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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2019)

Model validation is the process of evaluating how well a computational model represents reality. That is to say, does the model make predictions that adequately agree with the experimental evidence? Both model validation and uncertainty quantification have gained tremendous attention from researchers in engineering, physics, chemistry, and biology. Uncertainty quantification methods have been successfully applied to assessing model predictions of unmeasured quantities of interest and assisting in the development of computationally efficient, yet predictive, reduced-order models. In both cases, experimental data are incorporated into the analysis to refine the uncertainty estimate. However, with the amount of experimental data published and being generated through ongoing scientific endeavors, it is crucial to organize and integrate experimental data with the uncertainty quantification methods.

In this work, I develop tools for uncertainty quantification and construct a validation workflow that seamlessly integrates uncertainty quantification tools with an online database of chemical kinetics validation data. The first part of this dissertation discusses the need for structured experimental data, emphasizing its value towards model validation, and explore how online databases provide structure to data. An optimization-based framework for uncertainty quantification, Bound-to-Bound Data Collaboration, is employed throughout the dissertation to verify the compatibility of models with data. A novel strategy for surrogate modeling using Bound-to-Bound Data Collaboration is developed to guide the fitting procedure towards regions of the parameter space where the model predicts the data accurately. This technique is demonstrated in two simple examples and a solid-fuel combustion example. In the second part of this dissertation, three complex physics-based models are investigated, specifically H2/O2 combustion, a solid-fuel char oxidation model, and a semi-empirical quantum chemistry model. The efficacy of the validation workflow for developing predictive models, and the scientific insights uncovered from the analysis, is discussed.

    Cover page: Role of experimental data in validating and quantifying uncertainties in complex physical systems
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2014)
    Driven by its importance in nature and technology, droplet impact on solid surfaces has been studied for decades. To date, research on control of droplet impact outcome has focused on optimizing pre-impact parameters, e.g., droplet size and velocity. Here we follow a different, post-impact, surface engineering approach yielding controlled vectoring and morphing of droplets during and after impact. Surfaces with patterned domains of extreme wettability (high or low) are fabricated and implemented for controlling the impact process during and even after rebound--a previously neglected aspect of impact studies on non-wetting surfaces. For non-rebound cases, droplets can be morphed from spheres to complex shapes--without unwanted loss of liquid. The procedure relies on competition between surface tension and fluid inertial forces, and harnesses the naturally occurring contact-line pinning mechanisms at sharp wettability changes to create viable dry regions in the spread liquid volume. Utilizing the same forces central to morphing, we demonstrate the ability to rebound orthogonally-impacting droplets with an additional non-orthogonal velocity component. We theoretically analyze this capability and derive a We(-.25) dependence of the lateral restitution coefficient. This study offers wettability-engineered surfaces as a new approach to manipulate impacting droplet microvolumes, with ramifications for surface microfluidics and fluid-assisted templating applications.
      Cover page: Morphing and vectoring impacting droplets by means of wettability-engineered surfaces.
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2018)
      We report an evaluation of a semi-empirical quantum chemical method PM7 from the perspective of uncertainty quantification. Specifically, we apply Bound-to-Bound Data Collaboration, an uncertainty quantification framework, to characterize (a) variability of PM7 model parameter values consistent with the uncertainty in the training data and (b) uncertainty propagation from the training data to the model predictions. Experimental heats of formation of a homologous series of linear alkanes are used as the property of interest. The training data are chemically accurate, i.e., they have very low uncertainty by the standards of computational chemistry. The analysis does not find evidence of PM7 consistency with the entire data set considered as no single set of parameter values is found that captures the experimental uncertainties of all training data. A set of parameter values for PM7 was able to capture the training data within ±1 kcal/mol, but not to the smaller level of uncertainty in the reported data. Nevertheless, PM7 was found to be consistent for subsets of the training data. In such cases, uncertainty propagation from the chemically accurate training data to the predicted values preserves error within bounds of chemical accuracy if predictions are made for the molecules of comparable size. Otherwise, the error grows linearly with the relative size of the molecules.
        Cover page: Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical MethodCreative Commons 'BY' version 4.0 license
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