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Md Mahmudul Islam

Publications and source records attributed to Md Mahmudul Islam.

5 recordsLinked to original sources

An Efficient Second-Order-in-Time Penalty-Projection Ensemble Eddy Viscosity Method for Parameterized Navier-Stokes Flows

We propose a novel, robust, and second-order-accurate parameterized penalty-projection ensemble algorithm for incompressible Navier--Stokes flow problems. The resulting linearized algorithm, based on the second-order Backward Differentiation Formula (BDF-2), is computationally efficient because it shares the same coefficient matrix across all realizations for each subproblem at every time step. To enhance robustness in convection-dominated flows, the scheme incorporates Ensemble Eddy Viscosity (EEV) regularization. In addition, it is equipped with grad-div stabilization parameter $\gamma$, which controls the splitting error; under the assumptions of the analysis, the splitting error decreases and vanishes asymptotically as $\gamma\to\infty$. We establish the stability of the proposed scheme and rigorously prove its optimal convergence by demonstrating that, as $\gamma\to\infty$, the scheme converges to an equivalent coupled formulation. We further validate the method through a series of numerical experiments designed to verify the theoretically predicted convergence rates and evaluate its performance on benchmark convection-dominated problems. The numerical results are in excellent agreement with the theoretical analysis and confirm the effectiveness of the proposed scheme.

math.NA

Trust as a Field: A Macroscopic Representation for Vehicular Networks

Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of individual vehicles, making it difficult to reason about trust evolution across road segments. In this paper, we propose a spatio-temporal trust-field framework that aggregates microscopic vehicle-level trust into a continuous representation over space and time. The trust field is formally defined on road segments. We conducted simulation-based experiments using synthetic trajectories generated under controlled conditions, enabling analysis of trust-field behavior in simple road scenarios. Beyond theoretical modeling, we study an implication of the trust-field concept: reconstructing the full trust field from sparse roadside-unit (RSU) measurements. We compare (i) a coordinate-based deep learning baseline that learns a generic trust field from sparse samples and (ii) a field-informed deep learning method that treats trust as a latent quantity carried by vehicles and enforces measurement consistency through the aggregation mechanism. The field-informed approach more accurately recovers trajectory-aligned low-trust patterns and yields improved reconstruction error.

cs.RO

Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario

Physics-informed deep learning (PIDL) neural networks have shown their capability as a useful instrument for transportation practitioners in utilizing the underlying relationship between the state variables for traffic state estimation (TSE). Another efficient traffic management approach is implementing varying speed limits (VSLs) on transportation corridors to control traffic and mitigate congestion. However, the existing training architecture of PIDL in the literature cannot accommodate the changing traffic characteristics on a freeway with VSL. To tackle this challenge, we propose a novel framework integrating teacher-student ensemble training with PIDL neural networks for TSE under VSL scenarios. The physics of flow conservation law is encoded locally in the teacher models by PIDL, and the student model uses a multi-layer perceptron classifier (MLP) to identify traffic characteristics and selects the ensemble member of PIDL neural networks for TSE. This integrated framework provides a natural solution for capturing the heterogeneity of VSL and accurately addressing the TSE problem. The case study results validate the proposed ensemble approach, demonstrating its superior performance in TSE compared to other popular baseline methods, as indicated by relative L2 error.

cs.LG

Efficient and Optimally Accurate Numerical Algorithms for Stochastic Turbulent Flow Problems

In this paper, we first propose a filter-based continuous Ensemble Eddy Viscosity (EEV) model for stochastic turbulent flow problems. We then propose a generic algorithm for a family of fully discrete, grad-div regularized, efficient ensemble parameterized schemes for this model. The linearized Implicit-Explicit (IMEX) EEV generic algorithm shares a common coefficient matrix for each realization per time-step, but with different right-hand-side vectors, which reduces the computational cost and memory requirements to the order of solving deterministic flow problems. Two family members of the proposed time-stepping algorithm are analyzed and proven to be stable. It is found that one is first-order and the other is second-order accurate in time for any stable finite element pairs. Avoiding the discrete inverse inequality, the optimal convergence of both schemes is proven rigorously for both 2D and 3D problems. For appropriately large grad-div parameters, both schemes are unconditionally stable and allow weakly divergence-free elements. Several numerical tests are given for high expected Reynolds number ($\textbf{E}[Re]$) problems. The convergence rates are verified using manufactured solutions with $\textbf{E}[Re]=10^{3},10^{4},\;\text{and}\; 10^{5}$. For various high $\textbf{E}[Re]$, the schemes are implemented on benchmark problems which includes: A 2D channel flow over a unit step problem, a non-intrusive Stochastic Collocation Method (SCM) is used to examine the performance of the schemes on a 2D Regularized Lid Driven Cavity (RLDC) problem, and a 3D RLDC problem, and found them perform well.

math.NA

Existence of Trust-field in Vehicular Ad Hoc Networks: Empirical Evidence

Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety and traffic efficiency by enabling communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Robust trust management is necessary to ensure the reliability of information in decentralized systems. This paper presents the notion of a ``Trust Field" in VANETs, conceptualized as the behavior of the nodes that represents trust levels evolving in both spatial and temporal dimensions. Using the LogitTrust model, we provide empirical evidence of how trust fields in vehicular networks change over time in different scenarios, including when malicious nodes are present. The results of our study demonstrate that the trust domain can adjust to fluctuations in network conditions, thereby offering a comprehensive metric for assessing the reliability of nodes. This innovative method improves the dependability of VANET applications by efficiently detecting and mitigating malicious actions.

math.DS