This paper presents a three-dimensional formulation of the Localized Novel Implicit Iterative Particle Shifting (L-NIIPS) method within the Arbitrary Lagrangian-Eulerian Smoothed Particle Hydrodynamics (ALE-SPH) framework to minimize particle concentration gradients, thereby improving the regularity of the particle distribution. A multi-node parallelization approach is introduced for the L-NIIPS approach to efficiently handle complex 3D cases. The methodology also incorporates wall contribution terms into the Advection Correction Step (ACS) formulation to ensure consistent transport of physical quantities through the boundary integral method, thereby eliminating pressure oscillations near solid boundaries and yielding noise-free pressure fields. Through numerical evaluation, optimal L-NIIPS parameters are identified that strike a balance among accuracy, computational cost, and particle distribution regularity, proving robust across different flow configurations and eliminating the need for case-specific tuning. Validation is performed using established SPH benchmarks, including the Taylor-Green Vortex (TGV), moving square box, and 2D/3D impinging jets on a flat plate and further assessed through a demanding 3D Pelton bucket simulation. Results confirm that the proposed methodology significantly enhances accuracy and smoothness of the physical fields while maintaining a manageable computational overhead, making it suitable for industrial applications.
The growing energy demand has highlighted the need for reliable, decentralized, and self-sustained power systems. In this context, optimal integration of distributed energy resources (DERs) into distribution networks is vital for improving voltage stability and reducing power losses. However, conventional DER planning methods are computationally intensive and may produce inconsistent solutions under varying load conditions. This paper presents a sensitivity-based Generalized Particle Swarm Optimization (GEPSO) framework for optimal siting and sizing of DERs in radial distribution systems. The methodology is implemented in two stages. First, site-specific geographical and meteorological data are evaluated to determine realistic DER capacity limits. Second, these feasibility constraints are embedded within the GEPSO algorithm to identify optimal DER locations and sizes. Load flow analysis is performed using the forward–backward sweep method on a real 69-node Ramchandrapura feeder and the IEEE 33-node test system under multiple loading and DER penetration scenarios. Simulation results demonstrate an average active power loss reduction of approximately 36%, with up to 3.3% improvement compared to conventional PSO, while enhancing the minimum bus voltage by about 3–4%. The key contribution of this work is the integration of site-specific feasibility assessment with a sensitivity-guided GEPSO framework for efficient DER planning applications.
Engineering simulations are usually based on complex, grid-based, or mesh-free methods for solving partial differential equations. The results of these methods cover large fields of physical quantities at very many discrete spatial locations and temporal points. Efficient compression methods can be helpful for processing and reusing such large amounts of data. A compression technique is attractive if it causes only a small additional effort and the loss of information with strong compression is low. The paper presents the development of an incremental Singular Value Decomposition (SVD) strategy for compressing time-dependent particle simulation results. The approach is based on an algorithm that was previously developed for grid-based, regular snapshot data matrices. It is further developed here to process highly irregular data matrices generated by particle simulation methods during simulation. Various aspects important for information loss, computational effort and storage requirements are discussed, and corresponding solution techniques are investigated. These include the development of an adaptive rank truncation approach, the assessment of imputation strategies to close snapshot matrix gaps caused by temporarily inactive particles, a suggestion for sequencing the data history into temporal windows as well as bundling the SVD updates. The simulation-accompanying method is embedded in a parallel, industrialized Smoothed-Particle Hydrodynamics software and applied to several 2D and 3D test cases. The proposed approach reduces the memory requirement by about 90% and increases the computational effort by about 10%, while preserving the required accuracy. For the final application of a water turbine, the temporal evolution of the force and torque values for the compressed and simulated data is in excellent agreement.
