Search Results (4,320)

This paper presents a linear algebra-based control algorithm for multivariable gas turbine systems using matrix polynomial theory and the Kronecker product to assign block roots (i.e., block eigenvectors with prescribed latent structure). State and state-derivative feedback strategies are investigated and validated through simulations on an industrial gas turbine machine. The proposed method enables direct assignment of block roots governing closed-loop stability and transient response, while block eigenvectors shape the dynamic behavior of key turbine variables. Applicability of the approach requires block controllability and/or block observability, ensuring analytical transparency, design flexibility, and effectiveness for multivariable gas turbine control. Full article

Finite element analysis (FEA) remains the gold standard for simulating piezoelectric microactuators because it resolves coupled electromechanical fields with high fidelity. However, transient FEA becomes prohibitively expensive when thousands of actuators must be simulated. This work presents a data-driven surrogate modeling framework for tileable, PZT-5H microactuators enabling fast, dynamic, and parallel predictions of actuator displacement over multi-step horizons from short displacement history windows, augmented with the corresponding prescribed voltage and traction samples over that same history window. High-fidelity COMSOL simulations are used to generate a dataset aiming to encompass the full operational envelope of our actuator under stochastically sampled and procedurally generated input waveform families. From these families, we construct a supervised learning dataset of time histories, displacement, and applied loads. From this, we train a recurrent sequence-to-sequence neural network that predicts a multi-step open-loop displacement rollout conditioned on the most recent electromechanical history. The resulting model can be leveraged to perform batched inference for millions of actuators on GPU hardware, opening up a wide range of new applications such as reinforcement learning via digital twins, scalable design and simulation for piezoelectric artificial-muscle systems, and accelerated optimization. Full article

This paper proposes a fuzzy sliding mode controller optimized using particle swarm optimization (FSMC-PSO) for trajectory tracking of an autonomous underwater vehicle (AUV). Conventional sliding mode control (SMC) is well known for its robustness against external disturbances, unmodeled dynamics, and parameter uncertainties, ensuring stability under challenging operating conditions. In the proposed FSMC-PSO approach, fuzzy logic adaptively tunes the SMC parameters, while PSO optimizes the fuzzy output membership functions offline to improve tuning accuracy and overall control performance. During online operation, the optimized fuzzy system adaptively adjusts the SMC parameters with minimal computational cost. The effectiveness of the proposed method is evaluated through numerical simulations in the presence of random noise. Performance is assessed using standard tracking indices, including IAE, ITAE, ISE, ITSE, and RMSE. Comparative results show that FSMC-PSO achieves higher trajectory tracking accuracy, reduces steady-state and transient errors, and minimizes chattering compared to conventional SMC and SMC-PSO, as well as the super-twisting algorithm-based PSO (STA-PSO) controller.FSMC-PSO achieves up to an 86.58% reduction in ITAE and a 73.53% reduction in ITSE compared to classical SMC while also outperforming SMC-PSO and STA-PSO across all motion states (X, Y, and $\psi$). These results demonstrate the effectiveness of FSMC-PSO for high-precision and disturbance-resilient AUV trajectory tracking within the simulated scenarios. Full article

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control strategies, the PLL with grid following, VSG with grid shaping, VSG+BESS, VSG+STATCOM, and VSG+BESS+STATCOM, implemented within a coherent simulation framework based on Python. Unlike previous works that analyze these methods in isolation, this study provides a comprehensive quantitative comparison of their dynamic characteristics, including frequency root mean square deviation, maximum deviation, and composite resilience index (RI). To extend the analysis beyond static conditions, a multi-generator (multi-PMSG) scenario with heterogeneous inertia constants and variable load profiles is introduced. This dynamic model allows the evaluation of natural inertia diversity and the effects of inter-generator coupling compared to the synthetic inertia emulation provided by VSG-based control. The combined VSG+BESS+STATCOM configuration achieves the highest synthetic resilience, improving frequency and voltage stability by up to 15%, while the multi-PMSG system demonstrates comparable or even higher RI values due to its inherent mechanical inertia and decentralized response behavior. In addition, a cyber-physical scenario is included to evaluate the effect of communication delays and false data injection (FDI) on VSG frequency control. The results show that a communication delay of 50 ms reduces RI by approximately 0.2%, confirming that even minor cyber disturbances can affect synchronization and transient recovery. However, hybrid control architectures with local energy buffering (BESS) show superior resilience under such conditions. The main technical contribution of this work is the establishment of an integrated analytical and simulation framework that enables the joint assessment of synthetic, natural, and cyber-physical resilience in converter-dominated smart grids. This framework provides a unified basis for the analysis of dynamic stability, hybrid control interaction, and the impact of cyber uncertainty, thereby supporting the design of low-inertia, resilient, and secure next-generation power systems. Full article

Sliding mode control (SMC) is a robust nonlinear control framework that enforces system trajectories onto predefined manifolds, providing strong robustness guarantees against uncertainties. However, SMC inherently introduces unwanted transients or chattering in system state trajectories, which may cause issues especially for sensitive applications such as regulation of drug administration. This paper proposes a multi-input smooth sliding mode control (MISSMC) strategy that combines radiotherapy and chemotherapy for a nonlinear tumor–immune dynamical system described by ordinary differential equations. The closed-loop system is first analyzed to establish key qualitative properties: all state variables remain positive and bounded, the sliding surfaces exhibit asymptotic convergence, and explicit analytical upper bounds on the cumulative therapy doses are derived under clinically motivated constraints. On this basis, a smooth hyperbolic-tangent sliding manifold and associated control law are designed to regulate the radiation and drug infusion rates. While the use of a hyperbolic-tangent smoothing function effectively suppresses chattering, it introduces a small steady-state error due to the presence of a boundary layer. To address this limitation, integral action is incorporated into the sliding surfaces, ensuring asymptotic convergence of tumor state and reducing residual steady-state error, while enhancing robustness against model uncertainties and parameter variations. Numerical simulations, based on a brain-tumor case study, show that the proposed smooth SMC markedly suppresses transient overshoots in both states and control inputs, while preserving effective tumor reduction. Compared with a conventional (non-smooth) SMC scheme, the MISSMC controller reduces baseline radiation and chemotherapy intensities on average by roughly 70%. Similarly, MISSMC lowers the overall cumulative doses on average by about 40%, without degrading the therapeutic outcome. The resulting integral smooth SMC framework therefore offers a rigorous nonlinear-systems approach to designing combined radio-chemotherapy protocols with guaranteed positivity, boundedness, and asymptotic stabilization of the closed-loop system, together with explicit bounds on the control inputs. Full article

This study investigates ultrasonic wave propagation in carbon fiber reinforced polymer (CFRP) composites containing foreign object debris (FOD) by introducing a novel method to characterize the depth and size of FOD, from a single captured waveform generated by an out-of-focus spherically focused transducer. Current methods of inspection utilize a raster approach to both detect and quantify FOD, which is limited to identifying FOD smaller than 4 mm. The method introduced in the present paper allows for a single point scan to detect and quantify FOD, as small as 0.5 mm, with the highest error in the depth estimation being less than 8%. This paper presents experimental testing to inform a finite element analysis of a full waveform simulation of an immersion tank inspection environment and compares waveforms between testing and simulation. A transient pressure acoustic model is developed in the COMSOL Multiphysics environment to simulate wave propagations. Simulation results provide waveform reflection and transmission at material interfaces, which will occur when there is an acoustic mismatch between materials. The transmitted ultrasonic wave is partially reflected toward the transducer upon encountering material interfaces between the water, CFRP laminate, and the FOD. Simulation results show that the acoustic profile and pressure of the reflected wave captured by the transducer allows an accurate identification of FOD depth and size within the composite structure, suggesting an alternative method of inspection to quantify FOD characteristics faster than through conventional approaches. Results show an increase in captured signal pressure of over 125% between the 0.5 mm FOD and the 1 mm FOD located on the mid-plane of the laminate, and 500% between the same 0.5 mm FOD and 1 mm FOD placed near the front wall. These results suggest the potential sensitivity limits for physical component. This work demonstrates that small FOD, which are often difficult to resolve and quantify under conventional raster-based inspection, can be reliably identified by intentionally positioning the specimen within the defocused region of a spherically focused transducer. Results are presented to correlate the reflected acoustic pressure amplitude to defect depth, transducer–specimen distance, and FOD size, providing an approach to quantitatively discriminate small defects that would otherwise produce ambiguous signals. Full article

This study examines the impact of fluid excitation forces on the dynamic response of high-temperature molten salt circulating primary pump rotor systems. Unsteady simulations were conducted in ANSYS CFX to characterize pressure pulsation and radial forces across all impeller stages. Critical speeds and vibration modes were subsequently analyzed using SAMCEF to evaluate transient responses under varying flow rates. Key findings: Numerical performance predictions align with experimental data within a 5% error margin. The first-stage impeller exhibits a pressure-pulsation frequency of twice the rotational frequency (2 f_R), while the fifth-stage impeller oscillates at the guide-vane passing frequency (f_DPF). Under rated conditions, the radial force on the first stage is significantly larger than on the other stages. As the flow rate varies, the radial forces on the first and fifth stages change in opposite directions due to rotor–stator interaction. The rotor system’s critical speed (1894.5 r/min) exceeds the operating speed, eliminating resonance risk. Without radial forces, impeller displacements follow elliptical trajectories with maximum amplitude at the fifth stage. When radial forces are included, displacements become irregular, and shaft constraints cause peak displacement at the fourth stage. These findings provide useful insight for the design and analysis of molten salt primary pump rotor systems. Full article

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. Full article

Virtual acoustics enables the creation and simulation of realistic and ecologically valid indoor environments vital for hearing research and audiology. For real-time applications, room acoustics simulation requires simplifications. However, the acoustic level of detail (ALOD) necessary to capture all perceptually relevant effects remains unclear. This study examines the impact of varying ALOD in simulations of three real environments: a living room with a coupled kitchen, a pub, and an underground station. ALOD was varied by generating different numbers of image sources for early reflections, or by excluding geometrical room details specific for each environment. Simulations were perceptually evaluated using headphones in comparison to measured, real binaural room impulse responses, or by using loudspeakers. The perceived overall difference, spatial audio quality differences, plausibility, speech intelligibility, and externalization were assessed. A transient pulse, an electric bass, and a speech token were used as stimuli. The results demonstrate that considerable reductions in acoustic level of detail are perceptually acceptable for communication-oriented scenarios. Speech intelligibility was robust across ALOD levels, whereas broadband transient stimuli revealed increased sensitivity to simplifications. High-ALOD simulations yielded plausibility and externalization ratings comparable to real-room recordings under both headphone and loudspeaker reproduction. Full article

This paper proposes a collaborative optimization strategy of regenerative braking in heavy-duty electric logistics vehicles under complex driving conditions to improve energy recovery efficiency. Based on the actual operational data of 18-ton electric trucks in the southwestern region of China, three driving scenarios for heavy commercial vehicles are determined via the K-Means clustering algorithm. Key features are extracted using Recursive Feature Elimination and employed to train a Learning Vector Quantization neural network for precise real-time condition recognition. The identified driving condition parameters, including vehicle speed, remaining battery power, and braking force, collectively regulate the intensity of regenerative braking. Simulation results under double-WTVC (World Transient Vehicle Cycle) conditions indicate that the proposed strategy can effectively adapt regenerative braking behavior to diverse road conditions. In comparison with conventional control methods, this approach enhances battery energy recovery efficiency by 5.8% while preventing control discontinuities. Full article

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable of operating under uncertain environmental and load conditions. This study proposes a Physics-Informed Neural Network (PINN)-based charge allocation framework that explicitly embeds physical constraints—namely charge conservation and State-of-Charge (SoC) equalization—directly into the learning process, enabling real-time adaptive control under varying irradiance and load conditions. The proposed controller exploits real-time measurements of PV voltage, current, and irradiance to achieve optimal charge distribution while ensuring converter stability and balanced battery operation. The framework is implemented and validated in MATLAB/Simulink under Standard Test Conditions of 1000 W·m⁻² irradiance and 25 °C ambient temperature. Simulation results demonstrate stable PV voltage regulation within the 230–250 V range, an average PV power output of approximately 95 kW, and effective duty-cycle control within the range of 0.35–0.45. The system maintains balanced three-phase grid voltages and currents with stable sinusoidal waveforms, indicating high power quality during steady-state operation. Compared with conventional Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC) methods, the PINN-based approach achieves faster SoC equalization, reduced transient fluctuations, and more than 6% improvement in overall system efficiency. These results confirm the strong potential of physics-informed intelligent control as a scalable and reliable solution for smart PV–battery energy systems, with direct relevance to renewable microgrids and electric vehicle charging infrastructures. Full article

During the discharge process of a salt cavern compressed air energy storage (CAES) system, high-speed air flow may entrain salt slag from the cavern floor, posing a threat to pipeline safety. Currently, there is a lack of in-depth research into the transient mechanisms of the entrainment process, particularly the influence of particle shape. This study employs a CFD-DEM coupling approach to conduct, for the first time, a high-fidelity simulation of slag entrainment dynamics during the initial discharge phase of a salt cavern CAES system, with a focus on the motion patterns of three particle shapes: spherical, conical, and square. Results show that: (1) during the initial discharge stage, the flow field rapidly forms vortex structures that migrate toward the wellhead, which is the core mechanism driving particle mobilization; (2) particle shape significantly affects entrainment efficiency through frictional characteristics—spherical particles are most easily entrained (maximum entrainment rate of 0.42 kg/h), while non-spherical particles tend to accumulate below the wellhead; and (3) the entrainment process exhibits strong transient characteristics: the entrainment rate peaks rapidly (approximately 0.82 kg/h) within a short time and then declines sharply, and it is sensitive to particle size, with the most entrainable particle size being around 5 mm. This study reveals the coupling mechanism between transient vortices and multi-shape particle entrainment during discharge, providing a theoretical basis for the design of filtration systems, operational risk prevention, and slag removal strategies in salt cavern CAES power plants. Full article

This study introduces a multidimensional mathematical model and a robust numerical algorithm with second-order accuracy for modeling the complex coupled processes of heat and moisture transfer with gas-pressure-driven flow, based on time-fractional differential equations (with Caputo derivatives of order 0 < α ≤ 1), which capture the memory effects and anomalous diffusion inherent in heterogeneous porous media. The proposed model integrates conductive and convective heat transfer; moisture diffusion and phase change; and pressure dynamics within the pore space and their bidirectional couplings. It also incorporates environmental interactions through boundary conditions for heat and moisture exchange with the ambient air; internal heat and moisture release; transient influx of solar radiation; and material heterogeneity, where all transport coefficients are spatially variable functions. To solve this nonlinear and coupled system, we developed a high-order, stable finite-difference scheme. The numerical algorithm employs an alternating direction-implicit approach, which ensures computational efficiency while maintaining numerical stability. We demonstrate the algorithm’s capability through numerical simulations that monitor and predict the spatiotemporal evolution of coupled transport temperature, moisture content, and pressure fields. The results reveal how heterogeneity, diurnal solar radiation, and internal sources create localized hot spots, moisture accumulation zones, and pressure gradients that significantly influence the overall dynamics of storage and drying processes. Full article

The rising integration of renewable energy sources and power electronic converters in modern power grids imposes increasingly stringent requirements on electromagnetic transient (EMT) simulation in terms of both precision and computational speed. Conventional simulation tools face significant challenges in efficiently modeling large-scale systems with multi-timescale dynamics. To overcome these constraints, this paper presents a general-purpose accelerator for offline EMT simulation based on field-programmable gate array (FPGA) hardware. The proposed solver enhances computational performance by unifying component models and streamlining the accumulation calculation procedure, thereby substantially reducing FPGA processing latency and improving hardware resource utilization. Furthermore, a CPU-FPGA heterogeneous simulation framework is developed, capitalizing on the respective strengths of CPUs in handling complex control logic and FPGAs in executing parallel computations, to enable accelerated EMT simulation for generic, complex system models. The validity and efficiency of the proposed FPGA-based accelerator are demonstrated through a case study of a grid-connected photovoltaic system. Comparative evaluations against a CPU-only simulation platform confirm excellent agreement in results under both steady-state and transient operating conditions. Full article

As renewable energy integration accelerates, the displacement of synchronous generators by inverter-based resources (IBRs) necessitates advanced grid-forming (GFM) control strategies to maintain system stability. While techniques such as Droop control, Virtual Synchronous Generator (VSG), and Dispatchable Virtual Oscillator Control (dVOC) are well-established, their comparative performance under coordinated cyber-physical stress remains underexplored. This paper presents a comprehensive Controller Hardware-in-the-Loop (CHIL) assessment of these three GFM strategies within a networked microgrid environment. Utilizing a co-simulation framework that integrates an OPAL-RT real-time simulator with the EXata CPS network emulator, we evaluate the dynamic resilience of each controller under islanded, parallel, and fault-induced reconfiguration scenarios. Experimental results demonstrate that the VSG strategy offers superior transient performance, characterized by faster settling times and enhanced fault-ride-through capabilities compared to the Droop and dVOC strategies. Furthermore, recognizing the vulnerability of connected microgrids to cyber threats, this study investigates the impact of False Data Injection (FDI) attacks on the control layer. To address this, a model-reference resilience layer is proposed and validated on a TI C2000 DSP. The results confirm that this protection mechanism effectively detects and mitigates attacks on control references and feedback measurements, ensuring stable operation despite cyber-physical disturbances. Full article