arXiv subjects

Bingjia Xiao

Publications and source records attributed to Bingjia Xiao.

10 recordsLinked to original sources

arXiv2026

A transfer-learning-enhanced POD-FNN surrogate for rapid signal prediction and inverse fitting in thermoreflectance with patterned transducers

Patterned-transducer thermoreflectance enhances sensitivity to low-thermal-conductivity materials by suppressing lateral heat spreading in the metal transducer, but its wider use is limited by the cost of repeated high-fidelity forward evaluations in iterative fitting. Here, we develop a transfer-learning-enhanced POD-FNN surrogate for rapid phase prediction in patterned-transducer thermoreflectance, using patterned FDTR as a representative case. A validated COMSOL model is first constructed, and proper orthogonal decomposition is applied directly to the phase signals to build a compact reduced-order representation. A feedforward neural network is then trained to predict the POD coefficients from thermophysical and geometric parameters. Within the original parameter domain, the surrogate achieves mean and median RMSE values of 0.19 and 0.17 degrees, with a maximum RMSE below 0.47 degrees, while reducing the average prediction time per signal from 5.39 s to 0.01 s (about 534x). In inverse analysis, the fitting time for a representative case is reduced from about 18950 s to about 65 s with comparable accuracy. The framework is further applied to measured Al/SiO2 samples, yielding stable silica thermal conductivities of 1.44 +/- 0.088, 1.43 +/- 0.093, and 1.50 +/- 0.079 W/(m K) for conventional FDTR and patterned FDTR with pattern radii of 5.3 and 3.25 um, respectively. Transfer learning further improves performance in expanded parameter domains, with the TL-FR strategy giving the best overall results. Reducing the additional target-domain dataset from 6000 to 1000 samples also lowers the high-fidelity data-generation time from about 34179 s to about 5885 s. The proposed framework provides an accurate and efficient route for repeated forward evaluation, rapid inverse fitting, and cost-effective model updating in patterned thermoreflectance workflows.

physics.app-ph↗

arXiv2026

Simultaneous measurement of pressure-dependent bulk and interfacial thermal properties in thermal interface materials using square-pulsed source thermoreflectance

Thermal interface materials (TIMs) critically regulate heat dissipation from electronic chips to heat spreaders, yet their thermal conductivity (k), volumetric heat capacity (C), and interfacial thermal resistance (ITR) evolve with mechanical pressure and cannot be determined simultaneously using existing steady-state or transient techniques. As a result, the coupled roles of bulk compaction and interfacial contact in governing heat transport in TIM assemblies remain poorly resolved. Here, we present a square-pulsed source (SPS) thermoreflectance method that enables simultaneous determination of k, C, and ITR in TIM stacks under controlled mechanical loading. By spanning square-wave modulation frequencies from 1 Hz to 10 MHz, SPS probes a broad range of thermal penetration depths, enabling distinction between heat diffusion in the TIM bulk and interfacial heat transfer at the Al/TIM contact. Measurements on a thermally conductive gel, a thermal pad, and a high-vacuum grease during compression-unloading cycles reveal distinct pressure-dependent thermal transport mechanisms. The gel and pad exhibit increases in k and C, reduced ITR, and pronounced hysteresis, indicating coupled bulk densification and persistent interfacial conformity during loading cycles. In contrast, the grease shows nearly pressure-independent bulk properties but a strong pressure dependence of ITR, consistent with an interface-dominated response. These results resolve the long-standing challenge of simultaneously quantifying bulk and interfacial thermal transport in mechanically loaded TIM assemblies, enabling experimentally constrained thermal management and reliability analysis in electronic packaging.

physics.app-ph↗

arXiv2025

Hybrid Particle Swarm Optimization for Fast and Reliable Parameter Extraction in Thermoreflectance

Frequency-domain thermoreflectance (FDTR) is a widely used technique for characterizing thermal properties of multilayer thin films. However, extracting multiple parameters from FDTR measurements presents a nonlinear inverse problem due to its high dimensionality and multimodal, non-convex solution space. This study evaluates four popular global optimization algorithms: Genetic Algorithm (GA), Quantum Genetic Algorithm (QGA), Particle Swarm Optimization (PSO), and Fireworks Algorithm (FWA), for extracting parameters from FDTR measurements of a GaN/Si heterostructure. However, none achieve reliable convergence within 60 seconds. To improve convergence speed and accuracy, we propose an AI-driven hybrid optimization framework that combines each global algorithm with a Quasi-Newton local refinement method, resulting in four hybrid variants: HGA, HQGA, HPSO, and HFWA. Among these, HPSO outperforms all other methods, with 80% of trials reaching the target fitness value within 60 seconds, showing greater robustness and a lower risk of premature convergence. In contrast, only 30% of HGA and HQGA trials and 20% of HFWA trials achieve this threshold. We then evaluate the worst-case performance across 100 independent trials for each algorithm when the time is extended to 1000 seconds. Only HPSO, PSO, and HGA consistently reach the target accuracy, with HPSO converging five times faster than the others. HPSO provides a general-purpose solution for inverse problems in thermal metrology and can be readily extended to other model-fitting techniques.

cs.NE↗

arXiv2025

Correlation of the L-mode density limit with edge collisionality

The "density limit" is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot plant concepts must operate near or above the Greenwald limit to achieve their objectives. Evidence has also grown that the Greenwald scaling - in its remarkable simplicity - may not capture the full complexity of the density limit. In this study, we assemble a multi-machine database to quantify the effectiveness of the Greenwald limit as a predictor of the L-mode density limit and compare it with data-driven approaches. We find that a boundary in the plasma edge involving dimensionless collisionality and pressure, $ν_{*\rm, edge}^{\rm limit} = 3.5 β_{T,{\rm edge}}^{-0.40}$, achieves significantly higher accuracy (false positive rate of 2.3% at a true positive rate of 95%) of predicting density limit disruptions than the Greenwald limit (false positive rate of 13.4% at a true positive rate of 95%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. This boundary can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.

physics.plasm-ph↗

arXiv2025

PaMMA-Net: Plasmas magnetic measurement evolution based on data-driven incremental accumulative prediction

An accurate evolution model is crucial for effective control and in-depth study of fusion plasmas. Evolution methods based on physical models often encounter challenges such as insufficient robustness or excessive computational costs. Given the proven strong fitting capabilities of deep learning methods across various fields, including plasma research, this paper introduces a deep learning-based magnetic measurement evolution method named PaMMA-Net (Plasma Magnetic Measurements Incremental Accumulative Prediction Network). This network is capable of evolving magnetic measurements in tokamak discharge experiments over extended periods or, in conjunction with equilibrium reconstruction algorithms, evolving macroscopic parameters such as plasma shape. Leveraging a incremental prediction approach and data augmentation techniques tailored for magnetic measurements, PaMMA-Net achieves superior evolution results compared to existing studies. The tests conducted on real experimental data from EAST validate the high generalization capability of the proposed method.

physics.plasm-ph↗

arXiv2023

Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

physics.plasm-ph↗

arXiv2022

Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor

Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption discharges at high performance can hardly compose an abundant training set, which makes it difficult for current data-driven methods to obtain an acceptable result. A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem. The key is a disruption prediction model containing a feature extractor that is able to extract common disruption precursor traces in tokamak diagnostic data, and a transferable disruption classifier. Based on the concerns above, the paper first presents a deep fusion feature extractor designed specifically for extracting disruption precursor features from common diagnostics on tokamaks according to currently known precursors of disruption, providing a promising foundation for transferable models. The fusion feature extractor is proved by comparing with manual feature extraction on J-TEXT. Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment. The performance is comparable with a model trained with 1896 discharges from EAST. From the comparison among other model training scenarios, transfer learning showed its potential in predicting disruptions across different tokamaks.

physics.plasm-ph↗

arXiv2020

Robust control design for multi-input multi-output plasma shape control on EAST tokamak using H_infinity synthesis

Accurate plasma shape control is the basis of tokamak plasma experiments and physical research. Modeling of the linearized control response of plasma shape and position has been widely used for shape controller design in the last several years. But it usually contains much of the uncertainty, such as structured uncertainties and unmodeled dynamics. EAST tokamak plasma shape controller design is also based on a linear rigid plasma response model which integrated within a Matlab-based toolset known as TokSys. Meanwhile the PID control approach is currently used for EAST plasma shape control. This leads to strong coupling between different parameters describing the plasma shape. To handle these problems, a H_infinity robust control scheme for EAST multi-input multi-output (MIMO) shape control has been proposed. First, the plasma response is modeled as the linearized rigid RZIp model. Then, the controller design technique is introduced with two main stages: 1) loop shaping is used to shape the nominal plant singular values to give desired open-loop properties at frequencies of high and low loop gain; 2) a normalized coprime factorization and H_infinity technique is used to decouple the most relevant control channels and minimize the tracking errors. Finally, the simulation results show that the H_infinity robust controller combines good robust stability margins, speed of response, dynamic tracking characteristics, and closed-loop decoupling for EAST plasma shape control.

physics.plasm-ph↗

arXiv2018

The Design of Data Acquisition System for EAST Technical Diagnostic System

EAST (Experimental Advanced Superconducting Tokamak) Technical Diagnostic System (TDS) is used to monitor the outlet temperature of all superconducting coils, in case of temperature anomaly, it will trigger safety interlock system to meet EAST device safety. The data acquisition system of TDS is in charge of continuous data acquisition of the nitrogen and helium temperature signals, TDS security alarm and long-term data storage. It supports continuous data acquisition and pulse data acquisition. The data acquisition of the nitrogen temperature signals is based on the PXI technology while obtaining the helium temperature signals from Lake Shore model 224 temperature monitors with VISA standard. After data conversion, all the data will be stored in MySQL and MDSPlus for long-term storage. It should output TDS fault signal and status signal to trigger the safety interlock system to take actions after threshold evaluation of key temperature signals. It publishes part of real-time TDS data to the cryogenic system and provides an information inquiry service to the TDS administrator. The system has been used in 2018 EAST campaign.

physics.ins-det↗

arXiv2005

Computational Fluid Dynamic Approach for Biological System Modeling

Various biological system models have been proposed in systems biology, which are based on the complex biological reactions kinetic of various components. These models are not practical because we lack of kinetic information. In this paper, it is found that the enzymatic reaction and multi-order reaction rate is often controlled by the transport of the reactants in biological systems. A Computational Fluid Dynamic (CFD) approach, which is based on transport of the components and kinetics of biological reactions, is introduced for biological system modeling. We apply this approach to a biological wastewater treatment system for the study of metabolism of organic carbon substrates and the population of microbial. The results show that CFD model coupled with reaction kinetics is more accurate and more feasible than kinetic models for biological system modeling.

q-bio.QM↗