About

James Sutherland is an Associate Department Chair and Professor in the Department of Chemical Engineering at the University of Utah. His research interests encompass a broad range of topics related to combustion and energy systems, including carbon dioxide capture, coal combustion, combustion simulation, computational fluid dynamics, computational transport phenomena, energy efficiency, energy storage systems, high-performance computing, reacting flows, and machine learning/artificial intelligence. He focuses on developing advanced modeling techniques such as reduced-order modeling, neural networks, and data-driven approaches to improve the understanding and simulation of complex combustion processes. Professor Sutherland has received multiple awards recognizing his excellence in teaching and faculty contributions, including the College of Engineering Outstanding Teacher award and the Department of Chemical Engineering Outstanding Faculty award. His scholarly work includes numerous publications in combustion science, fluid mechanics, and energy systems, emphasizing the application of machine learning and high-fidelity simulations to optimize combustion models and energy technologies.

Research topics

• Computer Science
• Artificial Intelligence
• Algorithm
• Data Mining
• Machine Learning
• Virology
• Medicine
• Pathology
• Acoustics
• Mathematics
• Aerospace engineering
• Mechanical engineering
• Environmental science
• Telecommunications
• Physics
• Engineering
• Mathematical optimization

Selected publications

• Optimizing progress variables for ammonia/hydrogen combustion using encoding–decoding networks

  Combustion and Flame · 2025-04-10 · 2 citations

  articleOpen access

  We demonstrate a strategy to optimize parameterizations of combustion manifolds using an encoding–decoding artificial neural network architecture. Our focus in this work is on the combustion of ammonia (NH3) and hydrogen (H2) blends. The literature on NH3 combustion, to date, lacks an efficient definition of a reaction progress variable (PV) to parameterize the thermo-chemical state-space. A quality parameterization should be able to represent the thermo-chemical state variables accurately, as well as any functions of those, e.g. , the source terms of the non-conserved PVs. Our approach incorporates information about the reaction source term of a PV and about important combustion products into the PV optimization. A gradient descent optimizer is informed by the reconstruction quality of those important quantities of interest (QoIs) that enter the optimization as decoder outputs. The approach can be thought of as an iterative back-and-forth between defining a parameterization (encoding) and reconstructing QoIs from it (decoding). It thus naturally promotes parameterizations where each QoI is uniquely and smoothly represented over the manifold. This work can help advance the adaptivity of combustion models. First, we show that with an adequate definition of a PV, we can steer the model’s accuracy towards improved representation of selected products and pollutants. Second, the definition of a PV automatically adapts to best complement the remaining physics-based parameters, such as the mixture fraction or the enthalpy defect. These two achievements combined were not possible with the existing PV optimization methods which only impose monotonicity and scalar gradient magnitude in defining a PV. Novelty and Significance Statement We demonstrate a novel strategy to optimize the definition of a progress variable (PV) using an encoding–decoding artificial neural network. Our approach can be thought of as an iterative back-and-forth between defining a parameterization of a flame (encoding) and reconstructing important scalars from it (decoding). Notably, the PV definition and its corresponding source term are co-optimized. The definition of a PV automatically adapts to best complement the remaining physics-based parameters, such as the mixture fraction or the enthalpy defect. These achievements were not possible with the existing PV optimization methods which only impose monotonicity and scalar gradient magnitude in defining a PV. This work can help advance combustion models, paving the way for adaptive reduced-order models, where the model can be adjusted towards particularly good representation of target scalars, such as pollutants. Our optimization method is applicable to premixed and non-premixed combustion.

  PublisherDOI

• Reduced-order modeling with reconstruction-informed projections

  Combustion and Flame · 2023-10-18 · 6 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches

  Lecture notes in energy · 2023-01-01 · 2 citations

  book-chapterOpen access

  Abstract Data-driven modeling of complex dynamical systems is becoming increasingly popular across various domains of science and engineering. This is thanks to advances in numerical computing, which provides high fidelity data, and to algorithm development in data science and machine learning. Simulations of multicomponent reacting flows can particularly profit from data-based reduced-order modeling (ROM). The original system of coupled partial differential equations that describes a reacting flow is often large due to high number of chemical species involved. While the datasets from reacting flow simulation have high state-space dimensionality, they also exhibit attracting low-dimensional manifolds (LDMs). Data-driven approaches can be used to obtain and parameterize these LDMs. Evolving the reacting system using a smaller number of parameters can yield substantial model reduction and savings in computational cost. In this chapter, we review recent advances in ROM of turbulent reacting flows. We demonstrate the entire ROM workflow with a particular focus on obtaining the training datasets and data science and machine learning techniques such as dimensionality reduction and nonlinear regression. We present recent results from ROM-based simulations of experimentally measured Sandia flames D and F. We also delineate a few remaining challenges and possible future directions to address them. This chapter is accompanied by illustrative examples using the recently developed Python software, PCAfold . The software can be used to obtain, analyze and improve low-dimensional data representations. The examples provided herein can be helpful to students and researchers learning to apply dimensionality reduction, manifold approaches and nonlinear regression to their problems. The Jupyter notebook with the examples shown in this chapter can be found on GitHub at https://github.com/kamilazdybal/ROM-of-reacting-flows- Springer .

  PublisherOA PDFDOI

• Advancing Reacting Flow Simulations with Data-Driven Models

  Cambridge University Press eBooks · 2023-01-12 · 9 citations

  book-chapterOpen access

  The use of machine learning algorithms to predict behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and computer models. The performance of these tools is enhanced if all the prior knowledge and the physical constraints are embodied. In other words, the scientific method must be adapted to bring machine learning into the picture, and make the best use of the massive amount of data we have produced, thanks to the advances in numerical computing. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems. Examples of feature extraction in turbulent combustion data, empirical low-dimensional manifold (ELDM) identification, classification, regression, and reduced-order modeling are provided.

  PublisherOA PDFDOI

• Improving reduced-order models through nonlinear decoding of projection-dependent outputs

  Patterns · 2023-10-10 · 7 citations

  articleOpen accessSenior authorCorresponding

  A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection. This includes behavior such as overlapping, twisting, or large curvatures or uneven data density that can generate nonuniqueness and steep gradients in quantities of interest (QoIs). Here, we employ an encoder-decoder neural network architecture for dimensionality reduction. We find that nonlinear decoding of projection-dependent QoIs, when embedded in a dimensionality reduction technique, promotes improved low-dimensional representations of complex multiscale and multiphysics datasets. When data projection (encoding) is affected by forcing accurate nonlinear reconstruction of the QoIs (decoding), we minimize nonuniqueness and gradients in representing QoIs on a projection. This in turn leads to enhanced predictive accuracy of a ROM. Our findings are relevant to a variety of disciplines that develop data-driven ROMs of dynamical systems such as reacting flows, plasma physics, atmospheric physics, or computational neuroscience.

  PublisherOA PDFDOI

• Local manifold learning and its link to domain-based physics knowledge

  Applications in Energy and Combustion Science · 2023-03-23 · 10 citations

  articleOpen access

  In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting physically meaningful parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke–Schumann model, the chemical equilibrium model, and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed n-heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes. We shed the light on how data-driven techniques, such as local PCA, can be enhanced by using the available knowledge of the system.

  PublisherDOI

• PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

  SoftwareX · 2023-07-01 · 6 citations

  articleOpen accessSenior author

  We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

  PublisherDOI

• Local manifold learning and its link to domain-based physics knowledge

  arXiv (Cornell University) · 2022-07-01 · 1 citations

  preprintOpen access

  In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from the training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke-Schumann model, the chemical equilibrium model and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed $n$-heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes.

  PublisherOA PDFDOI

• Characterizing Tradeoffs in Memory, Accuracy, and Speed for Chemistry Tabulation Techniques

  Combustion Science and Technology · 2022-01-26 · 5 citations

  articleOpen accessSenior author

  Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. In this manuscript, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. We also demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.

  PublisherOA PDFDOI

• Manifold-informed state vector subset for reduced-order modeling

  Proceedings of the Combustion Institute · 2022 · 28 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence
  PublisherOA PDFDOI

Recent grants

• CDS&E: Extracting Models from Data - A Novel Data-Driven Simulation Strategy for Reacting Flows

  NSF · $453k · 2020–2025

• Integrated Experimental and Computational Studies Of MILD Oxy-Coal Combustion

  NSF · $493k · 2017–2022

Frequent coauthors

• Alessandro Parente

  30 shared

• Tony Saad

  15 shared

• Kamila Zdybał

  Harbin Institute of Technology

  13 shared

• Philip J. Smith

  The Ohio State University

  11 shared

• Josh McConnell

  Los Alamos National Laboratory

  9 shared

• Axel Coussement

  8 shared

• E. F. Armstrong

  University of Utah

  8 shared

• Mokbel Karam

  7 shared

Education

• Ph.D., Chemical Engineering

  University of Utah

  2004

• B.S., Chemical Engineering

  University of Utah

  1999

Awards & honors

• College of Engineering Outstanding Teacher award, University…
• Department of Chemical Engineering Outstanding Faculty award…
• Best lecturer award from Chemical Engineering class of 2016,…
• Oblad Award (2001)
• All-American Award in Pistol Shooting (1999)