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The purpose of this study is to understand experimentally the mixing characteristics of a two-stream exhaust system with a supersonic Mach 1.5 primary jet that exits the rectangular C-D nozzle surrounded by a sonic secondary jet from a convergent rectangular nozzle by varying the aspect ratio (AR = 2 and 3) similar to those that can be available for future high-speed commercial aircraft.

This paper focuses on the experimental results of effects of AR at various expansion levels of jets issued/delivered from a central rectangular convergent-divergent nozzle of AR 2 and 3 surrounded by a coflow from a convergent rectangular sonic nozzle. The lip thickness of the primary nozzle is 2.2 mm. various nozzle pressure ratios (NPRs) ranging from 2, 3, 3.69 and 4 were chosen for pressure measurements.

For all the NPRs, AR 3 had a shorter core than AR 2. Also, AR 3 was found to decay faster in the transition and fully developed zones. The lateral plots show that the AR has an influence on the jet spread.

The structure of waves existing in the potential core of the rectangular coflow jet along with the major and minor axis planes was visualized by the shadowgraph technique.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.