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Solutions of the twin plane jets HF chemical laser flow based on a turbulent kinetic theory, due to a modified Green’s function method, are presented. The calculated results of probability density function (PDF) of various chemical species in velocity space, and mass fraction concentration distributions of various reactants and products in the flow field, are revealed and discussed in this analysis. The transport phenomena of different pumping rate, collisional deactivation rate, and radiative deactivation rate in the interaction between the twin plane jets HF chemical laser show that the properties of species mass fraction concentrations, collisional reaction rate, and radiative incident intensity are the dominant factors. The present study provides the fundamentals for theoretical understanding of twin plane jets HF chemical laser and further application to multiple‐jet HF chemical laser analysis.

A turbulent kinetic theory due to Chung and a Green’s function method by Hong were employed to solve a reacting turbulent plane jet problem. An instantaneous mixing concept was used to simulate the steady state of turbulent plane jet with combustion. The probability density function description of the fluid elements in a turbulent reacting flow could properly explain the turbulent flame zone structure and the turbulent transport of heat, momentum and chemical species even under the infinitely fast reaction rate assumption. The calculated distributions of the various moments of the turbulent combustion field were found in good agreement with the available experimental data. The dynamic behaviour of combustion in the turbulent field could be better understood via the probability density function description of the present turbulent kinetic theory approach.

The swirling flow of sudden‐expansion dump combustor with central V‐gutter flameholder and six side‐inlets is studied by employing the SIMPLE‐C algorithm and Jones‐Launder k‐ε two‐equation turbulent model. Both combustion models of one‐step with infinite chemical reaction rate and two‐step with finite chemical reaction rate of eddy‐breakup (EBU) model are used to solve the present problem. The results agreed well with available prediction data in terms of axial‐velocity and total pressure coefficient along combustor centerline. The flowfield structure of combustor considered is strongly affected by swirling, flameholder and side‐inlet flow. For the fixed strength of swirling, the length of central recirculation zone is decreased when the angle of V‐gutter is increased. The outlet velocity of combustor in reacting flow is higher than that in cold flow because the released heat of combustion causes the decrease of density throughout the combustor flowfield. The distribution of mass fraction of various species in reacting process depends on the mixing effect, chemical kinetic and the geometric configuration of combustor.

Defining key artificial intelligence (AI) technologies is especially fundamental because AI applications involve the development of multiple technical fields and have the potential to generate numerous business opportunities in the future. However, most related studies have examined patent grants granted by or patent applications filed to major patent offices; few studies have employed the perspective of standard-essential patents (SEPs) from a holistic technical view. In addition, because few studies have explored the status signals of countries in relation to SEPs, the present study integrated “country” into the model and determined differences among countries in terms of their technological focus.

In this study, through patent technological network analysis in various periods, the author not only observed the focus fields of AI-related SEPs but also examined temporal trends to determine technical development trends.

This study identified technologies that have been key players in the SEP network in recent years; these technologies were centered on electric digital data processing, recognition of data and transmission of digital information. Moreover, many of these technologies have been applied in areas such as management and commerce and radio navigation. Furthermore, the USA plays a crucial role in the global development of AI technical network.

This study constructs a technical network model to identify key technologies and trends that can serve as a reference for national research and development resource allocation.