High-speed Surface Research Articles

Fluid-Structure Interactions of a Fin over a Compliant Panel in Supersonic Flow

The fluid-structure interactions of a blunt fin mounted over a compliant panel in Mach 2 flow were investigated. The test geometry consisted of a thin, flexible panel fixed on all edges with a blunt fin with a hemispherically rounded leading edge mounted in a manner that the leading edge of the fin overhung the panel. The blunt fin was also pivoted to several angles of attack with respect to the oncoming flow. This allowed for the study of the dynamic system created by the interactions between the compliant panel and the blunt fin geometries. Both rigid and compliant panel models were used in order to provide a comparison between the flow with and without the dynamics of the compliant panel. High-speed schlieren and surface oil flow visualizations showed that the time-averaged flow remained almost entirely unchanged between the rigid and compliant panels. The instantaneous flow fields, however, showed a highly dynamic system where the compliant panel induced oscillatory shock waves that were both stationary and freely moving. Simultaneous high-speed schlieren visualization and stereo digital-image correlation showed a strong linkage between the motion of the shock waves and the deformation of the panel. This was demonstrated through frequency analysis and modal decomposition of the panel deformation and shock motions.

Effect of dilution and Lewis number of the fuel in the fuel-air mixture on the heat flux during flame wall interaction (FWI)

Most combustion systems where flame is bounded by walls encounter Flame-wall interactions (FWI). Heat losses during FWI are in the magnitude of MW/m2 leading to concern about inefficiency, and thermal stresses. Hence, FWI is a topic of significant interest. With the goals of climate change pushing us toward renewable fuels with least carbon emissions, it is imperative to understand the FWI of renewable fuels like hydrogen-air mixture. In this study, FWI in a head-on-quenching configuration is carried out in a constant volume chamber. High-speed surface temperature measurement is carried out to compute instantaneous wall heat flux. Adding inert diluents to methane-air mixtures, the effect of dilution on the peak of heat flux during FWI is studied. Dilution affects the peak of heat flux predominantly by changing the adiabatic flame power of the fuel-air mixture. Furthermore, a large variation in flame power is carried out by changing the dilution fraction in the methane-air mixture to quantify its effect. It is found that the peak of heat flux and non-dimensional heat flux follows a nonlinear decrease with an increase in flame power. Knowing this effect, FWI of three different fuels, that is, methane, hydrogen, and a mixture of acetylene and hydrogen, having different Lewis numbers ( Le) of fuel in a fuel-oxidizer mixture are studied at equivalent flame power. This comparison shows that FWI of hydrogen-air mixtures in HOQ configuration have a lower peak of heat flux compared to other fuels which may be due to the preferential diffusion of hydrogen-air mixture.

A Study of Tribological Performance Prediction Based on Surface Texture Parameters

Surface texture parameters are a quantitative way of characterising surface topographical features and are closely related to tribological properties. In this paper, the correlation between surface topographic features and friction coefficient is investigated on the basis of the proposed improved correlation analysis model for high-speed milling surface topography of hardened steel. It was found that the friction coefficient could not be accurately reflected by a single parameter, so a prediction model for the friction coefficient based on Sxp, Sq, Sp, Sz, Sku and Sdq was developed. In this paper, the parameter screening was completed based on the changing characteristics of the data, and a multi-parameter prediction model of the friction coefficient in the stable wear stage was established, which provides a new idea to investigate the influence of the characteristics of surface topography on tribological performance.

ECM-YOLO: a real-time detection method of steel surface defects based on multiscale convolution

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network’s robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e., multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.

On the intrusiveness of phosphor thermometry for metal surface temperature measurements in reciprocating engines: an experimental and heat conduction modeling study

Abstract Lifetime-based phosphor thermometry has been applied in a wide variety of surface temperature measurement applications due to its relative ease of implementation and robustness to background interference when compared to other optical temperature measurement methods. It is often assumed that the technique is minimally intrusive if the thickness of the applied phosphor coating is < 20 µm. To evaluate this assumption, high-speed phosphor surface temperature and thermocouple measurements were performed on 4140 steel substrates installed in the cylinder head of an optically-accessible internal-combustion engine for four operating conditions. For phosphor thermometry measurements, four substrates were studied, each coated with a phosphor layer of different thickness ranging between 6 µm and 47 µm. The phosphor thermometry measured temperature swings during combustion were shown to be heavily impacted by the presence of the phosphor coatings, increasing by roughly a factor of 2 - 2.5 when increasing the thickness from 6 µm to 30 µm. A technique was implemented which utilizes the temperature data in combination with a heat conduction model to provide estimates of the temperature swing and heat transfer flux in the absence of the phosphor coating. It was shown that even the 6 µm phosphor coating could lead to an order of magnitude increase in the temperature swing relative to the uncoated 4140 steel substrate. Despite the intrusiveness of phosphor thermometry for the surface temperature measurements, reasonable agreement was demonstrated between heat flux estimates determined with the heat transfer modeling technique and those deduced from temperature-swing measurements using two different high-speed thermocouples. The results indicate that phosphor surface thermometry can be a reliable surface temperature and heat flux diagnostic for transient high heat flux environments, as long as proper care is taken to account for the impact of the phosphor layer on the measurement.

High-speed wafer surface defect detection with edge enhancement via optical spatial filtering in serial time-encoded imaging

With the rapid development of the chip and metasurface industries, high-speed defect detection of wafer during semiconductor batch manufacturing has become increasingly important for yield control. Here, we propose a high-speed, macro-scale wafer surface defect detection method with enhanced image edges, via optical spatial filtering in serial time-encoded imaging. By using blazed gratings to spatially project different wavelength components of pulsed light into a one-dimensional line, together with dispersive Fourier time stretching technology, a high-speed serial time-encoded imaging system based on a single pixel detector is constructed for testing the wafer surface defects, and optical spatial filtering is introduced to improve the imaging quality. Experiments show high-pass spatial filtering effectively restrains environmental interference and enhances image edge detection. Small target detection is achieved without a complex image algorithm. Our solution extends optical spatial filtering to serial time-encoded imaging, not only permitting wafer macro scale defect detection with edge enhancement, but also having potential significance for imaging lidar, imaging spectrometers, ghost imaging technology, and more.

High-Speed Surface Property Recognition with a 140 GHz Frequency

In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems’ decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method’s effectiveness in high-speed conditions, suggesting terahertz frequencies’ potential in future sensing and communication applications.

How Resilient is Wood Xylan to Enzymatic Degradation in a Matrix with Kraft Lignin?

Despite the potential of lignocellulose in manufacturing value-added chemicals and biofuels, its efficient biotechnological conversion by enzymatic hydrolysis still poses major challenges. The complex interplay between xylan, cellulose, and lignin in fibrous materials makes it difficult to assess underlying physico- and biochemical mechanisms. Here, we reduce the complexity of the system by creating matrices of cellulose, xylan, and lignin, which consists of a cellulose base layer and xylan/lignin domains. We follow enzymatic degradation using an endoxylanase by high-speed atomic force microscopy and surface plasmon resonance spectroscopy to obtain morphological and kinetic data. Fastest reaction kinetics were observed at low lignin contents, which were related to the different swelling capacities of xylan. We demonstrate that the complex processes taking place at the interfaces of lignin and xylan in the presence of enzymes can be monitored in real time, providing a future platform for observing phenomena relevant to fiber-based systems.

Coupling effect of machine tool dynamic characteristics and cutting conditions on the cutting process vibration and high-speed micro-planing surface mid-frequency waviness

In addition to the dynamic characteristics of the machine tool, cutting conditions significantly influences the vibration of the cutting system and mid-frequency waviness of the workpiece surface in ultra-precision machining (UPM). In this work, the effects of cutting conditions on machining vibration and surface waviness were investigated by high-speed micro-planning experiments. The origin of vibration and waviness were elucidated by frequency-domain processing of the vibration signal and surface waviness analysis. The dynamic characteristics of machine tool are determined using modal test and analysis. The results revealed that the cutting force has different contributions to the workpiece vibration and tool vibration. In the time domain, distinct differences can be found in stability and attenuation of various waviness components. The divergent effects of cutting force components in different vibration forms are confirmed through a novel cutting force model of micro-planing. The investigation can further reveal the formation mechanisms of cutting system vibration and surface waviness during the micro-cutting process, which can provide practical guidance for suppressing mid-frequency waviness on machined surfaces.

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

Enhanced Wear Properties of an Inspired Fish-Scale Film Structure in Terms of Microstructured Self-Lubrication Induced Effects by High-Speed Laser Surface Remelting Processing

Enhanced Wear Properties of an Inspired Fish-Scale Film Structure in Terms of Microstructured Self-Lubrication Induced Effects by High-Speed Laser Surface Remelting Processing

Nonparametric modeling of a high-speed USV at three speed regions based on Gaussian process regression with a hybrid kernel function

Due to the complexity and variability of the ocean environment, it is difficult for high-speed unmanned surface vehicles (USVs) to construct a unified mathematical model for nonlinear motion under different Froude numbers. In this paper, a nonparametric modeling method for high-speed USVs at three speed regions is presented based on Gaussian process regression with a hybrid kernel function (HKF-GPR). First, the hybrid kernel functions are designed based on the forecasting performance of Gaussian process regression with different single kernel functions (SKF-GPR) using sea trial speed data from the “Jiuhang 750” USV. Second, to better capture the dynamic performance of the USVs, the sea trial speed data are clustered into three speed regions by an improved k-means++ algorithm; these regions are the displacement, semiplanning, and planning regions. Finally, USV modeling and forecasting are performed in the three speed regions using HKF-GPR. The trained model shows good modeling accuracy and forecasting, verifying the effectiveness of the proposed method.

Automatic optimization of projection intensity for high dynamic range 3D surface measurement

Stripe projection profilers are widely used in many fields because of their high speed, high accuracy, and clear surface textures. In the structured light projection method, the complex reflectance distribution on the surface of the high-dynamic-range objective is an important factor that affects the measurement accuracy, as it can result in image over- and under-exposure. In this study, a method is proposed to adaptively determine the number of stripe patterns and the corresponding number of light intensities of the stripe patterns based on the complex reflectance of the surface of the object being measured. A multi threshold Otsu algorithm is used to classify the histogram of the surface reflectance distribution of the measured object, and several sets of stripe patterns with optimal light intensity are generated according to the light intensity response function. The original stripe patterns acquired at different light intensities are synthesized pixel by pixel into a high dynamic range (HDR) stripe image, and a four-step phase-shifting algorithm is used to obtain the unwrapped phase from the synthesized image. Experimental results show that this method can accurately measure the target of a surface-reflectance-transformed HDR.

Integrative Approach for High-Speed Road Surface Monitoring: A Convergence of Robotics, Edge Computing, and Advanced Object Detection

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries.

Design, Development and Commissioning of the Boldrewood Towing Tank – A Decade of Endeavour

The process of design, build and eventual commissioning of the towing tank on the Boldrewood Innovation Campus is described. The design brief required a facility that would have a capability to test models at a commercial scale but that would be effective as teaching environment for the next generation of Naval Architects as well as providing a flexible space for future fundamental research. Each of these provided their own challenges but the eventual solution of a 138 m long, 3.5 m deep, 6 m wide facility has more than met the initial aspirations. Equipped with 12 independent 0.5 m wavemaking flaps at the West end, a passive beach at the East end, a deployable side beach along the South wall for post run wave absorption and a monocoque Aluminium alloy carriage, the Boldrewood towing tank has now been successfully operating for more than a year. The carriage position and speed are controlled by a twin winch arrangement using a laser positioning system and low embodied energy composite cables. The carriage can reach a maximum speed of 10 m/s with controllable acceleration rates and can have up to four constant speed phases per run. Initial commissioning results and comparisons with benchmark data for the KCS hull confirm the accuracy and repeatability of the facility. In particular, the position and speed of the carriage are known to a high level of precision. To date research and consultancy work has spanned the performance of high speed vessels, uncrewed underwater and surface vessels, wave energy and tidal current systems, floating platforms for wind turbines, performance sport work for sailing, kayaking, rowing and swimming, open water propeller tests as well as conventional displacement vessel testing for self-propulsion and resistance. All ship science and maritime engineering students use the facility as part of their taught modules in every year of their programme as well as for individual, MSc and group projects as appropriate. It has also made a strong impact on the many thousands of visitors a year to the campus for science and engineering open days.

High-precision and high-speed surface topography measurement method of microstructures based on laser scanning transverse differential confocal

The manufacturing of microstructured devices requires higher performance regarding the increasing development of finer structures and more complex structural shapes, which poses a challenge for existing measurement techniques in terms of anti-scattering, high-precision and high-speed. Accordingly, this study proposes a new technology for surface topography measurement of microstructures based on laser scanning transverse differential confocal (LSTDCM), which inserts a D-shaped aperture into the existing confocal system, and performs split-focal spot detection on the focal plane. This method exhibits improved axial resolution by performing differential phase subtraction of the front- and back-focal signals to obtain a highly sensitive axial response signal. Further normalization of the transverse differential confocal signal eliminates the multiplicative noise of the system, avoids the impact of changes in roughness on the measurement results, and achieves an anti-scattering measurement. The linear region of the axial response signal around the zero-crossing point were used to achieve a “non-axial scanning” height measurement, which was combined with high-speed beam lateral scanning to achieve a high-speed areal topography measurement. Theoretical analysis and experimental results indicate that, for samples with height changes within the linear region of axial response, the method can complete areal topography measurement in 1.7 s with the axial resolution of 1 nm and the measurement range of 128 × 128 μm. A microstructured device processed using a nanoform single-point diamond machine tool and a semiconductor wafer were employed in the application validation experiment. And achieve a wide range stitching measurement of 1 mm × 1 mm under a 100 × objective along with the transverse scanning stage. The measurement efficiency is about three times higher than that of Olympus OLS4100 confocal microscope. The method provides a new and effective technical approach that is not limited by the surface roughness and complex structure of the tested devices, and can achieve high-precision as well as high-speed optical nondestructive measurement without moving the tested device. Thus, this work is expected to has important practical applications in the field of ultra-precision measurement.

Effect of complex ion-plasma treatment on mechanical properties of high-speed steel R6M5 surface

The paper describes the effect of complex ion-plasma treatment consisting of ion nitriding and subsequent application of a wear-resistant coating using a plasma source with a filament cathode on the physical and mechanical properties of the surface of high-speed steel P6M5. Two different technologies, a complex ion plasma technology and a protective coating of the (TiAl)N system, were compared. The results showed that the samples subjected to complex ion-plasma treatment have higher physical and mechanical properties.

Modeling and surface morphological study in drilling of Al 2024 alloy

With concern to profuse usability of drilling operations in various industries, almost 1/3rd of the all the machining operations, the main focus is to obtain a proper quality of hole in less time at optimum cost. Aluminium alloy 2024 (Al 2024) is largely applied in various industries specially, aircraft, automotive, marine etc. because of its outstanding union towards strength, corrosion resistance, and weldability. Cutting time, cutting temperature and surface morphological investigation during drilling is very much necessary for obtaining a quality hole that has not been substantially researched so much. Therefore, a proper and thorough study of these responses is quite important regarding the hole quality. With this view, in this work, drilling of Al 2024 alloy is performed with three process parameters (input) viz. spindle speed (v), feed rate (f) and drill bit diameter (d) and performance parameters (output) as cutting time (s), cutting temperature (°C) and surface roughness (μm). Three different drill bit diameters of high speed steel (HSS) of size viz. 6 mm, 8 mm and 10 mm are used in drilling. Each process parameter has three levels. The surface morphological study is also conducted to know the depth of hole morphology details after drilling. To make the drilling operation environment friendly, it is performed in dry atmosphere. Using full factorial design, total 27 experiments are conducted. The experimental results showed that high spindle speed along with high feed rate decreases the cutting time, whereas it increases the cutting temperature. For better surface finish medium spindle speed along with low feed rate is recommended. From analysis of variance (ANOVA), all the three models developed for cutting time, cutting temperature and surface roughness are found statistically significant. Spindle speed and feed are the influential parameters to cutting time, cutting temperature and surface roughness. A mathematical model is developed using response surface methodology (RSM) to predict the responses cutting time, cutting temperature and surface roughness with better accuracy. The coefficient of determination (R2) for cutting time, cutting temperature and surface roughness is found as 96.65%, 95.88% and 93.65% respectively, which indicated that the developed models could be used for prediction with better accuracy. The morphological study shows that scratches, trenches, abrasive wear are visible on the hole surface. At high spindle speed surface grain refinement is observed more which causes a better surface finish.

High speed surface acoustic wave and laterally excited bulk wave resonator based on single-crystal non-polar AlN film

One approach to extend the acoustic applications of aluminum nitride (AlN) in the GHz frequency range is to take advantage of the piezoelectric performance and high acoustic velocity (∼11 350 m/s) along the c-axis of this material. In particular, in the case of high-frequency micro-electromechanical systems, it should be possible to simplify the construction of resonators by using a-plane AlN-based structures. In the work described in this Letter, a single-crystalline a-plane AlN layer on an r-plane sapphire substrate is obtained by combining sputtering and high-temperature annealing. Based on this non-polar AlN, a resonator with only planar interdigital transducer electrodes is fabricated. Experiments on this resonator reveal simultaneous excitation of an anisotropic Rayleigh surface acoustic wave (SAW) at 2.38 GHz and a laterally excited bulk acoustic wave (LBAW) at 4.00 GHz. It is found that the Rayleigh SAW exhibits outstanding performance, with a quality factor as high as 2458 and great stability under variations in temperature. The LBAW at 4.00 GHz is excited by pure planar interdigitated electrodes without the need for any cavity or bottom electrode structure, thus demonstrating a promising approach to the construction of high-frequency resonators with a relatively simple structure.

Pelton turbine jet flow investigations using laser Doppler velocimetry

Hydropower is a sustainable and clean renewable energy source. The Pelton turbine is an impulse type of hydraulic turbine and operates under high head and low discharge. It comprises an injector, which converts high-pressure water into kinetic energy as a high-speed free surface water jet that impinges on the runner generating power coupled with an electric generator. The water is highly accelerated in the nozzle, which produces turbulent kinetic energy in the jet flow. Before impinging on the runner, the surrounding air is entrained into the jet, which causes the jet dispersion. The effective conversion of water energy into mechanical energy and the quality of a jet depend significantly on the uniformity of the jet. However, the jet encounters instabilities and non-uniformity caused by secondary flow and turbulence. Jet surface and velocity distribution do not remain symmetric. Investigation of the jet's velocity profile and turbulence intensities is important to understand the flow behavior and improvements in the design of injector. In this study, the experimental investigation of the jet has been performed using the laser Doppler velocimetry (LDV) system. The turbulent intensity and instantaneous velocity of the jet are analyzed at different locations along the jet flow. The velocity wake zone occurs around the center of the jet, which diminishes in the axial direction of the jet, with non-dimensional jet velocities values 0.91, 0.92, and 0.94 at x/Do of 0.5, 1, and 2 for ∼98% nozzle opening. The secondary flow velocity magnitude is larger for 90°–60° injector jet flow than for 90°–50°. The jet velocity profile obtained from the LDV has been compared with computational fluid dynamics analysis.