved AUC values, with the highest AUC in Model 2 (AUC = 0.889). High rWSS was significantly associated with future TIA and stroke events (p = 0.032). We speculated that high rWSS and diabetes mellitus were independent risk factors for unfavorable long-term outcomes in patients with MMD. rWSS and morphological parameters are crucial for predicting MMD progression and understanding its pathogenesis.

Assessing the Effect of Uneven Inlet Velocity on the 24 ft Diameter MinwaterCSP Fan Using a Unique Pressure Sensing System

An industrial-grade pressure sensing system was designed for installation on the surface of the 24 ft (7.315 m) diameter M-fan that is installed on the MinwaterCSP test facility. The facility has significant cross draft effects due to the proximity of a canal that runs underneath the centre of the facility. This canal causes disturbance of the inlet air flow distribution. Due to the constant rapid change in air velocity and direction with blade rotation, the pressure on the blade fluctuates. The velocity distribution was measured experimentally using the installed anemometers. The uneven inlet velocity distribution led to fluctuations in blade surface pressures, which were measured using the uniquely designed pressure sensing system. The measured results were compared to the results of a Computational Fluid Dynamics (CFD) model of the MinwaterCSP test facility. The CFD simulations consisted of two parts. Firstly a 3-dimensional representation of the facility, which was used to get the velocity distribution data and secondly a 2-dimensional airfoil simulation which was used to obtain the surface pressure distribution on the blade surface. The 3-dimensional CFD model made use of an actuator disk model to represent the effect of the fan. The experimental results showed a higher velocity and relative surface pressure distribution at locations correlating to the location of the canal. The simulation was able to replicate these distribution fluctuations. The results of the experimentally measured fan blade surface pressure at the fan suction side correlated within 9% to those of the simulation results.

Deep learning based hybrid POD-LSTM framework for laminar natural convection flow in a rectangular enclosure

Abstract Laminar natural convection in side-heated enclosures is characterized by transient phenomena of the working fluid till it reaches steady state. The side heating can done in several ways the most common way being heating one end at constant temperature and cooling the other end. One of the other ways is heating both sides of the enclosure at a constant heat flux. Mathematical modeling of such problems using Computational Fluid Dynamics (CFD) essentially involves considerable amount of computational time and power to predict the flow phenomena observed in actual experimentation. In the last few years, data driven model frameworks have proven to be anefficient way in saving both time and computational cost in several applications. In the present study, a data driven model framework using a combination of unsupervised machine learning (using Proper Orthogonal Decomposition [POD]) and supervised deep learning models (using Long Short Term Memory [LSTM]) has been developed and referred to as POD-LSTM framework. The selection of a few dominant spatial bases and accompanying temporal modes provides us with a reduced order model of the system. The flow is then reconstructed and compared with results of CFD simulations. The Rayleigh number (Ra) chosen for the study is 3.27 × 1010. The estimated time to reach stedy state for this Ra number is 15,000 s. The POD-LSTM framework is trained using data obtained from a validated CFD model for the first 1,000 s. The trained model was then tested to predict temporal dynamics for the entire 15,000 s. The predictions provided by POD-LSTM framework were found upto 98 % accurate compared to the ones predicted by CFD. The computational time and power was however an order of magnitude lower for the POD-LSTM framework than that required for the CFD model.

A Fast-Calibrated Computational Fluid Dynamic Model for Timber–Concrete Composite Ventilated Façades

Timber–concrete composite (TCC) systems join the positive aspects of engineered wood products (good seismftaic behaviour, low thermal conductivity, environmental sustainability, good behaviour under fire if appropriately designed) with those of concrete (high thermal inertia, durability, excellent fire resistance). TCC facades are typically composed of an internal insulated timber-frame wall and an external concrete slab, separated by a ventilated air cavity. However, there is very limited knowledge concerning the performance of TCC facades, especially concerning their thermal behaviour. The present paper deals with the development and optimization of a 2D Computational Fluid Dynamic (CFD) model for the analysis of TCC ventilated façades’ thermal behaviour. The model is calibrated and validated against experimental data collected during the annual monitoring of a real TCC ventilated envelope in the north of Italy. Also, a new solver algorithm is developed to significantly speed up the simulation (i.e., 45 times faster simulation at an error below 3.5 °C compared to a typical CFD solver). The final model can be used for the time-efficient analysis (simulation time of approximately 23 min for a full day in real-time) and the optimization of the thermal performance of TCC ventilated facades, as well as other ventilated facades with external massive cladding. Our simulation strategy partially avoids the expensive and time-consuming construction of mock-ups, or the use of comparably slow (conventional) CFD solvers that are less suitable for optimization studies.

Improved infiltration rate determination method for interlocking-block permeable pavements using standard ASTM test apparatus

ABSTRACT Current standard methods of measuring permeability of interlocking-block permeable pavements (IBPPs) do not correctly measure the actual infiltration rates of IBPPs. The test results vary with test area size and location. In this study, employing the standard ASTM-1781 test apparatus, an improved procedure that adopts an experiment-based numerical approach is developed to overcome the problem. It involves the following steps: (i) Perform falling-head infiltration tests using ASTM-1781 apparatus to obtain infiltration flow data; (ii) Develop computational fluid dynamic (CFD) finite-volume models for different infiltration test conditions; and (iii) Apply appropriate finite-volume model to calculate the effective IBPP infiltration rate under any given surface water depth on the IBPP. Experimental tests were conducted to validate the proposed method.

A numerical study on metallic melt infiltration in porous media and the effect of solidification

The melt infiltration in porous debris is of importance to severe accident prediction and mitigation in nuclear power plants (NPPs), but its mechanism remains elusive. In this study, a computational fluid dynamics (CFD) model is proposed to simulate the evolution of melt infiltration within porous media, incorporating both solidification and melting processes. The CFD model is validated against the experiment (REMCOD facility) and Moving Particle Semi-implicit (MPS) simulation results. Building upon this validated model, the influence of the melt superheat, the initial particle temperature, and its surface wettability on melt infiltration dynamics are mainly analyzed. It is found that increased initial melt superheat enhances melt infiltration length and rate; higher initial particle temperatures promote deeper and faster infiltration, while lower temperatures may result in solidification that blocks further infiltration. Additionally, the wettable particulate bed can enhance melt relocation and heat transfer, but it also accelerates the solidification of the melt, which complicates the infiltration process. Furthermore, phase changes could intensify melt flow instability. This work may expand our understanding of melt infiltration dynamics and pave the way to severe accident modeling in NPPs.

A fully coupled multiscale phase-change model at the porous interface for transpiration cooling: coupling dynamics pore-scale networks to continuum-scale free flow

Being a promising method for thermal protection system, transpiration cooling has received wide interest recently. Numerical simulation for transpiration cooling, however, has been limited due to the mis-matching between the pore-scale two-phase flow and external high-temperature aerothermodynamic environment, which induced by conventional decoupled or iterative one-way coupled simplification methods. In this work, a fully coupled continuum-scale and pore-scale model is established for transient transpiration cooling at the interface between boundary layer flow and porous medium through coupling Computational Fluid Dynamics (CFD) and Pore-Network Model (PNM), termed as the multiscale CFD-PNM coupled method. The coupled method allows to capture detailed displacement and phase change of two-phase flow at pore-scale, revealing the strong interaction of the water vapor with the external free flow within a high temperature boundary layer. After successfully validating the new coupled model by comparing with the Two-Phase Mixture Model (TPMM) solution, a number of cases mimicking the cooling at the interface of typical blunt bodies are simulated. The results show that the multiscale CFD-PNM coupled method can not only provide the thermal protection effect prediction, but also reveal many critical features that beyond the reach of continuum-scale studies. Some pore-scale phenomena that are important to the overall transpiration cooling effects are revealed, including transient phase change and composition variation of water vapor, imbibition and drainage of two-phase flow related to pore-scale capillary thresholds and applied boundary pressures, as well as the two-way mass transfer at the interface, such as the invasion of external hot air.

An entropy measure-based study on flow pattern of gas–liquid two-phase flow in a U-Tube

U-tubes are widely applied in gas–liquid two-phase transportation in chemical engineering. The diverse flow patterns within these tubes significantly affect the pressure loss, heat transfer efficiency, and even the fluid-induced vibration amplitude of the tubes. This study explores the complex flow pattern features in a U-tube in a vertical plane and focuses on recognizing them. For the acquisition and classification of flow patterns, a Computational Fluid Dynamics (CFD) model for gas–liquid two-phase flow is first established, and its quantitative calculation error is ensured to be less than 5%. Then, the spatiotemporal evolution characteristics of flow patterns is analyzed. The real-time pressure drop response is chosen as the representation signal, and its nonlinear features in the time and frequency domain under different flow patterns are explored. A nonlinear time series is constructed by extracting a segment from the real-time pressure drop data, and six entropy measures are applied to analyze and identify them. Finally, the sensitivity of entropy measures to both the time series lengths and the tested sections are evaluated. Results show that there are six typical flow patterns in a U-tube. According to most entropy measures, the bubble flow has the highest complexity; however, the plug flow presents the lowest complexity. In the U-bend, pressure drop signals for the bubble and annular flows show random fluctuations within a specific range, in contrast to the marked periodicity in plug flow signals, while wavy and slug flows exhibit intermittent peak values. Including the upstream and downstream straight pipes in the analysis, rather than focusing solely on the U-bend, significantly increases the complexity of the stratified, plug, and slug flows. Fuzzy entropy is an effective tool for identifying the six flow patterns, demonstrating good resilience to variations in the length of the data series. This characteristic makes it highly useful for real-time identification of flow patterns in the U-bend sections of non-transparent U-tubes, offering considerable potential in chemical equipment.

Performance enhancement of double slope solar still using cylindrical fins: Experimental and numerical analysis

This study explores the enhancement of a double slope solar still (DSSS) by integrating cylindrical fins on the absorber plate to improve heat transfer efficiency and water productivity. The experimental setup, made from galvanized iron sheets and insulated with a wooden box, features a glass cover angled at 34° for optimal solar radiation absorption. Testing was conducted in Gabes, Tunisia, evaluating solar radiation, wind speed, ambient temperature, and water productivity. Measurements were taken from 9:00 a.m. to 6:00 p.m., focusing on distillate yield, temperatures, and heat transfer coefficients. Both experimental and numerical analyses examined the effect of fin diameter on temperature distribution, heat transfer coefficients, and energy efficiency. Results demonstrate that the addition of fins significantly enhances both absorber and water temperatures, with the largest fin diameter (80 mm) achieving a 14.07 % increase. A validated Computational Fluid Dynamics (CFD) model showed a maximum temperature deviation of less than 3.5 °C from experimental data. The study recorded a peak energy efficiency of 71.03 % and a cumulative water productivity of 3252.55 mL/m2.

CFD and Artificial Intelligence-Based Machine Learning Synergy for the Assessment of Syngas-Utilizing Pre-Reformer in r-SOC Technology Advancement

This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming to enhance the preparation of quality r-SOC-ready fuel, which is an indispensable element for successful operation. Evaluating the intricate thermochemistry of syngas-containing reforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. The developed model forecasts and optimizes reforming processes across diverse fuel compositions, encompassing oxygen-containing syngas blends and controlled feedstock outlet process conditions. Impressively, the model’s predictions align closely with CFD outcomes with an error margin as low as 0.34%, underscoring its accuracy and reliability. This research significantly contributes to a deeper understanding and the qualitative enhancement of preparing high-quality syngas for SOC under improved process conditions. Enabling the early availability of valuable information drives forward sustainable research and ensures the safe, consistent operation assessment of r-SOC. Additionally, this strategic approach substantially reduces the need for resource-intensive experiments.

Thermal stress in externally irradiated tubes of solar central receivers with a gas–particle fluidized dense suspension

One of the objectives of the new generation of concentrating solar power plants is to increase the actual temperature limit (565 °C) up to or near 1000 °C. An alternative heat transfer fluid that could help to reach this objective is the use of a fluidized dense gas–particle suspension ascending through a vertical tube. Since the predominant cause of rupture in solar receivers is the creep-fatigue damage process, which is mostly influenced by the maximum temperatures and stress experienced along the tube, the main objective of this work is to numerically study the thermomechanical behavior of a non-uniform externally irradiated tube where particles are fluidized and moves upwards. The heat transfer problem in the two media present (i.e. dense suspension of particles and tube wall) was solved separately: a Computational Particles Fluid Dynamic model solves the heat transfer in the particles, whereas a three dimensional Finite Volume model simulates the heat conduction through the tube wall to obtain the temperature profile, which serves as input to calculate the thermal stress of the tube with an analytical method. Higher thermal stresses were obtained for an absorbed power of 250 kW/m2 (σVM,max=219MPa) compared to that for an absorbed power of 500 kW/m2 (σVM,max=72MPa) due to the lower temperature difference between the front and rear sides of the tube caused by the more pronounced increase of the radiative losses in the front side of the tube compared to that at the rear side. The thermomechanical behavior of a particle receiver was compared to that of a molten salt receiver. For an incident solar flux of 500 kW/m2, the particle receiver reduced the maximum thermal stress by 73% compared to the molten salt receiver. However, the tube’s maximum temperature exceeded the working limit for stainless steel tubes. In order for the receiver to survive heat fluxes characteristic of solar power plants, research into technological solutions that allow to improve the effectiveness of the heat transfer rate from the tube to the particle flow is necessary.

VALIDATION OF SPECIES TRANSPORT MODEL TO SIMULATE AGENT TRANSPORT IN A NACELLE

This is in continuation of work done to study the parameters that affect fire suppressant delivery and distribution in an idealized aircraft engine nacelle, based on the quarter scale model used at WPAFB. The primary objective of this paper is to outline the study of the computational fluid dynamics (CFD) approach to study the injection and spread of an inert fire suppressant in an idealized aircraft engine nacelle and to validate the model used. The inert gas (Nitrogen) is used as the fire suppressant. Initially, the work done by Hammins is used as the basis for validating the computational model. The model settings are chosen based on work done by Crawford and then modified to better match the experimental data. The computational model and assumptions used are documented and suggestions for improved accuracy of CFD model are suggested before applying the CFD model to the WPAFB geometry to conduct preliminary studies of agent spread in the nacelle.

Very low Ru loadings boosting performance of Ni-based dual-function materials during the integrated CO2 capture and methanation process

Herein, the effect of the Ru:Ni bimetallic composition in dual-function materials (DFMs) for the integrated CO2 capture and methanation process (ICCU-Methanation) is systematically evaluated and combined with a thorough material characterization, as well as a mechanistic (in-situ diffuse reflectance infrared fourier-transform spectroscopy (in-situ DRIFTS)) and computational (computational fluid dynamics (CFD) modelling) investigation, in order to improve the performance of Ni-based DFMs. The bimetallic DFMs are comprised of a main Ni active metallic phase (20 wt%) and are modified with low Ru loadings in the 0.1–1 wt% range (to keep the material cost low), supported on Na2O/Al2O3. It is shown that the addition of even a very low Ru loading (0.1–0.2 wt%) can drastically improve the material reducibility, exposing a significantly higher amount of surface-active metallic sites, with Ru being highly dispersed over the support and the Ni phase, while also forming some small Ru particles. This manifests in a significant enhancement in the CH4 yield and the CH4 production kinetics during ICCU–Methanation (which mainly proceeds via formate intermediates), with 0.2 wt% Ru addition leading to the best results. This bimetallic DFM also shows high stability and a relatively good performance under an oxidizing CO2 capture atmosphere. The formation rate of CH4 during hydrogenation is then further validated via CFD modelling and the developed model is subsequently applied in the prediction of the effect of other parameters, including the H2 inlet concentration, inlet flow rate, dual-function material weight, and reactor internal diameter.

Efficient aerodynamic shape optimization using transfer learning based multi-fidelity deep neural network

Computational efficiency and precision pose a classic contradiction in aerodynamic shape optimization. To address this challenge, this study introduces an effective optimization framework based on multi-fidelity fully connected neural network (MFFCN). The framework utilizes transfer learning (TL) to train a multi-fidelity surrogate model that establishes direct mappings between geometric configuration parameters and aerodynamic performance by adaptively capturing linear or nonlinear relationships concealed between high-fidelity (HF) and low-fidelity (LF) information. The HF and LF data are derived from fine and coarse grids, respectively, evaluated using the same computational fluid dynamics (CFD) model. The MFFCN-TL framework is applied to optimize the National Advisory Committee for Aeronautics 0012 (NACA0012) airfoil (12 design variables) and the Office National d'Études et de Recherches Aérospatiales M6 (ONERA M6) wing (50 design variables). Simulation results demonstrate that the NACA0012 airfoil achieves a 69.47% enhancement in lift–drag ratio in 1.069 s, compared to a 12.76% gain over 24.8 h in single-fidelity CFD-based optimization. The ONERA M6 wing achieves a 24.66% reduction in drag coefficient in 694 ms compared to 18.37% over 237.3 h in the CFD model. Statistical results show that the MFFCN-TL framework can reduce optimization cost by more than 90% compared to the single-fidelity CFD-based model. These findings suggest that the MFFCN-TL framework significantly enhances optimization efficiency and provides superior feasible solutions over single-fidelity methods.

Analysis and prediction of denitration performance of Mn1Co0.5Cr0.5Ox catalyst based on CFD and BP-GA method

Based on the experimental data of Mn1Co0.5Cr0.5Ox catalysts and the component transport model in Computational Fluid Dynamics (CFD), a kinetic model for the standard NH3-SCR (NH3 selective catalytic reduction) process was effectively established. The objective of the model development was to predict the denitrification reaction rate of the catalyst, which incorporates various factors such as the Arrhenius parameters (Pre-exponential factor and activation energy), inertial resistance, viscous resistance, and surface-to-volume ratio. To verify the practicability of the model, simulation results were compared with actual experimental data. The effects of NH3, NO, O2 concentrations, and gas hourly space velocity (GHSV) on NO conversion were simulated and analyzed. Subsequently, the NO conversion prediction model was trained and established using a combination of numerical simulation results, back-propagation neural network, and genetic algorithm (BP-GA). Furthermore, the significance of the impact that various factors had on the denitrification activity of the catalyst was determined.

Novel passive flow control method using leading-edge prism-shaped cylinder: Performance enhancement of vertical-axis wind turbines

Due to periodic dynamic stall at low tip speed ratios (TSRs), vertical-axis wind turbines (VAWTs) experience notable performance challenges during rotation, which leads to fluctuations in torque and a decrease in energy capture. This research aims to boost the aerodynamic performance of Darrieus VAWTs by employing a leading-edge (LE) prism cylinder (PC) to enhance energy extraction. This novel small-scale device functions as a passive method for controlling flow separation, aiming to energize the boundary layer and adjust the pressure distribution on the blades. Its effectiveness depends on factors such as size, shape, and placement, necessitating careful optimization. A three-dimensional (3D) computational fluid dynamics (CFD) analysis, combined with Taguchi optimization and analysis of variance, is conducted to determine the optimal design parameters for the LE PC tool. This 3D CFD method captures the full complexity of flow dynamics, including vortex structures and wake behavior, leading to more accurate wind turbine performance predictions than two-dimensional (2D) CFD models. The results highlight the crucial role of PC size (Factor A), which contributes nearly 85% to the total contribution factor, while the angle of PC influence is minimal. The optimized rotor demonstrates a 36% increase in maximum average power coefficient (CP) compared to an uncontrolled rotor at TSR = 1.5. However, the effectiveness of this control method diminishes at higher TSRs because the blades encounter angles of attack below the critical stall angle throughout the rotation cycle, naturally preventing flow separation and making the flow separation control method unnecessary. The PC installed on the optimized blade delays flow separation to 55% of the blade chord length, compared to 40% for the base blade. Consequently, the rotor operates efficiently, ensuring consistent, and reliable power generation without flow separation issues.

3-D full-field reconstruction of chemically reacting flow towards high-dimension conditions through machine learning

Monitoring chemically reacting flow is crucial for optimizing and controlling the conversion processes in various chemical engineering scenarios. These processes are often influenced by multiple factors, creating a high-dimensional condition space that imposes intensive computational cost to computational fluid dynamics (CFD) methods. We present a machine learning (ML) framework to reconstruct the 3-D scalar field preserving the accuracy and resolution of CFD modeling. We demonstrate the framework’s effectiveness through a case study on biomass combustion in an industrial-scale grate furnace, which exemplifies the challenges of modeling chemically reacting flow in complex geometric domains influenced non-linearly by multiple parameters. The framework implements three core principles: (1) a Reactor-Structure-Resembled Network (RSRNet) reflecting the operational logics of the reactor, (2) an incremental learning strategy based on random Latin Hypercube Sampling, and (3) the incorporation of self-extracted high-level features from data as soft constraints in the loss function. Ground truth data were calculated through a CFD model, with all cases sampled from a 11-variable condition space. Using only 240 randomly sampled cases (around 1 % total cases in the discretized condition space), RSRNet precisely replicated 1-D and 2-D scalar distributions through incremental training. Further reconstruction of 3-D temperature field only used 40 3-D CFD cases, resulting in the training and testing errors decreased to 7.94 × 10-4 and 1.55 × 10-3, respectively, approaching the level of the 2-D reconstruction, with the average error in temperature field predictions not exceeding 50 K. The generalization ability was validated using a separate test dataset with continuously changing excess air ratio and capacity ratio in wide ranges, including some extreme values, and the model successfully captured complex phenomena under unseen conditions.

Dual-functional adsorptive membranes for PFAS removal: Mechanism, CFD simulation, and selective enrichment

The remediation of emerging water contaminants, particularly per- and polyfluoroalkyl substances (PFAS), presents challenges due to their refractory nature and the presence of competing substances. Dual-functional adsorptive membranes, with hydrophobic backbone and quaternary ammonium moieties, were thereby designed to selectively intercept organic competitors while enrich PFAS. A 96.8 % removal of perfluorooctanoic acid (PFOA) was achieved and this effective removal (>90 %) maintained across five reuse cycles with a total treatment capacity of 650 L m−2. Rather than the rejection mechanism of nanofiltration process, these adsorptive membranes utilize synergistic electrostatic attraction and hydrophobic interactions, leading to a greater enrichment factor of 18.5 (PFOA over humic acid) and a permeability of 34.6 L m-2h−1 bar−1 (1.9- and 4.5-fold higher than reported NF 270 membranes, respectively). Furthermore, computational fluid dynamics (CFD) modeling revealed that the sponge-like matrix effectively prevents channeling flow and enhance access to adsorption sites. Sensitivity analysis and the high Damkohler number indicated that the adsorption process is mass transfer-controlled, with the key parameters ranked in order of significance: residence time > fluid viscosity > intrinsic adsorption rate. With consistent removal performance with co-existing competitors, efficient regeneration, and reusability, the dual-functional adsorptive membranes offer promising practical efficacy for PFAS remediation.

Influence of the stratification parameters on the wake field of an underwater vehicle in a two-layer stratified flow

Because the ocean exhibits different stratification states in different seasons and regions, the effect of different stratification parameters on a submarine wake must be studied. In particular, to investigate such effect in a two-layer stratified flow, this study established a computational fluid dynamics (CFD) model based on the URANS equations, shear-stress transport (SST) k-ω turbulence model, and Eulerian multiphase flow model. The volume of fluid (VOF) method was adopted to implement the free-surface capture method. The working conditions of the SUBOFF model under an infinite diving depth and near the free surface were compared with those of the experiments, and the convergence of the time step and grid number under stratified flow conditions was verified. These validation steps proved the feasibility of the CFD method. Finally, the effects of the stratification parameters on the free surface waves, internal waves, and turbulent kinetic energy behind the vehicle were observed by varying the stratification parameters to achieve different working conditions. The results show that when an underwater vehicle sails in a two-layer stratified flow, the interstratified density ratio and depth of the internal wave surface affect its wake field. When the underwater vehicle sails in the water layer, with an increase in the interstratified density ratio, the free-surface roughness increases and the turbulent kinetic energy decreases. When the underwater vehicle sails in the salt water layer, the result is just the opposite.

Source term estimation in the unsteady flow with dynamic mode decomposition

When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE.