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Assimilation of Satellite Active Fires Data
Authors:
James D. Haley
Abstract:
Wildland fires pose an increasingly serious problem in our society. The number and severity of these fires has been rising for many years. Wildfires pose direct threats to life and property as well as threats through ancillary effects like reduced air quality. The aim of this thesis is to develop techniques to help combat the impacts of wildfires by improving wildfire modeling capabilities by usin…
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Wildland fires pose an increasingly serious problem in our society. The number and severity of these fires has been rising for many years. Wildfires pose direct threats to life and property as well as threats through ancillary effects like reduced air quality. The aim of this thesis is to develop techniques to help combat the impacts of wildfires by improving wildfire modeling capabilities by using satellite fire observations. Already much work has been done in this direction by other researchers. Our work seeks to expand the body of knowledge using mathematically sound methods to utilize information about wildfires that considers the uncertainties inherent in the satellite data.
In this thesis we explore methods for using satellite data to help initialize and steer wildfire simulations. In particular, we develop a method for constructing the history of a fire, a new technique for assimilating wildfire data, and a method for modifying the behavior of a modeled fire by inferring information about the fuels in the fire domain. These goals rely on being able to estimate the time a fire first arrived at every location in a geographic region of interest. Because detailed knowledge of real wildfires is typically unavailable, the basic procedure for developing and testing the methods in this thesis will be to first work with simulated data so that the estimates produced can be compared with known solutions. The methods thus developed are then applied to real-world scenarios. Analysis of these scenarios shows that the work with constructing the history of fires and data assimilation improves improves fire modeling capabilities. The research is significant because it gives us a better understanding of the capabilities and limitations of using satellite data to inform wildfire models and it points the way towards new avenues for modeling fire behavior.
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Submitted 1 April, 2022;
originally announced April 2022.
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Extending Continuum Models for Atom Probe Simulation
Authors:
Daniel Haley,
Paul A. J. Bagot,
Michael P. Moody
Abstract:
This work describes extensions to existing level-set algorithms developed for application within the field of Atom Probe Tomography (APT). We present a new simulation tool for the simulation of 3D tomographic volumes, using advanced level set methods. By combining narrow-band, B-Tree and particle-tracing approaches from level-set methods, we demonstrate a practical tool for simulating shape change…
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This work describes extensions to existing level-set algorithms developed for application within the field of Atom Probe Tomography (APT). We present a new simulation tool for the simulation of 3D tomographic volumes, using advanced level set methods. By combining narrow-band, B-Tree and particle-tracing approaches from level-set methods, we demonstrate a practical tool for simulating shape changes to APT samples under applied electrostatic fields, in three dimensions. This work builds upon our previous studies by allowing for non-axially symmetric solutions, with minimal loss in computational speed, whilst retaining numerical accuracy.
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Submitted 27 February, 2018;
originally announced February 2018.
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Guided mass spectrum labelling in atom probe tomography
Authors:
Daniel Haley,
Pyuck-Pa Choi,
Dierk Raabe
Abstract:
Atom probe tomography (APT) is a valuable near-atomic scale imaging technique, which yields mass spectrographic data. Experimental correctness can often pivot on the identification of peaks within a dataset, this is a manual process where subjectivity and errors can arise. The limitations of manual procedures complicate APT experiments for the operator and furthermore are a barrier to technique st…
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Atom probe tomography (APT) is a valuable near-atomic scale imaging technique, which yields mass spectrographic data. Experimental correctness can often pivot on the identification of peaks within a dataset, this is a manual process where subjectivity and errors can arise. The limitations of manual procedures complicate APT experiments for the operator and furthermore are a barrier to technique standardisation. In this work we explore the capabilities of computer-guided ranging to aid identification and analysis of mass spectra. We propose a fully robust algorithm for enumeration of the possible identities of detected peak positions, which assists labelling. Furthermore, a simple ranking scheme is developed to allow for evaluation of the likelihood of each possible identity being the likely assignment from the enumerated set. We demonstrate a simple, yet complete work-chain that allows for the conversion of mass-spectra to fully identified APT spectra, with the goal of minimising identification errors, and the inter-operator variance within APT experiments. This work chain is compared to current procedures via experimental trials with different APT operators, to determine the relative effectiveness and precision of the two approaches. It is found that there is little loss of precision (and occasionally gain) when participants are given computer assistance. We find that in either case, inter-operator precision for ranging varies between 0 and 2 "significant figures" (2σ confidence in the first n digits of the reported value) when reporting compositions. Intra-operator precision is weakly tested and found to vary between 1 and 3 significant figures, depending upon species composition levels. Finally it is suggested that inconsistencies in inter-operator peak labelling may be the largest source of scatter when reporting composition data in APT.
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Submitted 17 November, 2017;
originally announced November 2017.
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Spatial resolution in atom probe tomography
Authors:
Baptiste Gault,
Michael P. Moody,
Frederic de Geuser,
Alex La Fontaine,
Leigh T. Stephenson,
Daniel Haley,
Simon P. Ringer
Abstract:
This article addresses gaps in definitions and a lack of standard measurement techniques to assess the spatial resolution in atom probe tomography. This resolution is known to be anisotropic, being better in the depth than laterally. Generally the presence of atomic planes in the tomographic reconstruction is considered as being a sufficient proof of the quality of the spatial resolution of the in…
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This article addresses gaps in definitions and a lack of standard measurement techniques to assess the spatial resolution in atom probe tomography. This resolution is known to be anisotropic, being better in the depth than laterally. Generally the presence of atomic planes in the tomographic reconstruction is considered as being a sufficient proof of the quality of the spatial resolution of the instrument. Based on advanced spatial distribution maps, an analysis methodology that interrogates the local neighborhood of the atoms within the tomographic reconstruction, it is shown how both the in-depth and the lateral resolution can be quantified. The influences of the crystallography and the temperature are investigated, and models are proposed to explain the observed results. We demonstrate that the absolute value of resolution is specimenspecific.
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Submitted 10 October, 2015;
originally announced October 2015.