Cloud-Based Simulation and Decision Support for Large-Scale Engineering System Design
Keywords:
cloud simulation, decision support, large-scale systems, engineering design, digital twins, governance, elasticity, multi-criteria decision analysis, resilience, sustainabilityAbstract
The design of large-scale engineering systems increasingly relies on distributed computational resources that can accommodate the combinatorial complexity of multi-domain trade-offs, stochastic operational environments, and extended life-cycle horizons. Cloud-based simulation and decision support platforms represent a transformative convergence of elastic infrastructure, high-fidelity modeling, and real-time analytics, yet their deployment demands careful architectural reasoning beyond the immediate technical capabilities. This paper examines the structural interplay between simulation architecture, decision governance, deployment topology, and long-term sustainability. It argues that the value of cloud-based environments lies not only in scalability but in their capacity to instantiate transparent, auditable, and adaptable socio-technical pipelines that link model-based systems engineering with policy-aware decision-making. The discussion explores federation across public and private cloud resources, containerized orchestration of simulation microservices, embedding of machine learning surrogates, and the integration of digital twin feedback loops. Particular attention is given to governance frameworks that address fairness, data sovereignty, and epistemic uncertainty propagation. Infrastructure considerations encompass vendor lock-in, carbon-aware scheduling, and the tension between rapid elasticity and deterministic reproducibility. A cross-domain synthesis is developed, drawing from transportation, energy, and aerospace sectors. The paper concludes that advancing cloud-based simulation for engineering design requires institutional mechanisms that align technical modularity with accountability, anticipatory regulation, and life-cycle stewardship, thereby transforming computational capability into enduring institutional knowledge.
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