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Asymmetric flow velocity measurement using multipath ultrasonic flowmeter with adaptive weighting method guided by tomography
Amri K.a, Suprijantoa, Kurniadi D.a, Juliastuti E.a
a Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 Penerbit UTM Press. All rights reserved.A conventional transit time ultrasonic flowmeter (USM) has a high accuracy for symmetric flow profiles but inaccurate for asymmetric flow profiles. Flow profile shapes can also change over time and difficult to predict. USM with tomographic configuration (USM-Tomo) can adapt to the flow profile changes but result in low temporal resolution. Meanwhile, USM with an adaptive weighting method can measure asymmetric flow velocity but limited to specific asymmetric flow profiles. An alternative scheme to determine adaptive weighting in various asymmetric flow profiles, we proposed a hybrid USM-Tomo. This scheme proposes programmable acoustic path configuration that could set the path mode between USM and tomography. Reducing computation of time of flight in each acoustic can be done by applying the dual-transducers technique. An adaptive weighting of hybrid USM-Tomo is calculated based on the mapping function between the set of velocity on 6 parallel paths of USM and average flow velocity from USM-Tomo. The mapping function is determined using machine learning, i.e., Artificial Neural Network (ANN) and Support Vector Regression (SVR). In the measurement phase, the average flow velocity is determined using the mapping function with input 6 parallel acoustic paths. Based on various types of asymmetric flow profiles used in the experiment, the 6 parallel acoustic paths of USM could produce average flow velocity with error below 1% compared to USM-Tomo. Therefore, the proposed hybrid USM-Tomo scheme has potential to be an alternative scheme for flow meter in industrial application.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Artificial Neural Network,Dual-transducers,Hybrid USM-Tomo,Support Vector Regression,Tomography,Transit time method,Ultrasonic flowmeter[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.11113/jt.v82.13785[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]