Arnab Bhadra | Searcharxiv

arXiv2024

Geometric Scaling Laws for Axial Flux Permanent Magnet Motors in In-Wheel Powertrain Topologies

In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based on the scaling concept of RFMs from the literature, specifically deriving the model of the main loss component in electric motors: the copper losses. We further present separate scaling models of motor parameters, losses and thermal models, as well as the torque limits and cost, as a function of the design variables. Second, we validate these scaling laws with several experiments leveraging high-fidelity finite-element simulations. Finally, we define an optimization problem that minimizes the energy consumption over a drive cycle, optimizing the motor size and transmission ratio for a wide range of electric vehicle powertrain topologies. In our study, we observe that the all-wheel drive topology equipped with in-wheel AFMs is the most efficient, but also generates the highest material cost.

eess.SY↗

arXiv2020

Site2Vec: a reference frame invariant algorithm for vector embedding of protein-ligand binding sites

Protein-ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm Site2Vec that derives reference frame invariant vector embedding of a protein-ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 datasets and against 23 other site comparison methods. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We provide Site2Vec as a stand alone executable and a web service hosted at \url{http://services.iittp.ac.in/bioinfo/home}.

q-bio.BM↗