Purpose Multi-phased mission system (MPMS) shows characteristics with multi-phase, uneven-length mission phase and different change trends per run, which bring new challenges on modelling and monitoring for the system. The purpose of this paper is to present a sequential local information increment-based condition monitoring method for the MPMS. Design/methodology/approach Firstly, the sequential local information increment is used to divide uneven-length mission phases. Local information gain indices are deduced through the local information increment, and then the information gain indices of the global mutations can be used to identify the transition point of the mission phase. Here, the sequential movement strategy reveals the order and real-time nature of the method. To ensure the accuracy of the division, a sliding window is used to specify the search range for the information gain index, and an adjusting strategy is used to determine the appropriate phase dividing parameter. Then, considering the uneven-length mission phase and the trends of change in mission phases may be different, similarity analysis is utilized to identify similar mission phases. Finally, several monitoring sub-models are developed for the divided mission phases based on principal component analysis, respectively. Findings Simulation for quick access recorder flight data illustrates that the proposed method can effectively reduce the false alarm rate and missed alarm rate. Originality/value This paper provides a sequential local information increment-based condition monitoring method for the MPMS.
Purpose Tomographic synthetic aperture radar (TomoSAR) missions require precise attitude control in the presence of low-frequency, weakly damped vibrations from large flexible appendages. To overcome the conservatism of model-based controllers and the lack of safety guarantees in data-driven approaches, this study aims to propose a control framework that integrates prescribed-time control with a learning-observation collaborative mechanism. Design/methodology/approach The architecture uses a prescribed-time time-varying sliding-mode controller to ensure tracking error convergence within a user-defined interval. A Gaussian process (GP) regression model is used for online learning and feedforward compensation of structured uncertainties. A fixed-time disturbance observer runs continuously, with its compensation selectively engaged when the GP residual exceeds a threshold, enabling rapid compensation for residuals and abrupt disturbances. Lyapunov analysis establishes the prescribed-time stability of the closed-loop system. Findings Simulations show that the proposed method reduces the attitude error to approximately 10−5 rad and achieves effective suppression of flexible vibrations compared with standard sliding mode controllers. Originality/value This study offers a collaborative framework that addresses the limitations of both conservative model-based control and unverified data-driven approaches, providing an effective solution for high-precision three-dimensional TomoSAR imaging in space environments.
Purpose To meet small/medium unmanned aerial vehicles’ (UAVs) rapid, covert deployment needs from the low-altitude economy and modern warfare, this paper aims to review mainstream launch technologies, analyzes core challenges and projects trends for military-civilian references. Design/methodology/approach This paper systematically reviews the development history, key technological breakthroughs and typical application scenarios of mainstream launch technologies for small- and medium-sized UAVs. It analyzes the core technical bottlenecks of existing launch systems and, based on multidisciplinary research progress, summarizes future development trends, aiming to provide a structured reference for related academic and engineering research. Findings As the core link between unmanned equipment and mission scenarios, small- and medium-sized UAV launch technology has evolved from “functional compliance” to “performance synergy” based on existing research. Literature indicates that advances in three key dimensions – lightweight design, low overload and low noise – coupled with improved intelligence and environmental adaptability, will drive the technology toward multiscenario compatibility, precise control and low-cost deployment. Originality/value This paper systematically compares domestic and international research progress, synthesizes key parameter characteristics of different launch technologies and summarizes core technical challenges (e.g. lightweighting, low noise and low overload) limiting future UAV system development. As a comprehensive literature review, it clarifies future development directions and provides theoretical references for subsequent research and practical applications.
Purpose In large-scale aerospace integration, prime contractors frequently procure externally mounted subsystems from tier suppliers for major platforms. Accurate aeroelastic assessment requires the subsystem’s Outer Mold Line (OML) and key dynamic properties, specifically mass, stiffness and inertia. However, strict intellectual property (IP) and confidentiality constraints often limit the disclosure of internal structural details, hindering accurate structural dynamics integration. This study aims to propose a novel “Inverse-Finite Element Model (FEM) Reconstruction Framework” to develop a simplified, equivalent surrogate model that reproduces the aeroelastic properties of a subsystem without revealing its internal architecture. Design/methodology/approach A decoupled two-stage gradient-based optimization protocol was implemented. First, beam cross-sectional parameters were optimized to minimize the deviation between deflection responses of the reduced-order and high-fidelity reference models at four specific checkpoint nodes. Second, nonstructural mass properties distributed along the beam elements were optimized to reproduce the total mass, center of gravity and inertia tensor. This case was demonstrated with a case study. Findings The optimization results demonstrated high fidelity: the simplified model achieved an average deflection error of 1.68%, a mass error of 0.588%, an inertia tensor discrepancy of 0.949% and a center of gravity deviation of 1.02%. Validation under inertial loading and modal analysis indicated that the simplified model exhibits a deflection difference of 1.35%, and a deviation of 1.57% in the first elastic bending mode frequency. These results confirm that the proposed approach successfully bridges the gap between IP protection and high-fidelity aeroelastic analysis requirements. Originality/value This paper addresses a critical conflict in the aerospace industry: balancing IP confidentiality with the necessity for accurate structural dynamic data. The proposed Inverse-FEM Reconstruction Framework provides a unique solution for tier suppliers. Crucially, the approach strictly preserves the exact OML of the subsystem while completely hiding its proprietary internal design architecture.
Purpose This study aims to homogeneously mix Al, Zn, Cu, and Si metal powders with graphene nanoplatet and SİC ceramic powder reinforcements at various times and different Zn ratios using a high-energy ball milling device (Spex-type Retsch MM 400 model mixer). Design/methodology/approach X-ray diffraction analysis (XRD) was used to investigate the effects of different Zn ratios and alloying times on the alloying process. The XRD data were used to calculate the crystal size using the Scherrer and Williamson-Hall equations. The alloyed powders were sintered and turned into billets. Then, images were taken from the polished surfaces using scanning electron microscopy. In addition, Vickers microhardness measurements of these billets were also examined. Findings The Vickers microhardness test results showed a general trend of increased hardness with prolonged alloying times and elevated Zn content. The increase in composite stiffness can be attributed to two main factors. First, the high concentration of hard ceramic particles in the softer aluminum matrix contributes to this increase. Second, the formation of a robust interface between the aluminum matrix and the ceramic particles also increases the stiffness. This interface ensures efficient load transfer and increases the overall stiffness of the composite. The EDX analysis results also support the XRD findings. Originality/value A high-strength aluminum alloy composite material has been produced and characterized for aircraft fuselage structures.
Purpose This study characterizes dynamic and thermal mechanical properties of ultrahigh molecular weight polyethylene (UHMWPE) fiber-reinforced Dyneema (R) HB-50 composite. The objective was to evaluate its structural stability and performance under thermal and dynamic loading conditions.Design/methodology/approach Laminates of the composite were fabricated using compression moulding method followed by hot pressing to improve structural integrity and specimen quality. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to assess thermal degradation behavior while dynamic mechanical analysis (DMA) was conducted to evaluate the viscoelastic properties. The thermomechanical properties of the composite were accessed over a frequency range of 0.1-10 Hz and a temperature range of 30 degrees C-140 degrees C.Findings TGA and DSC results inferred thermal degradation of Dyneema (R) HB-50 without residue and variation in thermal properties, confirming to fully crystalline nature and well-defined melting peak at 145 degrees C-150 degrees C without glass transition temperature. DMA demonstrated storage modulus, loss modulus and tan d are strongly dependant on frequency and temperature. Progressive decline in modulus from 30 degrees C to 130 degrees C was observed associated with crazing, microcrack formation and fiber-matrix bond weakening. Loss modulus and tan d peaks shifted consistently to higher temperatures with increasing frequency confirming viscoelastic behavior and adherence to the time and temperature superposition principle.Originality/value The novelty of this study lies in the integrated investigation of dynamic mechanical and thermal properties of Dyneema (R) HB-50 ballistic composite to provide deeper insight into its structural stability and thermomechanical performance. These findings contribute meaningfully to the development of improved lightweight ballistic protection systems and establishes a critical foundation for predicting the long-term reliability of the composite in dynamic, vibration-intensive and thermally demanding structural applications.
Purpose The leveling process is a critical metric during the non-forming ascent phase, yet research in this area remains insufficient. This paper aims to propose a leveling model based on the correlation between leveling and volume expansion to address this issue. Furthermore, the effects of the leveling process on vertical velocity, temperature and pressure differential are analyzed, refining the prediction of the non-forming ascent for airships.Design/methodology/approach The leveling model is integrated into the thermal-flight dynamic coupled prediction model. Variations in temperature, velocity and altitude during the ascent are analyzed. The validity of the model is validated by comparing results with those of other scholars. Furthermore, the effects of different trigger volumes and leveling rates are investigated.Findings The leveling process increases the vertical projection area and reduces the climbing velocity. For an airship with a volume of 8,578 m & sup3; and a ceiling height of 19.5 km, the preformed minimum vertical velocity is 18.7% to 40% lower than that of the nonleveling and the forming velocity is 20% lower. This forms a "step" in the velocity peak, which is strengthened as the leveling is achieved more quickly and earlier. Additionally, it also reduces the volume expansion, delays the initiation of super-pressure and alleviates the "supercooling" phenomenon, resulting in a helium gas supercooling temperature during forming that is 16% higher.Originality/value A leveling model is presented for the non-forming ascent of airships. At the same time, both the supercooling phenomenon and the vertical velocity undergo "step" variations. These findings provide insights into the lateral control and pressure differential control of airships, as well as the prevention of cold adhesion during the ascent process.
Purpose The aim of this study is to develop a valid and reliable measurement tool to assess individuals' perceptions of aviation safety and risk. Although aviation is considered a sector requiring high reliability, subjective perceptions of safety and risk are important for understanding the human factors that influence trust, behavior and decision-making processes in aviation systems. However, empirically validated and context-specific scales addressing aviation risk perception are limited in the literature. The proposed scale adopts a perception-based assessment perspective and does not aim to provide an objective engineering evaluation of aviation system performance, but rather to capture how aviation safety and risk are cognitively interpreted by individuals within complex socio-technical environments.Design/methodology/approach This study adopted a quantitative scale development design. An initial pool of items was created based on a comprehensive review of the aviation safety, risk perception and human factors literature. Following expert evaluation and pilot testing, data were collected from 386 participants using a structured questionnaire. Exploratory factor analysis (EFA) and confirmatory factor analysis were performed to examine the construct validity of the scale. Reliability was assessed using Cronbach's alpha coefficients and item-total correlations.Findings The results of the exploratory and confirmatory factor analyses confirmed a robust two-dimensional structure consisting of Safety Awareness and Risk Perception. The final 14-item scale demonstrated strong construct validity and high internal consistency. The findings indicate that individuals' perceptions of aviation safety are shaped by both institutional safety mechanisms and subjective evaluations of operational risks. The scale provides a reliable framework for identifying perceived vulnerabilities in civil aviation systems and for supporting evidence-based safety management and risk communication strategies.Originality/value This study introduces a context-specific and empirically validated measurement tool that integrates safety awareness and risk perception within a unified framework. By capturing both technical and human-centered dimensions of aviation safety, the Aviation Risk Scale offers a novel contribution to civil aviation risk assessment and supports proactive safety governance and resilience-oriented management practices.
This review aims to examine how Industry 4.0 (I4.0)-driven digital transformation can enhance supply chain resilience (SCR) in the aerospace sector. It aims to synthesize evidence on the benefits, challenges and strategic implications of integrating advanced digital technologies into aerospace supply networks to withstand and adapt to disruptions. The authors conduct a systematic review with a structured critical appraisal. For each technology–resilience link the authors classify study design (conceptual vs empirical), sector specificity (aerospace vs cross-industry) and relative effect direction; the authors also grade evidential strength and identify boundary conditions (certification, cyber-risk, organizational readiness). The study categorizes I4.0 technologies – including IoT, big data analytics, artificial intelligence, cloud computing, blockchain, additive manufacturing and digital twins – and maps their resilience-enhancing mechanisms (e.g. visibility, velocity, flexibility, collaboration, risk culture) to different disruption phases. Case studies and empirical findings from aerospace and comparable industries are analyzed to assess practical outcomes, challenges and integration strategies. The review finds that I4.0 technologies can significantly strengthen aerospace SCR by enabling real-time visibility, predictive risk analytics, agile decision-making and on-demand manufacturing. Evidence from COVID-19 and other disruptions shows that digitally enabled aerospace firms achieved faster recovery and maintained continuity more effectively than less-digitized counterparts. However, high implementation costs, legacy system integration hurdles, cybersecurity risks, skills gaps and cultural resistance remain major barriers. Successful adoption requires strategic alignment, organizational readiness, cross-firm collaboration and continuous improvement. Beyond synthesis, this review offers a critical appraisal of the strength, limits and contingencies of the evidence linking I4.0 to resilience in aerospace, including where empirical support is weak or mixed. The authors introduce an evidence-grading “map” that identifies boundary conditions (e.g. certification, cybersecurity, SME readiness) and delineates when specific technologies measurably improve robustness, agility and recovery.
This study aims to analyse global aviation research trends in maintenance, safety and regulation (2015–2024) to identify key themes and contributions. Probabilistic analysis, combined with text mining techniques, is used in this study based on Web of Science data to conduct thematic exploration through logistic regression (LR) and Markov chain modelling, providing a comprehensive assessment of contributions across countries, affiliations and journals. Logistic regression enables the prediction and forecasting of growth trends in UAVs, aviation operations and air traffic management using a dataset of N = 6,900. Markov process analysis applied at institutional, national and journal levels indicates predominantly positive transition behaviour, while aviation materials and structures demonstrate comparatively slower progression. This study used a unique combination of probabilistic analysis, text mining, LR and Markov chain modelling to examine research patterns. By integrating them, the study provides a novel means of mapping themes, identifying research gaps and analysing contributions from different countries, institutions and journals more effectively.
Purpose This study aims to address intake distortion in high-speed ducted fans during actual flight. Using the hybrid SST k - omega + LES numerical method and Ffowcs Williams-Hawkings (FW-H) acoustic theory, it investigates flow field and aerodynamic noise characteristics under normal, 20% and 40% occlusion conditions, reveals the distortion-induced noise mechanism and provides a theoretical basis for low-noise fan design and intake system optimization.Design/methodology/approach This study uses a hybrid numerical method combining the SST k - omega + LES model and LES, coupled with the FW-H acoustic analogy theory. It investigates three operating conditions (normal intake, 20% and 40% inlet occlusion) and verifies simulation reliability via semi-anechoic chamber experiments on a consistent prototype.Findings Inlet occlusion distortion breaks the axisymmetry of incoming flow, induces large-scale flow separation on the blocked side, intensifies tip vortex fragmentation and turbulence enhancement and significantly amplifies unsteady pressure pulsation on blade surfaces and duct inner wall. Compared with the clean inlet condition, the average overall sound pressure level (OASPL) increases by 12.4 dB under 20% occlusion and 14.8 dB under 40% occlusion. The fundamental blade passing frequency (BPF) tonal noise experiences a globally averaged enhancement of 10-15 dB, while in specific lateral focusing directions, the localized peak amplification reaches up to 20-25 dB, becoming the dominant component of radiated noise. The noise directivity shifts from a symmetrical butterfly-shaped distribution to a highly asymmetric pattern, with the strongest radiation concentrated in the 105 degrees-135 degrees and 210 degrees-240 degrees lateral rear directions. Meanwhile, distortion leads to significant redistribution of noise source contributions: the contribution of duct casing noise to total noise increases significantly, while the relative contribution of blade noise decreases.Originality/value The originality of this study lies in focusing on the high inlet occlusion ratio (up to 40%) distortion scenario rarely involved in existing literature. It reveals the intrinsic coupling mechanism between severe inflow distortion, flow unsteadiness evolution and aerodynamic noise mutation, clarifies the redistribution law of blade/casing noise source contributions under distorted conditions and provides experimental-validated quantitative data for the noise effect of high occlusion ratio distortion. This study fills the quantitative research gap on the aeroacoustic characteristics of ducted fans under severe inflow distortion and can provide direct technical guidance for acoustic optimization of ducted fan propulsion systems for VTOL aircraft and unmanned aerial vehicles in complex flight conditions.
Purpose The morphing aircraft adaptively changes its shape to achieve optimal performance according to the mission requirements in each flight phase. The purpose of this study is to design a new morphing scheme for the aircraft forebody that provides greater aerodynamic performance benefits than wing deformation.Design/methodology/approach A new multidimensional combined morphing scheme that considers flexible skin technology constraints and air tightness is proposed based on the mission profile, enabling multi-stage extension and deflection deformation of the aircraft forebody. Numerical simulations of the contraction, extension and deflection states of morphing aircraft are carried out at different Mach numbers and angles of attack.Findings The aerodynamic characteristics of three states are compared and the effects of deformation ratio are further explored. The results show that scheme can not only improve the lift-drag performance but also the pitching maneuverability.Originality/value First, a mission-profile-driven multidimensional combined morphing concept is proposed for the aircraft forebody, integrating contraction, extension and deflection states within a unified framework. Second, a fully rigid segmented forebody configuration is developed to avoid the load-carrying and airtightness limitations associated with flexible skins in supersonic and hypersonic applications. Third, the proposed scheme is evaluated not only in terms of aero-dynamic characteristics but also in terms of trajectory implications, showing that the extension state is beneficial for cruise efficiency, while the deflection state is advantageous for maneuverability.
Purpose This paper aims to address the actuator fault problem of the boost-glide rocket based on the proposed adaptive fault-tolerant control method.Design/methodology/approach The lock-in-place, loss of effectiveness and bias of the rocket actuator are equivalently regarded as the generalized drift fault. Based on the radial basis function (RBF) neural network, an adaptive update law is designed to diagnose the generalized drift fault. The nonlinear dynamic inversion control is used to adjust the control command based on the fault diagnosis result, then the control system still has a good performance when the actuator fault occurs. The integrated design method of RBF neural network-based fault diagnosis and nonlinear dynamic inversion control is used to achieve a fault-tolerant control effect.Findings The effectiveness of the proposed adaptive fault-tolerant control method is verified by the numerical simulation and the results show that the proposed method has excellent fault suppression capability.Social implications The proposed method effectively mitigates operational risks and associated economic losses of the boost-glide rocket.Originality/value An equivalent expression for actuator faults and an adaptive fault-tolerant control method based on an RBF neural network are proposed and the integrated design method is used to suppress the fault impact effectively.
Purpose This study aims to propose an audit-safe analytics architecture to integrate predictive maintenance decision support into regulated aircraft maintenance environments while preserving deterministic rule authority, regulatory compliance and human accountability.Design/methodology/approach A layered system architecture was developed that separates probabilistic analytics from deterministic rule engines derived from approved maintenance data. Governance controls, including advisory containment, traceability, version control and human-in-the-loop oversight, were embedded at the architectural level. A structured validation protocol evaluated rule primacy enforcement, audit reconstruction capability and advisory containment across representative maintenance scenarios.Findings The results demonstrate that predictive analytics can enhance situational awareness and planning without transferring decision-making authority from certified personnel. Structural separation between advisory models and compliance logic mitigates automation overreach and preserves audit defensibility.Practical implications This framework provides a governance-aligned blueprint for deploying analytics within Part 145 and airline maintenance organizations without compromising continuing airworthiness or safety management system obligations.Originality/value This study reframes predictive maintenance integration as an architectural governance problem rather than an algorithmic optimization challenge, offering a defensible pathway for analytics adoption in safety-critical aviation contexts.
Purpose On-orbit assembly technology is an important development direction in the field of spaceflight. Focusing on the cooperativity of microsatellite lightweighting and system dynamic performance, this study aims to propose a dynamic-consistent hierarchical decoupling optimization framework for modular satellite structural design under frequency constraints.Design/methodology/approach The dynamic stability of the satellite depends on the mechanical properties of its matrix. As it was difficult to establish an accurate model covering all structural and joint parameters for the multi-subsystem coupled satellite structure, instead of constructing a fully coupled global model at the early design stage, the multi-subsystem satellite structure is reformulated into sequential subsystem-level mass minimization problems under dynamic frequency constraints. A genetic algorithm is used as a global search engine within each subsystem optimization stage. System-level consistency is verified through finite element analysis to ensure that overall modal requirements are satisfied.Findings The genetic optimization algorithm is used in optimization design. The optimal matching of structural geometric parameters of the microsatellite is achieved. The result shows that within the base frequency range of the satellite, the proposed framework achieves significant lightweight performance while maintaining dynamic stability.Originality/value This paper presents a dynamic-performance-driven hierarchical decoupling methodology, which reduces modeling complexity and enhances engineering implementability. This framework provides a structured alternative to conventional fully coupled multidisciplinary design optimization approaches for modular on-orbit assembly systems. This method provides an effective engineering method and reproducible implementation path for the optimization design of microsatellite structure assembled on orbit by space robots or astronauts. It has reference significance for other similar satellite frame base structure design.
Purpose The purpose of this study is to bridge the gap between traditional unpowered aircraft design - which relies heavily on static aerodynamic polars and steady-state equilibrium flight models to maximize the theoretical lift-to-drag ratio (L/Dmax) - and the highly dynamic process of elite cross-country soaring, characterized by continuous spatial energy harvesting from atmospheric vertical motion.Design/methodology/approach This study uses Global Navigation Satellite System (GNSS) telemetry from the World Gliding Championships to empirically quantify the true flight mechanics of Standard Class sailplanes. By mathematically decoupling empirical vertical velocities from theoretical static sink rates, this paper introduces the Dolphin Efficiency Index to measure transient energy extraction.Findings The empirical data demonstrate that top-performing trajectories operate systematically above the static pole, achieving effective L/D ratios that exceed 60:1, with peak performances approaching double the theoretical mechanical limits. Furthermore, kinematic analysis of the thermalling phase reveals an empirical aerodynamic optimum, with elite pilots sustaining highly disciplined circling radii between 120 and 160 m to maximize climb rates. This demonstrates that competitive dominance relies on precise spatial tracking of the thermal core to avoid aerodynamic search penalties, rather than operating at extreme high-G or absolute stall boundaries.Originality/value These findings expose the fundamental limitations of evaluating sailplanes solely via idealized steady-state theoretical metrics. Therefore, this study proposes that future aerodynamic design directives shift focus towards transient-state drag reduction during pitch manoeuvres, robust performance at moderate-to-high lift coefficients and the integration of benign handling qualities as direct macroscopic performance multipliers.
Purpose The purpose of this study is to assess the accuracy and efficiency of the nano versions of the YOLOv5, YOLOv8, YOLOv10, YOLO11 and YOLO12 models in detecting foreign object debris (FOD) at airports and to compare these models using the most commonly used performance criteria to identify their strengths and weaknesses.Design/methodology/approach The data set used for this purpose is publicly available, referred to as FOD-A (FOD in airports), and it contains 31 object categories and over 30,000 annotation examples. The single runs, conducted to measure the potential of the evaluated models, are followed by a 10-run evaluation process to establish a reliable performance profile.Findings This study's findings highlight YOLOv8n's detection accuracy; however, all models generally perform well in foreign object detection at airports without substantial loss in precision. YOLOv10n, on the other hand, stands out for its performance under various perturbations and on edge computing platforms, as well as its better discrimination of small objects than other models. However, all models have limited capabilities in distinguishing the nail and nut categories and small-sized objects, which should be considered aspects for improvement in model development. In addition to scale- and category-specific challenges, Gaussian noise and image blurring are prominent perturbations that degrade performance.Originality/value Real-time object detectors with high performance and low inference times are of interest to both practitioners and researchers in restricted devices. YOLO, a continuously evolving real-time object detector, is still new for foreign object detection research in airports, and there is a need to summarise the available models and make comparisons.
Purpose Aircraft spare parts inventory management represents a critical challenge for airlines due to high capital costs, long repair lead times and strict service-level requirements. When the number of components increases, the resulting large-scale optimisation problem becomes difficult to solve using classical optimisation techniques, mainly because of the curse of dimensionality. The purpose of this paper is to investigate the applicability of a cooperative coevolutionary genetic algorithm to large-scale aircraft spare parts inventory optimisation and to evaluate its effectiveness in reducing total inventory cost while maintaining required service levels.Design/methodology/approach To address this problem, a cooperative coevolutionary genetic algorithm based on a divide-and-conquer strategy is developed. The proposed approach decomposes the original large-scale optimisation problem into smaller subproblems, which are optimised iteratively. The model minimises total inventory cost subject to service-level constraints derived from the minimum equipment list (MEL) criticality classifications. The methodology is tested using a real-world data set consisting of 940 aircraft spare parts, enabling direct comparison with classical Poisson-based approaches and linear programming (LP) methods.Findings The results demonstrate that the proposed cooperative coevolutionary genetic algorithm achieves a 24.10% cost reduction compared with the manufacturer-recommended policy while fully satisfying all service-level constraints. When evaluated against the LP results reported in the benchmark study under the same data set, the genetic algorithm-based solution produces a comparable total inventory cost and improved computational tractability for the large-scale integer decision space.Originality/value This study provides a novel application of cooperative coevolutionary genetic algorithms to large-scale aircraft spare parts inventory optimisation. The results highlight the potential of cooperative coevolution as a scalable and effective optimisation framework for complex inventory management problems in the aviation maintenance domain.
PurposeThe influence of moderate strut-angle variation on shock structure, mixing development and combustion performance in the German Aerospace Center (DLR) scramjet combustor has not been systematically clarified. This study aims to investigate the performance enhancement of a hydrogen-fueled DLR scramjet through targeted optimization of strut angles, focusing on 10 degrees and 14 degrees configurations relative to the baseline 12 degrees geometry.Design/methodology/approachSimulations were performed for both nonreacting and reacting flows using a high-speed compressible framework validated against experimental data. Fuel-air mixing and combustion efficiency were quantified along the combustor length to establish geometry-performance correlations.FindingsThe 10 degrees configuration demonstrated superior performance, achieving a combustion efficiency of 61.6%, exceeding the baseline (60%) and significantly outperforming the 14 degrees case (53%). Full mixing was attained at 260 mm, earlier than the baseline (280 mm) and 14 degrees (320 mm) configurations, indicating enhanced shock-induced mixing and accelerated combustion development.Practical implicationsImproved mixing compactness enables shorter combustor length requirements and supports the development of aerodynamically efficient scramjet architectures.Originality/valueThis study establishes a direct link between moderate strut-angle variation and shock-driven mixing enhancement, providing new design-oriented insight into geometry-controlled combustion optimization in hydrogen scramjet systems.
