Hydrodynamic Research Articles

An Assessment of Convergence of Climate Reanalyses from Two Coupled Data Assimilation Systems with Identical High-Efficiency Filtering

Abstract Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitime-scale high-efficiency filtering algorithm which includes a deep-ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously established CDA system within the Coupled Model, version 2.1 (CM2-MSHea-CDA), that is developed by the Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past 4 decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep-ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, El Niño–Southern Oscillation (ENSO), and Pacific decadal oscillation (PDO). Particularly, both CDA systems generate similar time mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations. Significance Statement A coupled climate model simulates the interactions of the atmosphere, ocean, sea ice, and land processes and derives change and variations of the climate system by combining these components. Due to imperfect numerical schemes, a model usually has systematic biases to the real world. Coupled data assimilation (CDA) combines coupled model dynamics and physics with atmospheric and oceanic observations to reconstruct the historical states of Earth’s climate system, forming the analysis of climate change and variability. However, because of the existence of model errors and individual data assimilation algorithm behaviors, currently, the ocean variability of reanalysis produced by different numerical systems is basically divergent. Here, we first apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely used IPCC coupled models to establish two CDA systems. Then, we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past 4 decades. We found, by some degree, that the results of these multimodel coupled climate reanalyses converged to what historical states ought to be in observational estimation. These convergent climate reanalyses are expected to significantly increase our understanding of CDA and climate assessment.

Large-scale Multiphysics Simulations of Small Modular Reactors Operating in Natural Circulation

Thanks to the advancements in high-performance computing, advanced modeling and simulation have become crucial in driving the development and deployment of next-generation nuclear reactors, such as small modular reactors (SMRs). SMRs offer the promise of cost-effective baseload electricity production and improved safety, while addressing some of the challenges associated with large reactor designs, such as high capital costs and extended construction timelines. As part of the Exascale Computing Project, the large-scale multiphysics simulation of an entire SMR primary system has been achieved by combining computational fluid dynamics and neutronics. In addition to the successful demonstration of full-core SMR simulations, the current study integrated the impact of natural circulation into the system. Natural circulation is the primary mechanism driving coolant circulation in SMRs. The mass flow rate in the core depends on the core power, and a numerical model has been developed to predict it. The pressure drop caused by the helical coil steam generator was also accounted for by developing a pressure drop correlation based on high-fidelity large eddy simulation results, further improving prediction accuracy. The results of the study demonstrate that the implemented natural circulation model is effective in predicting the responses of SMR full-core multiphysics simulations.

Emergence of Debye Scaling in the Density of States of Liquids under Nanoconfinement.

In the realm of nanoscience, the dynamic behaviors of liquids at scales beyond the conventional structural relaxation time, τ, unfold a fascinating blend of solid-like characteristics, including the propagation of collective shear waves and the emergence of elasticity. However, in classical bulk liquids, where τ is typically of the order of 1 ps or less, this solid-like behavior remains elusive in the low-frequency region of the density of states (DOS). Here, we provide evidence for the emergent solid-like nature of liquids at short distances through inelastic neutron scattering measurements of the low-frequency DOS in liquid water and glycerol confined within graphene oxide membranes. In particular, upon increasing the strength of confinement, we observe a transition from a liquid-like DOS (linear in the frequency ω) to a solid-like behavior (Debye law, ∼ω2) in the range of 1-4 meV. Molecular dynamics simulations confirm these findings and reveal additional solid-like features, including propagating collective shear waves and a reduction in the self-diffusion constant. Finally, we show that the onset of solid-like dynamics is pushed toward low frequency along with the slowing-down of the relaxation processes upon confinement. This nanoconfinement-induced transition, aligning with k-gap theory, underscores the potential of leveraging liquid nanoconfinement in advancing nanoscale science and technology, building more connections between fluid dynamics and materials engineering.

Heat transfer characteristics of printed circuit heat exchangers under mechanical vibrations

Purpose The purpose of this paper is to investigate the impact of mechanical vibration on the heat transfer and pressure drop characteristics of semicircular channel printed circuit heat exchangers (PCHEs), while also establishing correlations between vibration parameters and thermal performance. Design/methodology/approach By combining experimental and numerical simulation methods, the heat transfer coefficient and pressure drop characteristics of supercritical carbon dioxide (S-CO2) in a semicircular channel with a diameter of 2 mm under vibration conditions were studied. Reinforce the research by conducting computational fluid dynamics studies using ANSYS Fluent 22.0, the experimental results were compared with the numerical simulation results to verify the accuracy of the numerical method. Findings The use of vibration has the potential to attenuate the degradation of wall heat transfer caused by buoyancy-induced PCHEs on the upward-facing surface. The heat transfer enhancement (HTE) was maximized by an increase of 18.2%, while the pressure drop enhancement (PDE) was elevated by over 25-fold. The capacity to enhance the heat exchange between S-CO2 and channel walls through increasing vibration intensity is limited, indicating maximum effectiveness in improving thermal performance. Originality/value Conducting heat transfer experiments on PCHEs with mechanical vibration enhancement and verifying the accuracy of the vibration numerical model. The relation based on the dimensionless factor is derived. To provide theoretical support for using vibration to enhance the heat transfer capability of PCHEs.

Quantitative Measurements in the Exhaust Flow of a Rotating Detonation Combustor Using Rainbow Schlieren Deflectometry

Abstract This study employs Rainbow Schlieren Deflectometry (RSD) to characterize the unsteady, supersonic/subsonic exhaust plume of a Rotating Detonation Combustor (RDC). Firstly, RSD images are analyzed to quantify the frequency and strength of flow oscillations and their relationship to the detonation wave. Secondly, a 3D tomographic algorithm is used to obtain the local 3D density field across the whole region of interest (ROI). The tomographic analysis relies upon wave rotation to infer projection data of the 3D exhaust plume at multiple view angles using a single RSD/camera system and was previously validated using phantom data from computational fluid dynamics analysis of an RDC. The annular RDC operated on methane and 2/3 O2 - 1/3 N2 oxidizer mixture is equipped with a converging nozzle to pressurize the combustion chamber. The product flow exiting the nozzle throat expands across an unoptimized conical aerospike attached to the center body of the RDC. RSD images provide a temporal resolution of 369 ns and spatial resolution of 100 ?m in a 6.4 high and 25.6 mm wide ROI of the exhaust plume. A rainbow filter is calibrated to convert hue in color schlieren images into deflection angle data. This data is used to characterize the unsteady flow oscillations that show excellent agreement with PCB pressure probe measurements acquired inside the combustion chamber. Tomographic analysis yields a 3D local density field that shows distinct features, consistent with published numerical simulations of the RDC exhaust plume.

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing

A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process

Abstract This study presents a cost-effective and high-precision machine learning (ML) method for predicting the melt-pool geometry and optimizing the process parameters in the laser powder-bed fusion (LPBF) process with Ti-6Al-4V alloy. Unlike many ML models, the presented method incorporates five key features, including three process parameters (laser power, scanning speed, and spot size) and two material parameters (layer thickness and powder porosity). The target variables are the melt-pool width and depth that collectively define the melt-pool geometry and give insight into the melt-pool dynamics in LPBF. The dataset integrates information from an extensive literature survey, computational fluid dynamics (CFD) modeling, and laser melting experiments. Multiple ML regression methods are assessed to determine the best model to predict the melt-pool geometry. Tenfold cross-validation is applied to evaluate the model performance using five evaluation metrics. Several data pre-processing, augmentation, and feature engineering techniques are performed to improve the accuracy of the models. Results show that the “Extra Trees regression” and “Gaussian process regression” models yield the least errors for predicting melt-pool width and depth, respectively. The ML modeling results are compared with the experimental and CFD modeling results to validate the proposed ML models. The most influential parameter affecting the melt-pool geometry is also determined by the sensitivity analysis. The processing parameters are optimized using an iterative grid search method employing the trained ML models. The presented ML framework offers computational speed and simplicity, which can be implemented in other additive manufacturing techniques to comprehend the critical traits.

Impact of Lean-Burn Combustor Flow on Nozzle Guide Vane Performance

Abstract In this paper we investigate the impact of lean-burn-representative swirl and temperature distortion on the aerothermal performance of fully-cooled high-pressure nozzle guide vanes (NGVs) from a modern aero-engine. Experiments were carried out in the Engine Component AeroThermal (ECAT) facility at the University of Oxford. This is a fully-annular warm-flow engine parts facility, designed to operate at engine-representative conditions of Reynolds and Mach number. Inlet profiles of swirl, turbulence, and non-dimensional total temperature were generated using a non-reacting combustor simulator. The NGV outlet flow was experimentally characterized at three downstream planes in experiments with and without lean-burn-representative inlet conditions. Area-survey measurements included distributions of whirl angle, kinetic energy (KE) loss, and non-dimensional total temperature. Experimental data is compared to computational fluid dynamics (CFD) simulations. Fully-featured NGV geometry (including film cooling holes and internal passages) was used, to account for internal cooling flow redistribution resulting from altered external loading. We show that lean-burn inlet conditions result in significant surface flow redistribution, relatively high levels of residual swirl in the downstream flow, and a small increase of integrated KE loss.

Exploring the influence of concave-convex surface modifications on piezoelectric wind energy harvesting: A numerical analysis

Wireless health monitoring sensors are significant for the safety of sea-crossing bridges, and their operation is dependent on a durable and reliable power source. Piezoelectric wind energy harvesters (PWEHs) can be effectively utilized in windy marine environments and provide a sustainable power supply for these sensors. This paper examines the influence of concave-convex surface modifications of a circular cylinder on the performance of the PWEH through numerical research to improve the output and operational bandwidth of traditional harvesters using cylindrical bluff bodies. An electromechanical model, coupled with computational fluid dynamics on low-Reynolds-number flows, was developed and different concave-convex configurations were proposed to explore the impact by numerical experiments. The results show that the configuration with two protrusions plus one groove is the optimal design. This configuration exhibits a much higher output with a maximum voltage of 19.5 V, representing a 464% increase compared to the cylindrical bluff body's 4.2 V, and a broader bandwidth with no upper limit across the wind speed range. This study provides insight into effective and ineffective efforts to enhance energy harvesting through concave-convex surface modifications and is advantageous to the advancement of self-powered marine sensing systems, particularly in the health monitoring of sea-crossing bridges.

Research on Hydrodynamics of Trans-Media Vehicles Considering Underwater Time-Varying Attitudes

A trans-media vehicle is a new type of equipment that can adapt to two environments, water and air, to maintain optimal hydrodynamic and aerodynamic performance. However, no matter what kind of trans-media vehicle, its dynamics are much more complicated when traversing the interface of the medium, as the parameters are time-varying due to the change in ambient medium and vehicle attitudes. In order to improve the stability and performance of the trans-media vehicle in complex environments, an accurate mathematical model is established to characterize the dynamics of the trans-media vehicle in this process in this study. The time-varying hydrodynamic coefficients with different attitudes or depths are obtained using computational fluid dynamics software. The mathematical model is solved iteratively using a Runge–Kutta solver to calculate the dynamic response. A prototype of trans-media vehicle is fabricated, and motion experiments are performed in the pool. The experimental results confirm the effectiveness of the established model and lay the foundation for further controller design, providing a reference for the dynamic modeling of other similar equipment operating in complex environments. The primary novelty of this study lies in the fact that the established dynamic model considers the complex interaction between the attitude of the trans-media vehicle and the inherent different properties of water and air and utilizes computational fluid dynamics software to accurately obtain time-varying coefficients under different attitudes and depths. This approach not only recognizes the criticality of orientation-dependent hydrodynamic coefficients but also incorporates their temporal variations, which were often overlooked in previous studies.

Research on the technology of uniformly injecting nitrogen into the porous long pipes in the gob of the gob-side entry retaining mining mode with roof cutting and pressure relief

The implementation of the Gob-Side Entry Retaining Mining Mode with Roof Cutting and Pressure Relief (GERRCPR) results in the gob connecting to the retaining roadway, creating an open space that causes significant air leakage and increases the risk of spontaneous combustion. A study was conducted during the implementation of the GERRCPR in the Xiaonan Coal Mine N1-1502 working face to investigate spontaneous combustion characteristics, along with fire prevention and extinguishing measures. To analyze gob airflow, Computational Fluid Dynamics (CFD) was employed to collect data on airflow conditions, O2 concentration, and temperature. Based on this, this study focuses on exploring the effects of nitrogen injection treatment under various rates and positions to optimize parameters for buried pipe nitrogen injection. Results indicated that within the GERRCPR, air leakage in the gob increased, leading to an increase in O2 concentration, expansion of the oxidation zone, and an elevated risk of spontaneous combustion. Air leakage primarily occurred from the retaining roadway and the working face near the intake-air roadway, peaking at a retaining roadway length of 500 m, with a flow rate of 226 m3/min. Following nitrogen injection treatment, the oxidation zone was significantly reduced, with optimal treatment achieved at a nitrogen injection depth of 70 m and a rate of 600 m3/h. Field monitoring data showed that the inertization measure of using porous long pipes, a nitrogen injection spacing of 30 m, and a nitrogen injection rate of 600 m3/h significantly decreased the O2 concentration within the gob. This reduction meets safety production requirements and outperforms the effectiveness of traditional buried-pipe nitrogen injection methods, thereby validating the simulation accuracy. Understanding the laws governing spontaneous coal combustion in the GERRCPR and enacting preventive measures for nitrogen injection can improve safety standards in mining operations. This proactive approach can effectively prevent spontaneous coal combustion accidents, resulting in substantial social benefits.

Numerical Investigation of Effusion Cooling Air Influence On the Co Emissions for a Single-Sector Aero-Engine Model Combustor

Abstract Stricter aviation emissions regulations have led to the desire for lean-premixed-vaporized combustors over rich-quench-lean burners. While this operation mode is beneficial for reducing NOx and particulate emissions, the interaction of the flame and hot exhaust gases with the cooling flow results in increased CO emissions. Predicting CO in computational fluid dynamics (CFD) simulations remains challenging. To assess current model performance under practically relevant conditions, Large-Eddy Simulation (LES) of a lab-scale effusion cooling test rig is performed. Flamelet-based manifolds, in combination with the Artificial Thickened Flame (ATF) approach, are utilized to model the Turbulence-Chemistry Interaction (TCI) in the test rig with detailed chemical kinetics at reduced computational costs. Heat losses are considered via exhaust gas recirculation (EGR). Local transport effects in CO emissions are included through an additional transport equation. Additionally, a Conjugate Heat Transfer (CHT) simulation is performed for good estimations of the thermal boundary conditions. Extensive validation of this comprehensive model is conducted using the available experimental dataset for the studied configuration. Subsequently, model sensitivities for predicting CO are assessed, including the progress variable definition and the formulation of the CO source term in the corresponding transport equation. To investigate the flame thickening influence in the calculated CO, an ATF-postprocessing correction is further developed. Integrating multiple sophisticated pollutant submodels and evaluating their sensitivity offers insights for future investigations into modeling CO emissions in aero-engines and stationary gas turbines.

Revealing flow structures in horizontal pipe and biomass combustor using computational fluid dynamics simulation

AbstractComputational fluid dynamics (CFD) is a powerful tool to provide information on detailed turbulent flow in unit processes. For that reason, this study intends to reveal the flow structures in the horizontal pipe and biomass combustor. The simulation was aided by ANSYS Fluent employing standard ‐ model. The results show that a greater Reynolds number generates more turbulence. The pressure drop inside the pipe is also found steeper for small pipe diameters following Fanning's correlation. The fully developed flow for the laminar regime is found in locations where the ratio of entrance length to pipe diameter complies with Hagen–Poiseuille's rule. The sucking phenomenon in jet flow is also similar to the working principle of ejector. For the biomass combustor, the average combustion temperature is 356–696°C, and the maximum flame temperature is 1587–1697°C. Subsequently, air initially flows through the burner area and then moves to the outlet when enters the combustor chamber. Not so for particle flow, the particle experiences sedimentation in the burner area and then falls as it enters the combustor chamber. This study also convinces that secondary air supply can produce more circulating effects in the combustor.

Machine learning-assisted discovery of flow reactor designs

AbstractAdditive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterizations of reactor geometries are low dimensional with expensive optimization, limiting more complex solutions. To address this challenge, we have established a machine learning-assisted approach for the design of new chemical reactors, combining the application of high-dimensional parameterizations, computational fluid dynamics and multi-fidelity Bayesian optimization. We associate the development of mixing-enhancing vortical flow structures in coiled reactors with performance and used our approach to identify the key characteristics of optimal designs. By appealing to the principles of fluid dynamics, we rationalized the selection of design features that lead to experimental plug flow performance improvements of ~60% compared with conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with ‘augmented intelligence’ approaches can give rise to reactor designs with enhanced performance.

Dynamics of non-Newtonian Casson fluid and Cattaneo-Christov heat flux impacts on a rotating non-uniform surface due to Coriolis force: A comparison study of ANFIS-PSO and ANN

Aim of the present studyIt must study liquid flow over surfaces above the earth because its surface rotation stimulates liquids heated amid the sun. Consequently, the Coriolis impact force is investigated on Casson convective flow fluid with Cattaneo-Christov heat flux over the rotating paraboloid upper surface. Significance of the present studyThe simultaneous effects of the Lorentz force and Coriolis force occurring due to MHD flow contribute significantly to solar wind, sunspots, and more physical natural phenomena. As a result, Navier-Stokes equations governing the flow are derived and incorporated with the appropriate body forces. MethodologyPDEs are converted to ODEs by identical variants/variables that make the governing equations non-dimensional. The results of this problem are represented graphically using BVP4C. ConclusionIn the present study, some of the significant results are: While both ANN and ANFIS-PSO could accurately forecast the truth values, we found that ANFIS-PSO is more accurate; as the temperature rises, β and Gr consider and resist heat transmission with K and Ec, While Ec and Nt increase the concentration, K, β, Gr, Nb, and Sc impede mass transfer. table 3 compares the current study's findings with those of the previous research; the results are nearly identical, demonstrating the precision and accuracy of the current model.

A Pseudo-Spectral Method for Wall Shear Stress Estimation from Doppler Ultrasound Imaging in Coronary Arteries.

The Wall Shear Stress (WSS) is the component tangential to the boundary of the normal stress tensor in an incompressible fluid, and it has been recognized as a quantity of primary importance in predicting possible adverse events in cardiovascular diseases, in general, and in coronary diseases, in particular. The quantification of the WSS in patient-specific settings can be achieved by performing a Computational Fluid Dynamics (CFD) analysis based on patient geometry, or it can be retrieved by a numerical approximation based on blood flow velocity data, e.g., ultrasound (US) Doppler measurements. This paper presents a novel method for WSS quantification from 2D vector Doppler measurements. Images were obtained through unfocused plane waves and transverse oscillation to acquire both in-plane velocity components. These velocity components were processed using pseudo-spectral differentiation techniques based on Fourier approximations of the derivatives to compute the WSS. Our Pseudo-Spectral Method (PSM) is tested in two vessel phantoms, straight and stenotic, where a steady flow of 15mL/min is applied. The method is successfully validated against CFD simulations and compared against current techniques based on the assumption of a parabolic velocity profile. The PSM accurately detected Wall Shear Stress (WSS) variations in geometries differing from straight cylinders, and is less sensitive to measurement noise. In particular, when using synthetic data (noise free, e.g., generated by CFD) on cylindrical geometries, the Poiseuille-based methods and PSM have comparable accuracy; on the contrary, when using the data retrieved from US measures, the average error of the WSS obtained with the PSM turned out to be 3 to 9 times smaller than that obtained by state-of-the-art methods. The pseudo-spectral approach allows controlling the approximation errors in the presence of noisy data. This gives a more accurate alternative to the present standard and a less computationally expensive choice compared to CFD, which also requires high-quality data to reconstruct the vessel geometry.

Impact of human micro-movements on breathing zone and thermal plume formation

To enhance the realism and precision of indoor environment assessments and the evaluation of human health risks using computational fluid dynamics (CFD), it is crucial to accurately replicate human shape and physiological functions. This study investigates the influence of micro-movements resulting from posture control on the human breathing zone (BZ) using CFD analysis. A Computer-Simulated Person (CSP) incorporating a sophisticated nasal cavity model and multi-node thermoregulation was employed to precisely predict the microclimate around the human body, including the BZ. Two types of movements were considered to replicate micro-movements of a standing human body: angular and linear movements. The BZs were evaluated based on the Scale for Ventilation Efficiency (SVE) 4 and 5 for exhalation and inhalation modes, respectively. While the distribution of exhaled air remained generally consistent, distinct differences were observed in inhaled air distribution. In a representative case with a maximum angular velocity of 4°/s, the horizontal length increased by 12 cm, and the vertical distance of the SVE5>10 % distribution decreased by 14 cm compared to the stationary case. Linear movement, particularly in the mediolateral (ML) direction, led to a horizontal expansion of the inhaled air distribution. The findings of this study suggest that replicating human micro-movements has a minimal impact on general indoor climate analysis but significantly enhances the accuracy of predicting breathing air quality.

Numerical Calculation of the Oil Flow in a Truck Rear Axle Transmission and the Influence of Gear-Induced Air Flow

Abstract For the reliable operation of a gearbox, consistently sufficient lubrication of machine elements is necessary. Thus, the gearbox fluid flow plays an important role. The state of the art indicates that computational fluid dynamics (CFD) enables targeted support of the gearbox development process at an early stage. Computational time plays a prominent role in practical applications. The particle-based smoothed particle hydrodynamics (SPH) shows great potential for time-efficient calculations, especially with a simplified single-phase modeling approach. This study first examines the influence of gear-induced air flow on gearbox oil flow, focusing on a test gearbox to identify a suitable modeling approach for a truck rear axle transmission. The results indicate that the gear-induced air flow mainly impacts gearbox oil flow at higher circumferential speeds. For lower circumferential speeds, a single-phase model yields good results with significantly reduced computation times compared to a two-phase model. Applying the single-phase model to the truck rear axle transmission and comparing numerical results with experimental findings demonstrates a reliable representation of the oil flow characteristics.

Numerical Development of a Thermal Pre-processing Step for the Recycling of Lithium-Ion Batteries

AbstractIn the present study, a numerical model is being developed to simulate a step in the battery recycling chain, namely the thermal pre-treatment process. This process involves exposing battery cells to a high-temperature environment to induce a thermal runaway, with the aim of maximising the recovery of valuable metals in the subsequent downstream recycling steps. The proposed numerical model utilises the CFD-DEM framework. Computational Fluid Dynamics (CFD) is used to calculate the gas phase variables. The battery is considered a solid phase, using the Discrete Element Method (DEM) to model its behaviour under high temperature. In this context, an experiment was designed to reproduce conditions similar to a battery thermal deactivation process. Once elaborated, the results from the experiments are compared to the numerical model, seeking further simulations using more realistic furnace designs.

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.