arXiv subjects

Qianqian Ma

Publications and source records attributed to Qianqian Ma.

15 recordsLinked to original sources

arXiv2026

Multiphase Reactive Transport in a Heterogeneous Flow Field: Channel Formation in Hydrocarbon-Bearing Carbonate Rock

Carbon capture, utilization and storage (CCUS) is a key approach for reducing anthropogenic CO2 emissions. Currently the vast majority of CO2 stored is injected into depleted hydrocarbon reservoirs. Reactive transport in hydrocarbon-bearing carbonate reservoirs is controlled not only by the balance between reactant delivery and surface reaction, but also by the heterogeneous pore-scale flow field created by pore structure and fluid distribution. Here, we used time-resolved micro-CT imaging, pore-network analysis, and direct numerical simulation to investigate channelized dissolution during injection of CO2-saturated brine into oil-bearing Ketton limestone at 0.5 mL/min. The system remained in a high Pe, low Da regime throughout the 180 min injection. Despite the advection-dominated flow regime, dissolution became strongly localized. Preferential flow pathways were already present originally because the heterogeneous pore structure and remaining-oil occupancy restricted brine flow to a subset of the connected pore space. Dissolution progressively amplified these pathways. The associated reduction in hydraulic resistance further focused flow, producing a continuous tortuous channel by 180 min. Meanwhile, the effective reaction rate was only 1.6*10^5 mol/m2s,, approximately one order of magnitude lower than the batch reaction rate, showing that rapid advective transport did not translate directly into rapid overall dissolution under multiphase conditions. These results demonstrate that channel formation arose from the coupling between strong reactant delivery and pore-scale flow-field heterogeneity, rather than from the bulk Pe Da regime alone. Accounting for flow-field heterogeneity is therefore important for predicting reactive transport and pore-structure evolution during CO2 injection into hydrocarbon-bearing carbonate reservoirs.

physics.flu-dyn↗

arXiv2026

Time-Resolved Pore-Scale Imaging of Multiphase Dissolution during CO2-Saturated Brine Injection into a Carbonate: Competition between Hydrocarbon Mobilisation and Swelling

We present time-resolved pore-scale experiments in which CO2-saturated brine was injected into a water-wet Ketton limestone sample containing residual hydrocarbon under reservoir conditions (8 MPa, 50 °C) and monitored by 4D X-ray microtomography. Equivalent pore-network models were extracted at each scan time to track pore geometry, topology, and fluid occupancy, while fluid-fluid and fluid-rock interfacial areas and the effective reaction rate were determined from segmented images. The dissolution rate is non-monotonic in time and proceeds through three regimes, consistent with a shifting balance between hydrocarbon swelling and ganglion mobilisation, which control advective access to reactive surfaces. In the initial advection-dominated regime, pore-throat widening leads to ganglia mobilisation and efficient acidic brine delivery to reactive surfaces. The second, dissolution-inhibited regime is marked by up to two orders of magnitude reduction in effective reaction rate. Pore-network analysis shows that swollen hydrocarbon ganglia persistently occupy the largest throats throughout this regime. This occupancy is associated with a reorganisation of the advective flow field into preferential flow paths and stagnant zones. We interpret the rate suppression as primarily reflecting a path-dependent loss of advective access to reactive surfaces, with subordinate contributions from localised H+ depletion near ganglia and reduced near-wall mass transfer in widened flow paths. The inhibited state persists until hydrocarbon is displaced from the largest throats, after which, in the third stage, advective access improves and rock dissolution accelerates. These results show that the effective dissolution rate in residual-hydrocarbon-bearing carbonate depends dynamically on the competition between hydrocarbon swelling and ganglion mobilisation, governing advective access to surfaces.

physics.flu-dyn↗

arXiv2025

Pore-Scale Dynamics of Multiphase Reactive Transport in Water-Wet Carbonates under CO2-Acidified Brine Injection: Dissolution Patterns and Reaction Rates

Depleted carbonate hydrocarbon reservoirs are promising sites for geological CO2 storage, yet the presence of residual hydrocarbons introduces complex pore-scale interactions that influence the dynamics of solid dissolution. This study reveals how residual oil affects dissolution patterns and effective reaction rates during CO2-acidified brine injection into Ketton limestone under reservoir conditions. We combine time-resolved X-ray microtomography (micro-CT), core-flooding experiments, and direct numerical simulations to assess the impact of pore space heterogeneity, oil distribution and injection rate. We find that the coupling between pore structure, residual oil saturation and oil displacement control flow heterogeneity, reactive surface accessibility, dissolution patterns and the reaction rates. At low injection rate, dissolution by channel widening is enhanced by oil displacement. This mechanism is especially important when dissolution is suppressed by heterogeneity in the pore space and the residual oil. At high injection rates, a more uniform dissolution occurs and can be enhanced by re-mobilisation of oil blocking brine flow. Effective reaction rates in two-phase flow are found to be lower than in the the equivalent single-phase case and up to two orders of magnitude lower than the batch rates due to persistent transport limitations. These findings offer mechanistic insights into multiphase reactive transport in carbonates and highlight the need for accurate understanding of the impact of the hydrocarbon phase on reaction to improve predictions of CO2 storage efficiency.

physics.flu-dyn↗

arXiv2025

Thermal excitation of flexoelectricity in silicon

Flexoelectricity, an electromechanical coupling between strain gradient and polarization, offers a promising dimension to enrich silicon-based devices. Although the flexoelectricity of silicon is known, some fundamental aspects remain ambiguous, such as the discrepancy between experimental results and theoretical predictions, the influence of doping concentration, and the role of the bandgap. Here, we measured the flexoelectricity of intrinsic and heavily doped Si over the temperature range of 223 -473 K. The flexoelectric coefficient is of 2.6 μC/m and barely varies with temperature in doped silicon, while in intrinsic silicon it varies by nearly two orders of magnitude from 15.2 nC/m to 1.8 μC/m as temperature increases. We show that their different temperature dependencies correspond to the temperature-insensitive donor ionization in doped silicon and the temperature-sensitive intrinsic excitation in intrinsic silicon, with the latter captured by a quantitative relationship between flexoelectricity, temperature and bandgap. Furthermore, similar experimental results on germanium (Ge) suggest the universality of this relationship in first-generation semiconductors. These findings would offer valuable reference for developing Si-based electromechanical devices, as well as understanding the strain-gradient effects on semiconductor band structures (flexoelectronics).

cond-mat.mtrl-sci↗

arXiv2025

Flexoelectricity and surface ferroelectricity of water ice

The phase diagram of ice is complex and contains many phases, but the most common (frozen water at ambient pressure, also known as Ih ice) is a non-polar material despite individual water molecules being polar1,2. Consequently, ice is not piezoelectric and cannot generate electricity under pressure3. On the other hand, the coupling between polarization and strain gradient (flexoelectricity) is universal4, so ice may in theory generate electricity under bending. Here we report the experimental demonstration that ice is flexoelectric, finding a coefficient of 1.14+-0.13 nC/m, comparable to that of ceramics such as SrTiO3, TiO2, or PbZrO3. Additionally, and unexpectedly, the sensitivity of flexoelectric measurements to surface boundary conditions has also revealed a ferroelectric phase transition around ~160K confined in the near-surface region of the ice slabs. The electromechanical properties of ice may find applications for low-cost transducers made in-situ in cold and remote locations. Importantly, there are also consequences for natural phenomena. In particular, we have calculated the flexoelectric charge density generated in ice-graupel collisions, and found it to be comparable to the experimental charge transferred in such events, suggesting a possible participation of ice flexoelectricity in the charging up of thunderstorms.

cond-mat.mtrl-sci↗

arXiv2023

Entanglement Dynamics of Two V-type Atoms with Dipole-Dipole Interaction in Dissipative Cavity

In this work, we study a coupled system of two V-type atoms with dipole-dipole interaction in a dissipative single-mode cavity, which couples with an external environment. We obtain the analytical solution of this model by solving the time dependent Schrodinger equation after we diagonalize Hamiltonian of dissipative cavity by introducing a set of new creation and annihilation operators according to Fano theorem. We also detailedly discuss the influences of cavity-environment coupling, spontaneously generated interference (SGI) parameter and dipole-dipole interaction between two atoms on entanglement dynamics under different initial states. The results show that the SGI parameter has different effects on entanglement dynamics under different initial states. Namely, the SGI parameter will increase the decay rate of the initially maximal entangled state and reduce that of the initially partial entangled state. For the initially product state, the larger SGI parameter corresponds to the more entanglement generated. The entanglement monotonically decreases under the weak cavity-environment coupling, while the oscillation of entanglement will occur under the strong cavity-environment coupling. The larger the dipole-dipole interaction is, the slower the entanglement decays and the more the entanglement will be generated. So the dipole-dipole interaction can not only protect and generate entanglement very effectively, but also enhance the regulation effect of the SGI parameter on entanglement.

quant-ph↗

arXiv2023

Modulating quantum evolution of moving-qubit by using classical driving

In this work, we study quantum evolution of an open moving-qubit modulated by a classical driving field. We obtain the density operator of qubit at zero temperature and analyze its quantum evolution dynamics by using quantum speed limit time (QSLT) and a non-Markovianity measure introduced recently. The results show that both the non-Markovian environment and the classical driving can speed up the evolution process, this quantum speedup process is induced by the non-Markovianity and the critical points only depend on the qubit velocity. Moreover, the qubit motion will delay the evolution process, but this negative effect of the qubit velocity on the quantum speedup can be suppressed by the classical driving. Finally, we give the corresponding physical explanation by using the decoherence rates.

quant-ph↗

arXiv2022

A New Fuzzy $H_{\infty}$ Filter Design for Nonlinear Time-Delay Systems with Mismatched Premise Membership Functions

This paper is concerned with the fuzzy $H_{\infty}$ filter design issue for nonlinear systems with time-varying delay. To overcome the shortcomings of the conventional methods with matched preconditions, the fuzzy $H_{\infty}$ filter to be designed and the T-S fuzzy model are assumed to have different premise membership functions and number of rules, thus, greater design flexibility and robustness to uncertainty can be achieved. However, such design will also make the derived results conservative, to relax the result, a novel integral inequality which is tighter than the traditional inequalities derived from the Leibniz-Newton formula is applied, besides, a fuzzy Lypunov function and the information of the membership functions are also introduced. All the design methods are presented in LMI-based conditions. Finally, two numerical examples are given to prove the effectiveness and superiority of the proposed approach.

eess.SY↗

arXiv2022

Improved Fuzzy $H_{\infty}$ Filter Design Method for Nonlinear Systems with Time-Varing Delay

This paper investigates the fuzzy $H_{\infty}$ filter design issue for nonlinear systems with time-varying delay. In order to obtain less conservative fuzzy $H_{\infty}$ filter design method, a novel integral inequality is employed to replace the conventional Lebniz-Newton formula to analyze the stability conditions of the filtering error system. Besides, the information of the membership functions is introduced in the criterion to further relax the derived results. The proposed delay dependent filter design method is presented as LMI-based conditions, and corresponding definite expressions of fuzzy $H_{\infty}$ filter are given as well. Finally, a simulation example is provided to prove the effectiveness and superiority of the designed fuzzy $H_{\infty}$ filter.

eess.SY↗

arXiv2022

Optimal Lockdown for Pandemic Control

As a common strategy of contagious disease containment, lockdowns will inevitably weaken the economy. The ongoing COVID-19 pandemic underscores the trade-off arising from public health and economic cost. An optimal lockdown policy to resolve this trade-off is highly desired. Here we propose a mathematical framework of pandemic control through an optimal stabilizing non-uniform lockdown, where our goal is to reduce the economic activity as little as possible while decreasing the number of infected individuals at a prescribed rate. This framework allows us to efficiently compute the optimal stabilizing lockdown policy for general epidemic spread models, including both the classical SIS/SIR/SEIR models and a new model of COVID-19 transmissions. We demonstrate the power of this framework by analyzing publicly available data of inter-county travel frequencies to analyze a model of COVID-19 spread in the 62 counties of New York State. We find that an optimal stabilizing lockdown based on epidemic status in April 2020 would have reduced economic activity more stringently outside of New York City compared to within it, even though the epidemic was much more prevalent in New York City at that point. Such a counterintuitive result highlights the intricacies of pandemic control and sheds light on future lockdown policy design.

math.OC↗

arXiv2021

Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games

Learning in stochastic games is arguably the most standard and fundamental setting in multi-agent reinforcement learning (MARL). In this paper, we consider decentralized MARL in stochastic games in the non-asymptotic regime. In particular, we establish the finite-sample complexity of fully decentralized Q-learning algorithms in a significant class of general-sum stochastic games (SGs) - weakly acyclic SGs, which includes the common cooperative MARL setting with an identical reward to all agents (a Markov team problem) as a special case. We focus on the practical while challenging setting of fully decentralized MARL, where neither the rewards nor the actions of other agents can be observed by each agent. In fact, each agent is completely oblivious to the presence of other decision makers. Both the tabular and the linear function approximation cases have been considered. In the tabular setting, we analyze the sample complexity for the decentralized Q-learning algorithm to converge to a Markov perfect equilibrium (Nash equilibrium). With linear function approximation, the results are for convergence to a linear approximated equilibrium - a new notion of equilibrium that we propose - which describes that each agent's policy is a best reply (to other agents) within a linear space. Numerical experiments are also provided for both settings to demonstrate the results.

cs.GT↗

arXiv2021

Semi-supervised Domain Adaptive Structure Learning

Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of training data bias towards labeled samples. In this paper, we introduce an adaptive structure learning method to regularize the cooperation of SSL and DA. Inspired by the multi-views learning, our proposed framework is composed of a shared feature encoder network and two classifier networks, trained for contradictory purposes. Among them, one of the classifiers is applied to group target features to improve intra-class density, enlarging the gap of categorical clusters for robust representation learning. Meanwhile, the other classifier, serviced as a regularizer, attempts to scatter the source features to enhance the smoothness of the decision boundary. The iterations of target clustering and source expansion make the target features being well-enclosed inside the dilated boundary of the corresponding source points. For the joint address of cross-domain features alignment and partially labeled data learning, we apply the maximum mean discrepancy (MMD) distance minimization and self-training (ST) to project the contradictory structures into a shared view to make the reliable final decision. The experimental results over the standard SSDA benchmarks, including DomainNet and Office-home, demonstrate both the accuracy and robustness of our method over the state-of-the-art approaches.

cs.CV↗

arXiv2021

Optimal Vaccine Allocation for Pandemic Stabilization

How to strategically allocate the available vaccines is a crucial issue for pandemic control. In this work, we propose a mathematical framework for optimal stabilizing vaccine allocation, where our goal is to send the infections to zero as soon as possible with a fixed number of vaccine doses. This framework allows us to efficiently compute the optimal vaccine allocation policy for general epidemic spread models including SIS/SIR/SEIR and a new model of COVID-19 transmissions. By fitting the real data in New York State to our framework, we found that the optimal stabilizing vaccine allocation policy suggests offering vaccines priority to locations where there are more susceptible people and where the residents spend longer time outside the home. Besides, we found that offering vaccines priority to young adults (20-29) and middle-age adults (20-44) can minimize the cumulative infected cases and the death cases. Moreover, we compared our method with five age-stratified strategies in \cite{bubar2021model} based on their epidemics model. We also found it's better to offer vaccine priorities to young people to curb the disease and minimize the deaths when the basic reproduction number $R_0$ is moderately above one, which describes the most world during COVID-19. Such phenomenon has been ignored in \cite{bubar2021model}.

math.OC↗

arXiv2021

Contradictory Structure Learning for Semi-supervised Domain Adaptation

Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. To solve these challenges, we propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures (UODA). UODA consists of a generator and two classifiers (i.e., the source-scattering classifier and the target-clustering classifier), which are trained for contradictory purposes. The target-clustering classifier attempts to cluster the target features to improve intra-class density and enlarge inter-class divergence. Meanwhile, the source-scattering classifier is designed to scatter the source features to enhance the decision boundary's smoothness. Through the alternation of source-feature expansion and target-feature clustering procedures, the target features are well-enclosed within the dilated boundary of the corresponding source features. This strategy can make the cross-domain features to be precisely aligned against the source bias simultaneously. Moreover, to overcome the model collapse through training, we progressively update the measurement of feature's distance and their representation via an adversarial training paradigm. Extensive experiments on the benchmarks of DomainNet and Office-home datasets demonstrate the superiority of our approach over the state-of-the-art methods.

cs.CV↗

arXiv2020

Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion

We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknown and can be arbitrarily large. It is not known which revealed entries are corrupted. We propose a new algorithm combining alternating minimization with extreme-value filtering and provide sufficient and necessary conditions to recover the original rank-one matrix. In particular, we show that our proposed algorithm is optimal when the set of revealed entries is given by an Erdős-Rényi random graph. These results are then applied to the problem of classification from crowdsourced data under the assumption that while the majority of the workers are governed by the standard single-coin David-Skene model (i.e., they output the correct answer with a certain probability), some of the workers can deviate arbitrarily from this model. In particular, the "adversarial" workers could even make decisions designed to make the algorithm output an incorrect answer. Extensive experimental results show our algorithm for this problem, based on rank-one matrix completion with perturbations, outperforms all other state-of-the-art methods in such an adversarial scenario.

cs.LG↗