arXiv subjects

Cheng Huang

Publications and source records attributed to Cheng Huang.

At least 91 records · Page 5Linked to original sources

arXiv2021

Observation of nearly identical superconducting transition temperatures in pressurized Weyl semimetal MIrTe4 (M=Nb and Ta)

Here we report the observation of pressure-induced superconductivity in type-II Weyl semimetal (WSM) candidate NbIrTe4 and the evolution of its Hall coefficient (RH), magnetoresistance (MR), and lattice with increasing pressure to ~57 GPa. These results provide a significant opportunity to investigate the universal high-pressure behavior of ternary WSMs, including the sister compound TaIrTe4 that has been known through our previous studies. We find that the pressure-tuned evolution from the WSM to the superconducting (SC) state in these two compounds exhibit the same trend, i.e., a pressure-induced SC state emerges from the matrix of the non-superconducting WSM state at ~ 27 GPa, and then the WSM state and the SC state coexist up to 40 GPa. Above this pressure, an identical high-pressure behavior, characterized by almost the same value of RH and MR in its normal state and the same value of Tc in its SC state, appears in both compounds. Our results not only reveal a universal connection between the WSM state and SC state, but also demonstrate that NbIrTe4 and TaIrTe4 can make the same contribution to the normal and SC states that inhabit in the high-pressure phase, although these two compounds have dramatically different topological band structure at ambient pressure.

cond-mat.supr-con↗

arXiv2021

Flexible daytime radiative cooling enhanced by enabling three-phase composites with scattering interfaces between silica-microspheres and hierarchical porous coatings

Daytime radiative cooling has attracted considerable attention recently due to its tremendous potential for passively exploiting the coldness of deep-sky as clean and renewable energy. Many advanced materials with novel photonic micro-nanostructures have already been developed to enable highly efficient daytime radiative coolers, among which the flexible hierarchical porous coatings (HPCs) are a more distinguished category. However, it is still hard to precisely control the size distribution of the randomized pores within the HPCs, usually resulting in a deficient solar reflection at the near-infrared optical regime under diverse fabrication conditions of the coatings. We report here a three-phase (i.e., air pore-phase, microsphere-phase and polymer-phase) self-assembled hybrid porous composite coating which dramatically increases the average solar reflectance and yields a remarkable temperature drop of ~10 degC and 30 degC compared to the ambient circumstance and black paint, respectively, according to the rooftop measurements. Mie theory and Monte Carlo simulations reveal the origin of the low reflectivity of as-prepared two-phase porous HPCs, and the optical cooling improvement of the three-phase porous composite coatings is attributed to the newly generated interfaces possessing the high scattering efficiency between the hierarchical pores and silica microspheres hybridized with appropriate mass fractions. As a result, the hybrid porous composite approach enhances the whole performance of the coatings, which provides a promising alternative to the flexible daytime radiative cooler.

physics.app-ph↗

arXiv2021

Data-driven reduced-order models via regularized operator inference for a single-injector combustion process

This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends data-driven learning with physics-based modeling. The non-intrusive nature of the approach enables variable transformations that expose system structure. The specific contribution of this paper is to advance the formulation robustness and algorithmic scalability of the Operator Inference approach. Regularization is introduced to the formulation to avoid over-fitting. The task of determining an optimal regularization is posed as an optimization problem that balances training error and stability of long-time integration dynamics. A scalable algorithm and open-source implementation are presented, then demonstrated for a single-injector rocket combustion example. This example exhibits rich dynamics that are difficult to capture with state-of-the-art reduced models. With appropriate regularization and an informed selection of learning variables, the reduced-order models exhibit high accuracy in re-predicting the training regime and acceptable accuracy in predicting future dynamics, while achieving close to a million times speedup in computational cost. When compared to a state-of-the-art model reduction method, the Operator Inference models provide the same or better accuracy at approximately one thousandth of the computational cost.

cs.CE↗

arXiv2020

Reemergence of superconductivity in pressurized quasi-one-dimensional superconductor K2Mo3As3

Here we report a pressure-induced reemergence of superconductivity in recently discovered superconductor K2Mo3As3, which is the first experimental case observed in quasi-one-dimensional superconductors. We find that, after full suppression of the ambient-pressure superconducting (SC-I) state at 8.7 GPa, an intermediary non-superconducting state sets in and prevails to the pressure up to 18.2 GPa, however, above this pressure a new superconducting (SC-II) state appears unexpectedly. High pressure x-ray diffraction measurements demonstrate that the pressure-induced dramatic change of the lattice parameter c contributes mainly to the emergence of the SC-II state. Combined with the theioretical calculations on band strcture, our results suggest that the reemergemce of superconductivity is associated with the change of the complicated interplay among different orbital electrons, driven by the pressure-induced unisotropic change of the lattice.

cond-mat.supr-con↗

arXiv2020

RSAVS superconductors: materials with a superconducting state that is robust against large volume shrinkage

TThe transition temperature (TC) between normal and superconducting states usually exhibits a dramatic increase or decrease with increasing applied pressure. Here we present, in contrast, a new kind of superconductor that exhibits the exotic feature that TC is robust against large volume shrinkages induced by applied pressure (here naming them as "RSAVS superconductors"). Extraordinarily, the TC in these materials stays almost constant over a large pressure range, e.g. over 136 GPa in the (TaNb)0.67(HfZrTi)0.33 high entropy alloy and 141 GPa in the NbTi commercial alloy. We show that the RSAVS behavior also exists in another high entropy alloy (ScZrNbTa)0.6(RhPd)0.4, and in superconducting elemental Ta and Nb, indicating that this behavior, which has never previously been identified or predicted by theory, occurs universally in some conventional superconductors. Our electronic structure calculations indicate that although the electronic density of state (DOS) at the Fermi level in the RSAVS state is dominated by the electrons from the degenerate dxy, dxz and dyz orbitals, these electrons decrease in influence with increasing pressure. In contrast, however, the contribution of the degenerate dx2-y2 and dz2 orbital electrons remains almost unchanged at the Fermi level, suggesting that these are the electrons that may play a crucial role in stabilizing the TC in the RSAVS state.

cond-mat.supr-con↗

arXiv2020

Learning physics-based reduced-order models for a single-injector combustion process

This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations, and illustrates it in the challenging context of a single-injector combustion process. The method combines the perspectives of model reduction and machine learning. Model reduction brings in the physics of the problem, constraining the ROM predictions to lie on a subspace defined by the governing equations. This is achieved by defining the ROM in proper orthogonal decomposition (POD) coordinates, which embed the rich physics information contained in solution snapshots of a high-fidelity computational fluid dynamics (CFD) model. The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis. This is in contrast to traditional model reduction approaches that are constrained to use the physical variables of the high-fidelity code. Combining the two perspectives, the approach identifies a set of transformed physical variables that expose quadratic structure in the combustion governing equations and learns a quadratic ROM from transformed snapshot data. This learning does not require access to the high-fidelity model implementation. Numerical experiments show that the ROM accurately predicts temperature, pressure, velocity, species concentrations, and the limit-cycle amplitude, with speedups of more than five orders of magnitude over high-fidelity models. Our ROM simulation is shown to be predictive 200% past the training interval. Moreover, ROM-predicted pressure traces accurately match the phase of the pressure signal and yield good approximations of the limit-cycle amplitude.

physics.comp-ph↗

arXiv2020

Correlation between Fermi surface reconstruction and superconductivity in pressurized FeTe0.55Se0.45

Here we report the first results of the high-pressure Hall coefficient (RH) measurements, combined with the high-pressure resistance measurements, at different temperatures on the putative topological superconductor FeTe0.55Se0.45. We find the intimate correlation of sign change of RH, a fingerprint to manifest the reconstruction of Fermi surface, with structural phase transition and superconductivity. Below the critical pressure (PC) of 2.7 GPa, our data reveal that the hole - electron carriers are thermally balanced (RH=0) at a critical temperature (T*), where RH changes its sign from positive to negative, and concurrently a tetragonal-orthorhombic phase transition takes place. Within the pressure range from 1bar to PC, T* is continuously suppressed by pressure, while TC increases monotonically. At about PC, T* is indistinguishable and TC reaches a maximum value. Moreover, a pressure-induced sign change of RH is found at ~PC where the orthorhombic-monoclinic phase transition occurs. With further compression, TC decreases and disappears at ~ 12 GPa. The correlation among the electron-hole balance, crystal structure and superconductivity found in the pressurized FeTe0.55Se0.45 implies that its nontrivial superconductivity is closely associated with its exotic normal state resulted from the interplay between the reconstruction of the Fermi surface and the change of the structural lattice.

cond-mat.supr-con↗

arXiv2020

Dualism of the 4f electrons and high-temperature antiferromagnetism of the heavy-fermion compound YbCoC$_{2}$

We report on the first study of the noncentrosymmetric ternary carbide YbCoC$_{2}$. Our magnetization, specific heat, resistivity and neutron diffraction measurements consistently show that the system behaves as a heavy-fermion compound, displaying an amplitude-modulated magnetic structure below the Néel temperature reaching $T_{N}$ = 33 K under pressure. Such a large value, being the highest among the Yb-based systems, is explained in the light of our ab initio calculations, which show that the 4f electronic states of Yb have a dual nature -- i.e., due to their strong hybridization with the 3d states of Co, 4f states expose both localized and itinerant properties.

cond-mat.str-el↗

arXiv2019

LoadCNN: A Low Training Cost Deep Learning Model for Day-Ahead Individual Residential Load Forecasting

Accurate day-ahead individual residential load forecasting is of great importance to various applications of smart grid on day-ahead market. Deep learning, as a powerful machine learning technology, has shown great advantages and promising application in load forecasting tasks. However, deep learning is a computationally-hungry method, and requires high costs (e.g., time, energy and CO2 emission) to train a deep learning model, which aggravates the energy crisis and incurs a substantial burden to the environment. As a consequence, the deep learning methods are difficult to be popularized and applied in the real smart grid environment. In this paper, we propose a low training cost model based on convolutional neural network, namely LoadCNN, for next-day load forecasting of individual resident with reduced training cost. The experiments show that the training time of LoadCNN is only approximately 1/54 of the one of other state-of-the-art models, and energy consumption and CO2 emissions are only approximate 1/45 of those of other state-of-the-art models based on the same indicators. Meanwhile, the prediction accuracy of our model is equal to that of current state-of-the-art models, making LoadCNN the first load forecasting model simultaneously achieving high prediction accuracy and low training costs. LoadCNN is an efficient green model that is able to be quickly, cost-effectively and environmentally-friendly deployed in a realistic smart grid environment.

eess.SP↗

arXiv2019

Investigations and Improvement of Robustness of Reduced-Order Models of Reacting Flow

The impact of chemical reactions on the robustness and accuracy of projection-based Reduced-Order Models (ROMs) of fluid flows is investigated. Both Galerkin and Least-Squares Petrov Galerkin ROMs are shown to be less robust in reacting flows as compared to non-reacting flows. In particular, reacting flow ROMs show a strong sensitivity to \st{the} resolution and are often unstable. To identify the main underlying causes, a representative problem that contains the essential physics encountered in typical combustion dynamics problems is chosen. Comparisons with non-reacting solutions are used to assess the impact of reactions. Investigations are focused on three potential areas of significance: 1) preservation of conservation laws; 2) loss of dissipation; and 3) existence of unphysical local phenomena. Results indicate that conservation is relatively well-controlled and the global dissipation in the ROMs is actually larger than that in the underlying CFD solutions. Spurious local phenomena are, however, highly deleterious. Specifically, the steep temperature gradients that characterize combustion can lead to oscillations in local temperatures even in the absence of reactions. Representative calculations with physics-based temperature constraints verify that eliminating such excursions results in considerable improvement in both stability and future-state prediction capability.

physics.comp-ph↗

arXiv2019

Crossover from two-dimensional to three-dimensional superconducting states in bismuth-based cuprate superconductor

To decipher the mechanism of high temperature superconductivity, it is important to know how the superconducting pairing emerges from the unusual normal states of cuprate superconductors, including pseudogap, anomalous Fermi liquid and strange metal (SM). A long-standing issue under debate is how the superconducting pairing is formed and condensed in the SM phase because the superconducting transition temperature is the highest in this phase. Here, we report the first experimental observation of a pressure-induced crossover from two- to three-dimensional superconducting states in the optimally-doped Bi2Sr2CaCu2O8+delta bulk superconductor at a pressure above 2.8 GPa, through state-of-the-art in-situ high-pressure measurements of resistance, magnetoresistance and magnetic susceptibility. By analyzing the temperature dependence of resistance, we find that the two-dimensional (2D) superconducting transition exhibits a Berezinski-Kosterlitz-Thouless-like behavior. The emergence of this 2D superconducting transition provides direct and strong evidence that the SM state is predominantly 2D-like. This is important to a thorough understanding of the phase diagram of cuprate superconductors.

cond-mat.supr-con↗

arXiv2019

Reduced Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training

A multi-fidelity framework is established and demonstrated for prediction of combustion instabilities in rocket engines. The major idea is to adapt appropriate fidelity modeling approaches for different components in a rocket engine to ensure accurate and efficient predictions. Specifically, the proposed framework integrates projection-based Reduced Order Models (ROMs) that are developed using bases generated on truncated domain simulations. The ROM training is performed on truncated domains, and thus does not require full order model solutions on the full rocket geometry, thus demonstrating the potential to greatly reduce training cost. Geometry-specific training is replaced by the response generated by perturbing the characteristics at the boundary of the truncated domain. This training method is shown to enhance predictive capabilities and robustness of the resulting ROMs, including at conditions outside the training range. Numerical tests are conducted on a quasi-1D model of a single-element rocket combustor and the present framework is compared to traditional ROM development approaches.

physics.flu-dyn↗

arXiv2017

Isolate First, Then Share: a New OS Architecture for Datacenter Computing

This paper presents the "isolate first, then share" OS model in which the processor cores, memory, and devices are divided up between disparate OS instances and a new abstraction, subOS, is proposed to encapsulate an OS instance that can be created, destroyed, and resized on-the-fly. The intuition is that this avoids shared kernel states between applications, which in turn reduces performance loss caused by contention. We decompose the OS into the supervisor and several subOSes running at the same privilege level: a subOS directly manages physical resources, while the supervisor can create, destroy, resize a subOS on-the-fly. The supervisor and subOSes have few state sharing, but fast inter-subOS communication mechanisms are provided on demand. We present the first implementation, RainForest, which supports unmodified Linux binaries. Our comprehensive evaluation shows RainForest outperforms Linux with four different kernels, LXC, and Xen in terms of worst-case and average performance most of time when running a large number of benchmarks. The source code is available soon.

cs.OS↗

arXiv2017

An Efficient and Distribution-Free Two-Sample Test Based on Energy Statistics and Random Projections

A common disadvantage in existing distribution-free two-sample testing approaches is that the computational complexity could be high. Specifically, if the sample size is $N$, the computational complexity of those two-sample tests is at least $O(N^2)$. In this paper, we develop an efficient algorithm with complexity $O(N \log N)$ for computing energy statistics in univariate cases. For multivariate cases, we introduce a two-sample test based on energy statistics and random projections, which enjoys the $O(K N \log N)$ computational complexity, where $K$ is the number of random projections. We name our method for multivariate cases as Randomly Projected Energy Statistics (RPES). We can show RPES achieves nearly the same test power with energy statistics both theoretically and empirically. Numerical experiments also demonstrate the efficiency of the proposed method over the competitors.

stat.ME↗

arXiv2017

A Statistically and Numerically Efficient Independence Test based on Random Projections and Distance Covariance

Test of independence plays a fundamental role in many statistical techniques. Among the nonparametric approaches, the distance-based methods (such as the distance correlation based hypotheses testing for independence) have numerous advantages, comparing with many other alternatives. A known limitation of the distance-based method is that its computational complexity can be high. In general, when the sample size is $n$, the order of computational complexity of a distance-based method, which typically requires computing of all pairwise distances, can be $O(n^2)$. Recent advances have discovered that in the {\it univariate} cases, a fast method with $O(n \log n)$ computational complexity and $O(n)$ memory requirement exists. In this paper, we introduces a test of independence method based on random projection and distance correlation, which achieves nearly the same power as the state-of-the-art distance-based approach, works in the {\it multivariate} cases, and enjoys the $O(n K \log n)$ computational complexity and $O(\max\{n,K\})$ memory requirement, where $K$ is the number of random projections. Note that saving is achieved when $K < n/\log n$. We name our method a Randomly Projected Distance Covariance (RPDC). The statistical theoretical analysis takes advantage of some techniques on random projection which are rooted in contemporary machine learning. Numerical experiments demonstrate the efficiency of the proposed method, in relative to several competitors.

stat.ME↗

arXiv2015

A Distributed One-Step Estimator

Distributed statistical inference has recently attracted enormous attention. Many existing work focuses on the averaging estimator. We propose a one-step approach to enhance a simple-averaging based distributed estimator. We derive the corresponding asymptotic properties of the newly proposed estimator. We find that the proposed one-step estimator enjoys the same asymptotic properties as the centralized estimator. The proposed one-step approach merely requires one additional round of communication in relative to the averaging estimator; so the extra communication burden is insignificant. In finite sample cases, numerical examples show that the proposed estimator outperforms the simple averaging estimator with a large margin in terms of the mean squared errors. A potential application of the one-step approach is that one can use multiple machines to speed up large scale statistical inference with little compromise in the quality of estimators. The proposed method becomes more valuable when data can only be available at distributed machines with limited communication bandwidth.

stat.ME↗

arXiv2014

Revealing Correlated Electron-Nuclear Dynamics in Molecules with Energy-Resolved Population Image

We explore a new fashion, named energy-resolved population image (EPI), to represent on an equal footing the temporary electronic transition and nuclear motion during laser-molecular interaction. By using the EPI we have intuitively demonstrated the population transfer in vibrational H$_2^+$ exposed to extreme ultraviolet pulses, revealing the energy sharing rule for the correlated electron and nuclei. We further show that the EPI can be extended to uncover the origins of the distinct energy sharing mechanisms in multi-photon and tunneling regimes. The present study has clarified a long-standing issue about the dissociative ionization of H$_2^+$ and paves the way to identify instantaneous molecular dynamics in strong fields.

physics.atm-clus↗

arXiv2014

Revisiting the tunnelling site of electrons in strong field enhanced ionization of molecules

We investigated electron emissions in strong field enhanced ionization of asymmetric diatomic molecules by quantum calculations. It is demonstrated that the widely-used intuitive physical pic- ture, i.e., electron wave packet direct ionization from the up-field site (DIU), is incomplete. Besides DIU, we find another two new ionization channels, the field-induced excitation with subsequent ionization from the down-field site (ESID), and the up-field site (ESIU). The contributions from these channels depend on the molecular asymmetry and internuclear distance. Our work provides a more comprehensive physical picture for the long-standing issue about enhanced ionization of diatomic molecules.

physics.atom-ph↗