Technology Aviation The world's largest RC Boeing 777-9X takes flight Filmmaker Tyler Perry piloted the remote-controlled behemoth, which weighs 630 pounds with a 33-foot wingspan. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The remote-controlled aircraft is roughly the same size as a human-piloted Cessna 150. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The world's first'hovertrain' could reach speeds of 270 mph in the 1960s But the futuristic Aérotrain never saw the light of day. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. This cancelled Mongolian postage stamp shows the Aérotrain Orleans, circa 1979. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

A Ukrainian flying ace is leveraging his aerobatics skills to protect his countrymen from nightly attacks. The most challenging part of an international aerobatics contest is the Free Unknown. Pilots arrive at a competition after having polished sequences of loops, stall turns, and barrel rolls. But for the Free Unknown section they learn which assortment of tricks they must perform only a day in advance. Contestants plan out how they will string together the stipulated moves in the most pleasing fashion, but they cannot rehearse the routine, except in their minds. It's a test of imagination and airmanship that often decides the competition. In 2019, the World Intermediate Aerobatics Championship, which was held on an airfield in the Czech town of Břeclav, contained three Free Unknowns. The winner of the first was a twenty-five-year-old Ukrainian pilot named Timur Fatkullin. At the controls of his red-and-silver Extra 330LX--a nimble German sports plane--he made the unusual move of starting his sequence upside down. He then executed a complicated routine as if he'd practiced it for months. The Ukrainian team, boosted by Fatkullin's performance, won gold. Trevor Dugan, who served as a navigator with the R.A.F. in Afghanistan and Iraq, was on the British team, which took bronze. Fatkullin, he said, was "absolutely phenomenal." Not long after that championship, Fatkullin stopped entering aerobatics competitions: first came the pandemic, then the war with Russia. He moves through life impatiently. Now thirty-two, he has five children. He is tall, with a tight beard, pale-green eyes, and a square jaw. Even in casual situations, he stands ramrod straight, as though about to give or receive an order. He often wears a shirt with three buttons undone, a beige leather flying jacket with the collar turned up, combat pants, and Nike high-tops. He plays the guitar, a little piano. He often carries a thick fold of high-value bills. He speaks several languages, including English (almost perfectly) and Spanish (conversationally). He once spent thirty days in jail after breaking the ribs of a man who'd threatened his wife. He can dance the tango. When Fatkullin was in his mid-twenties, he started doing stunts with a group of other extreme athletes: parachutists, motorcyclists, a free diver.

The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, and a \textit{meta} data surrogate selection method to address constraint heterogeneity. Numerical benchmark problems and an aircraft conceptual design optimization problem are used to demonstrate the proposed methods. Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization, with improved prediction accuracy for both objective and constraint surrogate models.

Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, balancing precision and computational cost using variance reduction criteria. In this work, we propose novel multi-fidelity selection strategies. Specifically, we demonstrate how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution. We validate the proposed multi-fidelity optimization strategy by applying it to four analytical test cases, showcasing its effectiveness. The proposed method is used to efficiently solve a challenging aircraft wing aero-structural design problem. The proposed setting uses a linear vortex lattice method and a finite element method for the aerodynamic and structural analysis respectively. We show that employing our proposed multi-fidelity approach leads to $86\%$ to $200\%$ more constraint compliant solutions given a limited budget compared to the state-of-the-art approach.

Reinforcement learning (RL) demonstrates superior potential over traditional flight control methods for fixed-wing aircraft, particularly under extreme operational conditions. However, the high demand for training samples and the lack of efficient computation in existing simulators hinder its further application. In this paper, we introduce NeuralPlane, the first benchmark platform for large-scale parallel simulations of fixed-wing aircraft. NeuralPlane significantly boosts high-fidelity simulation via GPU-accelerated Flight Dynamics Model (FDM) computation, achieving a single-step simulation time of just 0.2 seconds at a parallel scale of $10^{6}$, far exceeding current platforms. We also provide clear code templates, comprehensive evaluation/visualization tools and hierarchical frameworks for integrating RL and traditional control methods. We believe that NeuralPlane can accelerate the development of RL-based fixed-wing flight control and serve as a new challenging benchmark for the RL community.

In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), whereby we look to learn a posterior distribution over the full eVTOL design space. To learn this distribution, we sample over discrete aircraft configurations (topologies) and their corresponding set of continuous parameters. Therefore, we introduce a hierarchical probabilistic model consisting of two diffusion models. The first model leverages recent work on Riemannian Diffusion Language Modeling (RDLM) and Unified World Models (UWMs) to enable us to sample topologies from a discrete and continuous space. For the second model we introduce a masked diffusion approach to sample the corresponding parameters conditioned on the topology. Our approach rediscovers known trends and governing physical laws in aircraft design, while significantly accelerating design generation.