AI 中文总结
研究先进反应堆数字孪生体在线参数反演问题，提出基于伴随的可微物理框架，通过隐式微分代数模型传播反向模式自动微分，驱动AD - 海森增量4D - Var估计器，在多工况下与多种基线对比，展现出不同优势。
AI 中文摘要
先进反应堆的数字孪生体必须在电厂很少处于稳态时，从有噪声的部分传感器数据流中在线反演物理参数。基于梯度的反演是完成这项任务的自然工具，但常规使用的正向模型很少能端到端可微，因此从业者只能依靠无导数滤波器。我们提出了一个闭式布雷顿气冷反应堆的端到端可微数字孪生体，它通过隐式微分代数电厂模型传播反向模式自动微分，揭示精确的参数灵敏度。该数字孪生体驱动一个AD - 海森增量4D - Var估计器，在一个二乘二矩阵上与集合、无迹和有限差分变分基线进行基准测试，该矩阵跨越稳态与瞬态激励以及全观测与部分观测。没有一个估计器在所有情况下都占优：无迹滤波器在受控稳态角落保持其最佳线性无偏优势，而所提出的估计器在其他三个角落的反射系数上获得最低平均误差——在瞬态全观测下达到0.43%，比无迹滤波器低约一个数量级，并且在组合瞬态 - 部分角落的低到中等噪声下与集合滤波器匹配。每个估计器的方差都在克拉美 - 罗界的一个小因子范围内，因此残余误差反映了由于数字孪生体的可微性简化导致的确定性偏差下限，而不是统计效率低下，其优势在于对由此产生的多模态损失景观具有鲁棒性，组件消融按工况定位了这种优势。因此，对第一原理电厂模型进行微分使得基于梯度的反演在反应堆的整个运行范围内与既定滤波器具有竞争力。
英文摘要
Digital twins for advanced reactors must invert physical parameters online from noisy, partial sensor streams while the plant is rarely at steady state. Gradient-based inversion is the natural tool for this task, but the forward models in routine use are seldom differentiable end to end, so practitioners fall back on derivative-free filters. We present an end-to-end-differentiable digital twin of a closed-Brayton gas-cooled reactor that propagates reverse-mode automatic differentiation through an implicit differential-algebraic plant model, exposing exact parameter sensitivities. The twin drives an AD-Hessian incremental 4D-Var estimator, benchmarked against ensemble, unscented, and finite-difference variational baselines over a two-by-two matrix that crosses steady with transient excitation and full with partial observation. No single estimator wins everywhere: the unscented filter keeps its best-linear-unbiased advantage on the controlled steady-state corner, while the proposed estimator attains the lowest mean error on the reflector coefficient on the other three corners --- reaching \SI{0.43}{\percent} under transient full observation, roughly an order of magnitude below the unscented filter, and matching the ensemble filter at low-to-moderate noise on the combined transient-partial corner. Every estimator's variance sits within a small factor of the Cramér--Rao bound, so the residual error reflects a deterministic bias floor from the twin's differentiability simplifications rather than statistical inefficiency, and the advantage is one of robustness to the resulting multi-modal loss landscape, which a component ablation localises by regime. Differentiating a first-principles plant model thus makes gradient-based inversion competitive with established filters across a reactor's operating range.
