Search Results (5,081)

Mode veering is a modal-interaction phenomenon found in vibration systems. For inertially coupled structures, the combined influence of coupling delay, nonlinear restoring force, and stochastic coupling perturbation remain insufficiently understood. This work analyzes an inertially coupled two-coordinate prototype in which a discrete delay, a delayed cubic stiffness, and positive multiplicative stochastic modulation all enter through the same relative-coordinate coupling channel. We formulate the delayed linear spectrum through a quasi-polynomial characteristic equation. We also characterize the veering by the two positive-frequency characteristic-root branches descending from the mechanical modes. Coupling delay shifts the veering center, alters the minimum frequency gap, and moves the tracked rightmost roots toward the stability boundary. An analytical imaginary-axis-crossing criterion is derived to determine the delay-induced stability boundary of the deterministic linearized system, and the resulting boundary is independently validated by direct multi-start characteristic-root searches and Chebyshev-collocation approximation of the DDE generator. A fixed-reference modal-coordinate representation identifies the off-diagonal modal terms associated with branch exchange while retaining the full delayed characteristic equation. A first-harmonic treatment of the delayed cubic term can yield an amplitude-dependent nonlinear veering backbone. For the stochastic problem, frozen lognormal coupling samples and a time-dependent Ornstein–Uhlenbeck-driven multiplier are constructed from the same unit-mean positive lognormal marginal law. The former is used to quantify realization-wise spectral broadening, whereas the latter retains temporal correlation and is used to evaluate finite-time branch residence and pathwise delayed-work statistics. The pathwise energy balance reveals that the delayed relative-coordinate work rate is sign-indefinite. This provides a common energy-transfer mechanism through which delay, nonlinearity, and stochastic modulation reshape mode veering in the inertially coupled system. Full article

Cross-domain distribution shifts severely degrade the diagnostic performance of rolling bearing models under unseen variable operating scenarios. Single-source domain generalization (SDG) builds fault diagnosis models using only single-source vibration data, which fits the practical limitations of industrial data collection. Existing contrastive learning methods adopt uniform spectral perturbations for data augmentation, which easily corrupt fault harmonic characteristics and require massive, labeled training samples. To tackle these drawbacks, this paper proposes an unsupervised contrastive learning framework named FDACL. An adaptive frequency-domain augmentation (AFA) module equipped with learnable weights is designed to separate fault-critical frequency bands from noise components. Differentiated amplitude perturbations are applied to two categories of spectral signals to generate diverse pseudo-samples while retaining intrinsic fault information. A shared encoder is trained with combined InfoNCE contrast loss and classification loss to learn domain-invariant fault representations. Validations are carried out on three datasets, namely Case Western Reserve University (CWRU), Paderborn University (PU), and the industrial CRRC Qingdao Sifang railway wheelset bearing dataset acquired from physical test benches. FDACL achieves average cross-speed diagnostic accuracies of 92.68% and 77.85% on CWRU and PU, respectively, and maintains competitive performance on the Qingdao Sifang industrial dataset. It outperforms state-of-the-art baselines by 4.23–8.71% across all SDG transfer tasks. Ablation experiments and hyperparameter analysis verify the efficacy of the AFA module and contrastive learning scheme, providing an unsupervised diagnostic approach for railway bearings under unknown working conditions. Full article

DC bias will cause the magnetization working point of the transformer core to shift and cause local saturation, and generate abnormal vibration through the magnetostrictive effect, which threatens the safe operation of the transformer. Aiming at the problem that the time–frequency characteristics of transformer vibration signals under DC bias are complex and the adjacent bias levels are difficult to distinguish, this paper proposes a 2DWT-CNN-Transformer diagnostic method that combines two-dimensional discrete wavelet transform, a convolutional neural network, and Transformer Encoder. Firstly, the multi-physical-field finite element model of three-phase three-column transformer is established, and the L0–L5 six-class DC bias dataset is constructed. Secondly, the one-dimensional vibration signal is reconstructed into a two-dimensional matrix, and the multi-subband time–frequency features of LL, LH, HL, and HH are extracted by two-dimensional discrete wavelet transform. The local texture features are extracted by the CNN, and the multi-head self-attention mechanism of Transformer Encoder is introduced to establish the global dependence and enhance the discrimination ability of adjacent bias levels. Compared with the traditional time–frequency-feature deep learning model, the proposed method achieves higher accuracy, especially in the high-noise environment of 15 dB, where it can still maintain accuracy of 96.23%. The visualization results further show that the model can form a more compact intra-class aggregation and a clearer inter-class boundary. This also provides an effective solution for the identification and evaluation of transformer DC bias states based on vibration signals in complex environments in the future. Full article

Organic–inorganic hybrid crystalline materials formed in multicomponent aqueous systems are of interest because their phase behavior and physicochemical properties can be controlled by composition and crystallization conditions. In this study, the phase equilibria and crystallization behavior of the ternary aqueous Ca(ClO₃)₂·2CO(NH₂)₂–CH₂ClCOOH·(C₂H₄OH)₃N–H₂O system were investigated over the temperature range of −24 to 60 °C using the visual-polythermal method. Experimental data obtained for the two boundary binary subsystems and eight internal sections were used to construct the polythermal phase diagram. The diagram revealed distinct crystallization fields corresponding to ice, Ca(ClO₃)₂·2CO(NH₂)₂·2H₂O, CH₂ClCOOH·(C₂H₄OH)₃N, and a separate crystallization region associated with a previously unreported crystalline phase with the proposed composition ClCH₂COOH·Ca(ClO₃)₂·(C₂H₄OH)₃N. The solid phase was isolated from its crystallization region, washed with cold distilled water, dried to constant mass, and characterized by complementary Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS), thermogravimetric analysis, derivative thermogravimetry, and differential scanning calorimetry (TG–DTG–DSC), and powder X-ray diffraction (PXRD). The experimentally determined Ca²⁺ and ClO₃⁻ contents were reasonably consistent with the proposed composition, while FT-IR spectroscopy revealed characteristic chlorate vibrations and changes in the vibrational environment of the organic component. SEM showed predominantly prismatic and plate-like crystalline morphologies, and EDS confirmed the presence of Ca, Cl, O, C, and N. Thermal analysis demonstrated multistage decomposition, with comparatively good thermal stability below approximately 150 °C. PXRD revealed a diffraction fingerprint distinct from those of the starting components and the corresponding physical mixture. Preliminary indexing of 19 principal reflections was consistent with a tetragonal candidate lattice with a = b = 7.7411(5) Å, c = 24.7182(10) Å, V = 1481.2(5) ų, and M₂₀ ≈ 23.0. The crystallographic analysis is considered preliminary because the diffraction profile was reconstructed from the available pattern and was not subjected to complete structure refinement. Overall, the combined phase-equilibrium, compositional, spectroscopic, morphological, thermal, and diffraction data support the isolation of a distinct organic–inorganic crystalline phase with the proposed composition. Full article

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions. Full article

This study investigates the hybrid squeeze–flow damping characteristics of a previously developed magnetorheological (MR) mount, which integrates two vertically symmetric squeeze gaps and four flow channels. Based on the magnetic-circuit configuration, a damping-force prediction model was established specifically for the proposed hybrid structure. Magnetostatic finite element analysis (FEA) was conducted to compare the magnetic field characteristics under co-directional and opposite-direction coil excitation, and the influence of magnetic isolation components on the magnetic field distribution was additionally investigated. The results indicate that co-directional current excitation generates higher magnetic flux density in both the squeeze gaps and flow channels, enabling the magnetorheological fluid (MRF) to approach magnetic saturation at an excitation current of 2 A. The magnetic isolation components further improve the magnetic flux distribution and enhance the magnetic flux density in the squeeze gaps and flow channels. A one-way coupled numerical method combining magnetostatic FEA and computational fluid dynamics (CFD) was employed. The rheological properties of the MRF were derived from the magnetic flux density and incorporated into the CFD model via a user-defined function (UDF) to calculate the pressure losses and predict the damping force of the MR mount. The proposed model was experimentally validated over an excitation frequency range of 5–30 Hz at an amplitude of 0.15 mm, showing good agreement with the experimental results under most operating conditions. Beyond the experimentally validated range, the model was further employed to investigate the predicted damping characteristics under extended excitation conditions. The extrapolated numerical results indicate that the total damping force can reach 958.2512 N at an excitation amplitude of 0.3 mm and a frequency of 200 Hz. This result should be regarded as a model-based prediction rather than experimentally validated high-frequency performance. The squeeze mode provides the dominant damping contribution, while the contribution of the flow mode becomes increasingly significant with increasing excitation frequency. The results provide a basis for evaluating the potential of the hybrid squeeze–flow MR mount for vehicle engine vibration isolation. Full article

Functionally graded (FG) beams have been used more and more in highly designed structures under dynamic loading due to their graded mechanical properties and excellent performance. Their transient response on complex elastic foundations is, however, not easily predicted due to the material heterogeneity, shear deformation, rotary inertia, and coupled effect of the foundation parameters. The purpose of this study is thus to propose an accurate and efficient computational model for the dynamic analysis of FG Timoshenko beams supported by biparametric Vlasov foundations under harmonic excitation. The formulation takes into account the space-varying material properties, Timoshenko shear deformation, rotary inertia, and coupled Winkler–shear interaction of the Vlasov foundation. The governing equations are numerically solved in space with the high-order spectral element method (SEM) and in time with the Hilber–Hughes–Taylor (HHT) scheme. The resulting framework is used to study the transient displacement and vibration response with respect to the excitation frequency, material gradation index, and stiffness and damping properties of the foundation. The numerical results prove that the results converge quickly in space and time and also indicate that the dynamic response is significantly affected by the interaction between the gradation of material and the parameters of the foundation. The displacement amplitude, resonance behavior, and vibration characteristics are significantly altered by any variations in the gradation index and foundation characteristics. The results obtained with the proposed formulation are in good agreement with those available from the benchmark solutions, thus validating the correctness and reliability of the formulation. The SEM–HHT methodology offers a reliable, precise, and low-computational-cost solution for transient analysis of FG Timoshenko beams on biparametric Vlasov foundations under harmonic excitation. The proposed framework offers a powerful predictive tool for vibration analysis, response control, and design of advanced FG beam systems that can be applied in aerospace, marine, smart infrastructure, and other high-performance engineering structures. Full article

Fine-grained bearing condition diagnosis is challenging because different bearing states within the same fault location often exhibit similar fault-characteristic-frequency responses, making them difficult to distinguish using envelope-spectrum information alone. To address this problem, a physics-guided raw-dominant gated fusion network (PG-RDGFN) is proposed for fine-grained bearing fault diagnosis. In the proposed framework, the raw vibration signal is retained as the dominant information source, while the envelope spectrum provides complementary fault-modulation evidence. A physics-aware descriptor derived from bearing characteristic-frequency responses is incorporated into a sample-wise gating mechanism to adaptively regulate the contribution of the envelope-spectrum features. Distinct from conventional direct multi-branch fusion or physics-informed schemes that mainly use physical knowledge as an auxiliary input or regularization constraint, PG-RDGFN uses mechanism-derived physical confidence to regulate how much auxiliary envelope-spectrum evidence participates in the fusion, rather than directly using the physical prior as a fine-grained classification cue. Meanwhile, a physical-consistency loss constrains the learned gate using a scalar physical-confidence target, and a raw-branch auxiliary loss preserves the discriminative capability of the dominant raw representation. Experiments are conducted on an eight-class diagnosis task constructed from the Paderborn University bearing dataset. Compared with SVM, MLP, 1D-CNN, CNN-LSTM, and TCN, the PG-RDGFN achieves the highest accuracy of 98.87%. Ablation and gate-consistency analyses further verify the effectiveness of the envelope branch, gated fusion, physics guidance, and auxiliary supervision. These results demonstrate that PG-RDGFN provides an accurate and physically interpretable solution for fine-grained bearing condition diagnosis. Full article

This paper presents a systematic experimental and computational investigation of the free-vibration characteristics of a Laser Powder Bed Fusion (LPBF) AlSi10Mg thick-walled cylinder, a geometry relevant to electric-machine housings, hydraulic sleeves, and pressure-carrying components exposed to resonance-critical service loads. The specimen has an outer diameter of 94 mm, an inner diameter of 64 mm, a wall thickness of 15 mm, and a height of 90 mm, placing it firmly in the thick-walled regime ($d/D=0.68$). A three-dimensional finite element model comprising 23,864 total elements (23,236 SOLID186 solid elements and 628 surface/contact elements) and 106,015 nodes was constructed in Ansys Mechanical using the AlSi10Mg material database entry (E = 75 GPa, $\rho$ = 2670 kg/m³, $\nu$ = 0.33) and solved with the Block Lanczos eigensolver under free–free boundary conditions. Experimental modal analysis (EMA) was conducted using Brüel & Kjær software with an impact hammer with a 260-node measurement grid covering the outer surface and both end rings; frequency response functions were acquired over 0–22,500 Hz. Fourteen flexible modes were identified in simulation; nine corresponding experimental modes were resolved with frequency deviations ranging from 0.13% to 1.10%. In addition to frequency correlation, this paper introduces a non-conservative damping characterization framework comprising: (i) Rayleigh (proportional) damping coefficient extraction from EMA data and assessment of its frequency-domain validity; (ii) a viscoelastic complex-modulus model relating the real storage modulus $E^{\prime }$ and imaginary loss modulus $E^{″}$ to the modal loss factor $\eta$ and damping ratio $\zeta$; and (iii) a practical design workflow for resonance mitigation of future AM structures including electric machine frames. Experimental damping ratios ($\zeta =0.013$–$0.311\%$) are converted to per-mode $E^{″}$ values and loss factors, revealing that energy dissipation in LPBF AlSi10Mg is strongly mode-shape-dependent and cannot be accurately represented by a single Rayleigh model. Full article

Accurate prediction of the state trend of pumped storage units (PSUs) is essential for timely anomaly detection and preventive maintenance to improve the overall economic performance of power plants. Nevertheless, the complex and time-varying characteristics of PSU vibration data increase the difficulty of accurately modeling their dynamic evolution. In response to this problem, an integrated vibration trend prediction (VTP) method for PSUs is developed by combining a temporal-enhanced generative adversarial network (TEGAN) with an improved bidirectional long short-term memory network (IBiLSTM). Firstly, TEGAN expands the original dataset by synthesizing artificial samples, thereby improving the structural diversity and representativeness of vibration data. Within TEGAN, a temporal characterization (TC) module is designed to collaboratively guide the generator and the discriminator, while a data processing module is adopted to incorporate structural priors into the learning process. Secondly, variational mode decomposition (VMD) is applied to decompose the original vibration data into intrinsic modes, followed by PSR to reconstruct the components of each modality into a higher-dimensional state space representation. Subsequently, by incorporating the proposed multi-order Kolmogorov–Arnold network (M-KAN) for high-order nonlinear fitting, IBiLSTM is employed to model each reconstructed sub-sequence. Finally, the outputs of all sub-sequences are aggregated to produce the final VTP results. The comparative evaluation verifies the advantages of the developed method in terms of prediction accuracy and robustness for PSU vibration trend forecasting. Full article

To elucidate the coupling characteristics between unsteady flow and hydrodynamic loads in the guide vane region of a pump–turbine under runaway conditions, a model pump–turbine of a high-head pumped storage power station was selected as the research object. A combined approach of model experiments and three-dimensional unsteady numerical simulations was employed to investigate the guide vane hydraulic torque, flow field structures, pressure distribution, and pressure fluctuation characteristics under different pre-opening guide vane conditions. In the experiments, the hydraulic torque of guide vanes was measured using a guide vane shaft strain testing method at five guide vane openings of 19 mm, 25 mm, 33 mm, 41 mm, and 45 mm. In the numerical simulations, a full-passage unsteady computational model was established based on the SST k-ω turbulence model, and the reliability of the numerical model was validated against experimental results. The results indicate that the guide vane hydraulic torque under runaway conditions exhibits pronounced periodic fluctuations, and the dominant period in the time domain is consistent with the blade passing frequency, demonstrating that rotor–stator interaction between the runner wake and guide vanes is the primary mechanism inducing unsteady hydraulic loads. As the guide vane opening decreases, the flow passage area in the guide vane region is reduced, and the high-speed swirling flow at the runner outlet generates significant jet impingement and local shear layers near the guide vane inlet, resulting in enhanced circumferential non-uniformity of the flow field and a substantial increase in the pressure difference across the guide vane surfaces. Among all operating conditions, the hydraulic torque fluctuation at a₀ = 19 mm is the most severe. Under small-opening conditions, flow separation, wake accumulation, and local backflow structures are prone to occur in the vicinity of the guide vanes, accompanied by pronounced high-frequency pressure disturbances and local impulsive pressure peaks. With increasing guide vane opening, the flow attachment behavior and flow field continuity are gradually improved, and the pressure fluctuations evolve from random oscillations to regular periodic pulsations, indicating a significant enhancement in flow stability. The study demonstrates that small guide vane opening conditions produce hydrodynamic load characteristics—specifically, higher-amplitude and more intermittent torque fluctuations, as well as lower minimum pressures—that are indicative of conditions conducive to increased vibration, fatigue accumulation, and cavitation risk; however, direct structural or two-phase cavitation analyses are required to confirm these implications. The present results can provide a theoretical basis for the optimal design of guide vane mechanisms and the safe operation of pump–turbines under runaway conditions, and quantitative coupling analysis reveals that the cross-correlation between inlet pressure and torque decreases from R = 0.87 at a₀ = 19 mm to R = 0.72 at a₀ = 45 mm, confirming that the flow–load coupling weakens substantially with increasing opening. Full article

Two-dimensional GaS_xTe_1−x alloy nanosheets with different compositions were synthesized by chemical vapor deposition using GaS and GaTe powders as precursors. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analyses confirmed their sheet-like morphology and the uniform distribution of S and Te, while Raman and photoluminescence measurements revealed composition-dependent vibrational and emission characteristics. Te-rich samples exhibited position-dependent emission ranging from the red to the near-infrared region, whereas increasing the S content gradually shifted the emission toward the blue-green region. Among the synthesized samples, GaS_0.9Te_0.1 showed a relatively stable photoluminescence peak near 520 nm and was therefore selected as a fluorescent powder for latent fingermark development. Its performance was evaluated on glass, stainless steel, plastic, and ceramic surfaces and compared with that of silver powder, gold powder, and commercial red fluorescent powder. GaS_0.9Te_0.1 produced clear fluorescent ridge patterns and strong background contrast, particularly on glass, plastic, and white ceramic. The mean contrast across the four substrates reached 25.83, exceeding that of the reference powders. These results demonstrate that GaS_xTe_1−x nanosheets possess tunable optical properties and that GaS_0.9Te_0.1 is a promising fluorescent material for latent fingermark development on non-porous surfaces. Full article

To address the insufficient representation of fault characteristics in rotating machinery vibration signals, the sensitivity of conventional Transformers to variations in input response amplitudes, and the limited ability of fixed sequence embedding to preserve continuous temporal information, a rotating machinery fault diagnosis method based on Amplitude-Calibrated Sequence Embedding (ACSE) and a Response-Normalized Transformer (RNformer) is proposed. First, ACSE is designed to construct local temporal feature representations through continuous convolutional mapping, while an amplitude response estimation and adaptive amplitude calibration mechanism is employed to dynamically recalibrate the response intensity at different temporal positions. Rather than simply rescaling the signal amplitude range, amplitude calibration adaptively strengthens the feature contribution of regions associated with fault-induced impacts according to the vibration response intensity, thereby highlighting fault-sensitive information while suppressing the influence of noncritical amplitude fluctuations. In this way, continuous temporal characteristics are preserved while fault-relevant information is enhanced. Second, RNformer is constructed by incorporating a response normalization mechanism into the Transformer encoder to mitigate the interference of abnormal amplitude responses with global feature modeling, thereby improving the stability and robustness of feature representations under complex operating conditions. Finally, a lightweight channel attention mechanism is introduced to further enhance critical fault features and perform fault classification. Experiments were conducted on the Paderborn University bearing dataset and the University of Connecticut gear dataset. The proposed method achieved average diagnostic accuracies of 99.36% and 99.28%, respectively, outperforming the best-performing baseline methods by 1.82 and 1.56 percentage points. These results demonstrated the effectiveness of the proposed method for fault diagnosis. Full article

As a key energy-conversion component of tower gravity energy storage systems (T-SGESs), the hoisting structure is susceptible to large-amplitude coupled vibrations when the dominant frequency of a continuous external excitation approaches one of its natural frequencies, potentially compromising operational stability and safety. To characterize this behavior, a two-degree-of-freedom nonlinear dynamic model is developed based on Hamilton’s principle. Eigenvalue and modal analyses are performed to determine the natural frequencies and modal characteristics of the coupled system, while the second-mode primary resonance is further analyzed using the method of multiple scales and validated through numerical frequency-sweep simulations. Near the second-mode primary resonance, the system exhibits a pronounced hardening-type nonlinear response characterized by multistability, saddle-node bifurcations, jump transitions, and hysteresis. Parametric analysis indicates that greater attention should be paid to short-rope and low-payload operating conditions, under which the system tends to exhibit stronger nonlinear responses and larger payload swing amplitudes near the second-mode primary resonance. Meanwhile, the nonlinear resonance response of the hoisting structure can be effectively mitigated through enhanced equivalent stiffness and damping, which substantially narrow the multistable frequency interval. At a damping ratio of 0.04, the system transitions from a multivalued response to a single stable branch, with a marked reduction in payload swing amplitude. These findings identify the second-mode primary resonance as a critical nonlinear operating regime and provide a quantitative basis for resonance avoidance and parameter regulation in T-SGES hoisting systems. Full article

A unified modeling framework is presented in this work to predict the free vibration and loss factor characteristics of damping sandwich rotational plates. The formulation starts from the first-order shear deformation theory, where the zigzag hypothesis and interlayer displacement continuity are combined to couple the displacement fields of the individual plies. An artificial spring scheme is adopted to enforce the layer–layer compatibility and the external boundary restraints, which leads to a Lagrangian functional composed of the kinetic energy, the strain energy, and the potential energies contributed by the boundary and coupling springs. The displacement unknowns are discretized with Chebyshev polynomials of the first kind, and the natural frequencies and damping loss factors are extracted by solving the resulting eigenvalue problem with the Rayleigh–Ritz method. Convergence tests are conducted, and the reliability of the model is validated against finite element results. Finally, a series of numerical examples is presented to systematically investigate the effects of key model parameters on the vibration characteristics of the structure. The results indicate that increasing the thicknesses of the inner and outer layers of the damping sandwich rotational plate structure can significantly raise the natural frequencies. Increasing the inner diameter helps to reduce the area of the low-frequency region, where the difference between the two sides exceeds 40 Hz, caused by the close thicknesses of the inner and outer layers. When only the outer boundary is clamped, the natural frequencies of the annular plate are more than twice those of the solid rotational plate, although the solid rotational plate yields a larger loss factor. When only the outer circular edge is fixed, increasing the total thickness of the structure can effectively raise the natural frequencies, with a maximum increase exceeding 110 Hz, while the loss factor decreases significantly. Full article