In future deep space exploration missions such as Mars sampling, manned landing on the moon, Jupiter system exploration, as the entry speed of the probe far exceeds the first cosmic velocity of the target planet, the system energy increases significantly. This leads to extreme flow and heat transfer phenomena accompanied by ultra-high temperature physical and chemical reactions, involving complex interactions between microscopic physical and chemical reactions and macroscopic flows, heat and mass transfer at the fluid-solid interface, and internal thermodynamic response of thermal protection materials. How to accurately predict such an extreme flow and heat transfer system with multi-phase, multi-scale, multi-process and strong coupling characteristics, so as to support the precise design of the probe for efficient kinetic energy dissipation and effective heat isolation, poses a severe challenge to fluid mechanics and related disciplines. This paper focuses on the extreme flow and heat transfer during planetary atmospheric entry, discusses and sorts out the key scientific problems in three aspects: ultra-high-speed flow, intense radiation heat transfer and ultra-high-temperature physical and chemical reactions, and prospects future research in this field.
- Article type
- Year
- Co-author
Open Access
Research Article
Issue
Open Access
Issue
Regenerative cooling technology for engines is a critical thermal protection method for high-speed propulsion systems, such as liquid rocket and scramjet engines. By circulating liquid fuel through regenerative cooling channels to absorb heat from combustion chamber walls, it safeguards structural integrity against overheating and failure while enhancing thermal efficiency by utilizing the heated coolant in the combustion process. This technology serves as a foundational enabler for achieving high-Mach-number flight and deep-space exploration missions.
A systematic review was conducted on the computational methods and research progress in fluid-structure coupled heat transfer for regenerative cooling systems. The review focused on the calculation models and critical influencing factors for heat transfer between hot gas and solid walls, as well as heat transfer within coolant channels. Three fluid-structure coupled heat transfer calculation methods were proposed. For gas-wall heat transfer, engineering practices commonly used empirical correlations such as the Bartz method and Eckert reference-enthalpy method combined with 1D (one-dimensional) or CFD (computational fluid dynamics) simulations. While these engineering methods (e.g., Bartz and Eckert approaches) offered high computational efficiency, they struggled to capture the influence of local flow field characteristics on heat flux. CFD methods, though capable of improving accuracy, heavily relied on high-fidelity physical models and involved significant computational costs. For coolant channel and solid wall heat transfer, three types of models—1D, quasi-two-dimensional (quasi-2D), and 3D—were predominantly used. One-dimensional models simplified complexity but neglected temperature stratification and local flow effects, resulting in lower computational precision. Three-dimensional models provided higher accuracy but incurred substantial computational expenses. Researchers used 3D CFD methods to analyze the effects of factors such as corrugated tubes and ribbed channels on enhanced heat transfer in channels. Additionally, special factors such as coolant secondary flow, supercritical property variations, and fuel pyrolysis had to be considered. These phenomena required empirical correlation corrections or CFD modeling to capture their unique physical characteristics. Regarding fluid-structure coupling, three primary methods were identified: (1) Monolithic solution approach: This reduced interface coupling errors by unifying governing equations but required significant computational resources for complex problems, which were challenging to achieve in engineering practice at the time. (2) Partitioned approach based on empirical correlations: This leveraged empirical formulas to simplify calculations but required careful selection of correlations, as each had specific applicability ranges. (3) Partitioned approach based on continuity boundaries: This achieved precise coupling via temperature and heat flux continuity conditions but relied on high-fidelity flow simulations. For transient heat transfer analysis, current studies at the time often adopted decoupling strategies for phased simulations due to time-scale effects. Future research was suggested to prioritize the development of spatiotemporal coupling methods that balanced computational efficiency and precision, enabling more robust and adaptive thermal analysis in dynamic operating conditions. This work provided a comprehensive overview of current advancements and challenges in regenerative cooling system design, emphasizing the need for integrated approaches that combined empirical models with high-fidelity simulations to address complex multi-physics phenomena.
High-Mach scramjet engines have become a critical direction for the development of next-generation aerospace propulsion systems. To meet the demands for refined design of engine thermal protection systems, it is essential to further enhance the predictive accuracy of thermal protection systems. For this purpose, research efforts on simulating heat transfer processes in regenerative cooling systems should focus on three key areas: (1) high-fidelity modeling of complex physicochemical processes, (2) high-confidence simulation of fluid-structure coupled heat transfer, and (3) high-efficiency simulation of transient fluid-structure coupled heat transfer. These advancements are crucial to fulfilling the precision requirements for advanced thermal protection system design.
As one of the fastest-growing directions in deep learning, the generative model has achieved remarkable success in realms such as computer vision and has also introduced novel paradigms and methodologies for research endeavors within the scientific fields like aerodynamics. This paper focuses on the latest research advancements of generative models in the field of aircraft aerodynamic configuration design, and systematically summarizes the relevant research achievements in recent years. Firstly, a representation-generation-evaluation framework for generative aerodynamic configuration design of aircraft is established. Subsequently, the key technologies and current development progress involved in aerodynamic configuration design are examined and discussed from the perspectives of aerodynamic configuration representation, the development of generative aerodynamic configuration design models, and methods for evaluating design quality. Additionally, a brief overview of aerodynamic data construction methods and typical datasets is provided, serving as a data foundation for generative aerodynamic design. Lastly, the future key development directions in the field of generative aerodynamic configuration design are discussed, including exploration of hybrid generative model architectures, construction of large models and domain-specific agents for aerodynamic design, establishment of a comprehensive evaluation system for generative aerodynamic design quality, and integration of domain knowledge into generative aerodynamic design models.
Open Access
Review
Issue
As one of the fastest-growing directions in artificial intelligence, the large model technology has achieved remarkable success in realms such as natural language processing and computer vision and is vigorously expanding its influence in empowering scientific research. It has also become a powerful tool in aerodynamics, possessing significant potential to expedite aerodynamic experiments and computations and assist aerodynamic theory and knowledge discovery. This paper begins by presenting an overview of large models for language processing, computer vision, and scientific computing. Subsequently, the paper outlines the conceptual framework of large models for scientific computing in aerodynamics, reviewing the current research progress from various perspectives, including flow field prediction, turbulence modeling, aerodynamic performance prediction, and aerodynamic configuration design. Furthermore, key techniques of large models in aerodynamics are discussed in-depth from the perspectives of model architecture, feedback alignment, and the generation of big aerodynamic data. Lastly, developing directions of large models in aerodynamics are prospected, including the construction of a unified pre-trained foundational model, the integration of aerodynamic knowledge to support scientific discoveries, and the development of discipline-specific agents.
Open Access
Issue
The deep integration of wind tunnel experiments, numerical calculations, and model flight tests is an inevitable requirement for developing future high-speed vehicle. This article focuses on constructing high-speed wind tunnel facilities, numerical calculation software and hardware, model flight testing capabilities, and self-developed flight test techniques for measuring the surface temperature, heat flux, pressure, friction drag, etc. Based on the characteristics of aerodynamic data fusion, an aerodynamic data fusion method, based on the correlation between aerodynamic data and physical models as well as deep learning, was developed to solve the problem of data association between different data sources, significantly improving the data's reliability. The data fusion method has been successfully applied in the flight-ground correlation of the pitching moment coefficient and aerothermodynamic data for high-speed vehicle. A comprehensive study of the high-speed shock/boundary-layer interaction problem using various methods results in an empirical formula of the interaction force/thermal load, a correction to the pressure/heat flow correlation, and the verification of the low-frequency oscillation of the separation bubble under real flight conditions.
Open Access
Review
Issue
Intelligent Aerodynamics (IA), a combination of artificial intelligence (AI) and aerodynamics that integrates the fourth research paradigm (Big Data), is growing to be a distinct cross-discipline. This paper first introduces IA's concept and connotation and sketches the main research methods. Secondly, the research progress and the work carried out in China Aerodynamics Research and Development Center (CARDC) are reviewed regarding AI-enhanced flow prediction, transition/turbulence modeling, multi-source data fusion, aerodynamic/aerothermal modeling, and extraction of critical flow-field information. Finally, the future research work and development trends of IA are prospected.
Open Access
Issue
Through the combination of wind-tunnel tests and numerical simulations, the shock-wave/boundary-layer interaction (SWBLI) of interstage flow fields within typical stage clearances of a Two-Stage-To-Orbit (TSTO) scaled model at Mach number 6 is studied. Flow fields in the interference areas are analyzed in detail. Results show that the impingement of shock waves induces the boundary layers to transit rapidly to turbulent states, which numerical simulations have accurately captured. SWBLI induces complex three-dimensional flow characteristics with prominent open features. The high wall pressure induced by the strong shock wave spreads downstream in an arc. The incident shock wave generated by the orbiter head constantly reflects between the two stages and gradually weakens. Meanwhile, the spanwise curved principal separation and reattachment lines, streamwise aligned secondary separation and reattachment lines, and critical points, including saddle, node, focus, and vortical structure induced by shear layers, are also observed in the interference areas. The shock wave impingement induces a larger separation area and more obvious open features in the laminar state than in the turbulent state.
Open Access
Review
Issue
Aerodynamics-related ground test capability is an important metric of the current development of a country’s flight vehicles. The United States is the world leader in terms of aerodynamic test and research capability, aircraft innovative research and aerodynamics-related disciplines. This paper summarizes the basic informations of USA aerodynamic research institutions, offers a list of 11 major institutions including their core test & experiment and supporting capabilities, and a classification of the status quo of USA test research capability and experimental techniques. A brief analysis on the advantages and disadvantages of USA aerodynamic ground test capability, and lessons learned is given subsequently, and a prediction of its future development is made.
京公网安备11010802044758号