Researchers at Saint Petersburg State University have created the PAINeT software suite. It will aid in refining the calculations needed by engineers when designing thermal shielding to reduce the mass of reusable space vehicles, which, in turn, will lower the expense of transporting cargo to space.
According to media reports, one key objective of the nation’s space industry development national project is to decrease the cost of orbiting one kilogram of payload from 500 thousand rubles to 200 thousand rubles by 2036.
Scientists at Saint Petersburg State University have developed the PAINeT software complex, which will assist in achieving this. Thanks to it, engineers will be able to enhance estimations of thermal loading and conceive lightweight thermal protection for recyclable spacecraft. This will lessen the vehicle’s weight, allowing for more supplies to be loaded, meaning more payload can be sent aloft for the same price. Thus, the unit cost of space delivery per kilogram will decline.
As explained by Vladimir Andreevich Istomin, the project lead and senior researcher at Saint Petersburg State University (Department of Hydroaeromechanics), thermal protection is necessary to prevent the vehicle from being destroyed by overheating (for instance, upon atmospheric reentry or during transit at altitudes of 10–20 km above Earth). When designing thermal shielding, numerous physical-chemical phenomena that cause nonequilibrium states (deviations from thermodynamic and physical-chemical equilibrium—Editor’s note) in flows of high-temperature mixtures of atmospheric gases must be considered.
Until recently, gas nonequilibrium effects were disregarded, and engineers would average available experimental data and perform necessary computations using approximate formulas.
The PAINeT software package will deliver precise outcomes. It comprises several computational modules and databases, including a dedicated neural network training module to expedite calculations. “Our developed software complex possesses a unique architecture that combines precise kinetic models and algorithmic capabilities. For this reason, PAINeT functions faster and superiorly compared to alternatives,” the researcher emphasized.
According to Vladimir Istomin, despite the multitude of existing programs, not all can furnish the user “out of the box” with the entire suite of necessary tools, as well as spectroscopic data. For example, one well-known analog to PAINeT can be utilized to calculate the transfer coefficient, but only without considering the electronic excitation of molecules at upper electronic levels. “Yet, this is vital when determining heat fluxes, which directly impacts the construction of the vehicle’s thermal protection,” Vladimir Istomin stressed. “To supplement the data, a specialist must dedicate time to modifying the program. Furthermore, the modification will not guarantee that they obtain calculations of the required precision, particularly due to the inadequacy of the underlying model. Additionally, the revised program might take a long time to compute results.”
Unlike its competitors, PAINeT employs detailed kinetic models alongside databases and acceleration methods through neural network training. This capability allows for rapid acquisition of physical properties, macroparameters, transfer coefficients, and all other requisite characteristics.
The innovation from Saint Petersburg State University scientists can be employed by designers at aerospace enterprises for thermal load calculations when engineering reusable craft for cargo transport to the ISS and subsequently to the Russian orbital station, as well as in preparations for missions to other celestial bodies.
Moreover, the software suite may prove beneficial to engineers designing high-speed aircraft, including hypersonic vehicles operating in dense atmospheric layers (at altitudes of 10–50 km). Naturally, PAINeT will also be useful in conducting scientific investigations.
The application scope of this development is further broadened by the possibility of using the complex’s modules and databases separately within various computational engineering programs to enhance calculation accuracy.
This work was carried out under the grants from the Russian Science Foundation: “Development of a software package for calculating macroparameters, transfer coefficients, and flux terms in various gas dynamics problems, accounting for the influence of strong nonequilibrium, chemical reactions, ionization, and electronic excitation” and “Application of machine learning methods in modeling physical-chemical processes in gas dynamics problems.”
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