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The capability of steel columns to support their design loads is highly affected by the time of exposure and temperature magnitude, which causes deterioration of mechanical properties of steel under fire conditions. It is known that structural steel loses strength and stiffness as temperature increases, particularly above 400 °C. The duration of time in which steel is exposed to high temperatures also has an impact on how much strength it loses. The time-dependent response of steel is critical when estimating load carrying capacity of steel columns exposed to fire. Thus, investigating the structural response of cold-formed steel (CFS) columns is gaining more interest due to the nature of such structural elements.

In this study, experiments were conducted on two CFS configurations: back-to-back (B-B) channel and toe-to-toe (T-T) channel sections. All CFS column specimens were exposed to different temperatures following the standard fire curve and cooled by air or water. A total of 14 tests were conducted to evaluate the capacity of the CFS sections. The axial resistance and yield deformation were noted for both section types at elevated temperatures. The CFS column sections were modelled to simulate the section's behaviour under various temperature exposures using the general-purpose finite element (FE) program ABAQUS. The results from FE modelling agreed well with the experimental results. Ultimate load of experiment and finite element model (FEM) are compared with each other. The difference in percentage and ratio between both are presented.

The results showed that B-B configuration showed better performance for all the investigated parameters than T-T sections. A noticeable loss in the ultimate strength of 34.5 and 65.6% was observed at 90 min (986℃) for B-B specimens cooled using air and water, respectively. However, the reduction was 29.9 and 46% in the T-T configuration, respectively.

This research paper focusses on assessing the buckling strength of heated CFS sections to analyse the mode of failure of CFS sections with B-B and T-T design configurations under the effect of elevated temperature.

In the present study, the application of a recent damage plasticity model is presented for nonlinear dynamic analysis of the Koyna gravity dam. This is a single surface isotropic damage plasticity concrete model, which is based on the decomposition of stresses and was proposed in a previous study. The theoretical aspects of the model are initially reviewed, and a few preliminary verification examples are illustrated. Thereafter, the HHT-α (i.e. Hilber–Hughes–Taylor) algorithm is presented for nonlinear dynamic analysis of concrete gravity dams.

Based on the prepared tools, nonlinear behavior of the Koyna Dam is studied by applying the invoked damage plasticity model. For this purpose, three cases are considered for the present study. Case A, which is based on the linear model, is mainly used for comparative purposes. The other two cases (B and C) correspond to the nonlinear (i.e. damage plasticity) model. The basic data for these two cases are similar. However, the employed damping algorithms are different and correspond to constant and variable damping algorithms, respectively. This means that the damping matrix is either kept constant or updated for all iterations of different time increments through the course of analysis.

The time histories of horizontal displacement at the dam crest were initially compared for the three cases: the linear Case A, and two nonlinear Cases B and C. It was observed that nonlinear cases’ responses begin to deviate from the corresponding linear case after the time of about 4.3 s. However, the amount of change for Case C (i.e. variable damping) was much greater than for Case B (i.e. constant damping). This was manifested initially in the peaks of response. It was also noticed that the period of response changed slightly for Case B in comparison with the linear Case A, while this change was significant for Case C. The obtained tensile and compressive damages were subsequently compared for the two nonlinear cases. For constant damping Case B, it was noticed that tensile damage occurred in the D/S face kink and on the U/S face slightly at a lower elevation. Moreover, it had a scattered nature. However, in variable damping Case C, it was noticed that tensile damage was much more localized and acted similar to a discrete crack. Of course, both cases also show tensile damages at the dam’s heel. In regard to compressive damages, it is observed that low values are occurring for both nonlinear cases as expected.

The application of a recent single surface isotropic damage plasticity concrete model is presented for nonlinear dynamic analysis of the Koyna gravity dam. The nonlinear response of the dam is investigated for two different damping algorithms. Moreover, the influence of variable characteristic length is also investigated in the latter part of this study.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The purpose of this study is to model the global dynamic hysteresis properties with an improved Jiles–Atherton (J-A) model through a unified set of parameters.

First, the waveform scaling parameters β, λ_k and λ_c are used to improve the calculation accuracy of hysteresis loops at low magnetic flux density. Second, the Riemann–Liouville (R-L) type fractional derivatives technique is applied to modified static inverse J-A model to compute the dynamic magnetic field considering the skin effect in wideband frequency magnetization conditions.

The proposed model is identified and verified by modeling the hysteresis loops whose maximum magnetic flux densities vary from 0.3 to 1.4 T up to 800 Hz using B30P105 electrical steel. Compared with the conventional J-A model, the global simulation ability of the proposed dynamic model is much improved.

Accurate modeling of the hysteresis properties of electrical steels is essential for analyzing the loss behavior of electrical equipment in finite element analysis (FEA). Nevertheless, the existing inverse Jiles–Atherton (J-A) model can only guarantee the simulation accuracy with higher magnetic flux densities, which cannot guarantee the analysis requirements of considering both low magnetic flux density and high magnetic flux density in FEA. This paper modifies the dynamic J-A model by introducing waveform scaling parameters and the R-L fractional derivative to improve the hysteresis loops’ simulation accuracy from low to high magnetic flux densities with the same set of parameters in a wide frequency range.

Elastic rings served as the elastic supporting elements which have been extensively used in the aeroengines for maneuverable planes with high overloading. However, under extreme conditions, the elastic ring contacts the bearing seat, causing elastic ring failure. Therefore, it is necessary to optimize the matching parameters of the elastic ring in order to suppress the occurrence of elastic ring failure under harsh working conditions.

In this paper, a rotor system supported by elastic rings is researched and a multi-objective parameter matching method of elastic ring is proposed, considering the elastic ring failure, rotor system’s frequency forbidden zone and rotor system’s dynamic response. Then, the particle swarm optimization algorithm is used to dynamically constrain the parameter matching space and obtain the ideal solution for the elastic ring parameter matching.

By analyzing the elastic ring’s matching results (different unbalanced forces and disk masses), the relationship between the trend of Pareto front changes and rotor system parameters is studied. In addition, the rotor system’s dynamic characteristics before and after parameter matching are analyzed.

This article provides guidance for the design of elastic rings by matching the parameters of elastic rings.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

The purpose of this paper is to derive a new stress tensor for calculating the Lorentz force acting on an arbitrarily shaped nonmagnetic conductive specimen moving in the field of a permanent magnet. The stress tensor allows for a transition from a volume to a surface integral for force calculation.

This paper derives a new stress tensor which consists of two parts: the first part corresponds to the scaled Poynting vector and the second part corresponds to the velocity term. This paper converts the triple integral over the volume of the conductor to a double integral over its surface, where the subintegral functions are continuous through the different compartments of the model. Numerical results and comparison to the standard volume discretization using the finite element method are given.

This paper evaluated the performance of the new stress tensor computation on a thick and thin cuboid, a thin disk, a sphere and a thin cuboid containing a surface defect. The integrals are valid for any geometry of the specimen and the position and orientation of the magnet. The normalized root mean square errors are below 0.26% with respect to a reference finite element solution applying volume integration.

Tensor elements are continuous throughout the model, allowing integration directly over the conductor surface.

This paper presents a novel method for identifying damage in reinforced concrete (RC) bridges, utilizing macro-strain data from distributed long-gauge sensors installed on the concrete surface.

The method relies on the principle that heavy vehicles induce larger dynamic vibrations, leading to increased strain and crack formation compared to lighter vehicles. By comparing the absolute macro-strain ratio (AMSR) of a reference sensor with a network of distributed sensors, damage locations can be effectively pinpointed from a single data collection session. Finite-element modeling was employed to validate the method's efficacy, demonstrating that the AMSR ratio increases significantly in the presence of cracks. Experimental validation was conducted on a real-world bridge in Japan, confirming the method's reliability under normal traffic conditions.

This approach offers a practical and efficient means of detecting bridge damage, potentially enhancing the safety and longevity of infrastructure systems.

Original research paper.

This study aims to optimize electrical systems represented by ordinary differential equations and events, using their frequency spectrum is an important purpose for designers, especially to calculate harmonics.

This paper presents a methodology to achieve this, by using a gradient-based optimization algorithm. The paper proposes to use a time simulation of the electrical system, and then to compute its frequency spectrum in the optimization loop.

The paper shows how to proceed efficiently to compute the frequency spectrum of an electrical system to include it in an optimization loop. Derivatives of the frequency spectrum such as the optimization inputs can also be calculated. This is possible even if the sized system behavior cannot be defined a priori, e.g. when there are static converters or electrical devices with natural switching.

Using an efficient sequential quadratic programming optimizer, automatic differentiation is used to compute the model gradients. Frequency spectrum derivatives with respect to the optimization inputs are calculated by an analytical formula. The methodology uses a “white-box” approach so that automatic differentiation and the differential equations simulator can be used, unlike most state-of-the-art simulators.

This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.