Numerical simulation of hydrogen arcjet thruster with coupled sheath model

In the present work,a complete 2D chemical and thermal non-equilibrium numerical model coupled with a relatively simple sheath model is developed for hydrogen arcjet thruster.Conduction heat transfer in the anode wall is also included in the model.The operating voltages predicted by the model are compared with those in the literature and are found to be in close agreement.Power distributions for the various operating conditions are obtained,anode radiation loss primarily determines the thruster efficiency.Higher thruster efficiency was found to be associated with longer arc length.At cathode ion diffusion contribution dominates except at low input current where thermo-field electron current is dominant.

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process

Chemical vapor infiltration(CVI)is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites.These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics.The densification process during CVI critically influences the final performance,quality,and consistency of these composite materials.Experimentally optimizing the CVI processes is challenging due to the long experimental time and large optimization space.To address these challenges,this work takes a modeling-centric approach.Due to the complexities and limited experimental data of the isothermal CVI densification process,we have developed a data-driven predictive model using the physicsintegrated neural differentiable(PiNDiff)modeling framework.An uncertainty quantification feature has been embedded within the PiNDiff method,bolstering the model’s reliability and robustness.Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data,the proposed method showcases its capability in modeling densification during the CVI process.This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding,simulation,and optimization of the CVI manufacturing process,particularly when faced with sparse data and an incomplete description of the underlying physics.