System for determining an ensemble characteristic of a particle-laden flow

    公开(公告)号:US11536638B2

    公开(公告)日:2022-12-27

    申请号:US16442067

    申请日:2019-06-14

    Abstract: A system and method are described for rendering a characteristic for a set of particles passing through a measurement volume of a particle optical measurement system. The method includes acquiring raw particle data for the particles passing through the measurement volume. The raw particle data comprises a set of raw particle records. Each particle record comprises at least: a trajectory of at least one particle, and a second primary mark of the at least one particle whose value influences an effective sampling area corresponding to the measurement volume. The method includes generating and storing an effective sampling area based upon: the trajectory of the at least one particle, and the second primary mark. Thereafter, an ensemble characteristic is rendered for the set of particles by performing an operation on the sampling area-corrected set of particle records.

    MACHINE LEARNING-BASED PARTICLE-LADEN FLOW FIELD CHARACTERIZATION

    公开(公告)号:US20210148802A1

    公开(公告)日:2021-05-20

    申请号:US16950011

    申请日:2020-11-17

    Abstract: A particle measurement system and method of operation thereof are described. The system and method render a characteristic for a set of particles measured while passing through a measurement volume. The system includes a source that generates a particle-laden field containing the set of particles. The system further includes a sensor that generates a raw particle data corresponding to the set particles passing through the measurement volume of the particle measurement system, where the raw particle data comprises a set of raw particle records and each of one of the raw particle records includes a particle data content. A preconditioning stage carries out a preconditioning operation on the particle data content of the set of raw particle records to render a conditioned input data. A machine learning stage processes the conditioned input data to render an output characteristic parameter value for the set of particles.

    Machine learning-based particle-laden flow field characterization

    公开(公告)号:US11709121B2

    公开(公告)日:2023-07-25

    申请号:US16950011

    申请日:2020-11-17

    CPC classification number: G01N15/0211 G01N15/0227 G06N3/04 G06N3/08 G06V10/70

    Abstract: A particle measurement system and method of operation thereof are described. The system and method render a characteristic for a set of particles measured while passing through a measurement volume. The system includes a source that generates a particle-laden field containing the set of particles. The system further includes a sensor that generates a raw particle data corresponding to the set particles passing through the measurement volume of the particle measurement system, where the raw particle data comprises a set of raw particle records and each of one of the raw particle records includes a particle data content. A preconditioning stage carries out a preconditioning operation on the particle data content of the set of raw particle records to render a conditioned input data. A machine learning stage processes the conditioned input data to render an output characteristic parameter value for the set of particles.

    SYSTEM FOR DETERMINING AN ENSEMBLE CHARACTERISTIC OF A PARTICLE-LADEN FLOW

    公开(公告)号:US20190383716A1

    公开(公告)日:2019-12-19

    申请号:US16442067

    申请日:2019-06-14

    Abstract: A system and method are described for rendering a characteristic for a set of particles passing through a measurement volume of a particle optical measurement system. The method includes acquiring raw particle data for the particles passing through the measurement volume. The raw particle data comprises a set of raw particle records. Each particle record comprises at least: a trajectory of at least one particle, and a second primary mark of the at least one particle whose value influences an effective sampling area corresponding to the measurement volume. The method includes generating and storing an effective sampling area based upon: the trajectory of the at least one particle, and the second primary mark. Thereafter, an ensemble characteristic is rendered for the set of particles by performing an operation on the sampling area-corrected set of particle records.

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