Computational Fluid Dynamics Calculations Research Articles

Spatial patterns of high-risk biomechanical metrics in plaques with abnormal vs. normal physiological flow indices

BackgroundPlaques associated with abnormally low physiological flow reserve indices are appropriate for percutaneous coronary intervention (PCI). However, recent trials demonstrate that PCI of ischemia-producing lesions does not reduce major adverse cardiac events (MACE). Low endothelial shear stress (ESS) or high ESS gradient (ESSG) are associated with MACE wherever they occur along the plaque. This study aims to determine the presence of high-risk ESS metrics in obstructive coronary plaques with high-risk (<0.80) vs. borderline-risk (0.80–0.89) vs. normal Instantaneous Wave-free Ratio (iFR) (>0.89). MethodsWe included 50 coronary arteries (50 patients) with variable iFR values who underwent coronary angiography and optical coherence tomography (OCT), followed by 3D reconstruction and computational fluid dynamics calculations of ESS/ESSG. The cohort was divided into 3 groups: iFR < 0.80, iFR 0.80–0.89, and iFR > 0.89. Spatial distribution of ESS metrics was reported along the course of each plaque, and high-risk ESS metrics and their location were compared among the 3 iFR subgroups. ResultsHigh-risk ESS features (Minimal ESS, Maximum ESSG) were similarly distributed along the course of the atherosclerotic plaque in the three iFR subgroups, both in absolute value and in location: Min ESS: 0.5 ± 0.3 vs. 0.4 ± 0.2 vs. 0.4 ± 0.2 Pa respectively (p = 0.60); Max ESSG any direction: 13.7 ± 9.4 vs. 10.4 ± 10.6 vs. 10.0 ± 7.8 Pa/mm respectively (p = 0.30). ESS metrics were spatially located up to ≥18 mm from the plaque minimal luminal area (MLA) in both directions. ConclusionHigh-risk ESS metrics are similarly observed in plaques with normal or abnormal iFR, both in absolute value and spatial location in reference to the MLA. Utilizing iFR to identify plaques likely to cause MACE would miss the majority of plaques mechanistically at high-risk to destabilize and cause future adverse cardiac events.

Machine Learning for Real-Time Building Outdoor Wind Environment Prediction Framework in Preliminary Design: Taking Xinjiekou Area of Nanjing, China as the Case

The incorporation of physical environmental performance as a primary consideration in building design can facilitate the harmonization of the built environment with the surrounding site and climate, enhance the building’s environmental adaptability and environmental friendliness, and contribute to the achievement of energy-saving and emission-reduction objectives through the integration of natural lighting and ventilation. General computational fluid dynamics (CFD) can help architects make accurate predictions and effectively control the building’s wind environment. However, CFD integration into the design workflow in the preliminary stages is frequently challenging due to program uncertainty, intricate parameter settings, and substantial computational expenses. This study offers a methodology and framework based on machine learning to overcome the complexity and computational cost barriers in simulating outdoor wind environments of buildings. In this framework, the machine learning model is trained using an automated CFD simulation system based on Butterfly and implemented within the Rhino and Grasshopper environment. This framework provides real-time simulation feedback within the design software and exhibits promising accuracy, with a Structural Similarity Index Measure (SSIM) ranging from 90–97% on a training dataset of 1200 unique urban geometries in Xinjiekou Area of Nanjing, China. Furthermore, we programmatically integrate various parts of the simulation and computation process to automate multiscenario CFD simulations and computations. This automation saves a significant amount of time in producing machine-learning training sets. Finally, we demonstrate the effectiveness and accuracy of the proposed working framework in the design process through a case study. Although our approach cannot replace CFD simulation computation in the later design stages, it can support architects in making design decisions in the preliminary stages with minimal effort and immediate performance feedback.

Performance optimization of latent heat storage device based on surrogate-assisted multi-objective evolutionary algorithm and CFD method

The design of fin structures and the formulation of heat conductivity enhancers have always been critical factors influencing the performance of latent heat storage devices. However, achieving the optimal parameters of fin structures and the fraction of heat conductivity enhancers when adjusting multiple parameters simultaneously remains a time-consuming and labor-intensive task. In this paper, a combination of machine learning methods and computational fluid dynamics (CFD) was employed to optimize the parameters of fin structures and the mass fraction of expanded graphite with minor computational resources and time costs, thereby obtaining a set of Pareto optimal latent heat storage (LHS) devices that simultaneously considers energy storage power and energy storage capacity. The combination essentially is a surrogate assistant multi-objective evolutionary optimization algorithm, which utilizes batch recommendation strategies to recommend multiple high-quality candidate solutions that achieve a balance between two conflicting optimization objectives in CFD simulations. This method significantly reduces the number of model iterations and saves the time and computational resources required for multi-objective optimization. The results showed that the proposed algorithm only required 80 cases of CFD calculations to obtain two well-fitted Gaussian process models, leading to a 91 % improvement in computational efficiency. According to the recommendations from the algorithm, the LHS device with 78 fins of 2.0 mm thickness and 20 % mass fraction of expanded graphite could achieve the optimal trade-off of energy storage power and energy storage capacity. Compared to the prototype, the energy storage power and total energy storage capacity increased by 46.7 % and 22.1 %, respectively. The method proposed in this study can provide guidance for the optimal configuration design of LHS devices.

The effective thermal conductivity of random isotropic porous media analysis and prediction

Effective thermal conductivity of porous media is a crucial parameter for heat transfer within them. Many studies have characterized various porous media by adjusting the control parameters generated through the Quartet Structure Generation Set method. The porous media effective thermal conductivity is then determined through Computational Fluid Dynamics calculations, which, however, necessitate significant computational resources and time. Thus, following an exploration of the influence of control parameters (i.e., porosity, core growth probability, and their coupling) on the effective thermal conductivity of isotropic porous media generated by the Quartet Structure Generation Set method, this study developed a multi-layer perceptron prediction model. The aim was to establish a prediction model from the porous media control parameters to effective thermal conductivity, thereby reducing the time spent on iterative calculations. The findings indicate that the effective thermal conductivity does not uniformly increase with the growth of core probability, and instead fluctuates after a certain threshold. Notably, the trained model exhibits a high prediction accuracy, with average deviations of 0.0887 and 0.0748 for the training and testing datasets, respectively.

Erosion-corrosion failure of overhead air cooler in atmospheric tower

ABSTRACT During the hydrogenation process in a petrochemical plant, the air cooler serves as a crucial heat exchanger in atmospheric towers. However, the frequent corrosion and leaks of air coolers significantly impact the safety of the process. Firstly, the Aspen software is utilized to simulate the operation of an air cooler. Then, Computational Fluid Dynamics (CFD) calculations are performed to analyze the internal flow in the air cooler. Finally, four inlet optimization strategies are proposed. It is indicated that the condensation of the oil phase results in the formation of liquid-phase oil droplets that cause erosion-corrosion. The absence of liquid-phase water condensation implies that there is minimal dew point corrosion in the tube bundle. The turbulence kinetic energy near the entrance exhibits significant variation, making erosion-corrosion more likely to occur near the entrance of the cooling tube bundle. Furthermore, vortexes form near the entrances of the first and second rows, exacerbating the corrosion in the tube bundle. By optimizing the air cooler, numerical simulations showed that the shear force of the vortex on the tube wall is reduced by nearly 50%. The internal erosion and scour effect of the improved air cooler is reduced.

Study on the internal waves induced by a submerged body moving in a continuously stratified fluid

This paper studies the characteristics of internal waves induced by the motion of a submerged body through a combination of experimental and numerical methods. First, by deploying an array of conductivity probes in an experiment, the temporal evolution of the internal wave at the plane of the conductivity probe array is obtained and compared with numerical simulation results based on the Navier–Stokes equations, thereby validating the accuracy of the Computational Fluid Dynamics (CFD) model. Subsequently, utilizing CFD calculations, further exploration is conducted on the spatiotemporal distribution characteristics of the internal wave, encompassing its generation and evolution, velocity field distribution, wake angle, and wave profile features. Finally, the variation of the internal wave with the Froude number (Fr) is investigated, revealing that it varies apparently with Fr, including the wave component, wave mode, wake length, wake angle, wave amplitude, and so on. It is found that when Fr is large, the internal wave converges toward the centerline behind the body, until forming a line. The wave magnitude changes with Fr in four stages, i.e., increasing with Fr from zero in the first stage, reaching a peak and decreasing thereafter in the second stage, then changing little in the third stage, and finally increasing approximately linearly in the fourth stage.

Deep learning assisted anode porous transport layer inverse design for proton exchange membrane water electrolysis

Proton exchange membrane water electrolysis cell (PEMEC) offers a clean and promising way for hydrogen production. The spatial internal current density and temperature distribution uniformity significantly determines its reliability and durability, as well as the hydrogen production performance. Here, a non-isothermal 3D computational fluid dynamics (CFD) model for PEMEC with parallel flow fields is employed to investigate the impacts of the heterogeneous porosity distribution within the anode porous transport layer (APTL) on the internal current density and temperature distribution uniformity and energy conversion performance. 800 heterogeneous APTL porosity distributions are applied for CFD calculation, providing dataset for deep learning. The deep operator network (DeepONet) is employed to mapping the heterogeneous APTL porosity distribution to the real physical fields such as temperature, oxygen molar fraction and current density distribution fields. Full-connected neural network is employed to construct the relationship between the heterogeneous APTL porosity distribution to the performance metrics. The gradient descent algorithm is applied to obtain APTL porosity distributions corresponding to the optimal internal current density and temperature distribution uniformity, respectively. Compared with the uniform porosity distribution, the current density and temperature uniformity are improved by 45.544 % and 26.680 %, respectively, at an average APTL porosity of 0.5.

Parabolic trough solar collector technology using TiO2 nanofluids with dimpled tubes

Nanoparticle concentrations have recently attracted attention as a means to increase the effectiveness of parabolic trough solar collectors (PTSC). Computational fluid dynamics (CFD) study has recently gained popularity for evaluating the performance of PTSC with Dimpled Tubes using a nanofluid made of TiO2 nanoparticles distributed in deionized water (DI-H2O). The effect of using Dimpled Tubes with varied turbulent flow rates, from 0.5 kg/minto 2.5 kg/min, is studied via a battery of tests and computational fluid dynamics simulations. TiO2nanofluid performance is compared to water, the “base fluid,” in this study.This study investigates the utilization of TiO2 nanofluids in enhancing the heat transfer performance of parabolic trough solar collector (PTSC) technology, particularly when integrated with dimpled tubes.The importance of this research lies in the quest for improving the efficiency of solar thermal systems, which is crucial for sustainable energy production.Variables such as solar collector efficiency, Reynolds number, and Nusselt number are tracked as the inquiry progresses. When TiO2 nanofluids are added to the base fluid within the dimpled tube, the convective heat transfer coefficient increases by an astonishing 34.25 percent.This improvement is most pronounced at a mass flow rate of 2.5 kg/min and a volume concentration of TiO2 nanofluid of 0.3 %. Parabolic trough solar collectors have recently been the focus of attempts to improve efficiency via nanoparticle concentrations. The paper uses Computational Fluid Dynamics analysis to investigate how well Dimpled Tubes work with TiO2/DI-H2O nanofluid. The significant increase in the convective heat transfer coefficient was clarified by experimental and CFD calculations that considered fluctuations in turbulent flow rates. The improvement is particularly striking at a mass flow rate and a volume concentration of TiO2 nanofluid.Findingsindicate a significant improvement in heat transfer rates when TiO2 nanofluids are used, particularly in conjunction with dimpled tubes. The combined effect leads to a notable thermal efficiency enhancement, representing the potential of nanofluid-based improvements in optimizing PTSC performance.

Conceptual design of coolant circuits and thermal stress analysis for JA-DEMO divertor

Engineering design of the JA-DEMO divertor concept, i.e. double-coolant circuits of 200°C coolant (5MPa) for high heat load targets (CuCrZr-pipe) and 290°C coolant (15MPa) for high neutron load Plasma Facing Units (F82H-pipe) and cassette body (CB), has been developed. Computational fluid dynamics (CFD) calculation of the coolant distributions to targets, baffles, reflectors, dome and to CB was performed in order to determine the feasibility of the key concepts. (i) All PFU support designs for the coolant distribution to PFUs were optimized to reduce variation of the flow velocity (Vcool) at the inlet of PFUs less than 5 – 6 %. Parallel coolant circuit design for the dome and reflectors was also developed, and mass flows were adjusted by orifices and the main mass flow rate. (ii) A new cooling concept with two layers of puddles and fins at the side routes was proposed for CB. The design provided Vcool = 0.55 – 1.27 m⋅s−1 and average Vcool = 1.04 m⋅s−1 with the fin transparent ratio of 0.1. It was enough to exhaust the nuclear heat, and the design issues were identified under the coolant condition. (iii) Heat exhaust on fish-scale surface target and stress-strain of the CuCrZr pipe were investigated under assuming a DEMO divertor condition. Steady-state heat load at wet region by plasma (qtwet) was restricted below 13.5 MWm−2 to avoid W-recrystallization. In the stress-strain cycle of higher qtwet ∼15.3 MWm−2, maximum tensile σ (120 – 200 MPa) and total change in strain during the heat load cycle (Δε ∼0.25 %) were relatively small. These evaluations suggested that such high heat load cycle may not be a critical lifetime issue, but reduction in Tcool is preferable to handle ITER-like slow transients such as 15 – 20 MWm−2.

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.

Investigation on the Static Performance of Surface-Throttling Frictionless Pneumatic Cylinder through Finite Element Method

The equilibrium system is essential for the high-precision movement of the ultra-precision vertical axis. However, the complex assembly process makes orifice-throttling frictionless cylinders difficult to manufacture and prone to air hammering. Surface-throttling frictionless pneumatic cylinders effectively avoid these problems. This paper establishes an improved finite element method (FEM) model of a novel surface-throttling frictionless pneumatic cylinder to investigate its static performance. Furthermore, the static equilibrium calculation of the dual-cylinder system is concerned. The radial bearing capacity and support force requirements for the surface-throttling aerostatic bearings are obtained. The outcomes provide theoretical guidance for optimizing cylinder parameters. It ensures that the ultimately optimized cylinder meets the requirements for radial bearing capacity and support force of the ultra-precision vertical axis while minimizing air consumption. Finally, the accuracy of the proposed method is verified through computational fluid dynamics (CFD) calculation and experiments.

Numerical Simulation of Folding Tail Aeroelasticity Based on the CFD/CSD Coupling Method

This paper presents a CFD/CSD coupling method for aeroelastic simulation of folding tail morphing aircraft. The unsteady aerodynamic analysis is based on an in-house computational fluid dynamics (CFD) solver for the Euler equations, and emphasis is made on developing an efficient dynamic mesh method for the tail’s hybrid fold motion/elastic vibration deformation. The structural dynamic analysis is based on the computational structural dynamics (CSD) technique for solving the structural equation of motion in modal space. The aeroelastic coupling was achieved through successive iterations of CFD and CSD computations in the time domain. An adaptive multi-functional morphing aircraft allowing tail fold motion was selected to be studied. By using the developed method, aeroelastic simulation and mechanism analysis for fixed configurations at different folding angles and for variable configurations during the folding process were performed. The influence of folding rate on tail aeroelasticity and its influence mechanism were also analyzed.

CFD Analysis of Counter-Rotating Vane-Type Wing Vortex Generator for Regional Aircraft

Vortex generator is a component that has a significant impact on aircraft performance. The function of the vortex generator is to create vortices that can optimize the aerodynamic performance of aircraft wings by avoiding air flow separation and increasing lift at high angle of attack. Vortex generator can provide increased lift during take-off and landing due to the increased wing angle of attack. Although the use of vortex generator can be carried out using an experimental approach, a computational fluid dynamic approach to determine the influence of geometric parameters and placement of the vortex generator needs to be carried out. The aim of this research is to determine the effect of parameters like placement on the wing chord, height of the boundary layer, length, shape, angle of incidence and distance between pairs on the lift and drag. The model used as a computational fluid dynamic calculation model is the Spalart Allmaras transient model. As a result, vortex generator does not always have a good effect on aerodynamics. All configurations have a negative influence on the lift and drag values, but the flow separation phenomenon can be reduced significantly. Of all the configurations, the best configuration is obtained by exhibiting an ogive shape, positioned at 13.8% of the chord length, set at a 13o angle of incidence. The vortex generator should have a height closely matching the boundary layer, with a length 6.5 times the height and a pair spacing of 6.7 times the height

Aerodynamic Stability Derivative Calculations Using the Compressible Source and Doublet Panel Method

This work presents the development of closed-form expressions for the aerodynamic stability derivatives of wings and aircraft flying at subsonic compressible conditions using the unsteady subsonic 3D source and doublet panel method (SDPM). The stability derivatives are obtained directly from the frequency domain version of the nonlinear unsteady subsonic Bernoulli equation and can be calculated for negligible additional computational cost around the true geometry of the wing or aircraft for each frequency of interest. The methodology is validated by applying it to an experimental test case of a swept wing oscillating in yaw and sideslip in the wind tunnel, demonstrating that the values of the aerodynamic stability derivatives obtained from the SDPM are in good agreement with the experimental data at low and moderate angles of attack. Then, the methodology is applied to a blended-wing–body unmanned aerial vehicle configuration and the SDPM stability derivative predictions are compared to estimates obtained from both steady computational fluid dynamics calculations and the USAF DATCOM methodology. It is shown that the SDPM approach is accurate and can yield more information about the stability of the aircraft than the DATCOM, given that the latter was developed for conventional aircraft configurations.

Mathematical model and numerical calculation of the movement of oil products in helicoidal heat exchangers

Heating of oil and oil products is widely used to reduce energy losses during transportation. An approach is developed to determine the effective length of the heat exchanger and the temperature of the cold coolant (oil) at its outlet in the case of a strong dependence of oil viscosity on temperature. The method of the log-mean temperature difference, modified for the case of variable viscosity, and the methods of computational fluid dynamics (CFD) are used for calculations. The results of numerical calculations are compared with the data obtained on the basis of a theoretical approach at a constant viscosity. When using a theoretical approach with a constant or variable viscosity, the heat transfer coefficients to cold and hot coolants are found using criterion dependencies. The Reynolds-averaged Navier-Stokes (RANS) and a turbulence model that takes into account the laminar-turbulent transition are applied. In the case of variable oil viscosity, a transition from the laminar flow regime to the turbulent one is manifested, which has a significant effect on the effective length of the heat exchanger. The obtained results of CFD calculations are of interest for the design of heat exchangers of a new type, for example, helicoidal ones.

Gas and powder flow characteristics of packed bed: A two-way coupled CFD-DEM study

A two-way coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) calculation was performed to numerically investigate the gas and powder flow characteristics within a packed bed. The analysis and discussion focused on the effects of gas phase velocity, particle shape, and size on the flow behavior of the powders. The results revealed that the average absolute velocity of the powder increased with an increase in the inlet gas flow rate. Conversely, the average dimensionless velocity exhibited the opposite trend. Particle shape significantly impacted the flowability of the powders. A critical value of sphericity equal to 0.96 was observed. For powders with sphericity less than 0.96, the average velocity fluctuated around a progressively decreasing value over time, and the powders tended to accumulate in the inlet region. In contrast, powders with a sphericity greater than 0.96 exhibited average velocities fluctuating around a constant value throughout the simulation. Additionally, a smaller number of these particles remained within the fluid domain, indicating a higher flow rate through the packed bed and ultimately exiting the fluid domain. Regarding the influence of particle size, the flowability reached a minimum value at a radius of 0.14 mm, with the average powder velocity mirroring this trend. Powders with a radius exceeding 0.14 mm demonstrated increasing flowability and average velocity. The observed impact on flowability was likely due to a combination of mechanisms. The findings presented in this paper offer valuable insights into the dynamics of powders within a packed bed. This knowledge can be instrumental in the design and analysis of packed bed reactors.

Investigating crossflow interactions in multi-perforation cooling systems: Experimental and numerical insights

This study investigates the effects of employing a multi-perforation cooling system on crossflow interactions, utilizing both numerical simulations and experimental methods. A 30° inclined multi-perforation cooling system is applied on a heated flat plate as part of the experimental setup. Large Eddy Simulation (LES) is utilized for Computational Fluid Dynamics (CFD) calculations to analyze the impact of multi-perforation on heat transfer dynamics. Validation is conducted by comparing the results with experimental investigations. The study encompasses both aerodynamic and thermal considerations, employing Particle Image Velocimetry (PIV) and infrared techniques for analysis, while wall heat flux is measured using electrical discharge. Experimental data on film cooling are collected through these methods, focusing on the impact of blowing ratio (M) and the number of open jet rows (R). The LES-based CFD simulations accurately predict the trajectory of the cooling film. Notably, the influence of flow through the multi-perforation on the primary flow is observable across both configurations of open jet rows (4R and 8R near the wall). The outcomes substantiate the formation of a cooling film emanating from the jet’s fourth row (4R configuration), thereby validating the LES simulations. Visualization of wall temperatures illustrates the formation of a cooling film, discernible through the visible heat distribution from row 4R. However, discrepancies arise in predicting the adiabatic cooling efficiency towards the exit of the 4R region and the commencement of the 8R region, potentially attributed to the alterations induced by the additional multi-perforated injection. The novelty and the main objective of this study is to evaluate the impact of the variation of geometric parameters between the jets and the main flow on the dispersion of the jets themselves. A novel function, tailored to the temperature evolution of the cooling gas, is employed for data analysis. Two geometry configurations, featuring varying open hole areas, are evaluated. The film-cooled heat transfer coefficient, depicted by the Nusselt number, and the adiabatic film cooling efficiency results, serve to appraise the film cooling efficacy of the configurations.

A two-phase flow node-solid joint simulation algorithm based on heat flow correction

In order to accelerate the temperature field calculation of fuel and tank wall protective materials, we propose a node-solid joint simulation method based on heat flow correction, in which the gas–liquid is used as a node for joint CFD (Computational Fluid Dynamics) calculation of the solid temperature field. Based on this method, we constructed a transient thermal analysis model of the fuel tank and performed a gas–liquid–solid conjugate heat transfer tight coupling simulation to obtain the wall heat transfer coefficient and the corrected heat flux under the specified thermal boundary conditions. The results show that the maximum relative errors of the average temperature of the gas–liquid node and the temperature field of the tank wall are less than 0.2 % and 6 %, respectively, and the calculation efficiency is about 160 times that of the traditional tight coupling calculation. When the solid boundary temperature is reduced by 200 K and increased by 500 K, the maximum relative error between the average temperature of the gas–liquid node and the solid temperature is less than 7 %. The algorithm has good robustness and accelerates the iterative design of the fuel tank. The accuracy of the algorithm was verified by the fuel temperature test experiment of the full flight profile.

A framework of a data-driven model for ship performance

An accurate assessment of a ship's required power is increasingly relevant for ship operations. We used a simplified framework of a data-driven model to predict ship's fuel consumption. Our approach was based on the learning capabilities of a generalized AutoML (Automated Machine Learning) process, trained with a variety of databases obtained from Computational-Fluid-Dynamics (CFD) simulations or from simplified numerical methods. These CFD simulations were conducted by solving the Reynolds Averaged Navier-Stokes (RANS) equations, using the STARCCM + commercial CFD software to calculate the ship resistance at speed under different operating conditions. Initially, we conducted a statistical analysis to select the independent variables before fitting the regression models and identifying potentially wrong assumptions. The AutoML process allowed optimizing the model's hyperparameters and designing the topology of the neural networks. For a set of unknown scenarios, comparative predictions obtained from the data-driven model and from numerical simulations showed that the data-driven model, trained with results obtained from CFD simulations, accurately and efficiently predicted ship operational parameters under realistic operating conditions, thereby dispensing with the need to perform elaborate CFD computations. Specifically, this low-cost and efficient operational data-driven technique forecasted the ship's operational fuel consumption although only a limited amount of recorded operational data was available.

Aerodynamic interference analysis and modeling of heterogeneous rotors

During closed-range rotorcraft missions, strong aerodynamic disturbances occur between multiple aircraft, thus affecting the stability and safety of cooperative flight processes. However, owing to the numerous control variables in rotor models, the cost and complexity of experiments and body-fitted grid simulation methods are high, thereby resulting in low analysis efficiency and difficulty in establishing a highly applicable aerodynamic-interference model. In this study, the effectiveness of a two-dimensional quasi-steady momentum source method for calculating the aerodynamic interference of heterogeneous rotors is validated using the sliding-mesh method. Using Latin sampling and momentum source methods to obtain aerodynamic-interference data points, a multiparameter-coupled heterogeneous-rotor aerodynamic-interference model is proposed and validated. The results indicate that the proposed aerodynamic-interference model provides clear physical significance and demonstrates high accuracy under different rotor configurations. In validation samples, the correlation coefficient between the predicted aerodynamic loss on the lower rotor and the computational fluid dynamics (CFD) calculation results is approximately 0.99, with the maximum errors in predicting thrust and moment losses are less than 7 % and 2 %. Meanwhile, the correlation coefficient between the predicted values on the upper rotor and the CFD calculation results is 0.82, with the maximum errors in predicting the dimensionless thrust and moment losses are less than 5.5 % and 1 %. The rapid aerodynamic-interference analysis model proposed herein can serve as a reference for the safe-flight strategy of rotorcraft during close-range cooperative missions.