Multidisciplinary Design Optimization (MDO) Review & Implementation | Peter Hacker | BlockScience скачать в хорошем качестве

Multidisciplinary Design Optimization (MDO) Review & Implementation | Peter Hacker | BlockScience

Explore Multidisciplinary Design Optimization (MDO). Learn how AAO, SAND, IDF & CO architectures solve complex, multi-team problems in engineering and socio-technical systems. This presentation is ideal for engineering leaders, system designers, and decision-makers interested in how structured optimization frameworks can improve coordination, problem formulation, and resource allocation in complex projects. Key Topics Covered: 🔹What is MDO and why it matters 🔹Challenges of disciplinary coupling and feasibility 🔹Monolithic vs. distributed architectures (AAO, SAND, IDF, CO) 🔹Applications in aerospace, simulation, and socio-technical systems 🔹Connections to BlockScience research In this presentation, BlockScience Research Engineer Peter Hacker reviews Multidisciplinary Design Optimization (MDO) architectures and explores their implementation as a systems engineering framework for solving complex, multi-team problems. MDO is framed as a powerful coordination tool for managing challenges such as computationally expensive disciplinary analyses and the need to manage coupling, feasibility, and decision-making across disciplines. Four main MDO architectures are reviewed—All-at-Once (AAO), Simultaneous Analysis and Design (SAND), Individual Disciplinary Feasible (IDF), and Collaborative Optimization (CO)—highlighting their strengths, trade-offs, and implications for both engineering practice and broader socio-technical systems. Example problems help to emphasize that the process of formulating a problem in an MDO framework is often as valuable as the final solution, providing an explicit and analytical description of the relationships and dependencies between different models The argument presented is that MDO offers a structured approach to managing the inherent complexity of multi-scale and multidisciplinary systems. The broader implication for BlockScience and other high-reliability organizations is that MDO frameworks can inform how multidisciplinary or multiscale socio-technical problems are structured, conditioned, and solved, even before computationally intensive simulations are run. The discussion also connects MDO to related concepts, such as generalized dynamical systems (GDS), graph-structured optimization, and cooperative strategies for solving multi-agent decision problems (MAIDs). These connections position MDO not only as an engineering methodology but as a cross-domain systems coordination framework, relevant to BlockSciences' research into computational social science, design architectures, and knowledge organization infrastructure. Show Me More! https://blog.block.science/multidisci... 00:00:01 Introduction 00:02:10 Project Walkthrough 00:03:13 Introduction to Multidisciplinary Design Optimization (MDO) 00:03:47 Background Context 00:04:37 Challenges in MDO 00:06:03 Variable Types in MDO 00:06:26 Single Discipline Analysis 00:06:50 Multiple Discipline Analysis Example 00:07:34 Disciplinary Coupling and Feasibility 00:08:34 Discussion: Cooperative Strategies for Solving MAIDS 00:10:02 Converging Disciplinary Coupling 00:10:53 Reformulating MDO Problems 00:14:43 MDO Architectures 00:14:43 Monolithic vs. Distributed 00:15:43 MDO Problem Formulations 00:16:21 Referencing Generalized Dynamical Systems (GSD) 00:20:02 NASA MBEE Team 00:21:59 Actionable Insights from DeSci Report 00:24:13 Exploring MDO Architectures 00:24:28 All-at-Once (AAO) Method 00:26:39 Simultaneous Analysis and Design (SAND) 00:27:12 Individual Disciplinary Feasible Method 00:29:04 Discussion of IDF 00:31:17 Collaborative Optimization (CO) 00:40:09 Example Problems & Implementation 00:40:29 Geometric Programming 00:41:02 Propane Combustion Problem 00:42:14 Disciplinary Analysis Function Calls 00:42:49 Project Conclusion: Learning & Challenges 00:44:00 Future Directions and Applications BlockScience® is a systems engineering firm that operationalizes emerging technologies for high-reliability organizations. We partner with organizations in healthcare, energy, finance, and government to develop and integrate new capabilities while maintaining reliable operations. Operationalization doesn't end with due diligence and procurement; it includes integration, calibration, and validation. Our R&D practice goes beyond exploration: We operationalize emerging technologies within our own organization first, to differentiate between impressive demonstrations and practical solutions. This hands-on experience, combined with rigorous engineering discipline, enables us to cut through hype and provide honest assessments of organizational readiness and technological fitness-for-purpose. We support accountable executives with responsibility for making complex technologies work within their operational constraints, ensuring that people, processes, and tools function together reliably. ► Discover More https://block.science/ | https://x.com/block_science |   / blockscience