Search Results (168)

Forced periodic operation (FPO) has emerged as a promising process intensification strategy for catalytic reactors. In this study, the nonlinear frequency response (NFR) methodology was applied to investigate square-wave FPO of an isothermal CSTR for methanol synthesis. The analysis focused on periodic modulation of the inlet CO and flow rate, considering both single-input and simultaneous-input forcing. The reactor response was evaluated using higher-order frequency response functions (FRFs) to quantify the non-periodic component responsible for time-averaged process improvement. The results showed that individual modulation of either inlet CO or flow rate does not provide significant improvement in reactor performance and may even reduce methanol productivity. In contrast, simultaneous modulation generates a strong positive nonlinear interaction that substantially improves reactor performance. Under optimal forcing conditions, methanol productivity increased from 336.9 mmol min⁻¹${\mathrm{k}\mathrm{g}}_{\mathrm{c}\mathrm{a}\mathrm{t}}^{-1}$ at steady-state to 553.6 mmol min⁻¹${\mathrm{k}\mathrm{g}}_{\mathrm{c}\mathrm{a}\mathrm{t}}^{-1}$, corresponding to a 64.3% improvement. Compared with previously reported cosine forcing, square-wave modulation nearly doubled the attainable productivity improvement while also improving hydrogen utilisation efficiency. The results indicate that square-wave FPO represents an effective intensification strategy for methanol synthesis in a laboratory-scale isothermal CSTR and confirm the capability of the NFR methodology for the a priori evaluation and optimisation of periodically operated catalytic reactor systems. Full article

Colorectal cancer remains a major cause of cancer-related mortality, and missed adenomas during colonoscopy are still a persistent clinical concern. Video-based computer-aided diagnosis can support polyp recognition, but real colonoscopy clips often include frame-specific degradation from motion blur, specular reflection, sensor noise, and compression. Standard temporal models usually aggregate sampled frames with similar confidence, so a few corrupted frames can distort the clip representation. This paper presents QBi-SSM, a reliability-guided bidirectional state space framework for clip-level polyp classification. QBi-SSM pairs a Frame Reliability Filter (FRF), which estimates frame reliability from sharpness, motion, and entropy cues and suppresses unreliable features, with a Bidirectional Context State Space Module (BC-SSM), which aggregates temporal evidence in both directions. The goal is to keep lightweight temporal modeling stable when frame quality varies within an endoscopic video. Here, “clean-set” denotes evaluation on the unmodified test clips before any synthetic degradation is applied. Experiments on LDPolypVideo and HyperKvasir show competitive clean-set performance rather than large clean-set gains, and both controlled synthetic degradation tests and an analysis of naturally degraded subsets show that the robustness advantage is more pronounced when frame quality is uneven. Ablation studies analyze the effects of reliability filtering, feature alignment, bidirectional scanning, and the loss formulation. Full article

This paper presents a practical and comprehensive guide for commissioning the controllers of bearingless machines (BMs). While significant research has been conducted on the commissioning of electric machines (EMs) and magnetic bearings (MBs), relatively little attention has been given to bringing up BMs. Motivated by this gap, the paper presents step-by-step methods for commissioning BM control systems. Two major contributions are outlined in the paper. First, static characterization and sensor calibration techniques, which are necessary for levitating the machine, are introduced. Second, a system identification (System ID) procedure for each plant is presented. Frequency response functions (FRFs) are used to identify the control plant transfer function parameters and evaluate the resulting feedback control performance (command tracking and dynamic stiffness). This paper emphasizes practical testing sequences that are immediately applicable to machines. By following the procedures provided in this paper, engineers can efficiently commission BMs by first ensuring precise sensor calibration and accurate models for control tuning. Full article

The resilience pads of sleeper floating tracks (STEDEF) are key components that absorb shock loads and vibrations induced by train traffic. Currently, under the Korean domestic guidelines for track facility performance evaluation, one sample per 500 m is collected from the field, and the static spring stiffness is assessed through laboratory testing. However, this approach requires nighttime track possession, incurs significant manpower and cost, and provides limited reliability because the condition of an entire section is inferred from a small number of samples. Therefore, this study proposes an impact-hammer–FRF-based method for evaluating the spring stiffness of resilience pads without pad extraction. Field impact-hammer tests were conducted to identify the dominant first-mode natural frequency of the track-support system. Configuration-specific finite element models were then used to derive frequency–stiffness relationships for the investigated STEDEF configurations. The novelty of the proposed method lies in converting the local first-mode frequency measured in situ into a static-equivalent stiffness index that can be directly compared with the maintenance reference value used in the Korean inspection framework. The finite element model reproduced the mean natural frequencies of the reference configurations with differences of 0.08–3.61%. Based on the configuration-specific relationships, estimation equations were developed to support in situ screening of resilience-pad stiffness at the measured locations. Full article

Ground vibration testing (GVT) plays a key role in the validation of numerical models and the assessment of aeroelastic stability in lightweight aircraft structures. This study presents an experimental and numerical investigation of a full-scale composite flying wing unmanned aerial vehicle (UAV) intended for vertical take-off and landing operations. Due to its low structural mass and highly integrated configuration, the aircraft exhibits increased sensitivity to modeling assumptions, boundary conditions, and measurement uncertainties. A finite element model was developed in Ansys, incorporating detailed laminate definitions and the internal sandwich structure. Experimental modal testing was performed under free-free boundary conditions using an electrodynamic shaker and a distributed measurement consisting of 94 response locations. Frequency Response Functions (FRFs), coherence analysis, and the Complex Mode Indication Function (CMIF) were employed to identify the dominant structural modes. Particular attention was given to the bending and torsional modes that govern aeroelastic behavior. Comparison of experimental and numerical results showed good agreement in mode shapes, while discrepancies in natural frequencies ranged from 10.4% to 20.1%. The results demonstrate that the model adequately captures the dynamic behavior of the aircraft and provides a reliable basis for future aeroelastic and flutter analyses of lightweight composite flying wing. Full article

To address the complex dynamic behavior of four-axis precision motion platforms under high-speed and high-acceleration conditions, as well as the difficulty of traditional modeling methods in balancing accuracy and efficiency, this paper proposes a data/model-driven dynamic modeling and analysis method that integrates generalized receptance coupling substructure analysis (GRCSA) with artificial intelligence (AI) algorithms. Based on the GRCSA theory, the initial analytical framework of the dynamic model of the precision motion platform is established, and the frequency response functions (FRFs) of the substructure and interface are preliminarily obtained. On this basis, the nonlinear prediction model of the dynamic parameters of the interface driving direction is established by using the AI algorithm, enabling fast and accurate prediction of the dynamic characteristics of the interface under different servo control parameters in the guide rail driving direction. Finally, based on the data/model-driven dynamic modeling and analysis method, the interface control parameters are optimized. The interface and substructure parameters are modified to reduce the prediction error of the FRFs from 3.50% to 2.47%. This method can achieve the prediction error of the dynamic characteristics of the interface under different control parameters of about 2.5%. Full article

The application of robotic milling manipulators demonstrates a promising method for the efficient manufacturing of large-scale structures. However, the cutting accuracy and efficiency of milling robots are predominantly subjected to their low stiffness, which may easily cause chatter during machining. Accurate prediction of chatter stability for robots is of practical importance and is challenging. This paper develops a dynamic model of flexible link elements by considering link flexibility and joint torsional deformation and then constructs a multi-link flexible coupled dynamic model using the receptance coupling substructure analysis (RCSA) method. Subsequently, the equivalent dynamic parameters are identified via the particle swarm optimization (PSO) algorithm. On this basis, the end-effector frequency response functions (FRFs) of the robot under different poses are predicted, and the stability lobe diagram (SLD) for milling is generated based on chatter theory. Finally, the predicted FRFs and stability regions are validated through modal tests and milling experiments. Experimental results demonstrate that the proposed model can predict the end-effector dynamic characteristics and chatter occurrence conditions under different poses, confirming its effectiveness in the analysis of milling chatter stability. Quantitative validation yields a maximum error of 3% for predicted first-order modal frequencies and relative modal amplitude errors below 10%, with experimentally confirmed critical depths of cut of 0.1–0.2 mm at 3000 rev/min and 0.5–0.6 mm at 5000 rev/min. Full article

This study aims to develop an efficient and accurate method for identifying joint parameters in assembled structures. A novel neural network-based joint identification framework is proposed. Frequency response function (FRF) datasets are generated by combining finite element simulation with frequency-domain substructure synthesis. The Uniform Manifold Approximation and Projection (UMAP) algorithm is employed for nonlinear dimensionality reduction in FRF sequences, preserving critical characteristics. A multilayer perceptron (MLP) network is then trained to regress joint parameters from the reduced-dimension FRF data. The necessity of the nonlinear dimensionality reduction within this joint identification framework is verified through comparison with the linear dimensionality reduction technique of principal component analysis (PCA). This methodology is implemented and validated using a tool–collet–holder system. Comparative studies with the global optimization method reveal that the proposed approach maintains superior identification accuracy while achieving significant improvements in computational efficiency across varying preload conditions. Furthermore, the identified joint parameters exhibit strong predictive capability when tested under tool/holder component changes, preload variations, and when coupled with a spindle, proving robustness under complex operational scenarios. This study provides a new technical pathway for the joint identification of assembly structure. Full article

The FAR-RED IMPAIRED RESPONSE 1 (FAR1) and FAR-RED ELONGATED HYPOCOTYL 3 (FHY3) transcription factors, together with other members of the FAR1-RELATED SEQUENCE (FRS) and FRS-RELATED FACTOR (FRF) families, represent a striking example of transposable element domestication in plants. Derived from ancient Mutator-like element (MULE) transposases, these proteins have been repurposed as transcriptional regulators throughout the plant kingdom. FHY3 and FAR1 were first identified in Arabidopsis thaliana as positive regulators of phytochrome A (phyA) signaling. They participate in the coordination of light signaling with the circadian clock, chlorophyll biosynthesis, hormone pathways, stress responses, flowering time, shoot branching, leaf senescence, seed dormancy, and phosphate homeostasis. At the molecular level, FHY3 and FAR1 regulate gene expression mainly by binding to the conserved FHY3/FAR1-binding site, FBS, with the sequence CACGCGC, in the promoters of target genes. They also act through protein interactions with key signaling regulators, including HY5, PIFs, EIN3, TOC1, and SPL transcription factors. In this review, we summarize the molecular basis of FHY3/FAR1 gene family function, discuss the roles and mutant phenotypes of characterized family members, and highlight recent advances from other plant species beyond Arabidopsis. Collectively, this gene family illustrates how domesticated transposase-derived proteins have evolved into key regulators of plant development and environmental adaptation. Full article

This study utilized three controlled Sitika spruce beam specimens and established a parameterized transfer-function model based on force–acceleration frequency response functions (FRFs) to characterize and reconstruct the frequency-domain modal response of beam specimens. The specimens were tested using non-contact magnetic swept-sine excitation, laser Doppler vibration measurement, and synchronous FFT analysis methods under free–free boundary conditions. In the experiment, one specimen was used for modeling and the other two specimens were used for consistency verification. Based on the measured complex FRF, a 1st–5th order modal transfer-function model was established in the frequency range of 0–1000 Hz. The experiment identified five resonance frequencies of the specimen, which were 65.0, 198.5, 370.5, 620.0, and 930.0 Hz, respectively. The model can reconstruct the measured magnitude and phase responses, with magnitude residuals within ±5 dB, resonance-peak magnitude errors of 0.03–0.73 dB, and wrapped-phase deviation around the poles of 0.20–5.08°. The Nyquist trajectory was continuous and smooth, with all poles located in the left half-plane, indicating that the model has stable pole behavior. The research results support the specimen vibration response as an approximate linear time-invariant system under small-magnitude and controlled testing conditions. The model can provide a physically interpretable and reconstructable modal-parameter expression for evaluating frequency-domain vibration responses of controlled wooden beam specimens. Full article

This study experimentally investigates the evolution of natural frequencies of premium threaded connections under varying interface contact stiffness, aiming to establish a non-destructive vibration-based method for evaluating sealing contact conditions. The sealing interface features a sphere-on-cone configuration, and Hertzian contact theory is used to derive the contact pressure distribution, which shows a nonlinear increase in peak pressure with increasing normal load. Modal experiments were conducted under free–free boundary conditions using an impact hammer on a Φ88.9 mm × 6.45 mm P110 premium threaded connection. Three make-up torque levels (4081 N·m, 4393 N·m and 4691 N·m) were applied to create distinct contact states, and the first five orders of natural frequencies were extracted from the measured acceleration responses, using frequency response function (FRF) analysis with peak-picking identification. The results demonstrate that natural frequencies increase significantly with make-up torque, following a power-law relationship f = αT^β with R² > 0.97 for the first three modes. A critical torque range of 4200–4400 N·m is identified, below which frequencies rise sharply and above which the increase slows due to contact stiffness saturation. Lower-order modes are more sensitive to contact stiffness variations than higher-order modes. The findings confirm that natural frequency can serve as an effective non-destructive indicator for assessing tightening quality and detecting loosening in premium threaded connections, offering practical guidance for torque optimisation and structural health monitoring in oilfield operations. Although only three torque levels are used, the observed trend is physically consistent with contact mechanics theory and widely reported joint stiffening behavior. Therefore, the fitted relationship should be interpreted as a physically guided empirical model rather than a purely statistical fit. Full article

The impact hammer, equipped with a force transducer, is a portable and practical tool for inducing measurable excitations in structural health monitoring (SHM). However, its reliability is often limited by uncontrolled factors such as swing power, angle, impact location, and operator consistency, particularly in nonlinear structures operating at low frequencies. While many researchers have avoided hammer testing by instead using better controlled drop mass systems or operational modal analysis (OMA) techniques, this study presents a new experimental modal analysis (EMA) approach that improves the accuracy of impact hammer testing: variable impulse hammer impact testing (VIHIT) using a single-input single-output (SISO) roving hammer and single fixed accelerometer. For a mode of interest, the imaginary component of the frequency response function (FRF) is evaluated at each test location using multiple impulses of varying magnitude. This output quantity exhibits an inverse power relationship with the input autopower spectral density (APSD) at the modal frequency. Evaluating the trend at a reference input APSD from sufficiently excited tests produces a very accurate mode shape for that input. For a given structure, nonlinear damping ratios vary with excitation and can be extracted using inverse FRF analysis. This method addresses variability in impact hammer testing by establishing reproducible trends for different impulse levels and test locations. Application to degraded timber beams demonstrated reductions in mode shape variability relative to conventional averaging and revealed impulse-dependent damping ratios ranging from approximately 0.02 to 0.04, highlighting the method’s ability to characterize nonlinear dynamic behavior. The result is a more accurate approach for extracting modal properties and mode shapes and characterizing nonlinear dynamic behavior using a SISO roving impact hammer system. Full article

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

Various types of turbine engines have been chosen as the primary power source of the long-endurance unmanned aerial vehicles (UAVs) because of their high propulsive efficiency and low specific fuel consumption. To ensure the healthy operation of UAV turbine engines, rotor unbalance should be monitored and constrained to a preset limit. This paper proposes an efficient and physically interpretable method to achieve rotor unbalance monitoring. This method enables the frequency response function (FRF) to inform the neural network design, bringing the physics-informed convolutional neural network (PICNN). Firstly, the FRF gives a qualitative judgment of the axial positions of dominant faulty parts. Then, the following subnet proceeds to achieve quantitative identification. This method is demonstrated on a series of numerical cases and on a twin-disk rotor-bearing-casing experimental setup with anisotropic supporting stiffness. This setup is representative of engine installation status on the UAV platform. The results show that the PICNN can achieve higher precision compared to pure data-driven or model-based benchmarks. The PI layer does not require a high-fidelity model that generates responses identical to the actual ones. The robustness against modeling errors in stiffness and damping ratios is demonstrated. The achieved relative errors are less than 1.5% under various experimental datasets. Full article

The integration of mechanics-based analysis and materials design procedures has become central to the development of multi-material structures with tailored mechanical and dynamic performance. In this study, the dynamic and flexural behaviour of multi-material FDM sandwich beams composed of PETG face sheets and an ABS core is experimentally investigated. Seven different infill patterns Grid, Line, Wavy, Honeycomb, Gyroid, Cubic, and Triangle were implemented in the core layer to assess their influence on damping and natural frequency behaviour. Experimental modal analysis was performed using impact testing to identify the first three vibration modes. Natural frequencies were extracted from Frequency Response Functions (FRFs), and modal damping ratios were determined using the half-power bandwidth method. The reliability of the damping results was evaluated through statistical analysis. Additionally, quasi-static three-point bending tests were conducted to assess flexural strength and load-carrying capacity. The results demonstrate that infill topology has a significant impact on both dynamic and mechanical responses. In particular, geometrically complex infill patterns exhibit enhanced stiffness, higher natural frequencies, and improved damping performance. Among the investigated designs, the Triangle infill exhibited the highest natural frequency values across the first three vibration modes (f₁ ≈ 24.910 Hz, f₂ ≈ 162.609 Hz, f ≈ 466.595 Hz), indicating its superior stiffness characteristics. In terms of damping behaviour, the Cubic infill showed the highest loss factor in the first vibration mode (0.0426), while the Line and Gyroid patterns exhibited the highest damping in the second (0.0439) and third modes (0.0354), respectively. Moreover, the force–displacement results revealed that the Triangle infill exhibited the highest load-bearing capacity, further confirming its superior structural stiffness among the investigated designs (SEA = 110.83 J/kg). These findings highlight the potential of multi-material FDM for designing polymer-based sandwich structures with tailored vibration and energy dissipation characteristics. Full article