High-fidelity Computational Fluid Dynamics Research Articles

CFD/FEA Co-Simulation Framework for Analysis of the Thermal Barrier Coating Design and Its Impact on the HD Diesel Engine Performance

Thermal barrier coatings (TBCs) have been investigated both experimentally and through simulation for mixing controlled combustion (MCC) concepts as a method for reducing heat transfer losses and increasing cycle efficiency, but it is still a very active research area. Early studies were inconclusive, with different groups discovering obstacles to realizing the theoretical potential. Nuanced papers have shown that coating material properties, thickness, microstructure, and surface morphology/roughness all can impact the efficacy of the thermal barrier coating and must be accounted for. Adding to the complexities, a strong spatial and temporal heat flux inhomogeneity exists for mixing controlled combustion (diesel) imposed onto the surfaces from the impinging flame jets. In support of the United States Department of Energy SuperTruck II program goal to achieve 55% brake thermal efficiency on a heavy-duty diesel engines, this study sought to develop a deeper insight into the inhomogeneous heat flux from mixing controlled combustion on thermal barrier coatings and to infer concrete guidance for designing coatings. To that end, a co-simulation approach was developed that couples high-fidelity computational fluid dynamics (CFD) modeling of in-cylinder processes and combustion, and finite element analysis (FEA) modeling of the thermal barrier-coated and metal engine components to resolve spatial and temporal thermal boundary conditions. The models interface at the surface of the combustion chamber; FEA modeling predicts the spatially resolved surface temperature profile, while CFD develops insights into the effect of the thermal barrier coating on the combustion process and the boundary conditions on the gas side. The paper demonstrates the capability of the framework to estimate cycle impacts of the temperature swing at the surface, as well as identify critical locations on the piston/thermal barrier coating that exhibit the highest charge temperature and highest heat fluxes. In addition, the FEA results include predictions of thermal stresses, thus enabling insight into factors affecting coating durability. An example of the capability of the framework is provided to illustrate its use for investigating novel coatings and provide deeper insights to guide future coating design.

Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator

<div class="section abstract"><div class="htmlview paragraph">Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice. The results show a strong influence of the amount of non-condensable gas on the level of cavitation as well as the overall shape of the gas-phase structures at the orifice exit. The emulator framework involves the construction of deep autoencoders for efficient dimensionality reduction of the flowfields. Deep artificial neural networks were then employed for prediction of the flowfields for unknown operating conditions. The emulated flowfields were then tested by evaluating spray and combustion predictions from one-way coupling spray simulations. The analysis of the spray predictions from CFD-generated and emulator-predicted injections maps revealed that the emulation framework is capable of reproducing spray predictions with similar level of accuracy, yet at a fraction of the computational cost. The maximum achievable speed-up using the emulator framework is up to 2 million times over the traditional CFD approach for generating static coupling injection maps. The emulation framework provides an efficient pathway for integrating detailed injector simulations into spray and engine simulations.</div></div>

Some improvements of hybrid trim method for a helicopter rotor in forward flight

The main difficulty of the trimming for helicopter rotor CFD (Computational Fluid Dynamics) lies in the high computational cost and low computational efficiency. The BET/CFD hybrid trim method, which uses the low-fidelity BET (Blade Element Theory) to calculate the Jacobian matrix and high-fidelity CFD method to get the aerodynamic force and moment for comparison with the target values, has been widely used in industry due to its high efficiency and low computational cost compared with the pure CFD method. However, this method also has two disadvantages: a complex C81 table needs to be established before the BET works; because of the low accuracy in computing the Jacobian matrix, the number of trim iterations is too large and the trimming may not even converge. In this paper, two improvements of the hybrid trim method have been proposed. Firstly, an Artificial Neural Networks (ANN) model has been developed to predict the aerodynamic coefficients in the BET. The ANN model greatly improves the efficiency and works for all kinds of airfoils without any extra operation. The second improvement is to divide the trimming process into two stages: the preliminary stage in which the Jacobian matrix is calculated using the BET with an ANN model, and the accurate stage in which the Jacobian matrix is obtained by the CFD method. The two-stage method avoids the second shortcoming mentioned above. The AH-1G rotor and ONERA 7A rotor in forward flight have been investigated to validate the present method. The trim results agree well with the flight test results and other computational results.

Propulsion optimization of a quadcopter in forward state

A propulsion system suitable for the forward state of a quadcopter is optimally designed in this study. The forward model of the rotor in tilt flow is established using a linear regression method to process the aerodynamics data acquired from the empirical blade element momentum theory (BEMT). Fuselage model is constructed by high fidelity computational fluid dynamics (CFD) method. Based on the models, the performance of the UAV can be derived precisely and quickly with the propulsion system parameters. Then, the propulsion system is optimized to better realize the forward flight endurance by using genetic intelligent optimization algorithm. Finally, wind-tunnel tests are conducted and the results indicate that the optimal propulsion system can efficiently improve the forward flight endurance.

A Case Study on Pathogen Transport, Deposition, Evaporation and Transmission: Linking High-Fidelity Computational Fluid Dynamics Simulations to Probability of Infection

A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.

A CFD data-driven aerodynamic model for fast and precise prediction of flapping aerodynamics in various flight velocities

Abstract

Data-based approach for wing shape design optimization

Aircraft design is a trade-off among different objectives and constraints, so multiple design rounds are usually required. Aerodynamic shape optimization based on high-fidelity computational fluid dynamics is consequently expensive, especially for wing design in the transonic regime. To address the interactive demand in aircraft design, we present a data-based approach to real-time high-fidelity wing shape optimization. Accurate and fast data-based models are constructed to perform aerodynamic analyses in lieu of costly computational fluid dynamic simulations. For the versatility of the data-based models, 135,108 training data that cover wing samples of different aerodynamic shapes, flight speeds, and flight altitudes are used. The verification on 47,967 wings shows that mean relative errors of CL, CD, and CM compared to computational fluid dynamic simulations are all within 0.4%. The models are further verified in multiple single-point, multi-point, and multi-objective wing design optimization problems. The optimized wings have similar shapes to those obtained by computational-fluid-dynamics-based optimization, and the differences in CD are merely one∼two counts. These results demonstrate the effectiveness of the data-based approach to fast and high-fidelity wing design. This work showcases a real-time high-fidelity optimization approach to high-dimensional nonlinear engineering problems using data-based models.

Innovative Box-Wing Aircraft: Emissions and Climate Change

The PARSIFAL project (Prandtlplane ARchitecture for the Sustainable Improvement of Future AirpLanes) aims to promote an innovative box-wing aircraft: the PrandtlPlane. Aircraft developed adopting this configuration are expected to achieve a payload capability higher than common single aisle analogues (e.g., Airbus 320 and Boeing 737 families), without any increase in the overall dimensions. We estimated the exhaust emissions from the PrandtlPlane and compared the corresponding impacts to those of a conventional reference aircraft, in terms of Global Warming Potential (GWP) and Global Temperature Potential (GTP), on two time-horizons and accounted for regional sensitivity. We considered carbon dioxide, carbonaceous and sulphate aerosols, nitrogen oxides and related ozone production, methane degradation and nitrate aerosols formation, contrails, and contrail cirrus. Overall, the introduction of the PrandtlPlane is expected to bring a considerable reduction of climate change in all the source regions considered, on both the time-horizons examined. Moreover, fuel consumption is expected to be reduced by 20%, as confirmed through high-fidelity Computational Fluid Dynamics (CFD) simulations. Sensitivity of data, models, and metrics are detailed. Impact reduction and mitigation strategies are discussed, as well as the gaps to be addressed in order to develop a comprehensive Life Cycle Assessment on aircraft emissions.

Artificial Neural Networks based wake model for power prediction of wind farm

In the wind industry, power prediction of wind farm is commonly implemented by analytical wake models, which is low-cost but insufficient in accuracy for high-turbulent wake modelling. In this study, a novel machine-learning-based wake model is developed to improve the power prediction of wind farms. The presented model can reproduce the velocity and turbulence fields in turbine wakes commensurate to the high-fidelity Computational Fluid Dynamics (CFD) simulations while achieving good computational efficiency. Driven by massive CFD simulation dataset, the implicit relationship between inflows and wake flows is established using the Artificial Neural Networks (ANN) technique based on back-propagation algorithm. The reduced-order method Actuator Disk Model with Rotation (ADM-R) and modified k−ε turbulence model are implemented into RANS simulations to save the computational costs dramatically in producing the big-data of wake flows. The ANN wake model is deployed in the Horn Rev wind farm, and validated against LES, onsite measurement, and analytical wake models. The conclusions show that the ANN model can appreciably improve the power predictions compared with the existing analytical models and match the LES and measurement data well. The validated model is also adopted to investigate the influence of wind direction and turbine layout on power production of wind farms.

Dynamic Instability Analysis of a Spring-Loaded Pressure Safety Valve Connected to a Pipe by Using Computational Fluid Dynamics Methods

Abstract As the ultimate protection of a pressure system, pressure safety valves (PSV) can respond in an unstable manner in the form of flutter and chatter, which will affect service life, reliability, and performance. In order to study the dynamic instability caused by multisource forces including the flow force, the spring compression force, and the pressure wave forces, a high-fidelity computational fluid dynamics (CFD) model of the system is proposed. A complete CFD model, incorporating the PSV, connected pipes, and the pressure vessel, is developed, in which advanced techniques in Fluent using User Defined Function (UDF) and Dynamic Layering method are combined to allow the PSV to be coupled to the system dynamics. Based on this model, the valve's opening and reclosing process is monitored to examine the influence of design parameters on the dynamic instability of the PSV. Specifically, the propagation of pressure waves along the connecting pipes is successfully captured, helping to assess the instability mechanism and provide the ability to optimize the design and setup of pressure relief systems.

High-Fidelity Computational Fluid Dynamics Modeling and Analysis of a Pressure Vessel-Pipe-Safety Valve System in Gas Service

Abstract A pressurized vessel-pipe-safety valve (PVPSV) system is a common configuration for many energy management systems, and a better understanding of their dynamics is helpful for system design and optimization. In this paper, a method for high-fidelity computational fluid dynamics (CFD) modeling is presented, which can be used to predict dynamic responses of PVPSV systems. For modeling, regions from the vessel outlet to the safety valve exit flange are modeled using a CFD approach; the pressure vessel is set as the boundary and the movement of the valve disk is represented by a one-dimensional (1D) rigid body motion model. Simulations are performed, and both stable and unstable operation are investigated. To establish accuracy, an experimental test rig is designed and constructed to measure the motion of the valve disk and the pressures at different system locations. Comparisons are performed for different dynamic modes and good agreement is obtained, supporting the accuracy of the high-fidelity model in reproducing the dynamic response of PVPSV systems. With the developed model, influences of other variables, such as piping length and safety valve configurations, can also be evaluated. The method presented in this paper can also be used to develop CFD models for other similar systems and should facilitate system design and optimization.

Statistical Mechanics-Based Surrogates for Scalar Transport in Channel Flow

Thermal hydraulics, in certain components of nuclear reactor systems, involve complex flow scenarios, such as flows assisted by free jets and stratified flows leading to turbulent mixing and thermal fluctuations. These complex flow patterns and thermal fluctuations can be extremely critical from a reactor safety standpoint. The component-level lumped approximations (0D) or one-dimensional approximations (1D) models for such components and subsystems in safety analysis codes cannot capture the physics accurately, and may introduce a large degree of modeling uncertainty. On the other hand, high-fidelity computational fluid dynamics codes, which provide numerical solutions to the Navier–Stokes equations, are accurate but computationally intensive, and thus cannot be used for system-wide analysis. An alternate way to improve reactor safety analysis is by building reduced-order emulators from computational fluid dynamics (CFD) codes to improve system scale models. One of the key challenges in developing a reduced-order emulator is to preserve turbulent mixing and thermal fluctuations across different-length scales or time-scales. This paper presents the development of a reduced-order, non-linear, “Markovian” statistical surrogate for turbulent mixing and scalar transport. The method and its implementation are demonstrated on a canonical problem of differentially heated channel flow, and high-resolution direct numerical simulations (DNS) data are used for emulator or surrogate development. This statistical surrogate model relies on Kramers–Moyal expansion and emulates the turbulent velocity signal with a high degree of accuracy.

Experimental determination and analysis of the transverse pressure difference in a wire-wrapped rod bundle

Wire-wrapped rod bundles are widely used in different applications for their enhanced heat transfer capability. The complex behavior of the flow in these rod bundles is strongly affected by the geometrical parameters, and flow regime. Experimental measurements of the transverse pressure difference (between adjacent faces) of a replica of a 61-rod wire-wrapped hexagonal bundle have been conducted under a wide range of Reynolds number (3,000–18,000), to study the effects of the wire azimuthal position for transition and turbulent flow regimes. As a result of the higher horizontal velocity, the transverse pressure difference was found to increase with the bundle Reynolds number. At higher Reynolds number, the maximum (positive) transverse pressure difference between two adjacent faces occurred when the wire is directed at the corner between these faces (θ=0∘), with the minimum (negative) occurring at a wire azimuthal location of θ=120∘. Pressure at adjacent walls equalizes at an angle 60∘<θ<120∘, and near θ=−90∘. For the lower Reynolds numbers investigated, a positive minimum pressure difference was observed at θ=0∘. The experimental results improved the understanding of the flow behavior in wire-wrapped bundles, and provided a unique set of data that can be used for validation of high-fidelity computational fluid dynamics codes.

Simulation-based study of COVID-19 outbreak associated with air-conditioning in a restaurant.

COVID-19 has shown a high potential of transmission via virus-carrying aerosols as supported by growing evidence. However, detailed investigations that draw direct links between aerosol transport and virus infection are still lacking. To fill in the gap, we conducted a systematic computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a restaurant setting, where likely cases of airflow-induced infection of COVID-19 caused by asymptomatic individuals were widely reported by the media. We employed an advanced in-house large eddy simulation solver and other cutting-edge numerical methods to resolve complex indoor processes simultaneously, including turbulence, flow–aerosol interplay, thermal effect, and the filtration effect by air conditioners. Using the aerosol exposure index derived from the simulation, we are able to provide a spatial map of the airborne infection risk under different settings. Our results have shown a remarkable direct linkage between regions of high aerosol exposure index and the reported infection patterns in the restaurant, providing strong support to the airborne transmission occurring in this widely reported incident. Using flow structure analysis and reverse-time tracing of aerosol trajectories, we are able to further pinpoint the influence of environmental parameters on the infection risks and highlight the need for more effective preventive measures, e.g., placement of shielding according to the local flow patterns. Our research, thus, has demonstrated the capability and value of high-fidelity CFD tools for airborne infection risk assessment and the development of effective preventive measures.

Fluid-structure coupled analysis of tandem 2D elastic panels

The aeroelastic characteristics of tandem positioned two-dimensional elastic panels have been numerically investigated. High-fidelity Computational Fluid Dynamics and Computational Structural Dynamics (CFD/CSD) methods have been used to simulate the aeroelastic interactions between the panels. Moreover, a CFD-based reduced order model is established through system identification method for auxiliary analyses. The results indicate strong fluid-mediated interactions between the adjacent panels. At low supersonic speeds, affected by the self-sustained vibration of upstream panel, the downstream panel exhibits the characteristics of forced vibration state for some parameter ranges where the flutter should occur. Beyond these parameter ranges, the vibration frequency of downstream panel gradually departs from the wake flow frequency and coincides with the flutter frequency of its isolated state, exhibiting the characteristics of limit cycle flutter. At high subsonic speeds, the aeroelastic response of downstream panel has a stabilizing effect on the upstream panel, and increases the dynamic stability of the entire tandem panels system. The aeroelastic behavior of the adjacent panels may enlighten a new perspective on the flutter control of the shell structures.

Thermoelectric Generation with Impinging Nano-Jets

In this study, thermoelectric generation with impinging hot and cold nanofluid jets is considered with computational fluid dynamics by using the finite element method. Highly conductive CNT particles are used in the water jets. Impacts of the Reynolds number of nanojet stream combinations (between (Re1, Re2) = (250, 250) to (1000, 1000)), horizontal distance of the jet inlet from the thermoelectric device (between (r1, r2) = (−0.25, −0.25) to (1.5, 1.5)), impinging jet inlet to target surfaces (between w2 and 4w2) and solid nanoparticle volume fraction (between 0 and 2%) on the interface temperature variations, thermoelectric output power generation and conversion efficiencies are numerically assessed. Higher powers and efficiencies are achieved when the jet stream Reynolds numbers and nanoparticle volume fractions are increased. Generated power and efficiency enhancements 81.5% and 23.8% when lowest and highest Reynolds number combinations are compared. However, the power enhancement with nanojets using highly conductive CNT particles is 14% at the highest solid volume fractions as compared to pure water jet. Impacts of horizontal location of jet inlets affect the power generation and conversion efficiency and 43% variation in the generated power is achieved. Lower values of distances between the jet inlets to the target surface resulted in higher power generation while an optimum value for the highest efficiency is obtained at location zh = 2.5ws. There is 18% enhancement in the conversion efficiency when distances at zh = ws and zh = 2.5ws are compared. Finally, polynomial type regression models are obtained for estimation of generated power and conversion efficiencies for water-jets and nanojets considering various values of jet Reynolds numbers. Accurate predictions are obtained with this modeling approach and it is helpful in assisting the high fidelity computational fluid dynamics simulations results.

Experimental study of a suspended subsea module at different positions in the splash zone

During the lifting of submerged marine modules, the highest dynamic loads occur in the splash zone. These forces are highly dynamic and cannot easily be estimated with sufficient accuracy using simple calculations or high-fidelity computational fluid dynamics because of their irregular and complex geometries. We conducted an experimental study in a wave tank to investigate the dynamics of a large subsea module in the splash zone subject to irregular waves. The experiments were conducted using a 1:8 scale model of a subsea module, similar to that designed by Offshore Oil Engineering Co., Ltd., for the subsea pipeline from the Wenchang gas fields to the Yacheng pipeline. The model was examined at five different positions relative to the free surface, from completely above the surface to fully submerged. The resulting data are analyzed in the time and frequency domains. Spectral analysis and a dynamic effect parameter are used to analyze the dynamic loads on the subsea module. Several time-domain numerical analyses are performed with different peak periods, and the tension of the sub-slings is obtained. The experimental results for the relative forces and motions of the subsea module are presented, providing a reference for the design of other similar modules and their installation and validation data for computational modeling.

Cooling system design for the internal combustion engine of a BWB UAV prototype

The current study presents an analytical design methodology for a compact thermal management system (cooling system) of a high-power density internal combustion rotary engine. The engine is installed on a fixed wing, tactical Blended-Wing-Body (BWB) Unmanned Aerial Vehicle (UAV) prototype that has been developed for the aerial delivery of cargo and lifesaving supplies. The design methodology is based mainly on a low-fidelity thermodynamic model and on high-fidelity Computational Fluid Dynamics (CFD) analyses. The results are validated against wind tunnel experiments for the cooling system of the engine. The overall methodology is divided into two distinct segments, the design of the heat exchanger (HEX) and the sizing of the cold-air inlet ducts. The low-fidelity thermodynamic model is based on 0D and 1D textbook methods and well-established formulae from the existing literature. The thermodynamic model is used for the calculation of the geometrical characteristics of the HEX. The high-fidelity CFD modeling is employed to yield a more detailed investigation of the flow field around the HEX and provide information about whether the airflow velocity requirements, for the 0D and 1D model, are met. Additionally, the detailed CFD computations assist the design of high-efficiency inlet ducts, in order to secure optimal airflow through the HEX and around the engine compartment. Using the proposed methodology, a functional HEX experimental prototype is designed and manufactured, as a proof of concept, in order to be thoroughly examined on a test rig in laboratory conditions. Multiple experiments are conducted in a variety of inlet temperatures and airflow velocities, for the validation and fine-tuning of the overall proposed design methodology. The study concludes with the final HEX design, as well as an analysis of its integration aspects on the tactical BWB UAV prototype.

High-Fidelity CFD Validation and Assessment of Ducted Propellers for Aircraft Propulsion

This paper presents validation and assessment of ducted propellers for aircraft propulsion. Numerical methods and simulation strategies are put forward, including steady/unsteady high-fidelity computational fluid dynamics (CFD) simulations and simpler momentum-based methods. The validation and comparisons of the methods are made using a ducted propeller proposed by NASA. Simulations are also performed and analyzed at extended advance ratios, blade pitch setting, and cross-wind angles. Comparisons are also made with open propeller counterparts. The ducted propeller shows superior performance over its unducted counterpart in hover and at low advance ratios. The major thrust gain is identified from the combination of duct leading-edge suction and the higher pressure at the diffuser exit. The propeller is off-loaded due to the higher inflow velocities. The ducted propeller is also shown to have less intrusive wake features at low axial speeds. However, as the advance ratio increases, the duct thrust contribution becomes negative and the ducted propeller becomes deficient, due to growing high-pressure areas at the leading edge. At cross-wind, high-fidelity CFD simulations offer accurate aerodynamic loads predictions despite the complex flow features. The duct surface separation is found to be delayed due to the propeller suction, while the propeller is shown shielded by the duct, thereby suffering less from the unbalanced inflow velocities. Decomposition of induced velocities by each part is carried out and presented. A large, nonlinear extra induction component, due to mutual interactions of the duct and the propeller, is observed and found favorable for the performance augmentation.

Aeromechanical Analysis of a Complete Wind Turbine Using Nonlinear Frequency Domain Solution Method

Abstract The high-fidelity computational fluid dynamics (CFD) simulations of a complete wind turbine model usually require significant computational resources. It will require much more resources if the fluid–structure interactions (FSIs) between the blade and the flow are considered, and it has been the major challenge in the industry. The aeromechanical analysis of a complete wind turbine model using a high-fidelity CFD method is discussed in this paper. The distinctiveness of this paper is the application of the nonlinear frequency domain solution method to analyze the forced response and flutter instability of the blade as well as to investigate the unsteady flow field across the wind turbine rotor and the tower. This method also enables the aeromechanical simulations of wind turbines for various interblade phase angles in a combination with a phase shift solution method. Extensive validations of the nonlinear frequency domain solution method against the conventional time domain solution method reveal that the proposed frequency domain solution method can reduce the computational cost by one to two orders of magnitude.