Search Results (11)

Multiple-fault events can initiate overload propagation and cascading outages, resulting in severe load loss and reduced system resilience. Therefore, beyond conventional protection concepts based on the (n − 1) criterion, there is also a need to address multiple-fault events to minimize loss of load. This paper presents an optimized overload tripping scheme to mitigate cascading outages in high-voltage grids under multiple-fault conditions, where selected line switches or circuit breakers are opened in a controlled manner to isolate limited grid sections, minimize interrupted load, and prevent further overload propagation. The method combines inverse definite minimum time relay modeling with a heuristic graph-search algorithm implemented in pandapower to identify feasible switching actions that minimize load loss while preventing overload propagation. The approach is demonstrated on SimBench high-voltage urban and mixed benchmark grids under double-line fault scenarios. In the urban grid, the proposed scheme reduces the maximum load loss from 34.0% to 2.4%, while in the mixed grid, the reduction is from 50.3% to 5.2%. A SAIFI-inspired resilience proxy is introduced to quantify the reduction in customer/load interruptions, showing a resilience improvement factor of about 3.6 for cascading scenarios. In addition, thermal inertia analysis indicates that corrective switching must be completed within approximately 5 min to remain within line-temperature limits. The analysis is based on quasi-steady-state power-flow and relay simulations; transient stability effects are outside the scope of this study. The results demonstrate that the optimized overload tripping scheme is a promising adaptive protection strategy for improving grid resilience under severe contingency conditions. Full article

Contactless transmission of mechanical power, which is characteristic of coaxial magnetic gears (CMGs), offers significant advantages over conventional mechanical gears, in particular, reduced maintenance frequency and inherent overload protection. At the same time, there is a lack of design methodologies for this type of gear based on the analysis and systematization of experience gained from already implemented designs. This paper presents a method for determining the maximum magnetic torques of CMGs on the basis of an equivalent magnetic-circuit model. The error associated with the proposed methodology does not exceed ±15%, which enables the influence of geometric parameters and the magnetic properties of materials on the key performance indicators of the gear to be assessed already at the preliminary design stage. A mathematical model of CMG dynamics was also developed, based on a quasi-stationary two-dimensional approximation of the magnetic field, accounting for the geometry of the magnetic circuit, the spatial distribution of the magnetic vector potential, and magnetic-circuit saturation. The proposed mathematical model was verified using the results of physical experiments. The discrepancy between the calculated and experimental values of the torque on the low-speed shaft in the steady state does not exceed 5.5%. Based on the optimization procedure, the dependence of the maximum linear torque density on the outer diameter of the CMG, the number of poles of the high-speed rotor, and the transmission ratio was determined. It was shown that, as the number of poles increases, the linear torque density also increases and, for example, for diameters of approximately 800 mm, it may exceed 100 N·m/m. Full article

This study develops a physics-informed digital twin framework for quasi-real-time assessment and forecasting of marine plate heat exchanger performance under variable environmental and operational conditions. Unlike conventional steady-state or purely data-driven approaches, the proposed framework integrates first-principles thermohydraulic modeling, an iterative successive-approximation solver, and continuous synchronization with operational ship data, enabling adaptive state estimation and degradation tracking. The methodology explicitly accounts for coupled thermal, hydraulic, and fouling processes, and incorporates uncertainty-aware validation under real ship operating conditions. A case study based on a central cooling system of a cargo vessel demonstrates that seawater temperature variations of 3–4 K can induce nonlinear system responses, including up to a fourfold increase in coolant demand, a 10–15% reduction in heat-transfer efficiency, and a 15–25% rise in hydraulic losses. A threshold operating regime is identified, characterized by rapid degradation and fouling amplification. Comparative analysis against a static baseline model shows that the digital twin improves predictive accuracy and enables early detection of performance deterioration. Energy-efficiency assessment indicates that adaptive cooling control supported by the digital twin can reduce auxiliary power demand and contribute to fuel savings. The proposed framework provides a scalable foundation for predictive maintenance and intelligent thermal management in maritime systems. Full article

This paper develops a rigorous operator-theoretic framework for learning-augmented quasi-gradient methods in constrained optimization. We consider the minimization of an objective function over a closed convex feasible set, where feasibility is enforced via projection and directional updates may incorporate data-driven corrections. Such settings arise naturally in modern optimization algorithms that integrate artificial intelligence components under structural constraints. The proposed formulation introduces an explicit contraction–bias–variance decomposition of the iterative dynamics. Curvature induces deterministic contraction, alignment distortion—quantified by a geometric parameter—modifies the effective contraction margin, and stochastic learning components inject controlled dispersion. Explicit error recursions yield convergence guarantees under strong convexity, the Polyak–Łojasiewicz condition, and smooth nonconvexity. The analysis establishes that stability regions and first-order complexity bounds are preserved whenever alignment distortion remains below unity and bounded second-moment conditions hold. A fully reproducible computational study provides quantitative validation: the empirically observed steady-state error closely matches the theoretical prediction proportional to ${\sigma }^2/\mu (1-\eta )$. Comparative experiments with gradient, stochastic gradient, and momentum methods confirm that the proposed operator retains classical stability margins and conditioning sensitivity while enabling principled integration of learned directional components. The results provide a transparent mathematical bridge between stochastic approximation theory and contemporary AI-enhanced constrained optimization. Full article

Atherosclerosis, a chronic inflammatory cardiovascular disease closely related to plaque formation during arteriosclerosis, poses a significant threat to global health. To deepen the understanding of the multifaceted interactions driving atherosclerosis progression and provide theoretical support for designing targeted therapeutic strategies, this study establishes a nonlinear coupled atherosclerotic free-boundary model integrating inflammatory immune cells, cytokines, and oxidized low-density lipoprotein. By applying the compression mapping principle, the local and global existence and uniqueness of solutions are proven, while revealing certain symmetries in the model’s solution structure. Under specific assumptions, a quasi-steady-state approximate model is derived, and the existence of its solution is demonstrated. Through numerical simulations of the quasi-steady-state approximate model using the finite difference method, the temporal and spatial evolution of pro-inflammatory macrophages and oxidized low-density lipoprotein is analyzed. The findings highlight the model’s strength in capturing the intricate dynamics of atherosclerosis, uncovering underlying mechanisms and identifying therapeutic targets. By evaluating inflammatory dynamics across plaque types and stenosis levels, the experimental design further validated the model’s ability to replicate clinical processes and reinforced its predictive accuracy. Notably, in the process of model analysis and solution, symmetries in the equations and boundary conditions play a crucial role in determining the solution properties. However, the current one-dimensional model has limitations. Future research should focus on developing higher-dimensional models and integrating more influencing factors to enhance the model’s clinical applicability and deepen the understanding of this complex disease. Full article

This paper presents a data-driven kinematic model for the end-effector pose control applied to a variety of manipulator robots, focusing on the entire end-effector’s pose (position and orientation). The measured signals of the full pose and their computed derivatives, along with a linear combination of an estimated Jacobian matrix and a vector of joint velocities, generate a model estimation error. The Jacobian matrix is estimated using the Pseudo Jacobian Matrix (PJM) algorithm, which requires tuning only the step and weight parameters that scale the convergence of the model estimation error. The proposed control law is derived in two stages: the first one is part of an objective function minimization, and the second one is a constraint in a quasi-Lagrangian function. The control design parameters guarantee the control error convergence in a closed-loop configuration with adaptive behavior in terms of the dynamics of the estimated Jacobian matrix. The novelty of the approach lies in its ability to achieve superior tracking performance across different manipulator robots, validated through simulations. Quantitative results show that, compared to a classical inverse-kinematics approach, the proposed method achieves rapid convergence of performance indices (e.g., Root Mean Square Error (RMSE) reduced to near-zero in two cycles vs. a steady-state RMSE of 20 in the classical approach). Additionally, the proposed method minimizes joint drift, maintaining an RMSE of approximately 0.3 compared to 1.5 under the classical scheme. The control was validated by means of simulations featuring an UR5e manipulator with six Degrees of Freedom (DOF), a KUKA Youbot with eight DOF, and a KUKA Youbot Dual with thirteen DOF. The stability analysis of the closed-loop controller is demonstrated by means of the Lyapunov stability conditions. Full article

The particulars of stimulus–response experiments performed on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports and other data limitations rule. For linear systems, exponential modes, visible and hidden, play an important role in understanding data limitations, embodied in what we call dynamical signatures in the data. We show here how to circumscribe and analyze modal response data in compartmentalizing model structures—so that modal analysis can be used constructively in systems biology mechanistic model building—for some nonlinear (NL) as well as linear biosystems. We do this by developing and exploiting the modal basis for dynamical signatures in hypothetical (perfect) input–output (I-O) data associated with a (mechanistic) structural model—one that includes inputs and outputs explicitly. The methodology establishes model dimensionality (size and complexity) from particular I-O datasets; helps select among multiple candidate models (model distinguishability); helps in designing new I-O experiments to extract “hidden” structure; and helps to simplify (reduce) models to their essentials. These modal analysis tools are introduced to NL enzyme-regulated and protein–protein interaction biosystems via nonlinear normal mode (NNM) and quasi-steady state approximation (QSSA) analyses and unified with linear models on invariant 2-dimensional manifolds in phase space, with properties similarly informative about their dominant dynamical properties. Some automation of these highly technical aspects of biomodeling is also introduced. Full article

A Cascadia Subduction Zone (CSZ) earthquake will cause widespread damage to numerous lifelines and infrastructure along the northern US west coast. The goal of the presented research is to provide a bottom up estimate of the impact on and subsequent recovery of a Cascadia Subduction Zone earthquake on the US western grid to supplement and enhance the expert opinion estimates provided to date. The scope is limited to only consideration of shaking damage to utility substation equipment components of a power system model. The analysis utilizes probabilistic models of damage and recovery for substation power system assets, along with graph techniques for modeling connectivity, and Monte Carlo quasi steady state power flow solutions. The results show that a conservative estimate of the initial damage and loss of load is approximately 4000 MW, with a recovery estimate of 230 days. Full article

Based on unsteady state heat conduction, a mathematical model has been developed to describe the simultaneous heat and moisture transfer during potato frying. For the first time, the equation was solved using both enthalpy and Variable Space Network (VSN) methods, based on a moving interface defined by the boiling temperature of water in a potato disc during frying. Two separate regions of the potato disc namely fried (crust) and unfried (core), were considered as heat transfer domains. A variable boiling temperature of the water in potato discs was required as an input parameter for the model as the water is evaporated during frying, resulting in an increase in the soluble solid concentration of the potato sample. Pulsed electric field (PEF) pretreatment prior to frying had no significant effect on the measured moisture content, thermal conductivity or frying time compared to potatoes that did not receive a PEF pretreatment. However, a PEF pretreatment at 1.1 kV/cm and 56 kJ/kg reduced the temperature variation in the experimentally measured potato center by up to 30%. The proposed heat and moisture transfer model based on unsteady state heat conduction successfully predicted the experimental measurements, especially when the equation was solved using the enthalpy method. Full article

Natural convection induced by carbon dioxide (CO₂) dissolution from a gas cap into the resident formation brine of a deep saline aquifer in the presence of a capillary transition zone is an important phenomenon that can accelerate the dissolution process, reducing the risk of CO₂ leakage to the shallower formations. Majority of past investigations on the instability of the diffusive boundary layer assumed a sharp CO₂–brine interface with constant CO₂ concentration at the top of the aquifer, i.e., single-phase system. However, this assumption may lead to erroneous estimates of the onset of natural convection. The present study demonstrates the significant effect of the capillary transition zone on the onset of natural convection in a two-phase system in which a buoyant CO₂ plume overlaid a water-saturated porous layer. Using the quasi-steady-state approximation (QSSA), we performed a linear stability analysis to assess critical times, critical wavenumbers, and neutral stability curves as a function of Bond number. We show that the capillary transition zone could potentially accelerate the evolution of the natural convection by sixfold. Furthermore, we characterized the instability problem for capillary-dominant, in-transition, and buoyancy-dominant systems. In the capillary-dominant systems, capillary transition zone has a strong role in destabilizing the diffusive boundary layer. In contrast, in the buoyancy-dominant systems, the buoyancy force is the sole cause of the instability, and the effect of the capillary transition zone can be ignored. Our findings provide further insight into the understanding of the natural convection in the two-phase CO₂–brine system and the long-term fate of the injected CO₂ in deep saline aquifers. Full article

The dynamics of a system of hard spheres enclosed between two parallel plates separated a distance smaller than two particle diameters is described at the level of kinetic theory. The interest focuses on the behavior of the quasi-two-dimensional fluid seen when looking at the system from above or below. In the first part, a collisional model for the effective two-dimensional dynamics is analyzed. Although it is able to describe quite well the homogeneous evolution observed in the experiments, it is shown that it fails to predict the existence of non-equilibrium phase transitions, and in particular, the bimodal regime exhibited by the real system. A critical revision analysis of the model is presented , and as a starting point to get a more accurate description, the Boltzmann equation for the quasi-two-dimensional gas has been derived. In the elastic case, the solutions of the equation verify an H-theorem implying a monotonic tendency to a non-uniform steady state. As an example of application of the kinetic equation, here the evolution equations for the vertical and horizontal temperatures of the system are derived in the homogeneous approximation, and the results compared with molecular dynamics simulation results. Full article