Achieving clean combustion systems is crucial in terms of solving environmental impacts, decarbonization needs and sustainability matters. Traditional combustion modeling techniques via computational fluid dynamics with accurate chemical kinetics face obstacles in computational cost and accurate representation of turbulence-chemistry interactions. Physically Informed Neural Networks (PINNs) as a new framework, merges physical laws with data-driven learning and shows great potential as an alternative methodology. By directly integrating conservation equations into their training process, PINNs achieve accurate mesh-free modeling of complex combustion phenomena despite having limited data sets. This review examines how this approach applies to clean combustion systems while focusing on their impact in aerospace applications including flame dynamics, turbulent combustion, emission prediction, and instability management in propulsion systems. Next-generation aerospace engines rely on PINNs to reduce computational costs while increasing predictive performance and enabling real-time control methods. This analysis concludes by exploring current barriers and future paths, while demonstrating how PINNs can revolutionize sustainable and efficient combustion technologies in aerospace propulsion systems.

In the development and verification of safety-critical aero-space software, Linear Temporal Logic (LTL) has been widely used to specify complex system properties derived from requirements. However, a significant gap remains in industrial practice: translating natural language (NL) requirements into formal LTL properties is a labor-intensive and error-prone process that requires rare expertise in both aerospace control engineering and formal methods. While recent NL-to-LTL tools (e.g., NL2SPEC, NL2TL, NL2LTL) are capable of automating parts of this process, they often fail on real requirement documents in industrial settings, due to complex domain terminology or implicit temporal and logical structure. To address these challenges, we present AeroReq2LTL, a framework that automates LTL property generation for aerospace requirements using large language models (LLMs), with two key industrial innovations: (i) a data dictionary that normalizes technical jargon into precise atomic propositions; and (ii) a template-based requirement language that makes temporal cues and logical relations explicit before translation. On a real aerospace dataset, AeroReq2LTL achieves 85% precision and 88% recall in LTL generation, and its outputs can be directly consumed by existing verification tools.

Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities. This shift will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation. Furthermore, AI's potential in aerospace extends to control systems, where its application can range from full autonomy to enhancing human control through assistive features. AI, particularly deep reinforcement learning (DRL), can offer significant improvements in control systems, whether for autonomous operation or as an augmentative tool.

The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use artificial-intelligence (AI)-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities) and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with clear differences in some SDGs. Finally, the number of abstracts the code cannot identify is decreasing with time, possibly showing the scientific community's awareness of SDG.

One of today's most propitious immersive technologies is virtual reality (VR). This term is colloquially associated with headsets that transport users to a bespoke, built-for-purpose immersive 3D virtual environment. It has given rise to the field of immersive analytics---a new field of research that aims to use immersive technologies for enhancing and empowering data analytics. However, in developing such a new set of tools, one has to ask whether the move from standard hardware setup to a fully immersive 3D environment is justified---both in terms of efficiency and development costs. To this end, in this paper, we present the AeroVR--an immersive aerospace design environment with the objective of aiding the component aerodynamic design process by interactively visualizing performance and geometry. We decompose the design of such an environment into function structures, identify the primary and secondary tasks, present an implementation of the system, and verify the interface in terms of usability and expressiveness. We deploy AeroVR on a prototypical design study of a compressor blade for an engine.

There are two main methods of nulcear fusion: inertial confinement fusion (ICF) and magnetic confinement fusion (MCF). Existing thermonuclear reactors are very complex, expensive, large, and heavy. They cannot achieve the Lawson creterion. The author offers an innovation. ICF has on the inside surface of the shell-shaped combustion chamber a covering of small Prism Reflectors (PR) and plasma reflector. These prism reflectors have a noteworthy advantage, in comparison with conventional mirror and especially with conventional shell: they multi-reflect the heat and laser radiation exactly back into collision with the fuel target capsule (pellet). The plasma reflector reflects the Bremsstrahlung radiation. The offered innovation decreases radiation losses, creates significant radiation pressure and increases the reaction time. The Lawson criterion increases by hundreds of times. The size, cost, and weight of a typical installation will decrease by tens of times. The author is researching the efficiency of these innovations. Keywords: Thermonuclear reactor, Multi-reflex AB-thermonuclear reactor, aerospace thermonuclear engine. This work is presented as paper AIAA-2006-7225 to Space-2006 Conference, 19-21 September, 2006, San Jose, CA, USA.

Model rockets have been employed in student projects, but very few papers in aerospace education offer concise summaries of activities at university-course levels. This paper aims to address this gap in the literature. The rockets used by our students reached some 500 m (~1,640 feet) in altitude, deployed a parachute, and spent 2-3 minutes descending to the ground. We present a series of analyses and experiments that students performed in order to predict the flight time, the maximum altitude, and the landing location of these rockets. They wrote computer programs to numerically integrate equations of motion, and experimentally measured input parameters (e.g., the thrust profile and drag coefficients). Once launched, these rockets could not be controlled; targeting the landing location would thus mean tilting the launch rail to a required angle. The largest source of error in landing location came from the difficulty in modeling wind velocities. Also discussed in this paper are the infrared spectroscopy and the extraction experiment as novel additions to model rocket projects.

STOCKSBRIDGE Engineering Steels (SES), a division of United Engineering Steels Ltd., is to invest £5 million in vacuum arc remelting (VAR) equipment that will significantly increase its capacity to produce the most specialised steels for the aerospace industry. The investment will make Stocksbridge the world's largest facility controlled entirely by the latest computerised technology.

Machine Learning methods have extensively evolved to support industrial big data methods and their corresponding need in gas turbine maintenance and prognostics. However, most unsupervised methods need extensively labeled data to perform predictions across many dimensions. The cutting edge of small and medium applications do not necessarily maintain operational sensors and data acquisition with rising costs and diminishing profits. We propose a framework to make sensor maintenance priority decisions using a combination of SHAP, UMAP, Fuzzy C-means clustering. An aerospace jet engine dataset is used as a case study.

The momentum conservation law is applied to analyse the dynamics of pulsejet engine in vertical motion in a uniform gravitational field in the absence of friction. The model predicts existence of a terminal speed given frequency of the short pulses. The conditions that the engine does not return to the starting position are identified. The number of short periodic pulses after which the engine returns to the starting position is found to be independent of the exhaust velocity and gravitational field intensity for certain frequency of the pulses. The pulsejet engine and turbojet engine aircraft models of dynamics are compared. Also the octopus dynamics is modelled. The paper is addressed to intermediate undergraduate students of classical mechanics and aerospace engineering.

ONE machine tool manufacturer that the production engineers in the aerospace industries seem to have taken to their hearts is the Matsuura Machine Company of Japan.

Aerospace engines, as critical components in aviation and aerospace industries, require continuous and accurate fault diagnosis to ensure operational safety and prevent catastrophic failures. While deep learning techniques have been extensively studied in this context, they typically output logits or confidence scores, necessitating post-processing to obtain actionable insights. Furthermore, the potential of large-scale audio models for this task remains largely untapped. To address these limitations, this paper proposes AeroGPT, a novel framework that transfers knowledge from the general audio domain to aero-engine bearing fault diagnosis. AeroGPT leverages a large-scale audio model and incorporates Vibration Signal Alignment (VSA) to adapt general audio knowledge to domain-specific vibration patterns, along with Generative Fault Classification (GFC) to directly generate interpretable fault labels. This approach eliminates the need for label post-processing and supports interactive, interpretable, and actionable fault diagnosis, thereby enhancing industrial applicability. Through comprehensive experimental validation on two aero-engine bearing datasets, AeroGPT achieves 98.94% accuracy on the DIRG dataset and 100% accuracy on the HIT bearing dataset, outperforming representative deep learning approaches. Qualitative analysis and further discussion also demonstrate its potential for interactive diagnosis and real-world deployment, highlighting the promise of large-scale audio models to advance fault diagnosis in aerospace applications.