This paper presents and compares two delay-free methods to parallelize Electromagnetic Transient (EMT) simulations when there is no line propagation delay for decoupling. Both methods involve transforming the network matrix into a Bordered-Block Diagonal form, allowing for parallel decoupling. In the first one, BBD (node tearing), the BBD form is obtained with no variable addition. In the second method, the Compensation Method (CM), additional variables, such as compensation currents, are required to achieve a parallelizable BBD form. Graph partitioning techniques (Metis) are used to automate the network decoupling in both methods, which are implemented in a Julia-EMT code. Each BBD-based method achieves substantial performance gains on practical networks.
Highly dynamic fluid–granular flows transitioning from subaerial to submerged conditions exhibit strongly nonlinear behaviour involving large deformations, evolving pore pressure, seepage, and granular dilatancy or compaction, posing significant challenges for computational modelling. This study presents a novel mixture-theory-based two-phase Moving Particle Semi-implicit (MPS) model for transitional fluid–granular dynamics. We develop the first volume-adaptive MPS formulation that explicitly accounts for particle volume variations induced by fluid seepage and granular deformation, enabling consistent modelling of dilatancy, compaction, and dry-to-saturated transitions. The granular phase is represented as a deformable porous medium using a generalized constitutive law that captures solid- and fluid-like behaviour under varying saturation conditions. A unified coupling strategy ensures conservative interphase interactions between overlapping continua with evolving volumes. Stabilization techniques are reformulated to accommodate variable-volume particle behaviour while maintaining numerical stability. The model is validated against benchmark problems involving subaerial granular column collapse, dam-break flows into porous media, and hydrostatic stress development in initially dry granular columns, and is further applied to two- and three-dimensional granular collapses interacting with free-surface flows. The results demonstrate accurate predictions of granular dynamics, pore-pressure evolution, and impulse-wave generation and propagation, establishing a volume-consistent particle framework for transitional fluid–granular flows.
This paper presents a new variable time-step method for the electromagnetic transient (EMT) simulation of power systems. Trapezoidal discretization with a constant time-step is commonly employed in EMT simulations of power systems. During an EMT simulation, power systems typically encounter high-frequency transients that demand small time-steps for accuracy, as well as low-frequency and steady-state conditions that allow larger time-steps for efficiency. With a fixed time-step, either the simulation accuracy or speed is compromised, and achieving a trade-off is not feasible for all cases. Consequently, this paper proposes a novel variable time-step strategy for maintaining both simulation accuracy and speed. The performance of the proposed method is verified through the WECC 240-bus system.
This study investigates the electric dial-a-ride problem, which involves scheduling a fleet of electric vehicles to provide ride-sharing services for customers with specified origins and destinations. The problem incorporates several real-world characteristics often overlooked in the literature, including (1) a concave piecewise linear charging function, (2) capacity constraints for charging stations, (3) time-dependent charging pricing policies, (4) multiple types of charging infrastructure, and (5) partial charging strategies. To address the computational challenges posed by this problem structure, we propose a variable neighborhood search metaheuristic enhanced with multiple neighborhood structures and embedded dynamic programming components to determine optimal charging strategies for the fleet. Computational experiments on large-scale and very large-scale instances, including those with up to 10,000 requests derived from benchmark instances and real-world data, confirm the high efficiency and robustness of the proposed method. The results demonstrate that the algorithm not only produces competitive solutions compared to state-of-the-art methods but also effectively addresses the operational challenges.
Model-based engineering (MBE) is a powerful paradigm that leverages models as essential pillars of the development process, enabling teams to clarify requirements, streamline design, specify behavior, and perform rigorous verification and validation tasks across the entire system life cycle. Digital twins (DTs) represent revolutionary software systems that mirror cyber-physical, socio-economic, or biological entities, systems, or processes. Built from robust models and data, DTs are deployed for high-impact applications such as planning, monitoring, control, and optimization of the twinned entity. The model-centric nature of DTs has naturally ignited recent exploration into harnessing MBE for the engineering and operation of DTs. However, this organic evolution has created a fragmented landscape of (partial) solutions. To confront this challenge, this article presents a rigorous and systematic literature survey on the field of model-based DT engineering (MBDTE), accompanied by a novel taxonomy for categorizing MBDTE approaches. We also introduce crisp definitions of both the field of MBDTE and the models themselves. We conclude by highlighting research gaps and outlining avenues for further exploration.
