Uncertainty in counting ice nucleating particles with continuous flow diffusion chambers

Abstract. This study investigates the measurement of ice nucleating particle (INP) concentrations and sizing of crystals using continuous flow diffusion chambers (CFDCs). CFDCs have been deployed for decades to measure the formation of INPs under controlled humidity and temperature conditions in laboratory studies and by ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in models by relating the formation of ice crystals to state variables such as temperature and humidity as well as aerosol particle properties such as composition and number. We show here that assumptions of ideal instrument behavior are not supported by measurements made with a commercially available CFDC, the SPectrometer for Ice Nucleation (SPIN), and the instrument on which it is based, the Zurich Ice Nucleation Chamber (ZINC). Non-ideal instrument behavior, which is likely inherent to varying degrees in all CFDCs, is caused by exposure of particles to different humidities and/or temperatures than predicated from instrument theory of operation. This can result in a systematic, and variable, underestimation of reported INP concentrations. We find here variable correction factors from 1.5 to 9.5, consistent with previous literature values. We use a machine learning approach to show that non-ideality is most likely due to small-scale flow features where the aerosols are combined with sheath flows. Machine learning is also used to minimize the uncertainty in measured INP concentrations. We suggest that detailed measurement, on an instrument-by-instrument basis, be performed to characterize this uncertainty.

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Uncertainty in counting ice nucleating particles with continuous diffusion flow chambers

10.5194/acp-2016-1180 ◽

2017 ◽

Cited By ~ 2

Author(s):

Sarvesh Garimella ◽

Daniel A. Rothenberg ◽

Martin J. Wolf ◽

Robert O. David ◽

Zamin A. Kanji ◽

...

Keyword(s):

Ice Nucleation ◽

Small Scale ◽

Learning Approach ◽

State Variables ◽

Laboratory Studies ◽

Diffusion Flow ◽

Detailed Measurement ◽

Particle Properties ◽

Machine Learning Approach ◽

Flow Features

Abstract. This study investigates the measurement of ice nucleating particle (INP) concentration and sizing of crystals using continuous flow diffusion chambers (CFDCs). CFDCs have been deployed for decades to measure the formation of INPs under controlled humidity and temperature conditions in laboratory studies and by ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in models by relating the formation of ice crystals to state variables such as temperature, humidity, and aerosol particle properties such as composition and number. We show here that assumptions of ideal instrument behavior are not supported by measurements made with a commercially available CFDC, the SPectrometer for Ice Nucleation (SPIN), and the instrument on which it is based, the Zurich Ice Nucleation Chamber (ZINC). Non-ideal instrument behavior, which is likely inherent to varying degrees in all CFDCs, is caused by exposure of particles to different humidities and/or temperatures than predicated from theory. This can result in a systematic, and variable, underestimation of reported INP concentrations. We use a machine learning approach to show that non-ideality is most likely due to small scale flow features where the aerosols are combined with sheath flows and to minimize the uncertainty associated with measured INP concentrations. We suggest that detailed measurement, on an instrument-by-instrument basis, be performed to characterize this uncertainty.

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Inside the Black Box: A Physical Basis for the Effectiveness of Deep Generative Models of Amorphous Materials

10.26434/chemrxiv.14696595 ◽

2021 ◽

Author(s):

Michael Kilgour ◽

Lena Simine

Keyword(s):

Machine Learning ◽

Amorphous Materials ◽

Physical Simulation ◽

Generative Models ◽

Black Box ◽

Small Scale ◽

Learning Approach ◽

Computational Costs ◽

Training Samples ◽

Machine Learning Approach

<p>We have recently demonstrated an effective protocol for the simulation of amorphous molecular configurations using the PixelCNN generative model (J. Phys. Chem. Lett. 2020, 11, 20, 8532). The morphological sampling of amorphous materials via such an autoregressive generation protocol sidesteps the high computational costs associated with simulating amorphous materials at scale, enabling practically unlimited structural sampling based on only small-scale experimental or computational training samples. An important question raised but not rigorously addressed in that report was whether this machine learning approach could be considered a physical simulation in the conventional sense. Here we answer this question by detailing the inner workings of the underlying algorithm that we refer to as the Morphological Autoregression Protocol or MAP. <br></p>

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A Machine Learning Approach to Improve Turbulence Modelling from DNS Data Using Neural Networks

International Journal of Turbomachinery Propulsion and Power ◽

10.3390/ijtpp6020017 ◽

2021 ◽

Vol 6 (2) ◽

pp. 17

Author(s):

Yuri Frey Marioni ◽

Enrique Alvarez de Toledo Ortiz ◽

Andrea Cassinelli ◽

Francesco Montomoli ◽

Paolo Adami ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Model Development ◽

Turbulent Channel Flow ◽

Boussinesq Approximation ◽

Machine Learning Algorithms ◽

Navier Stokes ◽

Cooling Flow ◽

Machine Learning Approach ◽

Flow Features

In this paper, we investigate the feasibility of using DNS data and machine learning algorithms to assist RANS turbulence model development. High-fidelity DNS data are generated with the incompressible Navier–Stokes solver implemented in the spectral/hp element software framework Nektar++. Two test cases are considered: a turbulent channel flow and a stationary serpentine passage, representative of internal turbo-machinery cooling flow. The Python framework TensorFlow is chosen to train neural networks in order to address the known limitations of the Boussinesq approximation and a clustering based on flow features is run upfront to enable training on selected areas. The resulting models are implemented in the Rolls-Royce solver HYDRA and a posteriori predictions of velocity field and wall shear stress are compared to baseline RANS. The paper presents the fundamental elements of procedure applied, including a brief description of the tools and methods and improvements achieved.

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Localization of Thermal Wellbore Defects Using Machine Learning

Journal of Energy Resources Technology ◽

10.1115/1.4053516 ◽

2022 ◽

pp. 1-13

Author(s):

Kathryn Bruss ◽

Raymond Kim ◽

Taylor A. Myers ◽

Jiann-cherng Su ◽

Anirban Mazumdar

Keyword(s):

Machine Learning ◽

Experimental Data ◽

Mean Shift ◽

Small Scale ◽

Localization Algorithm ◽

Test Bed ◽

Mean Shift Clustering ◽

Machine Learning Approach ◽

Detection And Localization ◽

Oil Gas

Abstract Defect detection and localization are key to preventing environmentally damaging wellbore leakages in both geothermal and oil/gas applications. In this work, a multi-step, machine learning approach is used to localize two types of thermal defects within a wellbore model. This approach includes a COMSOL heat transfer simulation to generate base data, a neural network to classify defect orientations, and a localization algorithm to synthesize sensor estimations into a predicted location. A small-scale physical wellbore test bed was created to verify the approach using experimental data. The classification and localization results were quantified using this experimental data. The classification predicted all experimental defect orientations correctly. The localization algorithm predicted the defect location with an average root mean square error of 1.49 in. The core contributions of this work are 1) the overall localization architecture, 2) the use of centroid-guided mean-shift clustering for localization, and 3) the experimental validation and quantification of performance.

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