Measurement Of Dynamic Response Research Articles

Dynamic Response of a Rotor Supported on Hybrid Bearings in Air, Water, and Liquid Nitrogen: Measurements and Comparisons to Predictions

Abstract Modern liquid rocket engine turbopumps implement hybrid bearings, which combine hydrostatic and hydrodynamic fluid film principles, in compact and lightweight units known for their superior reusability, durability, and reliability. This study aims to provide extensive and reliable test data for the purpose of anchoring and benchmarking predictive tools for these bearings. This paper provides comprehensive dynamic response measurements of a rotor weighing 10.40 kg and approximately 60 mm in diameter at the bearing locations. The test rotor is supported on two hybrid journal bearings with a bearing length to bearing diameter ratio of approximately 0.42 and a bearing radial clearance to rotor radius ratio of around 0.0017. The bearings are tested with air, water, and liquid nitrogen, serving as surrogate test fluids for common propellants in liquid rocket engines. Rotor run-up and speed coast-down tests are conducted to measure shaft motion responses. The results clearly demonstrate that the rotordynamic characteristics of the test rotor supported on the hybrid journal bearings largely rely on properties of the test fluids and their feeding conditions. Notably, when air is pressurized into the test bearings, the shaft motion responses differ markedly from those observed with water and liquid nitrogen, primarily due to significantly lower stiffness and damping coefficients. Furthermore, rotor speed coast-down tests for each test fluid exhibit notable differences in coast-down speed over time characteristics, depending on the test fluid properties. The predicted rotor imbalance responses exhibit excellent correlation with the measurement data. The steady increase of demand and growth of the fluid film bearing technology for reusable rocket engines require accurate bearing design tools, the extensive component testing, and the implementation of the technology. The validated and developed bearing predictive models from previous research demonstrate well the characteristics of bearings under various operating fluids conditions. The findings and database presented in this work contribute not only to comprehending the performance of hybrid bearings but also to understanding and enhancing the dynamic characteristics of rotor systems supported on hybrid bearings.

Numerical Simulation and On-Site Measurement of Dynamic Response of Flexible Marine Aquaculture Cages

Flexible cages are widely used in marine aquaculture, yet their mechanical features in extreme seas are still unclear. This study proposes a numerical algorithm to solve the coupled response of the multiple cage systems. The net and mooring lines are modeled using the lumped-mass model, while the flexible floating collar system is assessed with the large-deformation FEM model, and the two models are coupled through an iterative scheme. Sea trials are conducted, and the motion of the cage is obtained using an image processing technique, which validates the numerical algorithm. Using the proposed numerical algorithm, a series of simulations are performed to investigate the response of flexible cages in extreme seas. Motions, line tensions, and structural sectional forces are studied, and the effects of factors such as the wavelength of incident waves and the diameter of collar pipes are investigated.

Potential of Non-Contact Dynamic Response Measurements for Predicting Small Size or Hidden Damages in Highly Damped Structures.

Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam's natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.

Dynamic transient response measurement method for parity-time-symmetric LC telemetry sensors

Parity-time symmetry concept has been utilized to develop high precision LC passive wireless sensors. However, they often use the traditional frequency sweeping method for measurements, thus the measurement precision and speed are strongly influenced by the performance of the frequency domain analysis instrument. To solve this issue, herein we proposed a time domain measurement method and extracted sensing information from the transient response signals of the reader. Its measurement speed was much faster than that using the frequency domain analysis instrument. A distance sensing system was developed to demonstrate the feasibility of the new method. It showed a resolution of less than 300 nm for detections of centimeter range, and the measurement time was as short as 100 μs, which was at least 1000 times faster than that using the traditional method. This technology can be explored as an innovative strategy for LC passive telemetry sensing.

Feature extraction based on dynamic response measurements for structural damage identification: a comparative study

ABSTRACT Sudden unexpected loads, such as wind and earthquakes, can cause significant damage and notable changes in the characteristics of structures. Additionally, unfavorable environmental conditions can lead to erosion and deterioration of materials and structural integrity. Therefore, structural health monitoring (SHM) and damage identification have become critical concerns in civil engineering. Feature extraction from dynamic response measurements is a crucial aspect of structural damage identification. Various methods and techniques have been developed to extract features for identifying damage and monitoring the health of structures. This paper presents a comparative study of three different damage-sensitive features: resonant frequency, discrete wavelet transform component energy (DWTCE), and wavelet packet transform component energy (WPTCE), which are used in damage identification. The displacement and acceleration responses were used in the feature extraction process. This study assesses the sensitivity of each feature to structural damage using a derivative-based method. The results showed that WPTCE extracted from the acceleration response data can effectively capture structural damage due to its high sensitivity.

CNN-based model updating for structures by direct use of dynamic structural response measurements

This study presents a convolutional neural network (CNN)-based model updating method for structures using dynamic structural responses. In the presented method, the dynamic structural response is used directly for model updating, thereby omitting the manual process of extracting modal parameters. In the method, the type of stiffness variable in the target structure for model updating is selected, and a number of candidate models are generated by multiple selected stiffness variables in the structures and possible values of variables. Dynamic structural responses are then extracted by time history structural analysis of the candidate models, and a CNN sets dynamic structural response as input, and information on stiffness variables as output is introduced to learn the relationship between the two data. CNNs trained using dynamic displacement response and dynamic acceleration response are presented as Model_XT and Model_XTa, respectively. The presented models can rapidly estimate stiffness of the target structure by entering dynamic responses into the models. These models are applied to model updating for three example structures, and the stiffness estimation performance for model updating is evaluated. Furthermore, the length of data used for CNN training, the stiffness estimation performance according to the CNN input layer settings, and the stiffness estimation applied with limited data to the updated model are analyzed.

Effect of overheat and direct flow loading on the MEMS bistable flow sensor

We present the findings from an experimental study of a MEMS flow sensor in which an initially curved, double-clamped bistable microbeam is the primary sensing element. Our research explores how the overheat ratio, direct flow loading, and turbulence-induced vibration affect the sequential snap-through (ST) buckling and snap-back (SB) release of an electrostatically actuated beam heated by an electric current. The sensor is fabricated from highly doped single-crystal silicon using a silicon-on-insulator (SOI) wafer. Positioned at the chip’s edge, the microbeam is exposed to airflow, enabling concurrent dynamic response measurements with a laser Doppler vibrometer and a video camera.Our research demonstrates that the overheat ratio can be significantly lower for this sensing principle than conventional thermal sensing elements, pointing to the potential for substantial energy savings. We also emphasize the significant impact of flow angles and vibrations on the critical ST and SB voltages, which are vital for the flow sensor’s output. Additionally, we introduce the first direct experimental observation of the beam profile’s time history during the snap-through/snap-back transition.The potential impact of this research lies in developing more robust MEMS flow sensors with enhanced sensitivity and a better understanding of their response to environmental factors, which could have broader applications in fields such as aerospace, environmental monitoring, and industrial process control.

Vision-Based Monitoring and Frequency analysis for Structural beams

Real-time assessment presents a significant challenge in Structural Health Monitoring (SHM), where the timely detection of structural defects and determination of in-service strength are crucial. In the developing field of SHM, the integration of diverse technologies is becoming increasingly essential, especially given the different challenges brought on by climate change and the complex nature of civil engineering structures. Traditional approaches, such as vibration analysis, acoustic emission, ultrasonic testing, and thermal imaging, despite their widespread use, face challenges in accurately pinpointing structural damage due to their reliance on a single technology. Meanwhile, dynamic response measurements of structures present a promising method for gaining internal insights into structures. Additionally, the advent of Unmanned Aerial Vehicles (UAVs) has enhanced the viability of vision-based techniques, such as digital image correlation (DIC), for surface assessment. This paper investigates damage in lightly reinforced concrete beams using a dual-technique approach that exploits the strengths of the DIC technique and frequency measurement technique, aiming to enhance the detection and analysis of damage in reinforced concrete beams. This integration signifies a step forward in SHM especially if integrated with other advancements leading to a better monitoring of structures.

Development of multi-directional piezoelectric sensor doped with graphene by near-field electrospinning technology for calligraphy writing

A novel multi-directional (MD) circular-shaped fiber piezoelectric sensor was developed to detect calligraphy writing force from various directions quantitatively. The sensor was fabricated using polyvinylidene fluoride (PVDF) piezoelectric fibers doped with Graphene and spun using direct-write near-field electrospinning (NFES) technology. The NFES collector, designed as a rotating disc system, orderly collected the fibers to enhance the piezoelectric effect and dipole moments, forming the circular sensor. Flexible electrical electrodes with multiple signal output circuits were incorporated for calligraphy stroke sensing, bonded with circular PVDF fibers to create the flexible MD sensor. With inner and outer diameters of 20[Formula: see text]mm and 60[Formula: see text]mm, the circular sensor responded to deformable signals induced by calligraphy strokes. Parameters of sensor fabrication were optimized using the uniform design experimental method. Calibration involved tapping tests at 1–10[Formula: see text]Hz to correlate fiber output voltage with the corresponding force. The single sensor reached a maximum voltage output of approximately 908[Formula: see text]mV and detected forces ranging from 0.1 to 50[Formula: see text]N. After validating the MD circular-shaped piezoelectric sensor, a [Formula: see text] sensor array was configured for calligraphy writing force sensing, providing quantitative measurements of dynamic responses during writing, valuable as a data source for apprentice learning.

Dynamic Response Measurement and Finite Element Analysis of Large-Span Pedestrian Corridor

The natural frequency of the long-span steel structure corridor is close to the pedestrian step frequency, which makes it very easy to cause resonance. This paper aims to study crowd-induced vibration control of long-span steel pedestrian corridors with different dynamic characteristics by combining methods of site measurement and numerical simulation. First, based on the steel structure corridor project of a multi-tower structure, the field modal test and the acceleration response under pedestrian load excitation are measured, and the dynamic characteristics and acceleration response under different frequency pedestrian loads are studied. Then, the finite element model of the large-span corridor is established, and the results of the measured and numerical simulation are compared and analyzed. Finally, with the relevant norms, a reasonable evaluation of pedestrian comfort is carried out. The results show that this paper’s measured and finite element results have a certain accuracy. The damping characteristics of humans can absorb the vibration energy of the structure to reduce the vibration acceleration of the structure, and the results are conservative when human action is not considered. After installing the TMD system, the acceleration response of the corridor is significantly reduced, and the vibration reduction effect reaches 54%, which meets the comfort control requirements of the large-span corridor under pedestrian load excitation. The research results and methods in this paper can have particular engineering practical values for carrying out field measurements and comfort control in similar projects and provide a reference for engineering designers.

A Feasibility Study on Extension of Measurement Distance in Vision Sensor Using Super-Resolution for Dynamic Response Measurement.

The current civil infrastructure conditions can be assessed through the measurement of displacement using conventional contact-type sensors. To address the disadvantages of traditional sensors, vision-based sensor measurement systems have been derived in numerous studies and proven as an alternative to traditional sensors. Despite the benefits of the vision sensor, it is well known that the accuracy of the vision-based displacement measurement is largely dependent on the camera extrinsic or intrinsic parameters. In this study, the feasibility study of a deep learning-based single image super-resolution (SISR) technique in a vision-based sensor system is conducted to alleviate the low spatial resolution of image frames at long measurement distance ranges. Additionally, its robustness is evaluated using shaking table tests. As a result, it is confirmed that the SISR can reconstruct definite images of natural targets resulting in an extension of the measurement distance range. Additionally, it is determined that the SISR mitigates displacement measurement error in the vision sensor-based measurement system. Based on this fundamental study of SISR in the feature point-based measurement system, further analysis such as modal analysis, damage detection, and so forth should be continued in order to explore the functionality of SR images by applying low-resolution displacement measurement footage.

Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review

Structural health monitoring (SHM) involves the control and analysis of mechanical systems to monitor the variation of geometric features of engineering structures. Damage processing is one of the issues that can be addressed by using several techniques derived from image processing. There are two types of SHM: contact-based and non-contact methods. Sensors, cameras, and accelerometers are examples of contact-based SHM, whereas photogrammetry, infrared thermography, and laser imaging are non-contact SHM techniques. In this research, our focus centres on image processing algorithms to identify the crack and analyze its properties to detect occurred damages. Based on the literature review, several preprocessing approaches were employed including image enhancement, image filtering to remove the noise and blur, and dynamic response measurement to predict the crack propagation.

Active flutter suppression on a flexible wing via leading-edge blowing and circulation control

Flutter is a classical aeroelastic phenomenon that seriously affects the performance of flexible wings. This study investigates flutter suppression through flow control for a flexible wing. Aerodynamic force, flow field, and dynamic aeroelastic response measurements are conducted to analyze the mechanism of flutter suppression through flow control on a flexible wing modified to include leading-edge blowing (LEB) and circulation control (CC) actuators around the wing tip. Furthermore, the flutter control effects of two control strategies, i.e., steady state control and proportion–integral–differential (PID) control, are compared. The results show that steady LEB and steady CC can effectively reduce the flutter amplitude and increase the critical flutter velocity. When the mass flow coefficient CQ≥1.46×10−3, the Coanda jet has better flutter suppression effect than the leading-edge jet. Moreover, a closed-loop CC control based on the PID algorithm demonstrates that PID control can effectively improve the flutter control efficiency. Compared to steady flow control, PID control increases the critical velocity by more than 52.60% and reduces the air consumption by 90.44%.

Shake Test on Light Combat Helicopter

This paper deals with the Shake test (Ground Vibration Test) performed on a Light Combat Helicopter (LCH) at Rotary Wing Research and Design Centre, Hindustan Aeronautics Limited, Bangalore. Modal characteristics (frequency, mode shape and damping) and dynamic response characteristics of the prototype LCH have been determined experimentally. Tests are conducted with helicopter resting on landing gear and helicopter in free-free condition. Single input excitation and multipoint output data acquisition is used for determination of modal parameters. Multipoint excitation and multipoint output data acquisition is used for the dynamic response measurement to access the effectiveness of the Anti Resonance Isolation System (ARIS) of LCH. The test setup, instrumentation, data acquisition and analysis procedures are enumerated herein. The modal parameters are comparable to the finite element modal results. It is shown experimentally that, ground resonance condition does not occur and this has been proved during the ground run of the helicopter.

Identification of track irregularities with the multi-sensor acceleration measurements of vehicle dynamic responses

Track irregularities induce potential risks to the safety and stability of railway track systems. This paper proposes a novel methodology to identify vertical and lateral track irregularities. The method involves measuring system-based attitude calculation and a model-based unknown input observer estimator, based on the dynamic responses of distributed multi-sensors on the vehicle and bogie. First, a mechanical model of wheel-rail contacts is built with dynamic methods. The model considers the different directions of motion for a railway vehicle and consists of two bogies and four wheelsets. Based on the multi-sensor acceleration measurement, the vertical and lateral acceleration signals of the vehicle and bogies are integrated into the displacement signal. Then a state-space description of the vehicle suspension model is established for inverse dynamical analysis to extract the input signals. A suitable unknown input observer is constructed to estimate the track irregularities by transforming the state space equations of the vehicle into an augmented system that can monitor the track irregularities in-service. This method provides an opportunity to reduce the costs of the monitoring infrastructure and provide quicker and more reliable information about the status of a track.

A novel residual improved deep learning indirect measurement algorithm (RIDLA) for nonlinear dynamic responses

Accurately measuring points-of-concern in nonlinear structures is of great importance for structural optimization analysis, vibration reduction and isolation design, and structural health assessment. For complicated structures with strong nonlinear characteristics, it can be challenging to directly measure the strain, displacement, and other responses at points-of-concern, so sometimes measurements can only be indirectly deduced using data from measurable points. To address the disadvantages of traditional dynamic response analysis and prediction methods, such as their time-consumption and low accuracy, this study made full use of the ability of deep learning to capture nonlinear features. A novel residual improved deep learning indirection measurement algorithm (RIDLA) for predicting dynamic responses of nonlinear structures is proposed. The long short-term memory (LSTM) neural network with time-series feature prediction feedback was selected as the basic algorithm, and a multi-level residual improving deep learning model was constructed using interactive iterations between measurable point responses and calculated residuals, which effectively acquired the dynamic responses of the point-of-concern with improved accuracy. The performance of the RIDLA method in nonlinear response regression was evaluated using a simulation analysis of a six-degree-of-freedom mass-damping-spring nonlinear system and by using random vibration test data of a satellite structure. The results showed that the proposed RIDLA method can achieve high measurement accuracy and is suitable for indirect measurements of dynamic responses in complex nonlinear systems.

Numerical Simulation and Experimental Measurements of Dynamic Responses of Asphalt Pavement in Dry and Saturated Conditions under Full-Scale Accelerated Loading

Asphalt pavement presents diverse dynamic responses to vehicle loading in dry and saturated conditions, which can be systematically explored by numerical simulation. Building a numerical model based on the actual conditions of asphalt pavement is necessary, and relevant field tests should be subsequently conducted to monitor dynamic responses to calibrate and validate the numerical model. On the basis of strictly controlling the paths of vehicle wheels during field tests, this study numerically analyzed the dynamic responses of asphalt pavement in dry and saturated conditions under full-scale accelerated loading. The trends of the modeling results were consistent with those of field measurements. The increase in vehicle load significantly increased the magnitudes of stress, strain, and pore water pressure, while vehicle speed showed an obvious impact on pore water pressure. The dynamic responses decreased with pavement depths. Water made the dynamic responses more complex, and pore water pressure significantly decreased with depth within the upper layer of saturated asphalt pavement. Transverse distributions of indicators presented obvious compressive states in the regions in direct contact with vehicle wheels, while tensile states were found in the range of the middle vehicle axle. The numerical results provided a basis for field measurements in future studies, especially for the exploration of factors of temperature and layer depth.

Vision-Based Structural Modal Identification Using Hybrid Motion Magnification

As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.

Vision-Based Dynamic Response Measurements of Structures by Using Smartphone Videos and Alternative Techniques

This study aimed to measure dynamic responses of structural systems using smartphone videos and vision-based sensing processes. Two algorithms, based on template matching and feature extraction, are employed for this purpose. They were verified on the shake table experiments with single and multi-degree freedom steel specimens where their videos were captured by an ordinary smartphone during excitation. Dynamic responses such as strains at discrete sections as well as displacements, velocities, and accelerations at the floor levels of the specimens were obtained by following the signs of physical or virtual markers during the video recording. Through comprehensive experiments and one available full-scale multi-story shake table experiment in the literature, the developed vision-based algorithms were validated to be used with smartphone videos. It was also shown that regardless of the quality of the video record, substantial characteristics of a specimen or a structure could be determined reasonably by smartphone videos since the absolute mean relative differences varied between 10% to 20%.

Dynamic response and emergency measures under failure conditions of sCO2 Brayton cycle

AbstractThe sCO2 Brayton cycle has gained interest because of its flexibility and ability to provide higher thermomechanical conversion efficiency. Studies on failure conditions and corresponding emergency measures are of great significance to ensure the safety of the system, while there are limited studies related to the sCO2 Brayton cycle because the test circuit has not been applied in practice at present. In this study, we have developed a dynamic model of the CO2 Brayton cycle, and carefully validated its components against experimental data. Preliminary safety assessment and dynamic response under loss of cooling water (LOCW), loss of heat source (LOHS), and pipeline leakage have been analyzed. Emergency strategies are proposed to maintain the safe operation under failure conditions. The primary objective of this study is to reveal the dynamic performance and emergency measures under failure conditions of the sCO2 Brayton cycle. The results demonstrate that the system net power decreases at rates close to 10%/s and less than 0.1%/s under LOCW and LOHS, respectively. The system responds more dramatically under LOCW compared with LOHS. The system needs emergency measures under LOCW, while it has sufficient time to realize a normal slow shutdown under LOHS. The leakage rate is at least five times higher in the high‐pressure pipeline compared with the low‐pressure pipeline for the same area of the leakage hole. Under pipeline leakage, the heat source should be cut‐off slowly, and the rotation speeds of the main compressor and recompressor should be reduced simultaneously to protect the system.