Exploration of The Duality Between Generalized Geometry and Extraordinary Magnetoresistance

We outline the duality between the extraordinary magnetoresistance (EMR), observed in semiconductor-metal hybrids, and non-symmetric gravity coupled to a diffusive $U(1)$ gauge field. The corresponding gravity theory may be interpreted as the generalized complex geometry of the semi-direct product of the symmetric metric and the antisymmetric Kalb-Ramond field: ($g_{\mu\nu}+\beta_{\mu\nu}$). We construct the four dimensional covariant field theory and compute the resulting equations of motion. The equations encode the most general form of EMR within a well defined variational principle, for specific lower dimensional embedded geometric scenarios. Our formalism also reveals the emergence of additional diffusive pseudo currents for a completely dynamic field theory of EMR. The proposed equations of motion now include terms that induce geometrical deformations in the device geometry in order to optimize the EMR. This bottom-up dual description between EMR and generalized geometry/gravity lends itself to a deeper insight into the EMR effect with the promise of potentially new physical phenomena and properties.Comment: 13 pages and 6 figures. Revised/edited for clarity and purpose. Several references added. Updated title based on suggestions and comments received. Version accepted for publication in Phys.Rev.

PEM fuel cell with high-performance bipolar plates

The consumption of electricity has drastically increased over the last decade and the need for cheap energy efficient sources has augmented. Fuel cells have been proposed as possible power sources to address issues that involve energy production and the environment. A proton exchange membrane fuel cell (PEMFC) is a likely alternative as a zero-emission source due to its high efficiency, low temperature operation and high power density. The Bipolar plate is a vital element of PEM fuel cell, which supplies fuel and oxidant to reactive sites, removes reaction products, collects produced current and increases the effective contact area for higher heat and mass transport. It also represents more than 60% of the weight and 30% of the total cost in a fuel cell stack. Improving the layout of the flow-field and use of lightweight materials can significantly reduce the weight of the fuel cell stack. Different material combinations, flow-field layouts and fabrication techniques will be studied to achieve the previously mentioned functions efficiently, with the aim of obtaining higher performance. Bipolar plates with different flow field layouts have been designed and their effect on the PEM fuel cell will be measured using computational multiphysics models

Power Failure Cascade Prediction using Machine Learning

We consider the problem of predicting power failure cascades due to branch failures. We propose several flow-free models using machine learning techniques like support vector machines, naive Bayes classifiers, and logistic regression. These models predict the grid states at every generation of a cascade process given the initial contingency. Further, we also propose a model based on graph neural networks (GNNs) that predicts cascades from the initial contingency and power injection values. We train the proposed models using a cascade sequence data pool generated from simulations. We then evaluate our models at various levels of granularity. We present several error metrics that gauge the models’ ability to predict the failure size, the final grid state, and the failure time steps of each branch within the cascade. We benchmark the proposed models against the influence model proposed in the literature. We show that the proposed machine learning models outperform the influence models under every metric. We also show that the graph neural network model, in addition to being generic over randomly scaled power injection values, outperforms multiple influence models that are built specifically for their corresponding loading profiles. Finally, we show that the proposed models reduce the computational time by almost two orders of magnitude.S.M

DeepSRGM -- Sequence Classification and Ranking in Indian Classical Music with Deep Learning

A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a melodic framework for compositions and improvisations alike. Raga Recognition is an important music information retrieval task in ICM as it can aid numerous downstream applications ranging from music recommendations to organizing huge music collections. In this work, we propose a deep learning based approach to Raga recognition. Our approach employs efficient pre possessing and learns temporal sequences in music data using Long Short Term Memory based Recurrent Neural Networks (LSTM-RNN). We train and test the network on smaller sequences sampled from the original audio while the final inference is performed on the audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during inference on the Comp Music Carnatic dataset and its 10 Raga subset respectively making it the state-of-the-art for the Raga recognition task. Our approach also enables sequence ranking which aids us in retrieving melodic patterns from a given music data base that are closely related to the presented query sequence

Power Failure Cascade Prediction using Graph Neural Networks

We consider the problem of predicting power failure cascades due to branch failures. We propose a flow-free model based on graph neural networks that predicts grid states at every generation of a cascade process given an initial contingency and power injection values. We train the proposed model using a cascade sequence data pool generated from simulations. We then evaluate our model at various levels of granularity. We present several error metrics that gauge the model's ability to predict the failure size, the final grid state, and the failure time steps of each branch within the cascade. We benchmark the graph neural network model against influence models. We show that, in addition to being generic over randomly scaled power injection values, the graph neural network model outperforms multiple influence models that are built specifically for their corresponding loading profiles. Finally, we show that the proposed model reduces the computational time by almost two orders of magnitude.Comment: 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). Oct. 31, 2023. See implementations at https://github.com/sathwikchadaga/failure-cascad

Lateral transition metal dichalcogenide heterostructures for high efficiency thermoelectric devices

Increasing demands for renewable sources of energy has been a major driving force for developing efficient thermoelectric materials. Two-dimensional (2D) transition-metal dichalcogenides (TMDC) have emerged as promising candidates for thermoelectric applications due to their large effective mass and low thermal conductivity. In this article, we study the thermoelectric performance of lateral TMDC heterostructures within a multiscale quantum transport framework. Both $n$-type and $p$-type lateral heterostructures are considered for all possible combinations of semiconducting TMDCs: MoS$_2$, MoSe$_2$, WS$_2$, and WSe$_2$. The band alignment between these materials is found to play a crucial in enhancing the thermoelectric figure-of-merit ($ZT$) and power factor far beyond those of pristine TMDCs. In particular, we show that the room-temperature $ZT$ value of $n$-type WS$_2$ with WSe$_2$ triangular inclusions, is five times larger than the pristine WS$_2$ monolayer. $p$-type MoSe$_2$ with WSe$_2$ inclusions is also shown to have a room-temperature $ZT$ value about two times larger than the pristine MoSe$_2$ monolayer. The peak power factor values calculated here, are the highest reported amongst gapped 2D monolayers at room temperature. Hence, 2D lateral TMDC heterostructures open new avenues to develop ultra-efficient, planar thermoelectric devices

Grease material properties from first principles thermodynamics

Thermodynamics has historically been used to derive characteristic material properties. In this study, fundamental thermodynamics is applied to grease. First-principle formulations of existing material properties—heat capacity and storage modulus—and new properties—thermal strain and stress coefficients, chemical resistance and thermo-chemical decay coefficient—are derived, some of which are experimentally determined. A new group of Maxwell relations is introduced by replacing the classical compression work (Formula presented.) with the grease shearing work (Formula presented.). The physical interpretations and implications of these properties on grease behaviour and performance are presented. Experimental measurements of the derived properties are performed in accordance with the theoretical formulations. Six different grease types are studied. Obtained results are shown to conform with anticipated, observed and established grease behaviours. The proposed properties can be used in grease performance and degradation analyses, as well as grease selection for lubrication applications.</p