Ali Izadi | Searcharxiv

arXiv2026

Understanding Energy Flow and Inefficiency of a Thermomagnetic Generator by Transient Multi-Physics Modelling

Waste heat recovery improves energy efficiency and reduces greenhouse gas emissions; however, much industrial and environmental heat is wasted at low temperature. Thermomagnetic recovery of waste heat has a high potential for sustainable production of electric energy, especially for low-grade waste heat where conventional technology is inefficient or infeasible. Of particular interest are thermomagnetic generators (TMG) as they require almost no mechanically moving parts, which is beneficial for high reliability. However, all existing prototypes have two remaining challenges: low efficiency and low cycle frequency. In this work, we develop a digital twin of a recent TMG with genus 3 by using multi-physics simulations. We identify shortcomings of previous simulation approaches, and describe why simulations in three dimensions are necessary, which consider coupling between magnetic, thermal, fluid flow, and electrical physics domains. We validate our model, which only uses known geometry and material parameters, by experimental data of the TMG with highest power density today, and attain 96% accuracy in open-circuit voltage and 95% accuracy in power output. This high accuracy allows us to identify the origin of both challenges for TMGs, which are not accessible by experiments. First, we uncover inefficiencies by analyzing the energy flow within a Sankey diagram. Second, we trace the transient heat flow through the generator, which identifies the factors limiting frequency. This paves the way for more efficient and faster TMGs, and their development will be accelerated by our validated digital twin.

physics.app-ph↗

arXiv2024

Causal Order Discovery based on Monotonic SCMs

In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential to enable causal inference and causal discovery from observational data. While existing approaches either assume prior knowledge about the causal order or use complex optimization techniques to impose sparsity in the Jacobian of Triangular Monotonic Increasing maps, our work introduces a novel sequential procedure that directly identifies the causal order by iteratively detecting the root variable. This method eliminates the need for sparsity assumptions and the associated optimization challenges, enabling the identification of a unique SCM without the need for multiple independence tests to break the Markov equivalence class. We demonstrate the effectiveness of our approach in sequentially finding the root variable, comparing it to methods that maximize Jacobian sparsity.

cs.LG↗