Digital Twins

How high-performance computing is personalizing the future for complex systems

Professor Karen E. Willcox

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Director, Oden Institute for Computational Engineering & Sciences

Professor, Aerospace Engineering & Engineering Mechanics

University of Texas at Austin

Supercomputing Spotlights Webinar Series

Could you imagine one day having a dynamically evolving virtual replica of yourself that your doctor could use to drive personalized decisions to optimize your health and well-being? In engineering, these personalized dynamic computational models are known as digital twins, and are already being used to drive predictive maintenance decisions for aircraft and aircraft engines. This talk will discuss the high-performance computing and computational science that goes into creating a digital twin, and will discuss exciting new directions for digital twins in engineering, geosciences and medicine.

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Lead Forward

Mobility Air Force Command Nodes for Complex Operations

by Michael J. Lostumbo, Jeffrey S. Brown, Stephen W. Oliver, Jr.

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The future command and control (C2) structure of the United States Air Force (USAF) forces must be designed to withstand and cope with attacks on U.S. capabilities and to effectively adapt to a rapidly changing battle space. A future conflict with Russia or China is likely to involve precision missile attacks on airfields, attacks on command nodes, cyberattacks, and degradations to communications systems. The implications are far reaching. They threaten the long-standing USAF principle and practice of highly centralized C2. Determining how to adapt effectively to major disruptions in communications and chains of command is, thus, a crucial consideration for the USAF going forward.

To help the USAF envision how forces should be organized to prepare for such conditions, the authors identify the demands that the USAF's emerging agility concepts will place on expeditionary wings; develop alternative wing C2 constructs for expeditionary Mobility Air Force (MAF) forces — some potentially disaggregated — in forward areas under threat of missile attack; and provide a qualitative assessment of the alternative constructs.

Key Findings

USAF forces operating in forward areas will have to develop C2 capabilities designed to withstand attacks and to effectively adapt to a rapidly changing battlespace.

The physical and cyber vulnerabilities of command nodes and networks will need to be accounted for.

USAF forces must be designed to retain effective C2 in view of disruptions to communications and dispersals of forces.

A modular approach for MAF C2 force elements provides the most flexibility, given the wide scope of the global operations that MAF forces must perform.

New operating procedures for adaptive operations need to be standardized USAF-wide in order to maximize both force survivability and mission effectiveness.

Recommendations

For future high-end fights, the MAF should develop a wing-level capability for C2 of independent MAF forces operating from multiple locations.

MAF C2 force elements should be prepared to temporarily assume limited operational planning functions of higher headquarters.

MAF C2 forces should be organized, trained, and equipped to reflect new demands in areas such as operational planning, logistics, operational deception, and recovery.

The MAF should determine whether the communications capabilities of the command element are suited to future operating environments.

The MAF should develop a modular approach to C2 force elements.

The USAF should consider mechanisms to ensure integration of agility concepts and training across all major commands.

Table of Contents

Chapter One

Command Challenges and Analytic Approach

Chapter Two

Future Operating Environments

Chapter Three

Force Element Options

Chapter Four

Findings and Recommendations

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Complex Systems: A Survey

M. E. J. Newman

Department of Physics, University of Michigan, Ann Arbor, MI 48109 and

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Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109

A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation.

The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a short survey of the main themes and methods of complex systems science and an annotated bibliography of resources, ranging from classic papers to recent books and reviews.

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复杂系统科学及其应用的简介

An Introduction to Complex Systems Science and its Applications

Alexander F. Siegenfeld1;2  and Yaneer Bar-Yam2

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1Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA and

2New England Complex Systems Institute, 277 Broadway, Cambridge, MA

很多概念和定量的分析框架所基于的标准假设对于众多复杂的物理、生物和社会系统并不满足。复 杂系统科学可以告诉我们这些假设在什么情况下失效，以及为什么失效，并提供了可以理解复杂系统 属性的另一个框架。本综述介绍了复杂系统科学的一些基本概念，包括复杂度曲线，有效性和适应性 的权衡，系统复杂度和环境复杂度匹配的必要性，多尺度分析以及进化过程。复杂系统科学的目的是 对系统一般属性的理解和讨论，而不是对于具体系统动态的建模。相对于提供全面的综述，本文更希 望是来科普一些可用于对我们世界里的复杂系统进行理解和应用的简单概念和分析方法。本文将以简 单的语言进行，只需要高中层次的数学和科学背景即可，以便让各个领域的学者与各个行业、各级政 府和慈善机构的决策者以及其他任何对系统和社会感兴趣的人都可以看懂。

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An Introduction to Complex Systems Science and Its Applications

The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multiscale analysis, and evolutionary processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. This paper assumes only a high school mathematical and scientific background so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society.

REPORT FROM ICAS WORKSHOP ON

COMPLEX SYSTEMS INTEGRATION IN AERONAUTICS

Susan X. Ying

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Commercial Aircraft Corporation of China

Keywords: complex systems, integration, architecture, MBSE

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AIR UNIVERSITY

SCHOOL OF ADVANCED AIR AND SPACE STUDIES

Complex Adaptive Systems and the

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Development of Force Structures for the

United States Air Force

Eric M. Murphy

Lieutenant Colonel, USAF

Drew Paper No. 18

Air University Press

Air Force Research Institute

Maxwell Air Force Base, Alabama

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Graph-Based Digital Blueprint for Model Based Engineering of Complex Systems

Manas Bajaj1, PhD

Chief Systems Officer

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Intercax

Jonathan Backhaus

Staff Systems Engineer

Rotary & Mission Systems

Lockheed Martin Corporation

Tim Walden, LM Fellow

Chief Engineer

Advanced Systems

Lockheed Martin Corporation

Manoj Waikar

Senior Software Arch.

Intercax

Dirk Zwemer

President, Intercax

Chris Schreiber

Space Systems Company

Lockheed Martin Corporation

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