The population-based structural health monitoring paradigm has recently emerged as a promising approach to enhance data-driven assessment of engineering structures by facilitating transfer learning between structures with some degree of similarity. In this work, we apply this concept to the automated modal identification of structural systems. We introduce a graph neural network (GNN)-based deep learning scheme to identify modal properties, including natural frequencies, damping ratios, and mode shapes of engineering structures based on the power spectral density of spatially sparse vibration measurements. Systematic numerical experiments are conducted to evaluate the proposed model, employing two distinct truss populations that possess similar topological characteristics but varying geometric (size and shape) and material (stiffness) properties. The results demonstrate that, once trained, the proposed GNN-based model can identify modal properties of unseen structures within the same structural population with good efficiency and acceptable accuracy, even in the presence of measurement noise and sparse measurement locations. The GNN-based model exhibits advantages over the classic frequency domain decomposition method in terms of identification speed, as well as against an alternate multilayer perceptron architecture in terms of identification accuracy, rendering this a promising tool for PBSHM purposes.

Industrial mobile robots as service units will be increasingly used in the future in factories with Industry 4.0 production cells in an island-like manner. The differences between the mobile robots available on the market make it necessary to help the optimal selection and use of these robots. In this article, we present a concept that focuses on the mobile robot as a way to investigate the manufacturing system. This approach will help to find the optimal solution when selecting robots. With the parameters that can be included, the robot can be characterized in the manufacturing system environment, making it much easier to express and compute capacity, performance, and efficiency characteristics compared to previous models. In this article, we also present a case study based on the outlined method, which investigates the robot utilization as a function of battery capacity and the number of packages to be transported.

This paper presents a study in which modelling and simulation have been used to assess the effect of the aircraft lifts on the air flow over the flight deck of the Queen Elizabeth Class (QEC) aircraft carriers and the subsequent impact on helicopter operations. The aircraft lifts can be either raised or lowered, and they can also have aircraft on them. They can therefore significantly alter the geometry of the starboard side of the ship and, potentially, the air flow over the flight deck. The air flow over the flight deck of the QEC was investigated using experimental and Computational Fluid Dynamics (CFD) techniques. To assess how the air flows for the different lift configurations affected a helicopter landing on the flight deck, piloted flight simulation trials were performed in which a test pilot conducted helicopter deck landings in CFD-simulated Green 60 winds with speeds from 10 kt to 40 kt. Pilot assessment showed the operational wind speed limits, across all spots and lift configurations, were 30 kt or 35 kt and that the different lift configurations produced a 5 kt change in the maximum tolerable wind speeds. While the distribution of the workload experienced by the pilot along the flight path was different for the three lift configurations, it was judged that the difficulty of the overall landing task was not sufficiently affected to require different limiting wind speeds for the different lift configurations.

This paper presents a detailed robustness analysis of three nonlinear filtering algorithms: the unscented Kalman filter, the cubature Kalman filter, and the ensemble Kalman filter, applied to aircraft state estimation for fixed-wing flight dynamics. The study focuses on estimating critical longitudinal flight parameters such as true airspeed, angle-of-attack, pitch angle and pitch rate using pitch angle measurements. A nonlinear aircraft model is formulated, and each filtering technique is implemented and evaluated under multiple scenarios, including sensor noise, initial state mismatches and plant-model uncertainties. Simulation results across four cases, ranging from ideal conditions to $95{\mathrm{\% }}$ mismatch, demonstrate that the unscented Kalman filter consistently delivers the most accurate and robust estimates, especially for velocity and pitch rate. The cubature Kalman filter offers a trade-off between estimation accuracy and computational efficiency, while the ensemble Kalman filter shows significant sensitivity to uncertainties but performs relatively better in estimating the angle-of-attack under severe mismatch conditions. This comparative study provides valuable insights for selecting appropriate filtering strategies in aerospace applications, particularly where robustness and reliability under uncertainty are crucial.

Blade ice accumulation is a serious problem that changes turbine aerodynamics and dynamics, leading to lower power output and higher structural loading. Different from the literature, this paper investigates the performance effectiveness of baseline wind turbine controllers: the generator torque and collective blade pitch controllers against rotor blade ice accumulation. The NREL 5-MW turbine is utilised, and simulations of baseline controllers are conducted with the MS (Mustafa Sahin) Bladed Model for clean and iced blade cases. The performance of the controllers is examined in below (Region 2) and above (Region 3) rated regions under 1 m/s step rising wind speeds. Results are presented through various parameters, including turbine controllers’ gain(s), blade pitch angle, rotor speed, power, etc. Rotor speed response is used to evaluate the controllers’ performance. Even slight blade ice accumulation is estimated to affect turbine efficiency and characteristics, decreasing ${C_{pmax}}$ by 13.27%, slightly varying optimum blade pitch angle and tip speed ratio, altering the control input gain by up to 14.68%. Blade ice accumulation is observed to adversely affect baseline controllers’ performance. In Region 2, the torque controller exhibits reduced transient and steady-state performance, with rotor speed reaching the steady-state approximately 2 s later and showing a steady-state error of 1.86%. In Region 3, the pitch controller’s transient performance deteriorates at low wind speeds, particularly near the rated wind speed, leading to an increased decay time of up to 5.2 s. However, beyond 16 m/s, pitch controller performance gradually recovers, becoming nearly identical to the clean blade case at 21 m/s, while the controller steady-state performance remains unaffected.

The effect of the bio-inspired leading-edge modifications on the aerodynamic performance of non-slender delta wing models was investigated in a low-speed wind tunnel using force and surface pressure measurements. The measurements were performed at a Reynolds number of $Re = 1 \times {10^5}$ over an angle-of-attack range from $ - 4^\circ $ to $30^\circ $. Seven different sharp-edged delta wing models with a 45-degree sweep angle (${\rm{\varLambda }}$), including a base wing, were used to study the effect of sinusoidal and saw-tooth leading-edge modifications. Sinusoidal leading-edge wing designs were inspired by the leading-edge tubercles of the humpback whale’s pectoral fins. The results indicate that the bio-inspired wing modifications resulted in a delay in the stall angle by 4 degrees, smoother stall characteristics, a higher maximum lift coefficient, and increased post-stall lift. The drag coefficient of the modified wings was observed as higher than that of the base wing model. Regarding the longitudinal static stability, leading-edge modifications decreased the stability of the wing as the angle-of-attack surpassed $\alpha = 17^\circ $.

The Monte Carlo methods are frequently employed to evaluate the overall characteristics of non-monotonic, non-linear, non-superpositional performance functions. However, the multi-parameter, multi-objective spacecraft separation dynamics model is not amenable to decoupling to produce a result. This paper presents a parametric objective function that can be sampled. It combines the reliability analysis of the complex non-linear spacecraft separation model with Automated Dynamic Analysis of Mechanical Systems (ADAMS) and uses the Monte Carlo method to obtain the separation performance of the spacecraft separation system reliability profile, that is to say, the distribution of separation performance. The performance distribution of the spacecraft separation system was determined and parameters such as spring separation force, spring line of action, module mass and module centre of mass position were found to have a significant effect on the spacecraft separation dynamics by Adaboost machine learning regression.

A bandpass filter with reconfigurable band traps operating in the S-band with a wide tuning range for the transmission zeros is presented in this article. The filter employs a main transmission line path comprising two different step impedance resonator structure, with stopband formation achieved through four quarter-wavelength resonators coupled to both ends of the main path. These resonators folded into open-ring to decrease the area of the circuit, are loaded with reconfigurable elements (SMV2020), controlled via a voltage-based control system. The voltage control system, which is designed by microcontroller unit (MCU)-AT32F421K8T7, can change the power supply voltage linearly to make this filter system flexible. The filter is fabricated on a Rogers 4350 substrate with a relative dielectric constant of 3.66, a loss tangent of 0.004, and a thickness of 0.762 mm and simulated in high-frequency structure simulator. The filter demonstrates favorable passband characteristics on either side of the stopband, achieving an in-band insertion loss of less than 1 dB and a return loss exceeding 12 dB. The reconfigurable stopband spans from 2.1 to 3.0 GHz, with a stopband return loss greater than 13 dB and an out-of-band rejection exceeding 50 dB.

This paper presents a wideband balanced reflectionless filter based on the half-wavelength ring resonator. The proposed structure is simple and easy for manufacture. The design procedures are elaborately introduced. To promote understanding, the analysis of differential-mode (DM) and common-mode (CM) equivalent circuits are given. The corresponding equations are derived. For validation, a design example is fabricated and tested. The measured results verify its ability of transmitting DM signals and eliminating undesired CM signals. Specifically, the 21.6-dB CM suppression bandwidth can reach up to 273%, while the CM absorption bandwidth can reach up to 195%. The proposed balanced reflectionless filter exhibits excellent DM matching level, CM suppression level, and wide reflectionless bandwidth.