Commercial hypersonic point-to-point (P2P) travel promises intercontinental flights in under 90 min, yet viability depends on systems-level integration of advanced technologies and infrastructure. This paper addresses the critical question of whether such travel can be technically feasible, economically viable, and environmentally sustainable within the coming decades. We analyze two core technological pillars—Turbine-Based Combined Cycle (TBCC) propulsion and adaptive morphing aerodynamics—integrated within a multidisciplinary framework addressing thermal management. The central contribution is a coupled techno-economic model that quantifies interdependencies between propulsion efficiency, hydrogen costs, infrastructure capital expenditure, lifecycle carbon intensity, and operating costs. Liquid hydrogen (LH 2) is posited as indispensable due to its specific impulse and cooling capacity for air-breathing hypersonic propulsion. Three quantitative findings emerge: (1) green hydrogen must reach €2–3 kg −1 by 2035–2040 for levelized cost of travel (LCOT) to fall below €0.60 passenger-km −1; (2) a 100-aircraft fleet requires 1.65 Mt yr −1 LH 2, corresponding to 15–20 GW electrolysis capacity and €10–20B capital expenditure; (3) well-to-wake emissions for green-hydrogen pathways range from 150–350 g CO 2 e passenger-km −1, representing a 70%–80% reduction relative to subsonic Jet-A operations. Commercial viability necessitates realistic deployment beginning 2035–2045, following a staged pathway, with 2030 reframed as aspirational for flight demonstration rather than entry-into-service. The path forward requires concurrent advancement across the entire value chain—presenting a system-of-systems integration challenge with hydrogen at its core. We conclude that while hypersonic P2P travel is technically plausible, its realization hinges on coordinated progress in propulsion, infrastructure, and green hydrogen production, demanding immediate and sustained cross-sector investment.
The rapid emergence of advanced air mobility technologies such as air taxis and delivery drones is transforming the aviation landscape by introducing new layers of complexity for decision-making in dynamic and crowded airspaces. To support this evolving environment, we propose a Large Language Model (LLM)-based method that improves decision-making in aviation operations by leveraging users’ natural language queries alongside validated aviation expert models and real-time data sources. This method interprets user intent, selects appropriate expert tools such as models for trajectory prediction, runway configuration, and Traffic Management Initiative (TMI) forecasting, and provides contextualized responses using relevant aviation data sources. Unlike general purpose LLM applications, the proposed system grounds its outputs in aviation-validated tools and data sources, addressing the risk of hallucination in a domain where inaccurate information can have serious operational consequences. Evaluated on 1,000 domain-specific aviation queries across nine tools using two models, GPT-4o and GPT-4o-mini, the system achieves tool selection accuracy ranging from 86% to 100% across all tools, with results independently validated on a held-out test set. This work illustrates the potential of LLMs as decision support tools in aviation when carefully grounded in validated domain-specific models and datasets.
Omar D. Lopez Mejia, David E. Blanco, Luis E. Muñoz
The reduction of fuel consumption has become one of the main targets in the automotive industry. To achieve this objective, drag reduction plays a major role and one of the techniques that has become attractive in recent years is the use of active flow control (AFC). In the present paper, Computational Fluid Dynamics (CFD) simulations are used to study the application of synthetic jets (SJ) as AFC devices in the flow around a station wagon (SW). The main purpose of this study is to determine the viability of these devices in the overall aerodynamic performance of the vehicle but especially in drag reduction. A baseline case (no actuation) of the simulation of the flow around the SW was initially performed and the drag coefficient was validated with experimental data from a coast-down test. Using the baseline results, two zones for the synthetic jet outlets were defined in the rear part of the SW, one located in the Upper Side (US) and the other in the Lateral Side (LS). Numerical results show that the synthetic jets actuation effectively affects the dynamics of the flow and the near wake, and a reduction of the drag (∼1%) with an increment in the lift (∼20%) was observed with actuation in US. A lift coefficient reduction (∼33%) with a drag increment (∼4%) was also observed with SJ actuating in LS. Nevertheless, due to the high momentum coefficient required by the SJ, a simple estimation of the energy required to operate the SJ shows that it is higher than the energy saved due to the combination of drag reduction and lift increment.
Electromechanical actuators have become key components in next-generation aircraft architectures, particularly under More Electric Aircraft and Power-by-Wire paradigms. However, their operational complexity, compounded by mechanical-electrical interactions, introduces failure modes that are both difficult to detect and insufficiently represented in existing datasets. This paper presents a comprehensive digital twin-based framework developed to simulate and analyse EMA behaviour under both nominal and faulty conditions. Implemented using MATLAB Simulink and Simscape, the framework comprises modular voltage and load profiles, structured fault injection mechanisms, and labelled data generation tools. This work investigates the signal responses to various fault types, including mechanical backlash, voltage drop, and electrical resistance anomalies, both in isolation and combination. The simulation output enables systematic feature extraction and evaluation for diagnostics and health indicator development. The framework generated a high-fidelity dataset of 70,000 labelled samples, which demonstrated excellent feature separability for both single and compound faults under Principal Component Analysis. This research addresses the aerospace industry’s pressing need for synthetic, fault-labelled data to train and validate diagnostic algorithms and offers a scalable methodology applicable to diverse actuator configurations. The resulting openable, labelled dataset and modular scripts enable reproducible benchmarking for EMA health monitoring and will be extended to physics-informed prognostics in subsequent work.
Patrick K. Pischulti, Min Young Hwang, Christopher McComb, Katya Arquilla
NASA’s Moon to Mars campaign emphasizes the need for crews and habitat systems to operate with increasing autonomy as communication delays with Earth grow beyond 5 minutes. The digital twin framework has emerged as a promising solution to monitor, diagnose, predict, and optimize space systems, but prior aerospace applications have largely centered on system autonomy rather than crew autonomy. As a result, current approaches under-represent the interaction dynamics needed by mission control to continuously evolve procedure and accomplish mission objectives. This work introduces an Interaction Digital Twin (IDT) framework that twins the interactions between humans and systems rather than focusing only on individual entities. Built on a distributed digital twin architecture with bidirectional information flow, the framework integrates three complementary types of twins: Digital Twins for habitat systems, Human Digital Twins (HDTs) for individual crew members, and Interaction Digital Twins that capture emergent phenomena such as team cohesion, trust calibration, coordination, and adaptive autonomy. Twinning the interactions moves aspects of command and control on-board, giving crew mission-control-like capabilities even during periods of communication delay. We apply the framework to an Artemis Phase II mission scenario, demonstrating how interaction-level twinning extends system-level modeling to support cognitive workload management, information sharing, and human–autonomy teaming. By elevating interactions to first-class, inference-capable elements within the digital twin architecture, this framework bridges the gap between technical system models and the human teaming constructs essential for self-sufficient deep space exploration.
