In this paper, we present a deep learning based surrogate model to determine non-linear aerodynamic characteristics of UAVs. The main advantage of this model is that it can predict the aerodynamic properties of the configurations very quickly by using only geometric configuration parameters without the need for any special input data or pre-process phase. This provides a crucial and explicit design and synthesis tool for mini and small UAVs. To achieve this goal, a large data set, which includes thousands of wing-tail configurations geometry parameters and performance coefficients, was generated using the previously developed and computationally very efficient non-linear lifting line method. This data is used for training the artificial neural network model. The preliminary results show that the neural network model has generalization capability. The aerodynamic model predictions show almost 1-1 coincidence with the numerical data even for configurations with different 2D profiles that are not used in model training. Specifically, the results of test cases are found to capture both the linear and non-linear region of the lift curves, by predicting the maximum lift coefficient, the stall angle of attack, and the characteristics of post-stall region correctly. Similarly, total drag and pitching moment coefficients are predicted successfully. The developed methodology provides the basis for bidirectional design optimization and offers insight for an inverse tool that can calculate geometry parameters for a given design condition.
In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.
In this paper, we present a brief review of the state of the art physics informed deep learning methodology and examine its applicability, limits, advantages, and disadvantages via several applications. The main advantage of this method is that it can predict the solution of the partial differential equations by using only boundary and initial conditions without the need for any training data or pre-process phase. Using physics informed neural network algorithms, it is possible to solve partial differential equations in many different problems encountered in engineering studies with a low cost and time instead of traditional numerical methodologies. A direct comparison between the initial results of the current model, analytical solutions, and computational fluid dynamics methods shows very good agreement. The proposed methodology provides a crucial basis for solution of more advance partial differential equation systems and offers a new analysis and mathematical modelling tool for aerospace applications.
In this study, an intelligent wargaming approach is proposed to evaluate the effectiveness of a military operation plan in terms of operational success and survivability of the assets. The proposed application is developed based on classical military decision making and planning (MDMP) workflow for ease of implementation into the real-world applications. Contributions of this study are threefold; a) developing an intelligent wargaming approach to accelerate the course of action (COA) analysis step in the MDMP which leads creating more candidate COAs for a military operation, b) generating effective tactics against the opposite forces to increase operational success, and c) developing an efficient, visual wargame-based MDMP framework for future systems that require a small team of operators to supervise a network of automated agents. Several example engagement scenarios are performed to evaluate the system capabilities and results are given. Moreover, fleet composition issue for automated agents is investigated and the fleet composer algorithm with hyperparameter tuning architecture is proposed.
The next generation low-cost modular unmanned combat aerial vehicles (UCAVs) provide the opportunity to implement innovative solutions to complex tasks, while also bringing new challenges in design, production, and certification subjects. Solving these problems with tools that provide fast modeling in line with the digital twin concept is possible. In this work, we develop an artificial intelligence (AI) based multifidelity surrogate model to determine performance parameters of innovative modular UCAVs. First, we develop a data generation algorithm that includes a high-fidelity model based on computational fluid dynamics methods and a low-fidelity model based on computational aerodynamic approaches. In the next step, a new transfer learning-based surrogate model is generated using multifidelity data. Thanks to this approach, the developed AI model more accurately predicted the flow conditions that were missing in the high-fidelity data with the data obtained from the low-order model. The performance of the proposed AI-based surrogate model is to be investigated in terms of accuracy, robustness, and computational cost using a generic modular UCAV configuration.
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