arXiv subjects

Ian Addison-Smith

Publications and source records attributed to Ian Addison-Smith.

4 recordsLinked to original sources

arXiv2026

Mean-flow-based reduced-order models of turbulent channel flow

Reduced-order models (ROMs) for turbulent flows based on Galerkin projection can achieve reasonable accuracy using equation-based modal bases derived from the linearized Navier-Stokes equations through the controllability and observability Gramians. The use of the modal bases obtained from linearized equations around a mean state has been seen to enhance the first- and second-order statistics in the ROM, but the use of the mean state was not necessarily extended to the equations of motion, as it implies the treatment of the divergence of the Reynolds stresses in the Galerkin projection. In this work, we present a mean-flow-based framework for ROMs in which the projection of the Reynolds stresses is solved through a modified modal basis and the knowledge of the mean flow. This framework achieves turbulence statistics comparable to those of a reference direct numerical simulation (DNS) in a minimal channel at $Re_{\tau} \approx 185$. Short-time forecasting with this framework is assessed, where balanced truncation modal bases outperform controllability modes in ROMs, yielding a reconstruction of the velocity field comparable to the Galerkin projection of proper orthogonal decomposition (POD) modes. This framework can extend analysis based on linearisations around the mean turbulent flow, which became widespread in recent years, to include explicitly non-linear interactions between modes, enabling accurate models at higher Reynolds number.

physics.flu-dyn↗

arXiv2025

Train yourself: self-compressing reduced-order models of turbulent flows

Reduced-order models (ROMs) of turbulent flows based on Galerkin projection often require many degrees of freedom to resolve the dynamics of the turbulence, or simulation data to obtain an optimal modal basis. However, obtaining simulation data is computationally expensive, and the amount of data required to obtain a converged modal basis can increase this cost. Using the linearized Navier-Stokes equations, one can achieve spatial modes through the controllability and observability Gramians, which can yield a ROM without prior simulation data. In this work, we propose a self-compression of a ROM based on controllability modes, where the time series of the modal coefficients are leveraged to reduce the dimension of the ROM. In the self-compressed ROM (SCROM), we can maintain accurate first- and second-order statistics with respect to the DNS simulation, but in a further reduced dimension. The SCROM recovers spatial structures equivalent to proper orthogonal decomposition (POD) without relying on any simulation data, recombining spatial modes from linearized equations. This method leads to a novel ROM that can represent turbulence statistics in a data-free approach in a further reduced state space.

physics.flu-dyn↗

arXiv2025

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci↗

arXiv2023

Accurate boundary-integral formulations for the calculation of electrostatic forces with an implicit-solvent model

An accurate force calculation with the Poisson-Boltzmann equation is challenging, as it requires the electric field on the molecular surface. Here, we present a calculation of the electric field on the solute-solvent interface that is exact for piece-wise linear variations of the potential and analyze four different alternatives to compute the force using a boundary element method. We performed a verification exercise for two cases: the isolated and two interacting molecules. Our results suggest that the boundary element method outperforms the finite difference method, as the latter needs a much finer mesh than in solvation energy calculations to get acceptable accuracy in the force, whereas the same surface mesh than a standard energy calculation is appropriate for the boundary element method. Among the four evaluated alternatives of force calculation, we saw that the most accurate one is based on the Maxwell stress tensor. However, for a realistic application, like the barnase-barstar complex, the approach based on variations of the energy functional, which is less accurate, gives equivalent results. This analysis is useful towards using the Poisson-Boltzmann equation for force calculations in applications where high accuracy is key, for example, to feed molecular dynamics models or to enable the study of the interaction between large molecular structures, like viruses adsorbed onto substrates.

physics.chem-ph↗