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Xiong Yin

Publications and source records attributed to Xiong Yin.

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Muon-Catalyzed Nuclear Fusion: Physical Mechanism, Bottleneck Breakthroughs, and an Engineering Pathway

Muon-catalyzed nuclear fusion (\mucf) replaces atomic electrons with negative muons, compressing atomic orbitals by about two orders of magnitude and enabling deuterium--tritium (D--T) fusion under near-room-temperature conditions. This paper reviews the physical principles of \mucf{} and formulates its essential dynamics as a four-step cycle: muonic-atom formation, muon transfer, resonant \dtmu{} molecular formation, and D--T fusion with muon release and recycling. A kinetic model is used to quantify the number of catalysis cycles per muon and the corresponding energy gain. We focus on the central limitation of catalytic efficiency, namely the alpha-sticking effect, and discuss possible breakthrough routes including nuclear-spin and muon dual polarization, in-flight muon-catalyzed fusion, and heavy-ion-driven magneto-inertial fusion. Within the idealized assumptions of the present model, a four-dimensional synergistic scheme combining dual polarization, high-density confinement, electric-field-assisted muon recovery, and resonant enhancement may increase the number of catalysis cycles per muon from the present experimental record of about 150 to more than 500, potentially enabling an energy gain \(Q>2\). On this basis, we propose a conceptual fusion--fission fuel-breeding hybrid reactor, denoted as \mucf-FBR, which exploits the 14.1-MeV neutron yield of \mucf{} to breed \({}^{239}\mathrm{Pu}\) from a \({}^{238}\mathrm{U}\) blanket in a decoupled fusion--fission operating mode. This concept may offer advantages in engineering robustness, radiation-damage tolerance, and natural-uranium utilization.

hep-ph

State-of-charge estimation of lithium-ion batteries using a tree seed and genetic algorithm-optimized generalized mixture minimum error entropy-based square root cubature Kalman filter

The cubature Kalman filter based on minimum error entropy (MEE-CKF) offers accurate and robust performance in state of charge (SOC) estimation. However, due to the inflexibility of the minimum error entropy (MEE), this algorithm demonstrates limited robustness when confronted with more complex noise environments. To address these limitations, this paper proposes a generalized mixture minimum error entropy-based (GMMEE) square-root cubature Kalman filter (GMMEE-SRCKF). The square-root algorithm ensures improved numerical stability and avoids covariance degeneration, while the GMMEE criterion with two flexible kernels adapts effectively to non-Gaussian noise. Moreover, a hybrid tree seed and genetic algorithm (TSGA) is introduced to optimize the kernel parameters automatically. Experimental results confirm that the TSGA-optimized GMMEE-SRCKF outperforms existing robust filters, achieving the root mean square error (RMSE) of less than 0.5%.

eess.SP