About

Peter Hamlington is a Professor and the Department Chair at the Paul M. Rady Mechanical Engineering Department at the University of Colorado Boulder. He holds the Woodward-Vogel Leadership Chair and is a Vogel Faculty Fellow. His research interests include computational fluid dynamics, air quality, and design. He is associated with multiple departments including Aerospace Engineering Sciences, Chemical & Biological Engineering, Civil, Environmental & Architectural Engineering, and others. His work involves advancing understanding in thermo-fluid sciences and air quality, contributing to engineering education and research at the university.

Research topics

• Physics
• Mechanics
• Chemistry
• Meteorology
• Thermodynamics
• Geometry
• Marine engineering
• Engineering
• Classical mechanics
• Environmental science
• Materials science
• Statistical physics
• Aerospace engineering
• Electrical engineering
• Mathematics
• Optics
• Atomic physics
• Physical chemistry
• Computational physics

Selected publications

• Adaptive Computational Framework to Infer Flaring Source Conditions from Remotely-Sensed Measurements of Methane Emissions

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-27

  preprintOpen accessSenior author
  PublisherDOI

• Adaptive Computational Framework to Infer Flaring Source Conditions from Remotely-Sensed Measurements of Methane Emissions

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-27

  preprintOpen accessSenior author
  PublisherDOI

• High-speed thermal imaging of disk-shaped firebrands in a wind tunnel

  Fire Safety Journal · 2026-03-08

  article
  PublisherDOI

• Multifidelity Optimization of Turbulence in a Gas Turbine Combustor Simulator

  Journal of Engineering for Gas Turbines and Power · 2025-02-25

  articleSenior author

  Abstract Combustor turbulence in a gas turbine engine greatly influences the efficiency of the downstream high-pressure turbine stage. Here we use a multifidelity computational optimization methodology to modify the geometry of a nonreacting combustor simulator such that turbulence properties are optimized at the combustor-turbine interface. We modify the size, orientation, and positioning of the primary and dilution jets to minimize turbulence intensity at the combustor exit while demonstrating negligible or favorable changes to the pressure loss and mixing characteristics of the combustor. The optimization is performed using a machine learning surrogate-assisted genetic algorithm coupled with large eddy simulations (LES) and Reynolds-averaged Navier–Stokes (RANS) simulations. The optimization is performed in three phases: (i) we develop a continuously learning artificial neural network surrogate model, (ii) we perform a stochastic optimization with RANS simulations to narrow the parameter space, and (iii) we perform a stochastic optimization with a coarse-grid LES to identify the optimal solution. Using this approach, we are able to achieve a 5.35% reduction in turbulence intensity and a 0.42% reduction in pressure loss while maintaining good mixing uniformity at the combustor exit. These changes are enabled primarily by changing the aspect ratio, diameter, and spacing of the primary zone and dilution jets, as well as the chute height of the primary zone jets. This successful demonstration of multifidelity optimization in the combustor simulator can be extended in the future to the design of improved gas turbine combustors.

  PublisherDOI

• Global analysis of the interactions between small-scale ocean turbulence and carbonate chemistry

  2025-07-28

  preprintOpen accessSenior author

  The flux of carbon dioxide (\ce{CO2}) at the air-sea interface is affected by many factors, including the transport of dissolved \ce{CO2} away from the surface by turbulent advection. In this study, we characterize the effects of turbulence on \ce{CO2} fluxes by examining global variations in the time scales associated with small-scale turbulent mixing and carbonate chemistry. A map of the turbulence time scale, $\tau_t$, is obtained from a series of hydrodynamics-only large eddy simulations (LES) and the chemical time scale, $\tau_c$, is obtained from the Jacobian of the carbonate chemical system. The global map of the ratio $\tau_t/\tau_c$, known as the Damk\”{o}hler number (Da), shows that turbulence has the strongest effect on \ce{CO2} fluxes in mid- to high-latitude regions with lower surface temperatures and higher wind speeds and stresses. We verified these results using higher-resolution LES with coupled chemistry for five conditions corresponding to low, intermediate, and high Da.

  PublisherOA PDFDOI

• Automatic reduction of ocean biogeochemical models: a case study with BFM (v5.3)

  2025-08-05

  preprintOpen access

  Abstract. Modeling biogeochemical processes in ocean fluid dynamics simulations is computationally expensive, necessitating efficient model reduction techniques. Large-scale biophysical simulations, such as high-resolution large-eddy simulations (LES) of the upper ocean, require significant computing resources to capture small-scale turbulent processes while also resolving the evolution of reactive biogeochemical tracers. However, the complexity of existing biogeochemical models, such as the Biogeochemical Flux Model (BFM) which resolves 56 state variables, leads to unfeasibly high computational costs when represented in detailed LES. To address this, we applied model reduction techniques from the field of combustion to systematically reduce the complexity of the BFM while maintaining high fidelity. Specifically, we developed a modified version of the Directed Relation Graph with Error Propagation method and applied it to a 50-state-variable BFM. By analyzing 24 reduction scenarios, we produced five reduced models containing between 1 and 36 state variables capable of accurately capturing trends in concentration of the target fields. The results demonstrate the effectiveness of this reduction approach in preserving key biogeochemical dynamics while significantly reducing model size and complexity, paving the way for more efficient high-resolution ocean biogeochemical simulations.

  PublisherDOI

• Simultaneous versus sequential estimation of biogeochemical and physical parameters in coupled marine ecosystem models

  2025-08-29

  articleOpen accessSenior author

  Abstract. As computational resources have increased in availability and capability, so has the complexity of the models used to represent biogeochemical (BGC) processes in ocean simulations. To effectively calibrate the increasingly large number of uncertain parameters in these models, efficient parameter estimation methods are needed to ensure that the models can accurately represent the BGC processes under investigation. In this study, we address this challenge using a multistage automatic parameter estimation methodology that sequentially applies global sampling and local optimization to calibrate both the BGC model parameters and the parameters associated with the mathematical representation of physical ocean dynamics. We quantitatively compare the accuracy of sequential and simultaneous parameter estimations of moderately complex BGC and physical models at locations corresponding to the Bermuda Atlantic time series and the Hawaii Ocean time series. The results show that the best overall agreement with the observational data is obtained when the BGC and physical model parameters are estimated simultaneously, rather than sequentially. In particular, simultaneous estimation results in significantly improved predictions of oxygen and particulate organic nitrogen. Moreover, the agreement is improved in general when the physical model is included in the estimation, as opposed to calibrating the BGC model alone. This study also serves as a demonstration of a meta-algorithm for performing parameter estimation in high-dimensional models with local optimization approaches.

  PublisherOA PDFDOI

• Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments

  Frontiers in Forests and Global Change · 2025-10-17

  articleOpen access

  Pile burning is increasingly used in many forest and woodland ecosystems to reduce hazardous fuel loads following fuel hazard reduction or forest restoration efforts. Pile burning is often linked to thinning practices where residual fuel is piled and subsequently burned; the burning is typically done in winter months when conditions reduce the risk of unwanted fire behavior such as escapes. A key aspect of pile burning is estimating the amount of pile biomass and the amount of fuel consumed during burning as these two variables are critical for estimating treatment efficacy and smoke emissions. Methods to estimate pile masses have been studied and developed previously, however, they are time consuming and require extensive user training. Terrestrial laser scanning (TLS) is a remote sensing tool that has been successfully used on broadcast burning for fuel characterization and has the potential to estimate pile masses at prescribed burning sites. TLS reduces measurement error, requires less extensive user training, and eliminates observer bias in measurements. A total of 16 pile masses were measured across Colorado, United States, using a previously developed pile measurement methodology, using TLS, and by taking apart the pile and weighing the contents of the pile, to determine if TLS would be an adequate method for predicting pile masses. Individually, TLS did not do a good job predicting pile masses, however, when comparing across all 15 piles, using three TLS scans of a pile to estimate pile mass had the lowest median percent error across all piles.

  PublisherOA PDFDOI

• Adaptive Mesh Large Eddy Simulations of Transitional Jet Diffusion Flames in Crossflow

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• WindCline: Sloping wind tunnel for characterizing flame behavior under variable inclines and wind conditions

  Review of Scientific Instruments · 2024-02-01 · 3 citations

  articleOpen access

  Developing accurate computational models of wildfire dynamics is increasingly important due to the substantial and expanding negative impacts of wildfire events on human health, infrastructure, and the environment. Wildfire spread and emissions depend on a number of factors, including fuel type, environmental conditions (moisture, wind speed, etc.), and terrain/location. However, there currently exist only a few experimental facilities that enable testing of the interplay of these factors at length scales <1 m with carefully controlled and characterized boundary conditions and advanced diagnostics. Experiments performed at such facilities are required for informing and validating computational models. Here, we present the design and characterization of a tilting wind tunnel (the "WindCline") for studying wildfire dynamics. The WindCline is unique in that the entire tunnel platform is constructed to pivot around a central axis, which enables the sloping of the entire system without compromising the quality of the flow properties. In addition, this facility has a configurable design for the test section and diffuser to accommodate a suite of advanced diagnostics to aid in the characterization of (1) the parameters needed to establish boundary conditions and (2) flame properties and dynamics. The WindCline thus allows for the measurement and control of several critical wildfire variables and boundary conditions, especially at the small length scales important to the development of high-fidelity computational simulations (10-100 cm). Computational modeling frameworks developed and validated under these controlled conditions can expand understanding of fundamental combustion processes, promoting greater confidence when leveraging these processes in complex combustion environments.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Submesoscale-Resolving Large Eddy Simulations Using Reduced Biogeochemical Models

  NSF · $302k · 2019–2024

• CAREER: Structure and Dynamics of Highly Turbulent Premixed Combustion

  NSF · $501k · 2019–2025

• Collaborative Research: Reacting Tracers in a Turbulent Mixed Layer

  NSF · $401k · 2013–2018

Frequent coauthors

• Alexei Poludnenko

  University of Connecticut

  84 shared

• Colin Towery

  Los Alamos National Laboratory

  69 shared

• Nicholas T. Wimer

  51 shared

• Caelan Lapointe

  University of Colorado Boulder

  50 shared

• Gregory B. Rieker

  48 shared

• Jason Christopher

  Center for Discovery

  41 shared

• Elaine S. Oran

  32 shared

• Ryan Darragh

  32 shared