Airbus (Germany)

We optimize hierarchies of Time-to-Live (TTL) caches under network delays. A TTL cache assigns individual eviction timers to cached objects that are usually refreshed upon a hit where upon a miss the object requires a random time to be fetched from a parent cache. Due to their object decoupling property, TTL caches are of particular interest since the optimization of a per-object utility enables service differentiation. However, state-of-the-art exact TTL cache utility-based optimization does not extend beyond single TTL caches, especially under network delays. In this paper, we leverage the object decoupling effect to formulate the nonlinear utility maximization problem for TTL cache hierarchies in terms of the exact object hit probability under random network delays. We iteratively solve the utility maximization problem to find the optimal per-object TTLs. In addition, we propose a variant TTL policy, which we denote as exTTL to counteract the effect on the optimal utility of the storage mismatch between the actual realization of a TTL cache and its ideal infinite storage assumption. Further, we show that the exact model suffers from tractability issues for large hierarchies and propose a machine learning approach to estimate the optimal TTL values for large systems. Finally, we provide numerical and data center trace-based evaluations for both methods, showing the significant offloading improvement due to TTL optimization considering the network delays.

Abstract. High levels of bromine monoxide (BrO) observed in volcanic plumes indicate significant catalytic destruction of tropospheric ozone (O3) at local to regional scales. The underlying chemical mechanisms are still incompletely understood and the quantification of O3 concentrations and their distribution in volcanic plumes remain a major challenge. Common atmospheric O3 measurement techniques (UV absorption spectroscopy and electrochemical sensors) suffer from strong interferences, especially from sulphur dioxide (SO2), which is low in the atmospheric background but a main constituent of volcanic plumes (ppmv levels). This problem can be circumvented by using chemiluminescence (CL) O3 monitors, which have negligible interference with SO2 and other trace gases commonly found in volcanic plumes. However, volcanic plume measurements with modern CL O3 monitors are impractical because they are heavy and bulky. Here we report on the development and application of a lightweight version of a CL O3 instrument (l.5 kg, shoebox size) that can be mounted to a commercially available drone. Besides measurements of vertical O3 profiles over several hundred metres, we present drone-based CL O3 measurements in the volcanic plume of Mount Etna in Italy. Within 3 km of the emitting craters we measured an anti-correlation between SO2 and O3 concentrations, corresponding to ozone reductions by up to 60 % in the volcanic plume with respect to the surrounding atmosphere.

The utilisation of composite materials has the potential to play a vital role in the development of lightweight structures for future generations of aircraft, with the objective to reduce emissions. Ultrasonic welding is a process that has been proven to exhibit advantageous qualities, including the capacity to achieve welds with a comparatively short process time. Furthermore, its capacity to function as both a static and a continuous process makes it a viable candidate for facilitating the realisation of this objective. The present study investigates the potential of a novel explicit modelling approach for the static ultrasonic welding process to more accurately represent the welding process by incorporating a more precise representation of the hammering effect. The hammering effect describes the partial loss of contact between the sonotrode and the upper adherend. The model's validation was achieved through a multifaceted approach that incorporates high-speed camera recording, encompassing digital image correlation, laser displacement sensor measurements, and static ultrasonic welding experiments. These experiments encompassed varying welding times, followed by fracture surface analysis. The findings showed that an explicit time-domain model can effectively represent the static welding process of unidirectional materials utilising a film energy director. The experimental validation demonstrated a high degree of correlation between the thermal behaviour of the welding interface and the simulation results. The study demonstrated that the neutral position of the sonotrode exhibited an increase during the initial phase of the welding process due to dynamic stresses. This phenomenon enables reduced constraint movement of the adherends and the energy director, which results in the disconnection of the sonotrode from both the upper adherend and the energy director, as well as the adherends and the anvil. The higher neutral position of the sonotrode was then implemented in an explicit simulation of the static ultrasonic welding process.

Abstract The complexity of developing Virtual Commissioning (VC) architectures for modern automation systems requires a structured and adaptable methodology. This study addresses key challenges in applying the SPES (Software Platform Embedded Systems) methodology to VC, including limitations in decision support for technical configurations, a lack of clarity in control-level differentiation, and difficulty in integrating company-specific processes. To overcome these limitations, the SPES MBB (Methodological Building Blocks) framework is integrated with SPES, enhancing its capabilities through modular, reusable building blocks that provide clearer decision-making guidance, improve control-level task allocation, and support the integration of industry-specific knowledge. A case study from the iMOD project in aircraft manufacturing demonstrates the application of the SPES MBB framework to develop a more coherent and scalable VC architecture. The results highlight improvements in system integration, consistency across system viewpoints, and systematic configuration decision-making, leading to a more internally consistent and adaptable VC architecture, as demonstrated through a structured qualitative evaluation and a formal sensitivity analysis of expert judgments. The proposed approach addresses critical gaps in VC architecture development, demonstrating how SPES MBB can support the systematic design of VC architectures for complex automation systems.

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation.

This paper presents the multi-domain modelling and simulation framework developed within the Cryoprop project, which aims to deliver a 1-MW fully superconducting powertrain demonstrator. While detailed component-level physics models for superconducting hardware are well-established, a significant gap exists in system-level modelling for integrated powertrains, which is essential for understanding component interactions, developing control strategies, and optimizing overall system performance. Our work addresses this gap by creating a multi-fidelity simulation environment in Matlab/Simulink. This framework accommodates simulations across diverse timescales, from microsecond-resolution studies for electrical fault analysis to hour-long mission profiles for thermal management. The paper outlines the approaches for modelling key components, including the multiphase superconducting motor, cryogenic power electronics, and DC link. We outline the process we apply in model parameterization from high-fidelity 3D simulations and in verification and validation (V&V). A key finding is that validating model behavior under off-nominal conditions, particularly for superconducting quench events, remains a primary obstacle due to the inherent risks of destructive testing. We also highlight the need for up to date component sizing models when developing novel system architectures and describe their usage. We conclude that maturing these system-level modeling and V&V techniques is a prerequisite for the industrialization and eventual certification of superconducting aircraft propulsion systems.

• Numerical analysis of doubly curved unstiffened shells under axial compression. • Comparative study of MSI, LBMI, and SPLI imperfection scenarios is presented. • LBMI is the most conservative; MSI and SPLI show correlated, realistic prediction potential. • Considered convex shells are imperfection-sensitive but carry higher loads. • Some concave shells show imperfection insensitivity with higher prebuckling stiffness. The article concerns the use of various sources of initial imperfections to numerically assess the stability and load-carrying capacity of thin-walled segments of isotropic and composite toroidal shells subjected to compression. The considered sources of initial imperfections are: Linear Buckling Mode-shaped Imperfection, Single Perturbation Load Imperfection and actual maps of mid-surface (MSI) and thickness imperfections, originally measured for unstiffened cylindrical and conical shells. The most conservative Knock-Down Factor estimates were determined using Linear Buckling Mode approach, while the results provided by Single Perturbation Load approach were consistent with those obtained using measured MSI but were slightly more conservative. The studies demonstrated that a slight convexity can increase the load-carrying capacity of a composite doubly curved structure compared to the nominal cylinder or cone structure. Some of the analysed concave structures exhibited characteristics of imperfection-insensitive structures, where the load-carrying capacity significantly exceeded the critical buckling load determined from linear stability analysis.

This Special Issue contains a set of remarkable papers covering various recent research advances towards resilient Communication Networks for an hyper-connected World. Papers are organized into five categories: (i) Resilient Architectures for Next-Generation Networks, (ii) Edge, IoT, and Cyber-Physical Systems, (iii) Vehicular, Mobile, and Aerial Networks, (iv) Optical, Hybrid, and Satellite-based Resilient Communications, and (v) Security, Trust, and Resilience in Services and Applications. The editorial begins with an overview of the field and proceeds with a summary of the twenty-two papers included in this Special Issue.