arXiv subjects

Zhongtian Zhang

Publications and source records attributed to Zhongtian Zhang.

6 recordsLinked to original sources

arXiv2025

Induction Heating in Super-Earths: A Thermochemical Perspective

Electromagnetic induction heating has recently been proposed as an important internal heat source in the mantles of rocky exoplanets. However, its dependence on planetary interior properties remains poorly constrained. Here we construct electrical conductivity profiles for super-Earth mantles considering different temperatures and compositions, and evaluate induction heating in super-Earth mantles in both solid and partially molten states. We find that high mantle temperature, iron content, and melt fraction all suppress the overall induction heating efficiency due to increased mantle conductivity and magnetic shielding. In GJ 486b, induction heating likely exceeds both radiogenic heating and tidal heating, driving persistent surface volcanism and early volatile depletion, whereas HD 3167b and GJ 357b experience insignificant induction heating due to weak stellar magnetic fields. Our findings highlight induction heating as a critical factor in the thermal and atmospheric evolution of close-in super-Earths around magnetically active stars.

astro-ph.EP↗

arXiv2025

MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting

Time series forecasting is a critical and practical problem in many real-world applications, especially for industrial scenarios, where load forecasting underpins the intelligent operation of modern systems like clouds, power grids and traffic networks.However, the inherent complexity and dynamics of these systems present significant challenges. Despite advances in methods such as pattern recognition and anti-non-stationarity have led to performance gains, current methods fail to consistently ensure effectiveness across various system scenarios due to the intertwined issues of complex patterns, concept-drift, and few-shot problems. To address these challenges simultaneously, we introduce a novel scheme centered on fundamental waveform, a.k.a., meta-pattern. Specifically, we develop a unique Meta-pattern Pooling mechanism to purify and maintain meta-patterns, capturing the nuanced nature of system loads. Complementing this, the proposed Echo mechanism adaptively leverages the meta-patterns, enabling a flexible and precise pattern reconstruction. Our Meta-pattern Echo transformer (MetaEformer) seamlessly incorporates these mechanisms with the transformer-based predictor, offering end-to-end efficiency and interpretability of core processes. Demonstrating superior performance across eight benchmarks under three system scenarios, MetaEformer marks a significant advantage in accuracy, with a 37% relative improvement on fifteen state-of-the-art baselines.

cs.LG↗

arXiv2024

Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

This paper aims to introduce a new statistical learning technique based on sparsity promoting for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed that adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can successfully be employed for fault analysis studies, which distinguishes its capabilities compared with other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.

eess.SY↗

arXiv2024

Data-Driven Dynamic State Estimation of Photovoltaic Systems via Sparse Regression Unscented Kalman Filter

Dynamic state estimation (DSE) is vital in modern power systems with numerous inverter-based distributed energy resources including solar and wind, ensuring real-time accuracy for tracking system variables and optimizing grid stability. This paper proposes a data-driven DSE approach designed for photovoltaic (PV) energy conversion systems (single stage and two stage) that are subjected to both process and measurement noise. The proposed framework follows a two-phase methodology encompassing ``data-driven model identification" and ``state-estimation." In the initial model identification phase, state feedback is gathered to elucidate the dynamics of the photovoltaic systems using nonlinear sparse regression technique. Following the identification of the PV dynamics, the nonlinear data-driven model will be utilized to estimate the dynamics of the PV system for monitoring and protection purposes. To account for incomplete measurements, inherent uncertainties, and noise, we employ an ``unscented Kalman filter," which facilitates state estimation by processing the noisy output data. Ultimately, the paper substantiates the efficacy of the proposed sparse regression-based unscented Kalman filter through simulation results, providing a comparative analysis with a physics-based DSE.

eess.SY↗

arXiv2024

Quantification of classical and non-classical crystallization pathways in calcite precipitation

Crystal precipitation from aqueous solution occurs through multiple pathways. Besides the classical ion-by-ion addition, non-classical crystallization mechanisms, such as multi-ion polymer and nano-particle attachment, could be of great significance under certain circumstances. These non-classical crystallization processes have been observed with advanced microscopy, yet detailed quantification of their contribution in mineral precipitation remains challenging. Building from paired Ca and Sr isotope observations, we develop a new theoretical framework to quantify the relative contribution of classical and non-classical crystallization pathways on the precipitation of the calcium carbonate mineral calcite, one of the most common precipitates in nature. We demonstrate that the classical (ion-by-ion) crystallization pathway alone is insufficient to account for the observed isotope behaviors and, thus, the entire calcite precipitation process. We present a new kinetic surface reaction model to incorporate the non-classical crystallization pathway. This new model, for the first time, enables the detailed characterization of the roles of classical and non-classical crystallization mechanisms in calcite precipitation. The results suggest that the relative contribution of non-classical crystallization pathways increases with saturation state and can, under high supersaturation levels, be comparable to or greater than precipitation driven by the classical crystallization pathway. The presented theoretical framework readily explains observed trace element partitioning and isotope fractionation behaviors during calcite precipitation and can be further expanded onto other mineral systems to gain insights into crystal growth mechanisms.

physics.chem-ph↗

arXiv2024

Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge Platforms

As live streaming services skyrocket, Crowdsourced Cloud-edge service Platforms (CCPs) have surfaced as pivotal intermediaries catering to the mounting demand. Despite the role of stream scheduling to CCPs' Quality of Service (QoS) and throughput, conventional optimization strategies struggle to enhancing CCPs' revenue, primarily due to the intricate relationship between resource utilization and revenue. Additionally, the substantial scale of CCPs magnifies the difficulties of time-intensive scheduling. To tackle these challenges, we propose Seer, a proactive revenue-aware scheduling system for live streaming services in CCPs. The design of Seer is motivated by meticulous measurements of real-world CCPs environments, which allows us to achieve accurate revenue modeling and overcome three key obstacles that hinder the integration of prediction and optimal scheduling. Utilizing an innovative Pre-schedule-Execute-Re-schedule paradigm and flexible scheduling modes, Seer achieves efficient revenue-optimized scheduling in CCPs. Extensive evaluations demonstrate Seer's superiority over competitors in terms of revenue, utilization, and anomaly penalty mitigation, boosting CCPs revenue by 147% and expediting scheduling $3.4 \times$ faster.

cs.DC↗