arXiv subjects

Vijay P. Singh

Publications and source records attributed to Vijay P. Singh.

12 recordsLinked to original sources

arXiv2025

Intramolecular Singlet Fission Through a Coherently Coupled Excimer-like Intermediate

Singlet Fission (SF) into two triplets offers exciting avenues for high-efficiency photovoltaics and optically initializable qubits. While the chemical space of SF chromophores is ever-expanding, the mechanistic details of electronic-nuclear motions that dictate the photophysics are unclear. Rigid SF dimers with well-defined orientations are necessary to decipher such details. Here, using polarization-controlled white-light two-dimensional and pump-probe spectroscopies, we investigate a new class of contorted naphthalenediimide dimers, recently reported to have a favorable intramolecular SF (iSF) pathway. 2D cross-peaks directly identify the two Davydov components of the dimer along with strongly wavelength-dependent TT1 formation kinetics depending on which Davydov component is excited, implicating a coherently coupled intermediate that mediates iSF. Enhanced quantum beats in the TT1 photoproduct suggest that inter-chromophore twisting and ruffling motions drive the ~200 fs evolution towards an excimer-like intermediate and its subsequent ~2 ps relaxation to the TT1 photoproduct. Polarization anisotropy directly tracks electronic motion during these steps and reveals surprisingly minimal electronic reorientation with significant singlet-triplet mixing throughout the nuclear evolution away from the Franck-Condon geometry towards relaxed TT1. The observations of coherent excimer-like intermediate and significant singlet-triplet mixing throughout the iSF process need to be carefully accounted for in the synthetic design and electronic structure models for iSF dimers aiming for long-lived high-spin correlated triplets.

physics.chem-ph↗

arXiv2025

Observation of a Bilayer Superfluid with Interlayer Coherence

Controlling the coupling between different degrees of freedom in many-body systems is a powerful technique for engineering novel phases of matter. We create a bilayer system of two-dimensional (2D) ultracold Bose gases and demonstrate the controlled generation of bulk coherence through tunable interlayer Josephson coupling. We probe the resulting correlation properties of both phase modes of the bilayer system: the symmetric phase mode is studied via a noise-correlation method, while the antisymmetric phase fluctuations are directly captured by matter-wave interferometry. The measured correlation functions for both of these modes exhibit a crossover from short-range to quasi-long-range order above a coupling-dependent critical point, thus providing direct evidence of bilayer superfluidity mediated by interlayer coupling. We map out the phase diagram and interpret it with renormalization-group theory and Monte Carlo simulations. Additionally, we elucidate the underlying mechanism through the observation of suppressed vortex excitations in the antisymmetric mode.

cond-mat.quant-gas↗

arXiv2025

Detecting Phase Coherence of 2D Bose Gases via Noise Correlations

We measure the noise correlations of two-dimensional (2D) Bose gases after free expansion, and use them to characterize the in-situ phase coherence across the Berezinskii-Kosterlitz-Thouless (BKT) transition. The noise-correlation function features a characteristic spatial oscillatory behavior in the superfluid phase, which gives direct access to the superfluid exponent. This oscillatory behavior vanishes above the BKT critical point, as we demonstrate for both single-layer and decoupled bilayer 2D Bose gases. Our work establishes noise interferometry as an important general tool to probe and identify many-body states of quantum gases, extending its application to previously inaccessible correlation properties in multimode systems.

cond-mat.quant-gas↗

arXiv2023

How do chaos and turbulence affect the predictability of natural complex fluid flow systems?

Natural complex fluid flow systems exhibit turbulent and chaotic behavior that determines their high-level complexity. Chaos has an accurate mathematical definition, while turbulence is a property of fluid flow without an accurate mathematical definition. Using the Kolmogorov complexity (KC) and its derivatives (KC spectrum and its highest value), permutation entropy (PE), and Lyapunov exponent (LE), we considered how chaos and turbulence affect the predictability of natural complex fluid flow systems. This paper applied KC, Kolmogorov complexity spectrum, PE, and LE measures to investigate the turbulent and chaotic behaviors of the monthly streamflow of rivers from Bosnia and Herzegovina, the United States, and the Mendoza Basin (Argentina) and evaluated their time horizons using the Lyapunov time (LT). Based on the measures applied for river streamflow, we derived four modes of the interrelationship between turbulence and chaos. Finally, using those modes, we clustered rivers with similar time horizons representing their predictability. In summary, the calculated quantities of the measures were in the following intervals: (i) KC (0.484, 0.992), (ii) PE (0.632, 0.866), (iii) LE (0.108, 0.278), and (iv) LT (3.4, 9.3 months).

physics.flu-dyn↗

arXiv2023

Full-Range Approximation for the Theis Well Function Using Ramanujan's Series and Bounds for the Exponential Integral

The solution of the governing equation representing the drawdown in a horizontal confined aquifer, where groundwater flow is unsteady, is provided in terms of the exponential integral, which is famously known as the Well function. For the computation of this function in practical applications, it is important to develop not only accurate but also a simple approximation that requires evaluation of the fewest possible terms. To that end, introducing Ramanujan's series expression, this work proposes a full-range approximation to the exponential integral using Ramanujan's series for the small argument (u \leq 1) and an approximation based on the bound of the integral for the other range (u \in (1,100]). The evaluation of the proposed approximation results in the most accurate formulae compared to the existing studies, which possess the maximum percentage error of 0.05\%. Further, the proposed formula is much simpler to apply as it contains just the product of exponential and logarithm functions. To further check the efficiency of the proposed approximation, we consider a practical example for evaluating the discrete pumping kernel, which shows the superiority of this approximation over the others. Finally, the authors hope that the proposed efficient approximation can be useful for groundwater and hydrogeological applications.

cs.CE↗

arXiv2023

Complexity and chaotic behavior of the U.S. rivers and estimation of their prediction horizon

A streamflow time series encompasses a large amount of hidden information and reliable prediction of its behavior in the future remains a challenge. It seems that the use of information measures can significantly contribute to determining the time horizon of rivers and improving predictability. Using the Kolmogorov complexity (KC) and its derivatives (KC spectrum and its highest value), and Lyapunov exponent (LE), it has previously been shown that the degree of streamflow predictability depends on human activities, environmental factors, and natural characteristics. This paper applied the KC and LE measures to investigate the randomness and chaotic behavior of monthly streamflow of 1879 rivers from the United States for a period from 1950 to 2015 and evaluated their time horizons via the Lyapunov and Kolmogorov time (LT and KT, respectively).

physics.data-an↗

arXiv2022

Universal Scaling of the Dynamic BKT Transition in Quenched 2D Bose Gases

While renormalization group theory is a fully established method to capture equilibrium phase transitions, the applicability of RG theory to universal non-equilibrium behavior remains elusive. Here we address this question by measuring the non-equilibrium dynamics triggered by a quench from superfluid to thermal phase across the Berezinskii-Kosterlitz-Thouless transition in a 2D Bose gas. We quench the system by splitting the 2D gas in two and probe the relaxation dynamics by measuring the phase correlation function and vortex density via matter-wave interferometry. The dynamics occur via a two-step process of rapid phonon thermalization followed by slow dynamic vortex unbinding. We demonstrate universal scaling laws for the algebraic exponents and vortex density, supported by classical-field simulations, and show their agreement with the real-time RG theory.

cond-mat.quant-gas↗

arXiv2022

Observation of the BKT Transition in a 2D Bose Gas via Matter-Wave Interferometry

We probe local phase fluctuations of trapped two-dimensional (2D) Bose gases using matter-wave interferometry. This enables us to measure the phase correlation function, which changes from an algebraic to an exponential decay when the system crosses the Berezinskii-Kosterlitz-Thouless (BKT) transition. We determine the temperature dependence of the BKT exponent $η$ and find the critical value $η_c = 0.17(3)$ for our trapped system. Furthermore, we measure the local vortex density as a function of the local phase-space density, which shows a scale-invariant behaviour across the transition. Our experimental investigation is supported by Monte Carlo simulations and provides a comprehensive understanding of the BKT transition in a trapped system.

cond-mat.quant-gas↗

arXiv2020

Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.

eess.SP↗

arXiv2019

Mathematical Modelling of Streamwise Velocity Profile in Open Channels Using Tsallis Entropy

This study derived the vertical distribution of streamwise velocity in wide open channels by maximizing Tsallis entropy, in accordance with the maximum entropy principle, subject to the total probability rule and the conservation of mass, momentum, and energy. Entropy maximizing leads to a highly nonlinear differential equation for velocity which was transformed into a relatively weaker nonlinear equation and then solved analytically using a non-perturbation approach that yielded a series solution. The convergence of the series solution was proved using both theoretical and numerical procedures. For the assessment of velocity profile, the Lagrange multipliers and the entropy index were obtained by solving a system of nonlinear equations by Gauss-Newton method after approximating the constraint integrals using Gauss-Legendre quadrature rule. The derived velocity profile was validated for some selected sets of experimental and field data and also compared with the existing velocity profile based on Tsallis entropy. The incorporation of the above constraints and the effect of entropy index were found to improve the velocity profile for experimental as well as field data. The methodology reported in this study can also be employed for addressing other open channel flow problems, such as sediment concentration and shear stress distribution.

physics.comp-ph↗

arXiv2019

On the Role of Tsallis Entropy Index for Velocity Modelling in Open Channels

Following the work on Shannon entropy together with the principle of maximum entropy, Luo & Singh (J. Hydrol. Eng., 2011, 16(4): 303-315) and Singh & Luo (J. Hydrol. Eng., 2011, 16(9): 725-735) explored the concept of non-extensive Tsallis entropy for modelling velocity in open channels. Later, the idea was extended by Cui & Singh (J. Hydrol. Eng., 2013, 18(3): 331-339; 2014, 19(2): 290-298) by hypothesizing an accurate cumulative distribution function (CDF). However, these studies estimated the entropy index through a data-fitting procedure and the values of the index were different for different studies. The present study investigates the role of Tsallis entropy index for modelling velocity in open channels using the method of moments, based on conservation of mass and momentum. It is found that the entropy index depends on the normalized mean velocity and the momentum coefficient. In addition to the physical meaning of the index, it is also found that the modified velocity profile significantly improves for both wide and narrow channels, as shown by small predicted velocity errors. The proposed approach may be further employed for other open channel flow problems, such as sediment concentration, and shear stress distribution.

physics.flu-dyn↗

arXiv2018

Analysis of Daily Streamflow Complexity by Kolmogorov Measures and Lyapunov Exponent

Analysis of daily streamflow variability in space and time is important for water resources planning, development, and management. The natural variability of streamflow is being complicated by anthropogenic influences and climate change, which may introduce additional complexity into the phenomenological records. To address this question for daily discharge data recorded during the period 1989-2016 at twelve gauging stations on Brazos River in Texas (USA), we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures to assess complexity, and Lyapunov time (LT) to assess predictability. We find that all daily discharge series exhibit long memory with an increasing downflow tendency, while the randomness of the series at individual sites cannot be definitively concluded. All Kolmogorov complexity measures have relatively small values with the exception of the USGS (United States Geological Survey) 08088610 station at Graford, Texas, which exhibits the highest values of these complexity measures. This finding may be attributed to the elevated effect of human activities at Graford, and proportionally lesser effect at other stations. In addition, complexity tends to decrease downflow, meaning that larger catchments are generally less influenced by anthropogenic activity. The correction on randomness of Lyapunov time (quantifying predictability) is found to be inversely proportional to the Kolmogorov complexity, which strengthens our conclusion regarding the effect of anthropogenic activities, considering that KC and LT are distinct measures, based on rather different techniques.

physics.data-an↗