arXiv subjects

Shadi Heenatigala

Publications and source records attributed to Shadi Heenatigala.

3 recordsLinked to original sources

arXiv2026

A Local Macroscopic Conservative (LoMaC) low rank tensor method for the Vlasov-Maxwell system

The main computational challenges of solving the Vlasov-Maxwell (VM) system include the high dimensionality of the phase space, nonlinearity, inherent conservation properties, among others. In this paper, we develop a novel Local Macroscopic Conservative (LoMaC) low rank tensor method for the VM system, as a continuation of our previous work (arXiv:2207.00518). The method takes advantage of the tensor friendly structure of the Vlasov equation and employs the low rank hierarchical Tucker decomposition to approximate the Vlasov solution in high dimensions. Hence, the curse of dimensionality can be mitigated. Furthermore, to realize the LoMaC property, the algorithm simultaneously evolves the conservation laws of mass, momentum and energy alongside the Vlasov equation using a high order conservative method with the kinetic flux vector splitting. By a conservative orthogonal projection, the low rank solution is guaranteed to have the same macroscopic observables updated from the conservation laws. A collection of numerical tests on the VM system are presented to demonstrate the efficiency and efficacy of the proposed algorithm.

math.NA↗

arXiv2026

A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems

This study presents a statistical and machine learning framework for characterizing a hydrogen-based multi-energy system (H-MES) using one year of high-resolution operational data. Statistical analysis revealed a binary operation driven by renewable surplus, with solar irradiance explaining 45.7% of rank-based variance in hydrogen production, a large effect by conventional standards. Only high-irradiance periods triggered meaningful electrolyzer engagement, while electricity demand exerted a weaker inverse suppression effect ($ε^2 = 0.126$). Multiple regression confirmed electrolyzer power as the dominant linear predictor, with a synergistic solar-wind interaction. Notably, Random Forest analysis ranked wind output first in predictive importance despite its weak bivariate correlation (r = 0.167), revealing non-linear dynamics invisible to parametric methods. A sequence model exploited strong 24-hour autocorrelation (r = 0.845) for operational forecasting, while a reinforcement learning agent optimized hydrogen revenue dispatch. The core contribution is demonstrating that statistical and machine learning approaches are complementary for H-MES modeling and control.

cs.LG↗

arXiv2024

Insights from the Field: A Comprehensive Analysis of Industrial Accidents in Plants and Strategies for Enhanced Workplace Safety

The study delves into 425 industrial incidents documented on Kaggle [1], all of which occurred in 12 separate plants in the South American region. By meticulously examining this extensive dataset, we aim to uncover valuable insights into the occurrence of accidents, identify recurring trends, and illuminate underlying causes. The implications of this analysis extend beyond mere statistical observation, offering organizations an opportunity to enhance safety and health management practices. Our findings underscore the importance of addressing specific areas for improvement, empowering organizations to fortify safety measures, mitigate risks, and cultivate a secure working environment. We advocate for strategically applying statistical analysis and data visualization techniques to leverage this wealth of information effectively. This approach facilitates the extraction of meaningful insights and empowers decision-makers to implement targeted improvements, fostering a preventive mindset, and promoting a safety culture within organizations. This research is a crucial resource for organizations committed to transforming data into actionable strategies for accident prevention and creating a safer workplace.

cs.CY↗