Random Loading Research Articles

A neural network-based material cell for elastoplasticity and its performance in FE analyses of boundary value problems

This research focuses on evaluating the capacity and performance of a network-based material cell as a constitutive model for boundary-value problems. The proposed material cell aims to replicate constitutive relationships learned from datasets generated by random loading paths following a stochastic Gaussian process. The material cell demonstrates its effectiveness across three progressively complex constitutive models by incorporating physical extensions and symmetry constraint as prior knowledge. To address the challenge of magnitude gaps between strain increments in training sets and finite element simulations, an adaptive linear transformation is introduced to mitigate prediction errors. The material cell successfully reproduces constitutive relationships in finite element simulations, and its performance is comprehensively evaluated by comparing two different material cells: the sequentially trained gated recurrent unit (GRU)-based material cell and the one-to-one trained deep network-based material cell. The GRU-based material cell can be trained without explicit calibration of the internal variables. This enables us to directly derive the constitutive model using stress–strain data without consideration of the physics of internal variables.

Stochastic dynamics analysis of the rocket shell coupling system with circular plate fasteners based on spectro-geometric method

A numerical analysis model is established to predict the stochastic dynamic characteristics of the rocket shell coupling system. The virtual springs are employed to deal with the boundary conditions and assemble the sub structures. Firstly, the energy functional expressions of the sub structures are deduced with the medium-thick shell theory. Thereafter the equivalent dynamic model of the rocket shell coupling system is obtained. With the consideration of the random acceleration load excitation, the stochastic dynamic responses of the rocket shell coupling system are derived by solving the analytical model with the spectro-geometric method. By comparing with the results obtained with other numerical methods, the effectiveness of the proposed equivalent analysis model is successfully verified. Last but not the least, the parametric analysis is carried out to investigate the influence of some core factors on the stochastic dynamic response of the rocket shell coupling system.

Human-induced vibrations of floors: A probabilistic approach

The current design guidelines for assessing floor vibration performance rely on a simplified approach that considers a single pedestrian as a fixed load with multiple harmonic components applied to the anti-nodes of the floor mode shapes. However, these guidelines do not account for the influence of variability in the walking path on the dynamic response of floors. Moreover, the deterministic approaches used in these guidelines may yield unreliable estimates of vibration response due to the stochastic nature of pedestrian walking force. The aim of this paper is to investigate the dynamic response of floors under a single pedestrian walking load while considering the randomness of the walking path and the walking load itself. A probabilistic perspective is adopted to analyze the dynamic response under different random loading scenarios. Furthermore, the effectiveness of the current guidelines in predicting the dynamic response of floors is critically assessed. The importance of incorporating probabilistic approaches is emphasized to achieve a more comprehensive understanding of the vibration serviceability of floors and to enhance design practices in this context.

Experimental investigation on the thermal performance of a novel loop heat pipe (LHP) with micro-channel structure

Loop heat pipe (LHP) is widely used to solve the problem of high heat flux in electronic chips due to its excellent heat transfer efficiency, good thermal equilibrium, and large heat transfer limit. To further improve the heat transfer efficiency of the LHP, a novel LHP was designed with a parallel arrangement of 13 vapor channels (2 mm × 2 mm) capillary wick structure. Experimental investigations were conducted to evaluate the start-up and steady-state performance of the LHP under both regular and random variable loads ranging from 30 W to 180 W. The findings indicate that the LHP exhibits rapid start-up and operates smoothly within the heat load range of 30 W–180 W, maintaining the heat source temperature below 75 °C, thereby meeting the temperature requirements for chip operation.Under a regular variable heat load, the LHP experiences minimal temperature fluctuations, but the system operated with good stability. Meanwhile, under random variable heat loads, the LHP demonstrates quick responsiveness within 5 s and reaches a stable state within 8 min. The temperature fluctuations remain within 2 °C, effectively fulfilling the temperature control demands of electronic devices. Furthermore, the evaporator thermal resistance, condenser thermal resistance, loop thermal resistance and system thermal resistance of the LHP gradually decrease with increasing of heat load. When reaching a heat load of 180 W, the evaporator thermal resistance (Re), condenser thermal resistance (Rc), loop thermal resistance (RLHP), and system thermal resistance (Rsys) reach their minimum, with values of 0.0168 °C/W, 0.121 °C/W,0.137 °C/W and 0.229 °C/W, respectively. In comparison with other LHPs, the evaporator temperature was only 59.56 °C with the highest heat load of 210 W (52.5 W/cm2), which expanded the application range of the LHP for electronic devices cooling.

Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures

Data-driven methodologies have found increasing usage in the last decade for remaining useful life (RUL) prognostics of composite materials utilizing structural health monitoring (SHM) data. Of particular interest is the reliable RUL prediction in cases where the end-of-life is not in between the extreme values within the testing dataset. For example, when unexpected phenomena that severely compromise the structural integrity occur during the service life. Such cases are often referred as outliers and the RUL prognosis based on a data-driven model that learns from past data is often erroneous. This study addresses this challenge by proposing a new stochastic model; the Similarity Learning Hidden Semi Markov Model (SLHSMM), an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). Through the utilization of a nonparametric discrete distribution, which characterizes the similarity between the testing structure and the training structures, a dynamic re-estimation process is employed. This process assigns higher importance to the training structures that display greater similarity to the testing one. As a result, the estimated parameters effectively capture the specific characteristics of the testing structure. The training and testing SHM data sets consist of strain measurements collected from a case study where carbon–epoxy single-stringered panels, are subjected to constant, variable, and random amplitude fatigue loading until failure. RUL estimations from the SLHSMM, the NHHSMM, and the Gaussian Process Regression (GPR) are compared. The SLHSMM clearly outperforms its classical counterpart and GPR providing more accurate outlier and inlier prognostics, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data into a prognostic platform.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

First-of-Its-Kind Frequency Enhancement Methodology Based on an Optimized Combination of FLC and TFOIDFF Controllers Evaluated on EVs, SMES, and UPFC-Integrated Smart Grid

The most recent advancements in renewable energy resources, as well as their broad acceptance in power sectors, have created substantial operational, security, and management concerns. As a result of the continual decrease in power system inertia, it is critical to maintain the normal operating frequency and reduce tie-line power changes. The preceding issues sparked this research, which proposes the Fuzzy Tilted Fractional Order Integral Derivative with Fractional Filter (FTFOIDFF), a unique load frequency controller. The FTFOIDFF controller described here combines the benefits of tilt, fuzzy logic, FOPID, and fractional filter controllers. Furthermore, the prairie dog optimizer (PDO), a newly developed metaheuristic optimization approach, is shown to efficiently tune the suggested controller settings as well as the forms of the fuzzy logic membership functions in the two-area hybrid power grid investigated in this paper. When the PDO results are compared to those of the Seagull Optimization Algorithm, the Runge Kutta optimizer, and the Chaos Game Optimizer for the same hybrid power system, PDO prevails. The system model incorporates physical constraints such as communication time delays and generation rate constraints. In addition, a unified power flow controller (UPFC) is put in the tie-line, and SMES units have been planned in both regions. Furthermore, the contribution of electric vehicles (EVs) is considered in both sections. The proposed PDO-based FTFOIDFF controller outperformed many PDO-based traditional (such as proportional integral derivative (PID), proportional integral derivative acceleration (PIDA), and TFOIDFF) and intelligent (such as Fuzzy PID and Fuzzy PIDA) controllers from the literature. The suggested PDO-based FTFOIDFF controller has excellent performance due to the usage of various load patterns such as step load perturbation, multi-step load perturbation, random load perturbation, random sinusoidal load perturbation, and pulse load perturbation. Furthermore, a variety of scenarios have been implemented to demonstrate the advantageous effects that SMES, UPFC, and EV units have on the overall performance of the system. The sensitivity of a system is ascertained by modifying its parameters from their standard configurations. According to the simulation results, the suggested PDO-based FTFOIDFF controller can improve system stability despite the multiple difficult conditions indicated previously. According to the MATLAB/Simulink data, the proposed method decreased the total fitness function to 0.0875, representing a 97.35% improvement over PID, 95.84% improvement over PIDA, 92.45% improvement over TFOIDFF, 83.43% improvement over Fuzzy PID, and 37.9% improvement over Fuzzy PIDA.

Random Furrowing for a Stochastic Unit Commitment Solution

The Unit Commitment Problem involves the inherent difficulty of obtaining optimal combinatorial power generation schedules over a future short term period. The formulation of the generalized Unit Commitment Schedule formulation involves the specific combination of generation units at several de-rated capacities during each hour of the planning horizon, the load demand profile, load indeterminateness and several other operating constraints. This largely deterministic schedule continues to find favor with several plant operators, keeping in mind the close operating time-periods involved. However, the deterministic nature of the load profile is sought to be phased out by a stochastic pattern that is realistic and mirrors real-life situations, owing to modern trends in Demand side management. This shift is in tune with the ongoing power restructuring activities of electricity power reforms. The stochastic profile is obtained by a suitably tuned 2-parameter Weibull distribution that uses appropriate shape and scale parameters. The resulting band of generated load profiles are used to evaluate net power and penal costs associated with a set of pervasive randomized probability indices. The exact UCS comprises of a specific unit absolute state corresponding to a certain time period within the planning horizon. Subsequently, regression analysis is applied to establish the correlation between the absolute states and the cumulative randomized load demand against the intervals within the planning horizon. This method is analogous to random furrowing of probabilistic demand profile.

Tensile loading state sensing and estimation of opaque polymer materials using pulsed-terahertz waves

In this study, we used pulsed terahertz (THz) spectroscopy to analyze the THz optical properties of opaque acrylonitrile–butadiene–styrene (ABS). The phenomenon occurring upon the propagation of pulsed THz waves through ABS was analyzed through measurements of the variations in THz optical parameters (absorption constant, complex dielectric properties, and refractive index). When tensile loading of 0.1–1.5 kN was applied to a specimen, large optical coefficient variations were observed at 0.275, 0.471, and 0.686 THz, regardless of the applied tensile loading. The optical parameter variations were analyzed at 0.275 THz. Moreover, the accuracy of a tensile loading state estimation model established using principal component analysis was analyzed; consequently, random loading was confirmed. The input factor was extracted and calculated, with an error of approximately 3%, except for small loading (100 N).

Fatigue life estimate of metallic chain links of mooring systems assuming out of plane bending: From constant amplitude to random loading

In this work, a numerical and analytical study of the fatigue life of chains in the mooring system of FPSO platforms is carried out. Initially, analytical equations are proposed to calculate the stress at the hotspot of the critical chain link, considering axial stresses generated by axial loads and out-of-plane bending (OPB). The analytical equations consider the operating angle of the mooring systems as well as the interlink angle of the links. Thus, the validation of the equations is done by comparing the stress values obtained through numerical simulations of the mooring-fairlead system, where a satisfactory correlation was observed. In addition, loads with constant and variable amplitude are generated as well as random load histories, always between the maximum and minimum operating load of the system, which represent 1/3 and 1/6 of the minimum breaking load (MBL) of the chain link. Based on the Smith-Watson-Topper (SWT) approach, the fatigue life of the chain was estimated, considering the analytical value of the stress at the hotspot based only on the axial contribution and later, due to the axial and OPB contributions. Finally, it was verified that the analytical equations were able to determine the stress values at the hotspot and that the effect of normal stress at the hotspot due to OPB can cause a severe loss in the fatigue life of the mechanical component.

Finite element model-informed deep learning for equivalent force estimation and full-field response calculation

The measured data in structural health monitoring are discrete in space, which means that the full-field load distribution or structural responses cannot be captured. Conventional deep-learning-based data expansion methods require a huge volume of training datasets, and they tend to be overfitted because constraints are insufficient. To address these issues, a physics-informed neural network (PINN) is developed in this study by estimating an arbitrarily distributed load to an equivalent load. Subsequently, a finite element model is used to reconstruct structural responses. The proposed PINN is divided into connected trainable data model and untrainable physics-driven model. The data-driven model in the proposed PINN is a convolutional autoencoder architecture, while the physics-informed model incorporates the impulse response matrix derived from the time-invariant system. The autoencoder is fed with the monitored data, and an equivalent load is outputted. Consecutively, the impulse response function in the physics-informed model is used to calculate the responses at the sensor positions to the equivalent load. The difference between the measured and predicted responses is formulated as the loss function to train the PINN. In this way, the physics-driven model is integrated into the data model to guide its training direction in a novel self-supervised manner, thereby avoiding the overfitting effects in network training. In addition, the unmeasured and randomly-distributed load can be estimated automatically. The proposed PINN is applied to a numerical continuous beam and a laboratory-tested three-storey frame, where only a few sensors are required for response reconstruction. Results suggest that the PINN can accurately reconstruct the full-field structural responses via the estimated equivalent load and applies to complex conditions with random and unknown load distributions.

Multiverse optimized ANFIS scheduled fractional ordered proportional-integral-derivative controller for mitigation of frequency excursions in AC microgrid coupled with electric vehicles

ABSTRACT Owing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator.

A unified hybrid Ritz-SEA acoustic vibration coupling method of a rectangular plate coupled with fast multipole boundary integration

The Ritz method, based on a segmental strategy, is known for its fast convergence and ability to handle arbitrary boundary conditions while maintaining accuracy comparable to benchmark solutions. It is particularly effective in calculating vibrations in composite laminates, functionally graded plates, and other novel plate types. However, this method faces challenges in accurately calculating the mid-frequency problems that arise between plate structures and acoustic cavities. To fill this gap, this paper proposes a Ritz and statistical energy analysis (Ritz-SEA) hybrid method for calculating rectangular plate acoustic vibration coupling in the mid-frequency range for both deterministic and random loads. This method utilizes the fast multi-pole boundary element integral equation and region segmentation mapping as the governing equation for the coupling boundaries. The power flow equation of the statistical energy analysis (SEA) method is incorporated to solve for the average sound pressure level and sound power level of the coupled acoustic cavity. A method that combines the CHIEF and Burton-Miller methods has been proposed to address the issue of non-unique solutions. Validation and parametric studies are conducted. The results demonstrate that this approach can effectively filter out random fluctuations in mid-frequency domains while exhibiting exceptional stability and precision.

Fatigue Life Assessment in the Typical Structure of Large Container Vessels Based on Fracture Mechanics

Welding defects are known to cause crack propagation and reduce structural fatigue performance. Based on the Paris theory of fracture mechanics, research is conducted on evaluation methods for analyzing fatigue crack propagation by adopting random loads with long-term distribution that follows the Weibull distribution for the stress ranges of fatigue loads. This approach is combined with the corrective stress intensity factor (SIF) equation and the method for calculating the reference stress and failure criterion. A large container ship is selected for a simulation, and fatigue crack propagation analysis is conducted on typical critical locations. A detailed comparison of the forecasted fatigue life is carried out between fracture mechanics theory and the S-N curve. The results indicate that the fatigue life values obtained using the two methods are of the same magnitude. In general, for the welded structure, the fatigue life value obtained via the fracture mechanics method is shorter than that obtained via the S-N curve method, while, for the free edge of the structure and the unwelded structure, the predicted fatigue life value is closer than that predicted via the S-N curve method. Moreover, the influence of initial crack defects on the fatigue life is investigated, and the results show that the depth of the initial crack will greatly affect the fatigue life of the target ship in typical locations, but the influence of the shape ratio on the fatigue life is limited. Therefore, in the actual ship construction process, controlling the initial crack depth of components is effective for limiting crack propagation and improving fatigue life. The above conclusions and suggestions can serve as a reference for the structural design and fatigue life evaluation of large container ships.

Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.

Wind-Induced Vibrations and Gust Response Factors of the Cabin–Cable–Tower System

A large-scale radio astronomical telescope is a typical complex coupled system, consisting of a feed cabin, cables, and supporting structures. The system is extremely sensitive to wind loads, especially the feed cabin, which has high requirements for vibration displacement during operation, and excessive vibration may affect normal operation. To investigate the wind-induced vibration characteristics of such coupled systems, this study takes the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) as an example to conduct research. First, a refined finite element model of FAST is established, and a dynamic analysis using simulated random wind loads is conducted. The influence of the cable boundary on the time–frequency domain responses of the feed cabin is particularly considered. Then, the gust response factor (GRF) for different structural components within the coupled system is calculated. Finally, the evolution law of the GRF under various wind speeds and directions is revealed by parametric analysis. The parameter analysis only considers the wind directions ranging from 0° to 60°, because FAST is a symmetric structure. The results indicate that obvious differences are observed in both the rotational and translational displacements of the feed cabin under northward wind, especially the results along the east–west axis. When the supporting towers are considered, there is no change in the power spectral density (PSD) of the feed cabin in the low-frequency range. However, in the high-frequency range, taking the supporting towers into account leads to an increase in PSD and a resonance near the first-order natural frequency of the supporting tower. The GRF based on the dynamic response exhibits substantial deviations compared to those obtained from design codes, highlighting the need for an independent analysis when determining GRF for such coupled systems.

GWO- and SSA-Tuned PID Control for Frequency Regulation in Multi-Area PowerNetwork Integrated with Plug-in Electric Vehicle

ABSTRACT The Plug-in Electric Vehicles (PEVs) can be influential in containing power system frequency fluctuations. This study, therefore, investigates the efficacy of frequency regulation for PEV-integrated multi-area power network using Grey Wolf Optimizer (GWO) and Salp Swarm Algorithm (SSA) optimized Proportional-Integral-Derivative (PID) control. Instant investigation not only brings out the relative competence of GWO and SSA but also examines the impact of PEV in improving the system performance. The varying operating conditions are realized by subjecting the system to step and random load variations in either or both of the areas. With the proposed control scheme and involvement of PEV, system frequency and tie-line power excursions settle quicker with their peak swings also getting restricted to a lower value, while the oscillations are arrested as well to a great extent. Further, it’s the SSA that shows its superiority over GWO as per the simulation results executed in MATLAB.

MPC-based LFC for interconnected power systems with PVA and ESS under model uncertainty and communication delay

In this paper, a cloud-edge-end collaboration-based control architecture is established for frequency regulation in interconnected power systems (IPS). A model predictive control (MPC)-based load frequency control strategy for the IPS with photovoltaic aggregation and energy storage systems under model uncertainty and communication delay is proposed. This can effectively overcome the issues of model uncertainty, random load perturbation and communication delay. First, a state space model for the IPS is constructed. To coordinate the frequency and contact line power fluctuation of the IPS, a robust controller based on the theory of MPC is then designed. Then, considering the communication delay of frequency response commands during transmission, a predictive compensation mechanism is introduced to eliminate the effect of delay while considering model uncertainty. Finally, simulation results verify the effectiveness and robustness of the proposed control strategy.

Probabilistic fatigue cumulative damage model considering the randomness of load and material properties

A probabilistic fatigue cumulative damage model that can be applied to the fatigue life prediction of aluminum alloy under spectrum loading is proposed in this paper. The model is constructed by a novel nonlinear cumulative damage model, the P–S–N curve, and the probabilistic load–exceedance curve. It introduces the randomness of the loading process and fatigue resistance of the material to the deterministic model. In this model, it is derived that the cumulative damage under fleet spectrum loading exhibits an approximate lognormal distribution, and a method to calculate the distribution parameters is provided. Finally, the validity of the proposed probabilistic model is verified through fatigue tests on 2297-T87 aluminum–lithium alloy specimens under random loading, demonstrating its capability to capture the probabilistic nature of the fatigue behavior of aluminum alloy effectively.

Performance evaluation of RYU controller under distributed denial of service attacks

<span>Distributed denial of service (DDoS) attacks have been identified as one of the greatest threats to software-defined networking (SDN) because they are highly effective, hard to detect, and easy to use, and they take advantage of vulnerabilities in which the new architecture still exists. This paper describes one technique for denying the RYU controller's services, which can cause the controller's resources to be depleted if a significant number of packets from various zombie hosts are sent to the controller using spoofed source internet protocol (IP) addresses. In order to demonstrate the impact of the attack, we measure various metrics related to RYU controllers such as its central processing unit (CPU) usage and network throughput. In this work, Mininet was used to simulate the data plane and measure metrics such as random access memory (RAM) usage, CPU load, and link latency.</span>