Reference Velocity Research Articles

Spiralling Inverse Method: A New Inverse Method to Estimate Ocean Mixing

Abstract Here, we introduce the Spiralling Inverse Method (SIM) that provides estimates of the small-scale and mesoscale mixing strength. The SIM uses a vertical integral over a balance between the water mass transformation equation and the thermal wind equation. The result is an equation where all terms, except for the mixing strengths, can be obtained from hydrographic data of temperature and salinity. As an advantage, the SIM estimates the mixing strengths without the need for further knowledge of a reference velocity or streamfunction. Here, we apply the SIM to a small region in the North Atlantic. We find that the estimates obtained by the SIM compare well to observations and other (inverse) estimates of the mixing strength. The SIM therefore has the potential to improve and constrain parameterizations used for climate and ecosystem modeling using readily available hydrographic data. Significance Statement Ocean mixing is a combination of many different physical processes over a large range of scales in time (from seconds to years) and space (from millimeters to 100 km). Most of these processes are too small to compute in climate models and need to be simplified (parameterized). These parameterizations have a strong influence on climate projections, and their shape and magnitude need to be constrained using observations and indirect estimates of ocean mixing strength. The Spiralling Inverse Method (SIM) is a new method to obtain such constraints of mixing strengths using readily available observations of temperature and salinity. We here test the SIM and confirm its potential to improve mixing estimates and therewith ultimately climate simulations.

Assessment of gamified mixed reality environments for upper limb robotic rehabilitation: pilot study on healthy adults

Exoskeletons for rehabilitation have the potential to aid movement and promote intensive, task-oriented, and personalized motor training. However, robotic-assisted training and user experience could be further improve by including Mixed Reality Environments (MREs) and using a gamification approach. This paper presents the design and evaluation of a MR solution based on Microsoft Hololens 2 for robot-assisted bilateral shoulder training. It is based on gamified MREs designed to guide and encourage bilateral synchronous and asynchronous movements of the upper extremities while a 6 Degrees of Freedom (DOF) exoskeleton (FLOAT) provides motor assistance only to the impared limb. The robotic-assisted bimanual tasks involve the use of rehabilitative tools (such as a dowel rod), as a means to interact with the virtual world and to enable the intact limb guide and control the motions of the disable limb. The gamified MR training solution generates meaningful performance metrics from the kinematic analysis of hands movement, captured by Hololens. The subjective evaluation of the gamified MR solution focused on usability, cognitive load, and user experience. Meanwhile, the objective evaluation encompassed the analysis of the robot-assisted movements with and without gamified MREs, a comparative analysis between metrics obtained from Hololens and Vicon data, and the collection of reference data and trajectories. There were twenty-one healthy adults involved in the evaluation of the system. The results with the gamified MREs highlight excellent system usability, low cognitive load, and high user experience. Additionally, integrating gamified MREs into robot-assisted movements enhances shoulder movements. Data collection with Hololens demonstrated to be reliable and consistent. Furthermore, the normal reference values, paths, and velocity profiles obtained from healthy individuals offer a foundation for assessing the performance of individuals with disabilities. In summary, the introduction of gamified MREs for robot-assisted bilateral shoulder movements marks a significant and promising advancement in rehabilitation technology.

Rotor solidity and blade pitch influence on horizontal axis small wind turbine performance – Experimental study

The urgent need to reduce greenhouse gas emissions has accelerated the adoption of renewable energy sources such as wind power. In this context, small wind turbines (SWTs) have emerged as a viable solution for decentralized electricity generation. This paper investigates the combined influence of rotor solidity and pitch angle variation on the performance of a small horizontal axis wind turbine through experimental analysis conducted in a wind tunnel. Results show that increasing rotor solidity generally improves Cpmax, particularly for shorter chord blades, with up to a 25–35 % increase observed by increasing the number of blades and a 10–15 % increase by employing longer chord blades compared to benchmark designs. Additionally, the study highlights the significant sensitivity of turbine performance to slight changes in pitch angle resulting in a 4–8 % rise in Cpmax. Higher solidity rotors exhibit reduced sensitivity to pitch angle changes and reference velocity variations, contributing to improved stability in varying environmental conditions. The study highlights the influence of aerofoil Reynolds number on turbine performance, underscoring the importance of careful aerodynamic design considerations. It provides valuable insights for SWT design to enhance energy efficiency and promote the widespread adoption of wind energy technology.

Array analysis of P-wave polarization data for constraining near-surface shear wave speeds: Aplication to theValley of Mexico seismic network

SUMMARY We present an array method for constraining near-surface shear wave speed values from P-wave polarization data. As the direction of P-wave particle motion is determined both by the incident ray parameter and the local surficial shear wave speed, the latter can be inferred from three-component seismic records if the ray parameter is estimated first. Previous studies have analysed data recorded at a single station from various earthquakes in order to estimate near-surface shear wave speed. In contrast, this paper analyses data recorded at an array of stations. As such, this method can estimate both ray parameter data for the recorded earthquakes and near-surface shear wave speed values at the studied sites. And as an advantage, the method can analyse records of local and regional earthquakes where ray parameter information cannot be reliably estimated using 1D reference velocity models. Additionally, by processing polarization data spanning multiple events and stations, our method provides robust estimates for near-surface shear wave speed values. We apply this array-based method to a seismic array covering the Valley of Mexico. Analysed records correspond to regional earthquakes with epicentral distances of roughly 300 km. The corresponding ray parameter values for all earthquakes were estimated from the delay of the seismic waves arrival throughout the network as all stations have a reliable time reference. Near-surface shear wave speed values estimated from the array-based polarization analysis are correlated with major geological features of the area such as the Trans-Mexican Volcanic Belt and the local basin. We also apply the presented method to a minimal data scenario. The flexibility of this method allows it to employ a single ray parameter value as input, producing robust estimates for near-surface shear wave speed values within our case study. This feature becomes useful in a variety of scenarios, such as utilizing seismic data lacking timing information. The presented method constitutes an accessible and inexpensive alternative to study shallow seismic structure, opening opportunities to expand and complement current tools and techniques to assess seismic hazards.

Three-dimensional spatiotemporal wind field reconstruction based on LiDAR and multi-scale PINN

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmospheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimizing a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalized errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices.

Wind tunnel measurements of cross-ventilation flow in a realistic building geometry: Influence of building partitions and wind direction

Wind tunnel measurements have widely been used for validation of computational fluid dynamics simulations of natural ventilation airflows. However, the majority of such measurements employed simple generic single-zone buildings, while there is a lack of studies on realistic buildings including flow-critical geometrical features (e.g. internal partitions). To assess the effect of internal partitions at different incident flow angles (α = 0° and α = 30°), wind tunnel measurements of velocities in and around a cross-ventilated realistic residential building (with and without internal partition) were performed. Measurements were conducted at a geometric scale 1:40, using laser Doppler anemometry. Results indicate a large impact of the internal partition on indoor airflow distribution and resulting ventilation flow rates. For instance, for α = 0°, on the partitioned building side, regions of velocity increase (from ∼0 m/s to ∼80% of the outdoor reference velocity, Uref), but also regions of velocity decrease (from ∼50% of Uref to ∼0 m/s) were observed. The ventilation flow rate through the windows at the partitioned side decreased by 23% and 32%, respectively. For the partitioned building, a change from α = 0° to α = 30° resulted in regions of velocity increase from 0 m/s to ∼60% of Uref.

A low-complexity solution for LMIs of hardly constrained nonlinear MIMO tracking control systems

This research is dedicated to the discrete-time tracking control of nonlinear multi-input multi-output (MIMO) Newtonian mechanics systems with an equal number of inputs and outputs, in the presence of input saturation and high-order dynamics of the actuation system. Firstly, the continuous-time model of the plant and actuation system has been transformed into the discrete-time domain using Adams–Bashforth and Adams–Moulton discretization techniques. In the next step, considering saturation boundaries, the modified position and velocity references are derived using the discrete-time reaching law-based control approach. These modified references are obtained through an optimization algorithm that calculates the closest achievable position and velocity trajectories with respect to the desired reference trajectory. Modifying desired trajectories in MIMO systems results in the formation of a system of linear matrix inequalities (LMIs). Solving such systems of inequalities requires the use of complex methods, and this difficulty increases with the increase in the system’s degrees of freedom (DOFs). The proposed strategy offers a more straightforward approach to solving these inequalities. Once the modified references are calculated, the corresponding position and velocity control signals can be obtained using the mentioned reaching law strategies. The final step of the proposed algorithm is to combine the resulting position and velocity control signals using a Gaussian-weighted averaging method to form the main control signal. A desirable performance for the control system can be achieved by appropriately adjusting the standard deviations of the Gaussian weighing functions. To evaluate the performance characteristics of the proposed method, it has been applied in the tracking control procedure of a nonlinear 3-DOF mechanical system. The numerical simulation results demonstrate the effectiveness of the method.

Specific growth velocity reference charts for monochorionic twin pregnancies.

We aimed to create specific growth velocity reference charts for monochorionic (MC) twin pregnancies and provide additional information for assessing fetal growth in MC twins. This retrospective study collected data from uncomplicated MC twins with serial ultrasound parameters. The four ultrasound parameters, including biparietal diameter, femur length, head circumference, and abdominal circumference, were used to calculate the estimated fetal weight (EFW). Multilevel linear regression models were applied to fit growth velocity charts for each biometric parameter and EFW. Analysis of variance was used to examine differences in birthweight by whether EFW velocity and EFW values were <10th or ≥10th percentiles. The final analysis encompassed a total of 5956 ultrasound examinations conducted on 487 MC twins. The growth velocity of four biparietal diameters exhibited a gradual decrease in a nearly linear fashion progressing from 18 to 37 gestational weeks. The EFW velocity increased steadily from 18 to 36 gestational weeks, reaching a peak of 178.2 g/week, and then the velocity gradually decreased until delivery. At 32 weeks for illustration, the lightest birth weight was observed when both EFW and EFW velocity were <10th percentile (1899 g). The study also found that birth weight was higher when EFW velocity was ≥10th percentile compared with <10th percentile, regardless of EFW being below or above the 10th percentile (2263 and 1906 g, respectively; P < 0.001). We developed specific growth velocity reference charts for MC twins, which could provide a valuable reference point for a more precise evaluation of fetal growth in MC twins. Preliminary findings indicate that the inclusion of fetal growth velocity in monitoring fetal growth provides additional information beyond EFW alone.

A novel learning-based robust model predictive control strategy and case study for application in optimal control of FCEVs

With the gradual implementation of carbon-neutral policies, fuel cell electric vehicles (FCEVs) have gained widespread recognition as a promising solution. Due to multi-source electromechanical coupling, the powertrain of FCEVs constitutes a complex nonlinear system, necessitating an advanced energy management strategy for effective control to enhance economic performance. In this study, a novel learning-based robust model predictive control (LRMPC) is proposed for a 4WD FCEV, integrating a high-confidence velocity estimation model and an accurate state observation model to enhance energy-saving potential, robustness, and real-time application performance under various driving conditions. To enhance robustness and adaptability, deep forest with superior feature fusion abilities is employed to generate future velocity reference datasets based on FCEV operating scenarios. To improve state observation accuracy and control effectiveness, a novel built-in state observation model based on machine learning algorithms is established, leveraging knowledge of nonlinear systems within the FCEV powertrain. Subsequently, to enhance the real-time applicability of LRMPC, an explicit control scheme for multidimensional mapping based on explicit data tables is utilized for accurate state transformation, incorporating input features such as vehicle states and component states. Simulation evaluations and hardware-in-the-loop tests demonstrate the improved economic performance and real-time application ability of LRMPC, showcasing its promising performance.

Model predictive control based integrated fault tolerant controller for autonomous vehicles with four-wheel independent steering, driving and braking systems

This paper presents a fault-tolerant control algorithm for path and velocity tracking of an autonomous vehicle based on four-wheel independent steering, driving, and braking systems. The proposed algorithm is designed to distribute the control effort to normal actuators in the occurrence of faults. The proposed algorithm consists of three modules. The reference velocity and yaw rate to track the reference path are determined by the reference state decision module. The model predictive control (MPC)-based fault-tolerant controller determines the optimal inputs to compensate for the effect of the faulty actuator and maintain the tracking performance. A faulty-factor estimator is proposed based on a recursive covariance estimator to evaluate the performance degradation due to the failure of each actuator. The estimated faulty factors are applied to the objective function of the MPC such that the control input is distributed based on the level of the fault. The performance of the proposed algorithm was verified via performing simulations using MATLAB/Simulink and CarSim. The simulations proved that the proposed controller maintained the control performance of the normal case even in multiple actuator failures.

Complex Laplacian approach for formation tracking without velocity measurements

This paper is focused on formation tracking control of multi-agent systems in a leader–follower setting. The objective is to introduce control algorithms to steer a team of mobile agents into a desired, moving geometrical pattern while the agents are unaware of their own and other agents' velocity information during the entire process. We introduce a formation control law under which the agents asymptotically shape a desired geometrical pattern and track a constant reference velocity. Another control law is then introduced to address the case where the reference velocity is time-varying. Both control laws are based on the complex Laplacian approach and do not require agents' velocity information. The convergence of these algorithms are proved and their performance are examined via numerical examples. Simulation results verify the outperformance of the proposed control laws compared to the rival control schemes in the literature.

Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons.

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon.

Terminal Soft Landing Guidance Law Using Analytic Gravity Turn Trajectory

This paper presents an innovative terminal landing guidance law that utilizes an analytic solution derived from the gravity turn trajectory. The characteristics of the derived solution are thoroughly investigated, and the solution is employed to generate a reference velocity vector that satisfies terminal landing conditions. A nonlinear control law is applied to effectively track the reference velocity vector within a finite time, and its robustness against disturbances is studied. Furthermore, the guidance law is expanded to incorporate ground collision avoidance by considering the shape of the gravity turn trajectory. The proposed method’s fuel efficiency, robustness, and practicality are demonstrated through comprehensive numerical simulations, and its performance is compared with existing methods.

Uncertainty quantification for velocity measurement with 2D2C particle image velocimetry

Abstract In this paper, a theoretical model of dimensionless instantaneous and average velocity measurement uncertainty quantification with 2D2C particle image velocimetry (PIV) is established under the framework of generally adopted international uncertainty quantification standards. The effectiveness of the model is verified using uniform flow field testing data. Combined with semi-quantitative analysis of the theoretical model, uncertainty control suggestions for PIV measurements are given. The major sources affecting the uncertainty of instantaneous velocity measurements are the reference velocity, particle instantaneous pixel displacement, and their correlation term. For average velocity measurement uncertainty quantification, the uncertainty of particle average pixel displacement is effectively controlled by taking a large number of particle images. Thus, three single-component terms — the calibration factor, particle average pixel displacement and reference velocity — and two correlation terms — the particle average pixel displacement–calibration factor and the particle average pixel displacement–reference velocity — all make an important contribution to the average velocity measurement uncertainty. To reduce the uncertainty of PIV velocity measurement, one can reduce the reference velocity measurement uncertainty, optimize the PIV algorithm and improve the calibration factor by applying a high spatial resolution imaging system in experiments. In addition, reducing the reference velocity measurement uncertainty and improving the spatial resolution are key feasible methods.

An improved correction of radial velocity systematics for the SOPHIE spectrograph

High-precision spectrographs can on occasion exhibit temporal variations in their reference velocity or nightly zero point (NZP). One way to monitor the NZP is to measure bright stars, whose intrinsic radial velocity variation is assumed to be much smaller than the instrument precision. The variations of these bright stars, which is primarily assumed to be instrumental, are then smoothed into a reference radial velocity time series (master constant) that is subtracted from the observed targets. While this method is effective in most cases, it does not fully propagate the uncertainty arising from NZP variations. We present a new method for correcting for NZP variations in radial velocity time series. This method uses Gaussian processes based on ancillary information to model these systematic effects. Moreover, it enables us to propagate the uncertainties of this correction into the overall error budget. Another advantage of this approach is that it relies on ancillary data that are collected simultaneously with the spectra and does not solely depend on dedicated observations of constant stars. We applied this method to the SOPHIE spectrograph at the Haute-Provence Observatory using a few instrument housekeeping data, such as the internal pressure and temperature variations. Our results demonstrate that this method effectively models the red noise of constant stars, even with a limited number of housekeeping data, while preserving the signals of exoplanets. Using simulations with mock planets and real data, we found that this method significantly improves the false-alarm probability of detections. It improves the probability by several orders of magnitude. Additionally, by simulating numerous planetary signals, we were able to detect up to 10% more planets with small-amplitude radial velocity signals. We used this new correction to reanalyse the planetary system around HD158259 and to improve the detection of the outermost planets. We propose this technique as a complementary approach to the classical master-constant correction of the instrumental red noise. We also suggest to decrease the observing cadence of the constant stars to optimise the telescope time for scientific targets.

Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects

AbstractVegetation plays a crucial role in river hydrodynamic processes, and the accurate prediction of canopy drag force is essential for effective river management and ecosystem protection. The interactions within the vegetation canopies must be quantified to understand their impact on drag force. Through a series of flume experiments, we conducted an investigation into the canopy interaction mechanism of rigid emergent aquatic vegetation, particularly focusing on the blockage and sheltering effects. Our experimental design includes various combinations of lateral and longitudinal spacing, as well as special single‐row and single‐column arrangements. This allowed us to provide a more precise understanding of how lateral and longitudinal spacing affect the blockage and sheltering effects. Furthermore, we introduced a unified reference velocity that combines two effects, based on which we have established a widely applicable drag model that can predict drag under various density conditions. Lastly, we proposed a critical characteristic value for quantifying drag. This value is instrumental in revealing the ultimate performance of drag under different spacing arrangements. The findings provide a reliable approach for predicting drag in rigid emergent vegetation canopies, significantly enhancing our understanding of vegetation's influence on hydrodynamic processes and offering a practical tool for river management and ecosystem protection.

Design of an Intelligent Cascade Control Scheme Using a Hybrid Adaptive Neuro-Fuzzy PID Controller for the Suppression of Drill String Torsional Vibration

Eliminating the excessive stick–slip torsional vibrations of drill strings by utilizing an effective controller can significantly increase penetration rates and reduce drilling operation costs. The present study aims to develop a fast and intelligent cascade control structure for a multi-degree-of-freedom drill string system under torsional vibrations. The proposed control configuration consists of two control loops in a cascade arrangement. The first controller estimates the top drive velocity reference from the actual drill bit velocity and its reference. The role of the second controller is to regulate the top drive velocity to its reference by generating the necessary torque for the drilling operation process to progress. Each control loop is designed based on a hybrid adaptive neuro-fuzzy PID and feedforward term. The latter assures fast regulation when there are sudden changes in operation. To evaluate the performance of the suggested cascade feedforward neuro-fuzzy PID (CFF-NFPID) control structure, extensive simulations were conducted using Matlab/Simulink. The simulation results clearly showed that the proposed CFF-NFPID controller provided high-performance control with variations in the weight on the bit and the desired drill bit rotary speed in comparison with that of cascade feedforward fuzzy PID, sliding-mode, cascade feedforward PID, cascade PID, and conventional PID controllers. Furthermore, the control robustness is very suitable despite the change in the system parameters.

Tractor Three-Point Hitch Control for an Independent Lower Arms System

The three-point hitch, found on agricultural tractors, facilitates the raising and lowering of an attached implement. Some tractors include a rock shaft that comprises a physical shaft that interconnects and facilitates the raising and lowering of the lower arms of the three-point hitch in a synchronized manner. In this study, we deal with a hitch system with the lower arms actuated by two independent hydraulic cylinders. This innovative tractor hitch system design allows the implement to follow the terrain, instead of the tractor, about the fore–aft (roll) axis of the tractor. However, since the two lower arms are independent, a specialized controller is needed to move these arms in unison. First, we present a position controller for individual arms and a roll controller to move these arms together. Second, we present a unique algorithm to emulate a physical rock shaft while the implement is operating in float mode. The algorithm ensures that the implement does not roll around the fore–aft axis while making sure it moves up and down vertically to follow the terrain. We present experimental results from the step response of the hitch system’s height while tracking a velocity reference. With the roll of the implement defined as the difference between the left arm’s position in percentage and that of the right arm in percentage, we observe that the largest mean roll was 0.23% with a flail mower attached and 0.26% without any implement. We then present results from the implement’s position in the float mode when the software rock shaft was activated and compare them with the case without the software rock shaft. The experiments showed that, when the software rock shaft was turned on, the mean roll reduced from 4.64% to 0.58% with a seed drill implement and from −3.99% to −0.59% with a flail mower implement. The standard deviations in these two implement cases improved from 16.77% to 2.79% and 6.45% to 3.53%, respectively, proving the effectiveness of the software rock shaft and its potential to replace the physical rock shaft found on the traditional tractors.

Data-driven double-focusing resolution analyses for seismic imaging

Seismic imaging requires a supporting tool to measure its resolution characteristics as a basis for seismic interpretation. However, traditional focal-beam (FB) resolution analyses are usually applied to acquisition geometries by calculating the impulse response of a single point in a reference velocity model. Seismic data to directly estimate the spatial resolution of migrated images remain unaddressed. We address this data resolution by incorporating weighted FBs into the prestack migration process to develop a data-driven double-focusing (DF) resolution analysis method for complex media. Unlike traditional resolution analyses that define the system resolution of acquisition geometries using a unit point reflector, the data-driven resolution analysis for seismic imaging uses angle-trace gathers that contain all the information of acquisition geometries, migration velocities, propagation effects, and reflectivities. The data-driven resolution analysis consists of the detector- and source-focusing processes using common-shot and common-detector gathers, respectively, followed by a multiplication of weighted focal detector and source beams. The resulting resolution function can be used to calculate the horizontal and vertical resolutions and sharpness of a given imaging point. It is implemented along with prestack migration to share the same wavefield extrapolation without invoking extra computational cost. We benchmark the data-driven method for a homogeneous medium containing single- and double-point targets by conventional point-spread and FB methods. Numerical experiments with wedge-model synthetic data and field data indicate the performance of the DF resolution analysis, demonstrating the effects of propagation attenuation, incorrect migration velocity, and noise contamination, which significantly reduce the system resolution of acquisition geometries.

Convective heat transfer improvement for a single row of continuous air jets by the acoustic-actuator integration

An experimental study is performed to identify the roles of acoustic-actuator integration on improving convective heat transfer from a row of continuous air jets onto a flat target surface, by using a representative element that involves three continuous-jet pipes with a fixed jet-to-jet pitch (X/d = 5) and two acoustic actuators positioned at the intermediate centers of adjacent continuous jets. The tests are performed under Re = 3000–7000 (continuous jet), f = 150Hz–250Hz (acoustic actuator) and H/d = 2–10 (jet-to-target distance). In addition, several numerical simulations regarding the flow fields are also made to illustrate the mutual interaction mechanism between neighboring synthetic jet and continuous jet. The results clearly demonstrate that three featured local-Nu distribution patterns would happen, dependent on the reference velocity ratio (UR) and the jet-to-target distance. Within the limit of this study, the most possibilities in accordance with different featured local-Nu distribution patterns are put forward. As far as spatially-averaged Nusselt number (Nus-av) and heat transfer uniformity indicator (HTU) for a specified zone, the roles of acoustic-actuator integration on heat transfer performance are thoroughly evaluated. Its positive role on heat transfer enhancement is identified to behave significantly in the situations of UR > 1 and H/d ≥ 6, wherein Nus-av could be raised by 20 % at least in terms of the baseline continuous-jet configurations. However, when UR is far less than 1.0, a little negative influence on Nus-av would happen at larger jet-to-target distances. Meanwhile it is confirmed that the acoustic-actuator integration could always improve the heat transfer uniformity, especially at larger jet-to-target distances wherein HTU could be mostly reduced beyond 30 % with respect to the baseline continuous-jet situations.