High-resolution Grid Research Articles

Low agricultural potential exacerbates the effect of temperature on civil conflicts

We examine the impact of temperature variations on the incidence of civil state conflicts, focusing on a specific mechanism: the agricultural potential of a country, i.e., its initial agricultural conditions before human interventions. Using data for 172 countries from 1946 until 2014, we conduct a natural experiment and identify the effect of the interaction between time-wise variations in temperature within a country and the cross-country variation in agricultural potential on conflict incidence. We find that there is a significantly higher probability to be in conflict when annual temperature deviates from its mean in a country with low agricultural potential relative to a country with high agricultural potential. An analysis of the long-term relationship (decennial averages) points to an exacerbation of the negative impact of the agricultural potential-temperature interaction on civil conflicts. The findings also suggest that the interaction has similar effects across development levels, no matter the share of agriculture in a country's GDP, and no significant effect in countries with exportable resources such as oil. The results are robust to a high resolution grid analysis when we use a suitability index for wheat. For other cereals and other specifications, including standardized temperature and long differences, the results are significant when controlling for the distance to the capital. This is the first study to show that initial agricultural conditions play a significant role to understand the climate-conflict linkages on a global scale.

A hybrid method for characterizing tillage-induced soil physical quality at the profile scale with fine spatial details

Robust methods are needed to assess tillage-induced changes in the physical quality of agricultural soils. Most soil physical quality (SPQ) evaluation procedures are based on the analysis of a limited number of soil cores randomly sampled in the soil profile. Such procedures likely fail to properly capture the spatial patterns in soil structure induced by tillage implements and vehicle traffic, resulting in a misleading SPQ diagnosis. We propose a hybrid method that couples high-resolution soil penetration resistance measurements with soil core sampling to derive high-resolution data grids of SPQ indicators encompassing the soil profile structural heterogeneity. The method builds on strong experimental relationships between soil penetration resistance and bulk density, and between bulk density and the soil hydraulic properties. We tested our method on a silt-loam soil with three tillage treatments (moldboard plowing, deep loosening with a tine cultivator, and minimum tillage), each including a zone impacted by the wheel traffic. The results allow visualization and analysis of the effect of tillage treatments and vehicle traffic on the soil SPQ indicators with a high level of spatial detail. The proposed methodology can be used to compare various soil management techniques or to monitor the temporal evolution of SPQ. As such, it can be a valuable tool to guide agricultural soil management. Furthermore, the generated high-resolution soil physical parameter grids could be used to parameterize numerical hydrodynamic models and estimate water and solute fluxes with distributed parameters representing the actual soil profile heterogeneity.

Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys, and historical datasets

Abstract. High-resolution bathymetry forms critical datasets for marine geoscientists. It can be used to characterize the seafloor and its marine habitats, to understand past sedimentary records, and even to support the development of offshore engineering projects. Most methods to acquire bathymetry data are costly and can only be practically deployed in relatively small areas. It is therefore critical to develop cost-effective and advanced techniques to produce regional-scale bathymetry datasets. This paper presents an integrated workflow that builds on satellites images and 3D seismic surveys, integrated with historical depth soundings, to generate regional high-resolution digital elevation models (DEMs). The method was applied to the southern half of Australia's North West Shelf and led to the creation of new high-resolution bathymetry grids, with a resolution of 10 × 10 m in nearshore areas and 30 × 30 m elsewhere. The vertical and spatial accuracy of the datasets have been assessed using open-source Laser Airborne Depth Sounder (LADS) and multibeam echosounder (MBES) surveys as a reference. The comparison of the datasets indicates that the seismic-derived bathymetry has a vertical accuracy better than 1 m + 2 % of the absolute water depth, while the satellite-derived bathymetry has a depth accuracy better than 1 m + 5 % of the absolute water depth. This 30 × 30 m dataset constitutes a significant improvement of the pre-existing regional 250 × 250 m grid and will support the onset of research projects on coastal morphologies, marine habitats, archaeology, and sedimentology. All source datasets are publicly available, and the methods are fully integrated into Python scripts, making them readily applicable elsewhere in Australia and around the world. The regional digital elevation model and the underlying datasets can be accessed at https://doi.org/10.26186/144600 (Lebrec et al., 2021).

Embossed transparent electrodes assembled by bubble templates for efficient flexible perovskite solar cells

Flexible perovskite solar cells are promising power source candidates in wearable and portable electronics. However, the performance of flexible perovskite solar cells still lags the values reported in rigid devices. This issue is mainly due to the lack of suitable electrodes which possess low sheet resistance, high optical transmittance, and smooth surface. Here, we proposed a flexible electrode based on solution-processed silver grids. With the patterned micro-bubbles as templates, high-resolution sliver grids electrodes were fabricated, which showed a sheet resistance of 7.9 Ω/sq and optical transmittance over 85% (including the substrate, from 375 nm to 900 nm). Besides, the embossed sliver grids can assist the growth of perovskite crystals and provide localized channels for charge carrier transport. The corresponding flexible perovskite solar cells based on these flexible sliver grids electrodes achieved a power conversion efficiency of 18.49% with remarkable flexibility, which enhanced about 20% compared to the control devices.

ForestTemp - Sub-canopy microclimate temperatures of European forests.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation±1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

Modelling the global soundscape: Validating a study of the COVID-19 impact using in-shore and off-shore observatory data

This study presents a data-model comparison of the soundscape in the Northeast Pacific Ocean in 2019 and 2020. Focussing on frequencies below 400 Hz, where the soundscape is dominated by shipping, marine mammals (at selected frequencies) and large storms, a parabolic-equation model computes the propagation loss from a grid of points to a four-dimensional, high-resolution grid of receivers. The modelling data are extracted from a dynamic model of the global shipping and wind noise soundscape that utilizes AIS ship-tracking data. A comparison of the modelling results between 2019 and 2020 revealed a marked difference, reasonably due to the COVID-19 global shutdown. These results are compared to measurements from the long-term time series collected by Ocean Networks Canada’s in-shore and off-shore cabled observatories in the Salish Sea and Northeast Pacific Ocean.

A new algorithm for reconstructing the three-dimensional flow field of the oceanic mesoscale eddy* *Project sported by the National Natural Science Foundation of China (Grant Nos. 41875045 and 61371119) and the Blue Project of Jiangsu Province, China.

A new algorithm for reconstructing the three-dimensional flow field of the oceanic mesoscale eddies is proposed in this paper, based on variational method. Firstly, with the numerical differentiation Tikhonov regularizer, we reconstruct the continuous horizontal flow field on discrete grid points at each layer in the oceanic region, in terms of the horizontal flow field observations. Secondly, benefitting from the variational optimization analysis and its improvemen, we reconstruct a three-dimensional flow field under the constraint of the horizontal flow and the vertical flow. The results of simulation experiments validate that the relative error of the new algorithm is lower than that of the finite difference method in the case of high grid resolution, which still holds in the case of unknown observational errors or in the absence of vertical velocity boundary conditions. Finally, using the reanalysis horizontal data sourcing from SODA and the proposed algorithm, we reconstruct three-dimensional flow field structure for the real oceanic mesoscale eddy.

Decarbonization of Summer Cooling Energy Demands of Buildings Employing Absorption Systems in the Framework of Climate Change in Italy

Temperatures in the Mediterranean area have gradually risen in the last decades due to climate change, especially in the Italian Peninsula. This phenomenon has increased the cooling needs to ensure thermal comfort in buildings and, consequently, the use of refrigeration machines. Summer air conditioning is carried out mainly using compression machines powered by electricity supplied by the national network. All this contributes to the emission of climate-changing gases. To avoid this disadvantageous chain, compression machines could be replaced by absorption cooling systems powered by solar energy. The energy needs of the buildings in a time are directly proportional to the sum of positive differences between the outdoor air temperature and the indoor set point of the systems (equal to 26°C). The annual sum of hourly temperature differences defined above can be computed for each grid cell thanks to a numerical weather prediction model, namely the Weather Research and Forecasting model, that simulates the hourly temperatures on high-resolution computation grids and over fairly large extents. Maps of cooling consumption for buildings are thus produced. Choosing absorption solar energy-powered systems instead of vapor compression refrigeration systems leads to a drop in electrical energy consumption and therefore in emissions of greenhouse gases. In this work, different hypothetical scenarios of penetration of this technology have been considered. And the subsequent consumption of electricity withdrawn from the national grid has been estimated together with the reduction of greenhouse gas emissions.

Wind energy is not sustainable when balanced by fossil energy

Ensuring access to affordable, reliable, sustainable and modern energy for all is one of the Sustainable Development Goals. Some countries have therefore invested significantly in wind energy, but emissions, which is a common measure for sustainability in this context, have not fallen significantly. Reductions between 20% and 40% are typical. We therefore test the hypothesis that wind energy reduces emissions compared to using gas turbines when life-cycle emissions are included. The Irish grid is studied due to its record-high wind penetration. The model is based on high resolution grid data covering four years and input from 828 Life-Cycle Assessment cases to allow detailed analysis of demand, supply, life-cycle emissions and their changes due to the increased ramping of gas turbines and increased grid reserves required to maintain grid reliability when wind is deployed. Indirect effects are included to some extent. The model is sampled 10,000 times using Monte Carlo simulations. The results show that emissions are reduced by 10–20%, which supports the hypothesis. However, with an average wind penetration of 34% in 2019, reaching many times the 65% limit for non-synchronous generation set by the system operator to maintain grid reliability, such modest reductions logically imply that achieving an affordable, low-carbon grid using wind together with fossil energy balancing is infeasible with today’s technology, emissions and costs. This key finding is transferable to other grids where wind has large penetration and requires fossil energy balancing. Thus, wind energy is not sustainable when balanced by fossil fuel generators.

Application of Video-to-Video Translation Networks to Computational Fluid Dynamics

In recent years, the evolution of artificial intelligence, especially deep learning, has been remarkable, and its application to various fields has been growing rapidly. In this paper, I report the results of the application of generative adversarial networks (GANs), specifically video-to-video translation networks, to computational fluid dynamics (CFD) simulations. The purpose of this research is to reduce the computational cost of CFD simulations with GANs. The architecture of GANs in this research is a combination of the image-to-image translation networks (the so-called “pix2pix”) and Long Short-Term Memory (LSTM). It is shown that the results of high-cost and high-accuracy simulations (with high-resolution computational grids) can be estimated from those of low-cost and low-accuracy simulations (with low-resolution grids). In particular, the time evolution of density distributions in the cases of a high-resolution grid is reproduced from that in the cases of a low-resolution grid through GANs, and the density inhomogeneity estimated from the image generated by GANs recovers the ground truth with good accuracy. Qualitative and quantitative comparisons of the results of the proposed method with those of several super-resolution algorithms are also presented.

Analysis of Volcanic Thermohaline Fluctuations of Tagoro Submarine Volcano (El Hierro Island, Canary Islands, Spain)

Temperature and conductivity fluctuations caused by the hydrothermal emissions released during the degasification stage of the Tagoro submarine volcano (Canary Islands, Spain) have been analysed as a robust proxy for characterising and forecasting the activity of the system. A total of 21 conductivity-temperature-depth time series were gathered on a regular high-resolution grid over the main crater of Tagoro volcano. Temperature and conductivity time series, as manifestations of stochastic events, were investigated in terms of variance and analysed by the Generalised Moments Method (GMM). GMM provides the statistical moments, the structure functions of a process whose shape is an indicator of the underlying stochastic mechanisms and the state of activity of the submarine volcano. Our findings confirm an active hydrothermal process in the submarine volcano with a sub-normal behaviour resulting from anti-persistent fluctuations in time. Its hydrothermal emissions are classified as multifractal processes whose structure functions present a crossover between two time scales. In the shorter time scale, findings point to the multiplicative action of two random processes, hydrothermal vents, which carries those fluctuations driving the circulation over the crater, and the overlying aquatic environment. Given that both temperature and conductivity fluctuations are nonstationary, Tagoro submarine volcano can be characterised as an open system exchanging energy to its surroundings.

Lighthill's analogy applied to a automotive turbocharger compressor

Computing transient CFD simulations of turbocharger compressors is computationally very demanding. It is of fundamental importance to resolve turbulent structures at the location of their generation and to establish a fine enough grid to allow propagation of the resolved structures. This results in high-resolution grids, existing of more than 20 million cells. Applying Lighthill's analogy, it is possible to only resolve turbulent structures at their location of generation and compute the pressure propagation by using an additional, not that demanding, acoustic grid. This allows using coarser CFD grids in the inlet and outlet section. For transferring Lighthill's source terms from the CFD to the acoustic grid, advanced interpolation algorithms are used. The simulation results are validated by measurements of a cold gas test rig are considered.

Atmospheric Wind Field Modelling with OpenFOAM for Near-Ground Gas Dispersion

CFD simulations of near-ground gas dispersion depend significantly on the accuracy of the wind field. When simulating wind fields with conventional RANS turbulence models, the velocity and turbulence profiles specified as inlet boundary conditions change rapidly in the approach flow region. As a result, when hazardous materials are released, the extent of hazardous areas is calculated based on an approach flow that differs significantly from the boundary conditions defined. To solve this problem, a turbulence model with consistent boundary conditions was developed to ensure a horizontally homogeneous approach flow. Instead of the logarithmic vertical velocity profile, a power law is used to overcome the problem that with the logarithmic profile, negative velocities would be calculated for heights within the roughness length. With this, the problem that the distance of the wall-adjacent cell midpoint has to be higher than the roughness length is solved, so that a high grid resolution can be ensured even in the near-ground region which is required to simulate gas dispersion. The evaluation of the developed CFD model using the German guideline VDI 3783/9 and wind tunnel experiments with realistic obstacle configurations showed a good agreement between the calculated and the measured values and the ability to achieve a horizontally homogenous approach flow.

Bound-preserving inexact Newton algorithms on parallel computers for wormhole propagation in porous media

Simulating wormhole propagation during reactive dissolution of carbonates is often significantly challenging, because of the high nonlinearity of the governing equations with complex fluid physics. High resolution grids are often required to represent the complex geological heterogeneity, which demands massively parallel computers with a large number of processors. Herein, we present a parallel and scalable simulator on parallel computers for the fully implicit solution of the wormhole propagation model. Our approach is based on a family of mixed finite element methods for the spatial discretization and the implicit Backward Euler scheme for the temporal integration, to handle the combination of complicated flow physics and high resolution grids in their full complexity. Moreover, the active-set reduced-space method, as a class of bound-preserving inexact Newton algorithms, is proposed for the resultant nonlinear system of equations, to guarantee the nonlinear consistency of the fully implicit discretization in a monolithic way and to ensure the boundedness requirement of the solution. Numerical experiments are presented to demonstrate the effectiveness and efficiency of the proposed simulator for a set of heterogeneous medium problems. Large-scale results are provided to show the scalability for reservoir simulation with hundreds of millions of unknowns by using several thousand processors.

FDRN: A fast deformable registration network for medical images.

Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the performance of deformable registration in both accuracy and runtime, we propose a fast unsupervised convolutional neural network for deformable image registration. The proposed registration model FDRN possesses a compact encoder-decoder network architecture which employs a pair of fixed and moving images as input and outputs a three-dimensional displacement vector field (DVF) describing the offsets between the corresponding voxels in the fixed and moving images. In order to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each encoder and decoder stage. To facilitate the learning efficiency, we leverage skip connection within the encoder and decoder stages to enable residual learning and employ an auxiliary loss at the bottom layer with lowest resolution to involve deep supervision. Particularly, the low-resolution auxiliary loss is weighted by an exponentially decayed parameter during the training phase. In conjunction with the main loss in high-resolution grid, a coarse-to-fine learning strategy is achieved. Last but not least, we involve a proposed multi-label segmentation loss (SL) to improve the network performance in Dice score in case the segmentation prior is available. Comparing to the SL using average Dice score, the proposed SL does not require additional memory in the training phase and improves the registration accuracy efficiently. We evaluated FDRN on multiple brain MRI datasets from different aspects including registration accuracy, model generalizability, and model analysis. Experimental results demonstrate that FDRN performs better than the state-of-the-art registration method VoxelMorph by 1.46% in Dice score in LPBA40. In addition to LPBA40, FDRN obtains the best Dice and NCC among all the investigated methods in the unseen MRI datasets including CUMC12, MGH10, ABIDE, and ADNI by a large margin. The proposed FDRN provides better performance than the existing state-of-the-art registration methods for brain MR images by resorting to the compact autoencoder structure and efficient learning. Additionally, FDRN is a generalized framework for image registration which is not confined to a particular type of medical images or anatomy.

DG-GMsFEM for Problems in Perforated Domains with Non-Homogeneous Boundary Conditions

Problems in perforated media are complex and require high resolution grid construction to capture complex irregular perforation boundaries leading to the large discrete system of equations. In this paper, we develop a multiscale model reduction technique based on the Discontinuous Galerkin Generalized Multiscale Finite Element Method (DG-GMsFEM) for problems in perforated domains with non-homogeneous boundary conditions on perforations. This method implies division of the perforated domain into several non-overlapping subdomains constructing local multiscale basis functions for each. We use two types of multiscale basis functions, which are constructed by imposing suitable non-homogeneous boundary conditions on subdomain boundary and perforation boundary. The construction of these basis functions contains two steps: (1) snapshot space construction and (2) solution of local spectral problems for dimension reduction in the snapshot space. The presented method is used to solve different model problems: elliptic, parabolic, elastic, and thermoelastic equations with non-homogeneous boundary conditions on perforations. The concepts for coarse grid construction and definition of the local domains are presented and investigated numerically. Numerical results for two test cases with homogeneous and non-homogeneous boundary conditions are included, as well. For the case with homogeneous boundary conditions on perforations, results are shown using only local basis functions with non-homogeneous boundary condition on subdomain boundary and homogeneous boundary condition on perforation boundary. Both types of basis functions are needed in order to obtain accurate solutions, and they are shown for problems with non-homogeneous boundary conditions on perforations. The numerical results show that the proposed method provides good results with a significant reduction of the system size.

Modeling nearshore dynamics of extreme storms in complex environments of Connecticut

Flood hazard planning requires the accurate estimation of total water elevation due to predicted tide, surge, and wave runup to design flood protection structures and improve coastal risk planning for severe storms. The beach geomorphology and nearshore hydrodynamic conditions impact the conclusive flood inundation mapping in complex environments. The conventional approaches of flood modeling are limited due to either i) simple static estimates, ii) the application of a coupled circulation and phase-averaged wave models in coarse resolution, iii) failing to calibrate and validate with in-situ data, or iv) not considering sea-level rise projections in mapping the flood extent. We used a fully nonlinear Boussinesq wave model (FUNWAVE-TVD) on the nearshore area with a high-resolution grid to determine total water elevation on the shores. The model adopted the boundary conditions from coupled circulation and wave model of the Long Island Sound (Liu et al., 2020).We applied the model to Branford, Norwalk, and New Haven, CT to represent the complex topo-bathymetric features and the structural interference in shallow water wave dynamics. We compared the model results with the FEMA base flood elevation, the North Atlantic Coast Comprehensive Study (NACCS), and (Liu et al., 2020)’s Long Island Sound FVCOM-SWAVE model. The FUNWAVE-TVD model is found to model wave processes more accurately in shallow water regions compared to the empirical equation application of FEMA and coupled circulation-phase averaged model application of NACCS and FVCOM-SWAVE. We also examined local sea-level rise predictions of storms with 1% and 10% annual exceedance probability by the year 2050 in Connecticut and found that the flood extent of these two storms showed little to no difference. We suggest the planning approaches should consider the increase in the frequency of the storms in the predicted inundation zones due to sea-level rise.

High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

Aerosols play an important role in climate change, and ground aerosols (e.g., fine particulate matter, abbreviated as PM2.5) are associated with a variety of health problems. Due to clouds and high reflectance conditions, satellite-derived aerosol optical depth (AOD) products usually have large percentages of missing values (e.g., on average greater than 60% for mainland China), which limits their applicability. In this study, we generated grid maps of high-resolution, daily complete AOD and ground aerosol coefficients for the large study area of mainland China from 2015 to 2018. Based on the AOD retrieved using the recent Multi-Angle Implementation of Atmospheric Correction advanced algorithm, we added a geographic zoning factor to account for variability in meteorology, and developed an adaptive method based on the improved full residual deep network (with attention layers) to impute extensively missing AOD in the whole study area consistently and reliably. Furthermore, we generated high-resolution grid maps of complete AOD and ground aerosol coefficients. Overall, compared with the original residual model, in the independent test of 20% samples, our daily models achieved an average test R2 of 0.90 (an improvement of approximately 5%) with a range of 0.75–0.97 (average test root mean square error: 0.075). This high test performance shows the validity of AOD imputation. In the evaluation using the ground AOD data from six Aerosol Robotic Network monitoring stations, our method obtained an R2 of 0.78, which further illustrated the reliability of the dataset. In addition, ground aerosol coefficients were generated to provide an improved correlation with PM2.5. With the complete AOD data and ground coefficients, we presented and interpreted their spatiotemporal variations in mainland China. This study has important implications for using satellite-derived AOD to estimate aerosol air pollutants.

A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows

A sequential estimator based on the Ensemble Kalman Filter for Data Assimilation of fluid flows is presented in this research work. The main feature of this estimator is that the Kalman filter update, which relies on the determination of the Kalman gain, is performed exploiting the algorithmic features of the numerical solver employed as a model. More precisely, the multilevel resolution associated with the multigrid iterative approach for time advancement is used to generate several low-resolution numerical simulations. These results are used as ensemble members to determine the correction via Kalman filter, which is then projected on the high-resolution grid to correct a single simulation which corresponds to the numerical model. The assessment of the method is performed via the analysis of one-dimensional and two-dimensional test cases, using different dynamical equations. The results show an efficient trade-off in terms of accuracy and computational costs required. In addition, a physical regularization of the flow, which is not granted by classical KF approaches, is naturally obtained owing to the multigrid iterative calculations. The algorithm is also well suited for the analysis of unsteady phenomena and, in particular, for potential application to in-streaming Data Assimilation techniques.

Numerical investigation on noise reduction of rotor blade-vortex interaction using blade surface jet blowing

The effects of blade surface jet blowing on the reduction of rotor blade-vortex interaction (BVI) noise are investigated using the compressible Reynolds-averaged Navier-Stokes equations and the Ffowcs Williams-Hawkings equations. A detailed analysis of the accuracy of the Weighted Essentially Non-Oscillatory (WENO) scheme and Monotone Upstream-Centered Scheme for Conservation Laws for simulating the flow field and predicting the BVI noise is performed using a two-bladed scaled AH-1 helicopter main rotor undergoing BVI as the baseline case. Additionally, a grid convergence study is conducted. The BVI noise is accurately predicted using the fifth-order WENO scheme and high-resolution grid system (the prediction errors of the peak sound pressure are less than 4%). Surface boundary conditions are introduced to model the effect of jet blowing on the blade surface on the flow control of the baseline case. Blade surface blowing is analyzed near the trailing edge and the leading edge near the rotor tip. The results show that blowing near the blade trailing edge is effective for BVI mitigation by altering the tip vortex strength and increasing the blade-vortex miss distance. The sound pressure level is reduced by 2.6 dB, and the peak amplitude is attenuated by more than 30%, with a 9.8% loss in rotor thrust. Therefore, blowing near the trailing edge may be considered a promising approach for active BVI noise control.