This study explores the magnetohydrodynamic (MHD) flow of a Jeffrey-Hamel fluid within a convergent/divergent channel, a scenario relevant to both physical and biological sciences. The flow dynamics between nonparallel inclined walls are governed by highly nonlinear differential equations derived through conservation laws and similarity transformations. By applying similarity transformations, the governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs). The NDSolve approach is then utilized to obtain numerical solutions for these equations. A comparison with existing methods in the literature confirms the accuracy and reliability of the results. Additionally, the impact of various dimensionless physical parameters, such as the influence of the magnetic parameter, angle alpha, and the Deborah number on the velocity profile is investigated. The parameters angle alpha, Eckert number, and volume friction are examined on the temperature profile, followed by a detailed discussion of the findings.

Tornados are extremely catastrophic, and the global effect of natural calamities like tornados is enormous and needs prompt and effective management. We can tackle this problem by using measures like multi-criteria decision-making (MCDM) to identify high-risk areas of a potential tornado strike. We frequently use MCDM techniques to solve the complexities and uncertainties of modern-era problems. We present a study that builds a prediction model by combining the Dombi aggregation operator with a complex m-polar fuzzy set (CmFS) to accurately guess when a tornado will hit. Our proposed model determines an expert panel, criteria, and a set of alternatives after identifying the problem. We create summed-up decision matrices using complex m-polar fuzzy Dombi aggregation operators (CmFDAO) after experts evaluate criteria and options. The algorithm then presents the best option with the help of a final decision score matrix. Our model uses a set of eight meteorological elements and eight experts to assess four possible tornado locations and pinpoint an area with a high risk of tornado strikes. The results generated by our aggregation operator set demonstrate that our proposed method for handling complex and multi-polar data is concise and efficient when compared to other sets. This early prediction highlights the potential of significant risk reduction to the environment and human life due to catastrophic events like tornados by enhancing early warning systems and effective emergency management.