The Integration of ANN and FEA and Its Application to Property Prediction of Dual-Performance Turbine Disks.

Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present substantial challenges. The prediction of yield strength is typically derived from the analysis of microstructures under various heat treatment regimes. This method is time-consuming, expensive, and the accuracy often depends on the precision of microstructural characterization. This study successfully employed a coupled method of Artificial Neural Network (ANN) and finite element analysis (FEA) to reveal the relationship between the heat treatment process and yield strength. The coupled method accurately predicted the location specified and temperature-dependent yield strength based on the heat treatment parameters such as holding temperatures and cooling rates. The root mean square error (RMSE) and mean absolute percentage deviation (MAPD) for the training set are 50.37 and 3.77, respectively, while, for the testing set, they are 50.13 and 3.71, respectively. Furthermore, an integrated model of FEA and ANN is established using a Abaqus user subroutine. The integrated model can predict the yield strength based on temperature calculation results and automatically update material properties of the FEA model during the loading process simulation. This allows for an accurate calculation of the stress-strain state of the turbine disk during actual working conditions, aiding in locating areas of stress concentration, plastic deformation, and other critical regions, and provides a novel reliable reference for the rapid design of the turbine disk.

Investigation of Lithium-Ion Battery Performance Utilizing Magnetic Controllable Superionic Conductor Li3(V1-x Fe x )2(PO4)3/C (x = 0.05 and 0.10).

Lithium-ion batteries with Li3V2(PO4)3/C as the cathode have been a popular research topic in recent years; however, studies of the effects of external magnetic fields on them are less common. This study investigates the effects of an external magnetic field applied parallel to the direction of the anode and cathode on the ion transport through iron-doped Li3(V1-x Fe x )2(PO4)3, the outer carbon coating, the film/electrolyte/separator, and up to the lithium metal electrode on a microscopic level. The results reveal that for the x = 0.05 sample with lower doping, the magnetostriction expansion of Li3(V1-x Fe x )2(PO4)3 and the magnetostrictive contraction effect of the outer ordered carbon layer cancel each other out, resulting in no significant enhancement of the battery's energy and power density due to the external magnetic field. In contrast, the x = 0.1 sample, lacking magnetostrictive contraction in the outer ordered carbon layer, shows that its energy and power density can be influenced by the magnetic field. Under zero magnetic field, the cyclic performance exhibits superior average capacity performance in the x = 0.05 sample, while the x = 0.1 sample shows a lower decay rate. Both samples are affected by the magnetic field; however, the x = 0.1 sample performs better under magnetic conditions. In particular, in the C-rate tests under a magnetic field, the sample with x = 0.1 showed a significant relative reduction in capacity decay rate by 20.18% compared to the sample with x = 0.05.

An Improved Grain Growth Model and Its Application in Gradient Heat Treatment of Aero-Engine Turbine Discs.

A new grain growth model was developed by introducing the ultimate grain size to the traditional model. The grain growth behavior and its ultimate size under the Zenner pinning force are also discussed. This model was applied to the nickel-based superalloy and integrated into an FEM code. The grain evolution of a forged third-generation powder superalloy heat treated at different temperatures and holding times was studied. A gradient heat treatment setup was designed and implemented for a full-size turbine disc based on the model prediction to meet the accurate dual-microstructure requirements of an advanced aero-engine turbine disc design. The predicted temperature was validated by thermal couple measurements. The relative error between the prediction and the measurements is less than 2%. The metallographic examination of the whole turbine disk through sectioning showed that the grain size was ASTM 7-8 at the rim area and ASTM 11-12 at the bore region, which agrees well with the prediction. The predicted values of the three measurement areas are ASTM 12.1, ASTM 9.1, and ASTM 7.1, respectively, with a maximum error of 5% compared to the measured values. The proposed model was validated and successfully applied to help manufacture a dual-microstructure aero-engine turbine disc.

The muon Smasher's guide.

We lay out a comprehensive physics case for a future high-energy muon collider, exploring a range of collision energies (from 1 to 100 TeV) and luminosities. We highlight the advantages of such a collider over proposed alternatives. We show how one can leverage both the point-like nature of the muons themselves as well as the cloud of electroweak radiation that surrounds the beam to blur the dichotomy between energy and precision in the search for new physics. The physics case is buttressed by a range of studies with applications to electroweak symmetry breaking, dark matter, and the naturalness of the weak scale. Furthermore, we make sharp connections with complementary experiments that are probing new physics effects using electric dipole moments, flavor violation, and gravitational waves. An extensive appendix provides cross section predictions as a function of the center-of-mass energy for many canonical simplified models.