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Effect of Boric Acid on Volatile Fission Products in Conditions Simulating a Severe Nuclear Accident
No full textBoric acid is expected to play a role in severe nuclear accident chemistry, raising questions about of how it affects the volatile fission products iodine, cesium, and tellurium. Since tellurium and iodine are radiologically related (132Te decays into 132I/132mI with a half-life of 3.17 days) interactions between them are always possible in a severe accident scenario, but research focusing on their interactions is surprisingly scant. Experiments were undertaken at the VTT Technical Research Center of Finland using a setup involving the volatilization of tellurium, the injection of iodine as a gas, and boric acid and/or CsI dissolved in water and injected with the help of an atomizer. Analysis of the results included measurements with inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The results indicate that the volatility of tellurium is significantly increased if tellurium, iodine (I2), and boric acid are all present together, which was observed through a heightened concentration of tellurium in the liquid trap following such experiments. Furthermore, the formation of tellurium iodide is possible, as determined by SEM-Energy-dispersive X-ray spectroscopy (SEM-EDS) and supported by XPS. These results imply that studies of tellurium in combination with other relevant species should be continued. There is evidence that their volatility can be affected by one another, but the research into this type of interaction is scant.</p
Low-Complexity ZNN Model Handling Time-Varying Generalized Matrix Inversion Problems with Multilayered Sensor-Related Disturbances Applied to Robot Manipulator
No full textSensor-related disturbances and measurement uncertainties often degrade the performance of dynamic neural network methods in solving time-varying problems, especially the time-varying generalized matrix inversion (TVGMI) problem, which serves as the computational foundation for real-time control and signal reconstruction tasks. Traditional zeroing neural network (ZNN) models for handling TVGMI problems usually require matrix inversion or vectorization operations, leading to high computational complexity and poor robustness under sensor disturbance or time-varying perturbations. To overcome these challenges, this article proposes a novel low-complexity ZNN (LCZNN) model that achieves efficient and unified computation of time-varying matrix inversion and pseudoinverse without involving inverse matrix computation. To further enhance robustness, the LCZNN model is extended to handle multilayered sensor-related disturbances, giving rise to three variants: the state-disturbed LCZNN (SDLCZNN), the velocity-disturbed LCZNN (VDLCZNN), and the hybrid-disturbed LCZNN (HDLCZNN) models. Each variant introduces structured compensation dynamics that enable accurate convergence under different disturbance scenarios. Rigorous theoretical analyses establish their convergence and stability properties. Comprehensive numerical experiments on representative TVGMI problems validate the low computational burden, fast convergence, and superior multilayered disturbance tolerance of the proposed LCZNN framework. Moreover, discrete algorithms derived via Euler discretization are applied to the real-time path-tracking inverse-kinematics control of robotic manipulators. Simulation and physical experimental results confirm that the proposed LCZNN-based algorithms achieve high tracking precision, strong robustness, and computational efficiency, making them well-suited for real-time robotic and control applications.</p