Jiang Jiang

Cross-phase collaborative management in complex product systems (CoPS) development is inherently challenged by heterogeneous organizational coupling and stochastic disturbances. Although digital twin (DT) and model-based systems engineering (MBSE) technologies provide foundations for physical–virtual synchronization and model traceability, existing approaches remain fragmented in three respects: insufficient requirements-traceable architectural integration, limited cross-phase semantic interoperability and runtime evolution, and weak operational links between semantic reasoning and adaptive decision models. To address these gaps, this paper proposes an MBSE-driven digital twin framework with semantic enhancement. First, a four-layer architecture is derived using the MagicGrid methodology, encompassing physical–virtual mapping, semantic reasoning, decision support, and service interaction. Second, a collaboration-oriented SysML profile is developed to standardize the representation of tasks, resources, materials, disturbances, and management constraints across engineering phases. Third, a knowledge-driven adaptive collaboration mechanism maps runtime disturbance inputs into semantic states, propagates their cross-phase impacts, supports process-topology reconfiguration, and generates decision-ready constraints for adaptive management. A case-based prototype for aero-engine turbofan blade development demonstrates the feasibility of the mapping–reasoning–decision chain and provides case-level evidence of improved cross-phase coordination under controlled disturbance scenarios. The results indicate a feasible engineering pathway from perceptive DT functions toward reasoning-enabled collaborative decision support.

Accurately evaluating how aero-engine performance supports upper-level capability remains a challenging issue in the digital planning, demonstration, and design of complex equipment systems-of-systems. Existing studies mainly rely on two-level analyses at the subsystem and system-of-systems levels, which are insufficient to characterize the cross-level transmission relationships among the aero-engine, aircraft performance, and overall capability. To address this limitation, this paper proposes a multilayer network-based contribution evaluation method for aero-engines oriented toward digital equipment planning and demonstration. First, a three-layer evaluation index system is constructed, including the overall capability layer, the aircraft performance layer, and the aero-engine performance layer, based on the OODA loop concept and aviation physical constraints. This provides a structured and traceable basis for cross-level requirement decomposition and scheme evaluation. Second, by integrating expert prior judgment with mechanism-based sensitivity analysis, the interrelationships among indicators at different layers are quantified, and a multilayer evaluation index network is established. Third, topological structure analysis is employed to identify key indicators in the aero-engine layer, and a cascading propagation model is introduced to evaluate the supporting roles and contribution rates of both individual indicators and the overall aero-engine layer with respect to the overall capability layer. Simulation results show that the proposed method can effectively reveal the structural characteristics, propagation paths, and dynamic influence patterns of aero-engine-layer indicators within the multilayer network. The proposed method provides methodological support for digital equipment planning, scheme demonstration, design optimization, and capability-oriented decision-making of aero-engines.

Identifying the weak set of interdependent networks that influences both structure and function is of great importance for studying infrastructure resilience and supporting engineering management. However, the vulnerability of interdependent networks arises not only from their structural properties but also from the cascading propagation of failures, which increases the complexity of weak set identification and makes it difficult for classical methods to balance both accuracy and efficiency. In this study, a data-driven framework, termed the GCN-WSI (Graph Convolutional Network-based Weak Set Identification model), is proposed to identify the weak set that can disintegrate interdependent networks under cascading failures. First, the cascading failure mechanism and related properties of interdependent networks are characterized, which reformulates weak set identification as a prediction problem of estimating the probability that nodes belong to the weak set. Second, the concept of dual-stream graph embedding is introduced to aggregate neighbor information within and across layers, incorporating attributes associated with structural properties and cascading failures to construct comprehensive representations. Finally, a multilayer perceptron (MLP) regressor is employed to estimate the probability of node membership in the weak set. The GCN-WSI framework is trained on small-scale synthetic networks and is applicable to large-scale real-world systems. Experiments on diverse datasets validate the effectiveness of the proposed method and highlight the necessity and feasibility of specific components. In particular, under predefined disintegration targets, the model demonstrates strong capability in identifying a compact weak set whose removal leads to a rapid decline in network performance.

Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.

The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems (CSoS), which can be abstracted as a heterogeneous combat network (HCN). It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy's CSoS. To this end, this paper proposes an integrated framework called HCN disintegration based on double deep $Q$-learning (HCN-DDQL). Firstly, the enemy's CSoS is abstracted as an HCN, and an evaluation index based on the capability and attack costs of nodes is proposed. Meanwhile, a mathematical optimization model for HCN disintegration is established. Secondly, the learning environment and double deep $Q$-network model of HCN-DDQL are established to train the HCN's disintegration strategy. Then, based on the learned HCN-DDQL model, an algorithm for calculating the HCN's optimal disintegration strategy under different states is proposed. Finally, a case study is used to demonstrate the reliability and effectiveness of HCN-DDQL, and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.

Within the context of capability-based system-of-systems (SoS), the technology selection involves multiple stakeholders interactively participating in decision-making. In this article, a novel approach based on graph model for conflict resolution (GMCR) is proposed to handle the interactive technology selection decision across capability domains. First, a GMCR methodology based decision analysis framework for technology selection is presented, which allows decision-makers (DMs) with distinct risk attitudes, preference knowledge, and degrees of foresight to independently and interactively participate in technology selection. Then, the technology selection model is established by a four-step procedure that incorporates improved fuzzy option prioritization, followed by systematical technology selection analysis to provide strategic insights for identifying potential mutually accepted technology portfolios. Finally, an illustrative example is used to demonstrate the applicability and effectiveness of the proposed approach.

The evidential reasoning (ER) has been widely used in engineering practice owing to its powerful ability in dealing with various uncertainties. Typical ER methods require that evidence should be independent and mainly focus on the uncertainty caused by crisp values. However, when the evidence is obtained in interval forms, how to describe the interval uncertainty, analyze the evidence dependence, and explore the performance of the reasoning rule remains to be studied. As such, the modeling, inference, and robustness analysis of interval evidential reasoning (IER) rule is investigated. Specifically, the interval uncertainty is modeled through interval belief degree, interval weight, and interval reliability, and several calculation methods of interval evidential parameters are designed. The IER rule is developed theoretically based on the ER rule, and a minmax-based parameter optimization model is established to alleviate the subjective uncertainty in referential values. A robustness analysis method is put forward to study the performance of IER rule, where the perturbation threshold estimation model is built through the robustness coefficient. The health evaluation of laser gyroscope is conducted to demonstrate the implementation of IER rule and validate its effectiveness.