arXiv subjects

Shuchang Yan

Publications and source records attributed to Shuchang Yan.

5 recordsLinked to original sources

arXiv2026

DetMesh-Gadep: Triangulated Surface Modeling and GPU-based Monte Carlo Efficiency Calibration of High-Purity Germanium Detectors

Sourceless efficiency calibration of high-purity germanium (HPGe) detectors can provide accurate detector-response information without experiments using radioactive calibration sources, offering advantages in both convenience and safety. In many practical implementations, this process is performed using Monte Carlo simulation; however, its performance is constrained by the accuracy of detector modeling, the operational complexity of simulation frameworks, and the computational-resource requirements associated with CPU-based parallelization. In this study, a complete detector modeling and simulation framework is proposed. The detector modeling program DetMesh can generate triangulated surface geometry from parameterized detector models, providing advantages in the representation of complex geometric boundaries. It incorporates standard geometric operations and a geometric library, and is lightweight with strong extensibility. The generated geometry is then input into Gadep, a GPU-based Monte Carlo computational kernel, to enable rapid simulation. For $1\times 10^8$ particles, a single RTX 4090 achieved a speedup factor of 13.53 compared with simultaneous computation using 60 CPU cores. The proposed framework has low implementation cost and broad applicability, providing a complete solution for refined detector modeling and calibration.

physics.ins-det↗

arXiv2024

Constrained Optimal Fuel Consumption of HEVs under Observational Noise

In our prior work, we investigated the minimum fuel consumption of a hybrid electric vehicle (HEV) under a state-of-charge (SOC) balance constraint, assuming perfect SOC measurements and accurate reference speed profiles. The constrained optimal fuel consumption (COFC) problem was addressed using a constrained reinforcement learning (CRL) framework. However, in real-world scenarios, SOC readings are often corrupted by sensor noise, and reference speeds may deviate from actual driving conditions. To account for these imperfections, this study reformulates the COFC problem by explicitly incorporating observational noise in both SOC and reference speed. We adopt a robust CRL approach, where the noise is modeled as a uniform distribution, and employ a structured training procedure to ensure stability. The proposed method is evaluated through simulations on the Toyota Prius hybrid system (THS), using both the New European Driving Cycle (NEDC) and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). Results show that fuel consumption and SOC constraint satisfaction remain robust across varying noise levels. Furthermore, the analysis reveals that observational noise in SOC and speed can impact fuel consumption to different extents. To the best of our knowledge, this is the first study to explicitly examine how observational noise -- commonly encountered in dynamometer testing and predictive energy control (PEC) applications -- affects constrained optimal fuel consumption in HEVs.

cs.LG↗

arXiv2024

Constrained Optimal Fuel Consumption of HEV: A Constrained Reinforcement Learning Approach

Hybrid electric vehicles (HEVs) are becoming increasingly popular because they can better combine the working characteristics of internal combustion engines and electric motors. However, the minimum fuel consumption of an HEV for a battery electrical balance case under a specific assembly condition and a specific speed curve still needs to be clarified in academia and industry. Regarding this problem, this work provides the mathematical expression of constrained optimal fuel consumption (COFC) from the perspective of constrained reinforcement learning (CRL) for the first time globally. Also, two mainstream approaches of CRL, constrained variational policy optimization (CVPO) and Lagrangian-based approaches, are utilized for the first time to obtain the vehicle's minimum fuel consumption under the battery electrical balance condition. We conduct case studies on the well-known Prius TOYOTA hybrid system (THS) under the NEDC condition; we give vital steps to implement CRL approaches and compare the performance between the CVPO and Lagrangian-based approaches. Our case study found that CVPO and Lagrangian-based approaches can obtain the lowest fuel consumption while maintaining the SOC balance constraint. The CVPO approach converges stable, but the Lagrangian-based approach can obtain the lowest fuel consumption at 3.95 L/100km, though with more significant oscillations. This result verifies the effectiveness of our proposed CRL approaches to the COFC problem.

cs.LG↗

arXiv2019

Controlling Power and Virtual Inertia from Storage for Frequency Response

Nowadays, power imbalance happens more frequently due to the more integration of renewable energy sources. Energy storage is a kind of devices that can charge energy at one time and discharge energy at another time. This function makes that storage is widely envolved into promoting power balance of power system. Besides this function, storage can also emulate virtual inertia to respond to frequency deviations in the system. This work provides a generalized optimization framework to analyze how to control power and virtual inertia from storage to participate in frequency response when a large disturbance happens. Centralized and distributed model predictive control is employed here, and case study verifies the effectiveness of our optimization framework.

eess.SY↗

arXiv2019

Controlling Time-Variant Virtual Inertia from Storage by Dynamic Programming and PROPT

The integration of renewables gradually replaces the traditional power plants, and this makes that the rotational inertia provided by the power plants is decreasing with time. Virtual inertia emulated by power electronic devices is becoming a promising way to stabilize the frequency of power system when a disturbance happens. And how to control virtual inertia to achieve desired performance is an important question to be answered. In this work, authors formulate an optimal control problem to describe the behaviour of controlling the time-variant inertia from storage, and structure preserving model is utilized to describe the dynamics of system, where frequencies of all buses are preserved. Also, practical constraints such as frequency contraints for buses, power/energy constraints for storage are incorporated in the optimal control problem. To solve this optimal control problem, dynamic programming (DP) and PROPT MATLAB Optimal Control Software are employed to obtain global optimal solution (virtual inertia trajectory). The simulation results show the correctness and effectiveness of our problem formulation and solving method.

math.OC↗