About

James Allison is a Professor and the Jerry S. Dobrovolny Faculty Scholar at the University of Illinois Urbana-Champaign. He is a faculty member of the Industrial & Enterprise Systems Engineering and Aerospace Engineering (affiliate) departments. Prof. Allison holds MS degrees in Mechanical Engineering (2004) and Industrial and Operations Engineering (2005), as well as a Ph.D. in Mechanical Engineering (2008), all from the University of Michigan. He also earned a BS in Mechanical Engineering from the University of Utah in 2003 and an AAS in Automotive Technology from Weber State University in 1998. His research focuses on the creation and analysis of novel quantitative design methods for engineering systems that enable the identification of high-performance system designs, including control co-design, system architecture design, and Spatial Packaging of Interconnected Systems with Physical Interactions (SPI2). His investigations span a wide range of application domains such as mechatronics, power electronics, renewable energy systems, spacecraft attitude control, intelligent structures, scramjet design, automotive systems, manufacturing, and fluid systems. Prof. Allison is recognized as an internationally-leading researcher behind the impactful new era of control co-design (CCD), which has facilitated new levels of integration and performance in actively controlled engineering systems. He has contributed significantly to the field through over 150 research publications and has received numerous awards, including the NSF CAREER Award and the ASME Design Automation Young Investigator Award. His work in CCD has led to over $150 million in government funding and transformational advancements in renewable energy and aerospace systems. Prof. Allison has held various academic positions at UIUC since 2011, progressing from Assistant to Professor, and has industry experience as a Senior Engineer at MathWorks and a Consulting Engineer at Ford Motor Company.

Research topics

• Computer Science
• Systems engineering
• Engineering
• Artificial Intelligence
• Marine engineering
• Programming language
• Operating system
• Distributed computing
• Aerospace engineering
• Software engineering
• Theoretical computer science

Selected publications

• Impact of control strategies on the control co-design of spar floating offshore wind turbines

  Ocean Engineering · 2025-06-10 · 11 citations

  articleOpen accessSenior author

  • This study explores how control strategies impact the design of spar floating offshore wind turbines. • OLOC, MPC, and ROSCO are compared, revealing trade-offs between energy production and constraint satisfaction. • A full factorial evaluation of 81 tower designs assesses the impact of structural parameters on performance. • The model captures aerodynamics, hydrodynamics, and structural dynamics while maintaining efficiency. • Sensitivity analysis shows tower height significantly affects Annual Energy Production (AEP). • Mismatched control strategies between design and implementation lead to performance loss. • Higher tower stress limits enable taller towers that generate more energy. Offshore wind energy has the potential to transform global energy systems by providing clean and sustainable power. Optimizing both plant and control design is crucial for realizing this potential. This paper examines the impact of Open-Loop Optimal Control (OLOC), Model Predictive Control (MPC), and the ROSCO Controller on the Control Co-Design (CCD) of spar floating offshore wind turbines. By integrating plant and control design, we evaluate performance and feasibility under each strategy. Results show that OLOC achieves the highest Annual Energy Production (AEP) by leveraging full system information, while MPC balances performance and computational cost. The ROSCO controller, with its fixed structure, offers a straightforward implementation but faces challenges in meeting certain constraints. We analyze the role of MPC prediction horizon and the impact of control strategy mismatches between CCD and practical implementation. Findings highlight the need to account for practical constraints during design to minimize performance losses. Additionally, we explore AEP sensitivity to key design variables under different controllers, identifying critical factors for optimization. These insights contribute to advancing offshore wind turbine design by improving performance and reducing costs.

  PublisherDOI

• Multidisciplinary Modeling and Control Co-Design of a Floating Offshore Vertical-Axis Wind Turbine System

  Journal of Mechanical Design · 2025-02-27 · 8 citations

  article

  Abstract This study investigates the modeling and design of a floating vertical-axis wind turbine (FloatVAWT) system with multidisciplinary design optimization (MDO) and control co-design (CCD) approaches. By integrating various associated disciplinary models, the study aims to holistically optimize the physical and control designs of the FloatVAWT system. Through the identification of impactful design elements and capitalizing on synergistic interactions, the study aims to provide insights into subsystem designers and aid their detailed decisions. The model developed for this CCD framework utilizes automated geometric manipulation and mesh generation to explore various FloatVAWT configurations during the early design stages. Surrogate models facilitate efficient design studies within limited computing resources by exchanging model information between disciplinary models and subsystems without requiring extensive simulations during the optimization loop. The model incorporates an aero-hydro-servo dynamic representation of the FloatVAWT system, considering physical and control constraints. Additionally, the study investigates the potential benefits of varying both the average and intracycle rotational speeds of the VAWT rotor to enhance energy production and minimize adverse platform motions, thus reducing the levelized cost of energy. System-level design solutions are analyzed to identify design tradeoffs and propose mitigation strategies for potential mechanical failures of the rotor. In conclusion, this study provides modeling strategies for the FloatVAWT system and analyzes the system design solutions through MDO and CCD approaches. The outcomes of this study offer insights into system-optimal solutions for subsystem-level decisions considering multidisciplinary couplings.

  PublisherDOI

• Can Graph Neural Networks Help Identify Promising Thermal Management System Architectures Among Vast Numbers of Possibilities?

  Journal of Mechanical Design · 2025-09-16

  articleSenior author

  Abstract The more efficient a thermal management system must be across a diverse range of conditions, the greater the intricacy of its design is required. Yet, the computational bottlenecks of enumerating and analyzing potential system architectures often render the best solutions impractical. This study evaluates the feasibility of accelerating system architecture performance evaluation by leveraging graph neural network (GNN)-based regression as a cost-effective alternative to traditional open-loop optimal control (OLOC) analysis. We examine a case study where enumerating all possible system architectures is computationally feasible, but directly analyzing the performance of each is not. Instead, we analyze a small subset of architectures and use these data to train a generalizable surrogate model capable of evaluating the remaining architectures within the available computational budget. After the training, the predicted performance values are sorted to obtain the estimated best configurations. Then, there are two options: (1) select the highest-ranked configuration as the optimal solution or (2) choose a small subset of test data with the highest estimated ranks and evaluate them using OLOC to get a more accurate result for the correct optimal configuration. Our results show that training a GNN with approximately 30% of the architectures was sufficient to predict the performance of the remaining 70% with an average mean squared error (MSE) loss of 0.6, achieving a 92% reduction in computational cost overall.

  PublisherDOI

• Wind turbine control co-design using dynamic system derivative function surrogate model (DFSM) based on OpenFAST linearization

  Applied Energy · 2025-06-06 · 7 citations

  articleSenior author
  PublisherDOI

• Nested control co-design of a spar buoy horizontal-axis floating offshore wind turbine

  Ocean Engineering · 2025-03-29 · 13 citations

  articleOpen accessSenior author
  PublisherDOI

• Automated Enumeration of Reconfigurable Architectures for Thermal Management Systems in Battery Electric Vehicles

  ArXiv.org · 2025-11-26

  preprintOpen access

  As the automotive industry moves towards vehicle electrification, designing and optimizing thermal management systems (TMSs) for Battery Electric Vehicles (BEVs) has become a critical focus in recent years. The dependence of battery performance on operating temperature, the lack of waste combustion heat, and the significant effect of TMS energy consumption on driving range make the design of BEV TMSs highly complicated compared to conventional vehicles. Although prior research has focused on optimizing the configuration of thermal systems for varying ambient conditions, a holistic approach to studying the full potential of reconfigurable TMS architectures has not yet been fully explored. The complex design landscape of multi-mode reconfigurable systems is difficult to navigate. Relying solely on expert intuition and creativity to identify new architectures both restricts progress and leaves significant performance improvements unrealized. In this study, using graph modelling of TMS architectures, we propose a systematic method to automatically enumerate and simulate reconfigurable architectures for a TMS, given the desired operating modes, along with a framework to conduct transient performance analysis and optimization-based trade-off studies among system performance, energy consumption, and complexity. We explored more than 150 operating mode sequences, retaining 39 unique architectures for further evaluation. MATLAB Simscape models of these architectures were automatically created and their performance evaluated. The multi-objective optimization results provide decision support for selecting the best architecture based on user priorities.

  PublisherOA PDFDOI

• Extracting Design Information From Optimized Designs of Power Flow Systems: Application to Multisplit Thermal Management System Configuration

  Journal of Mechanical Design · 2025-04-04 · 1 citations

  articleSenior author

  Abstract As engineering systems grow more intricate and technological progress accelerates, traditional sources of design knowledge, such as historical data and expert intuition, struggle to keep pace with the complexity and the speed of knowledge generation. To address this challenge, additional sources of knowledge are necessary, particularly for designing unprecedented engineering systems lacking any design heritage. One promising approach involves analyzing optimized designs to extract valuable insights, enabling designers to break away from incremental improvements over existing designs. This article explores the extraction of design information from optimized designs in power flow systems using various classification machine learning methods, empowering designers to make informed decisions in future design endeavors. This design information can also serve as a foundation for synthesizing engineering system configurations that are more complex than those previously encountered. This approach offers several advantages over traditional methods, including its applicability in the absence of design heritage and its ability to provide normative guidance for system design. This article focuses on power flow systems that can be modeled as graphs with a tree structure, with the case study being multisplit fluid-based thermal management systems. The article presents four case studies demonstrating the effectiveness of using information from optimized designs to enhance the design of complex thermal management systems, in both human-directed and automated design processes. The results show that information extraction significantly improves the design process, with less than 1 percent error in approximating the true optimal configuration. This approach eliminates the need for solving complex control problems, leading to reduced computation costs.

  PublisherDOI

• A practical open-source approach to Model Predictive Control using the Legendre–Gauss–Radau pseudospectral method

  Software Impacts · 2025-05-22 · 2 citations

  articleOpen accessSenior author

  In a world increasingly reliant on technologies that sense and respond to their environment—from thermostats to energy grids—predictive capabilities are critical. However, uncertainties and complexity often hinder the adoption of advanced strategies like Model Predictive Control (MPC), leading many industries to rely on simpler, less effective methods. This paper presents a practical, open-source software tool based on the Legendre–Gauss–Radau pseudospectral method, designed to streamline MPC implementation. The software handles dynamics, constraints, and objectives efficiently while supporting black-box systems. A case study in this paper demonstrates its effectiveness, with additional examples in the supplementary material validating its versatility. • An open-source software tool streamlines the implementation of Model Predictive Control (MPC). • The Legendre–Gauss–Radau pseudospectral method is used to convert dynamic systems into nonlinear programming problems. • Supports both symbolic and black-box system dynamics for wide applicability. • Offers modular architecture enabling users to define dynamics, constraints, and objectives independently. • Demonstrates effectiveness through case studies and examples, with additional material provided online.

  PublisherDOI

• Numerical Estimation of the Sensitivity of Optimal Plant Design to Control Perturbations

  2025-08-17

  articleSenior author

  Abstract Control co-design (CCD) methods treat plant and control design in an integrated manner. Control co-design problems typically present design coupling, that is, control and plant design decisions affect each other. Quantitative design coupling information can be used to assist in problem formulation, solution strategy selection and can provide new design insights. This work focuses on the estimation of control-plant coupling, that is, the effect of control decisions on optimal plant design. The type of perturbation that affects the control inputs may be known a priori only in some specific cases. In this work, we estimate control-plant coupling for the general case where the nature of the perturbation is unknown. The procedure is based on the use of the Fourier series to approximate the perturbation function. Two methods are investigated to determine the Fourier coefficients, with the goal of maximally efficiently determining sensitivity to a wide variety of perturbations. Two examples demonstrate the use of this procedure, and show that a good estimate of the coupling strength may be obtained with a small sampling of perturbations.

  PublisherDOI

• Control Co-Design With Varying Available Information Applied to Vehicle Suspensions

  Journal of Dynamic Systems Measurement and Control · 2025-09-26 · 1 citations

  articleSenior author

  Abstract Recent optimization strategies for Control Co-Design (CCD) often utilize open-loop optimal control (OLOC) to explore the physical performance limits of actively controlled engineering systems. For most real systems, however, closed-loop control (CLC) is required for implementation. The physical (plant) design generated by an OLOC CCD method will normally not interact optimally with CLC, producing results that are not system optimal. In this article, an intuitive strategy is presented for investigating empirically the impact of information availability on CCD optimization results. Model predictive control (MPC) provides a flexible means to vary what information is used in making real-time control decisions. This is used as a proxy for the vast space of potential controllers, from simple to sophisticated. This method for studying information-based characteristics of CCD problems is demonstrated using a canonical CCD problem based on an active automotive suspension problem. Different plant architectures with various plant design variables are considered. Results show that varying the amount of information in the control design yields different plant designs and different objective values, and has the potential to yield insights into promising CLC architectures (beyond MPC), fruitful directions to head for plant design, and a deeper understanding of the interface between physical and control system design. This article introduces the concept of information-based studies in CCD, but utilizes an applied approach based on MPC to generate insights. A more theoretical approach could be taken in the future that yields a more generalizable understanding of how information limitations influence CCD optimization outcomes.

  PublisherDOI

Recent grants

• CAREER: Integrated Design of Intelligent Structures with Tailored Distributed Damping

  NSF · $618k · 2017–2023

• Toward a Method for Achieving Synergy between Heuristic Rules of Thumb and Quantitative Methods in Engineering Design

  NSF · $150k · 2015–2018

Frequent coauthors

• Daniel R. Herber

  Colorado State University

  27 shared

• Satya R. T. Peddada

  University of Illinois Urbana-Champaign

  24 shared

• Yong Hoon Lee

  University of Memphis

  20 shared

• Oscar S. Alvarez-Salazar

  Jet Propulsion Laboratory

  20 shared

• Albert E. Patterson

  Texas A&M University

  20 shared

• Soon‐Jo Chung

  California Institute of Technology

  19 shared

• Jack Aldrich

  Jet Propulsion Laboratory

  19 shared

• Kai A. James

  Georgia Institute of Technology

  17 shared

Awards & honors

• NSF CAREER Award
• ASME Design Automation Young Investigator Award
• Jerry S. Dobrovolny Faculty Scholar (2019)
• Selected as the 2013 ASME Design Automation Young Investigat…