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『pd meters|orifice meter』Related information(clamp on meter|electromagnetic meter|venturi meterrotameter|orifice meter|ultrasonic flow meter|mass flow meter|coriolis mass flow meter|coriolis flow meter|magnetic flow meter|magmeter flow meter|magflow flow meter|mag meter flow meter|electromagnetic flow meter|vortex flow meter|turbine flow meter|thermal mass flow meter|thermal flow meter|rotameter flow meter)

1. May I ask how to translate "vortex flowmeter" in English?

Vortex flowmeter: Vortex Flowmeter. Translation of other flowmeters for reference: Electromagnetic flowmeter: Electromagnetic flowmeter. Coriolis mass flowmeter. Open channel flowmeter: Orifice Plate flowmeter. Rotary flowmeter: Rotameter. Turbine flowmeter: Turbine flowmeter. Thermal flowmeter: Calorimetric flowmeter. Volume flowmeter: Positive Displacement (PD) flowmeter. Ultrasonic flowmeter: Ultrasonic flowmeter. Venturi flowmeter: Venturi flowmeter. Thermal mass flowmeter: Thermal Mass flowmeter. Multiphase Flowmeter: Flumes and Weirs Flowmeter

2. Supply Chain Multi Site Selection Supreme Edition - with DC Capacity Constrained Rapid Solution (Source Code)

Supreme Version of Supply Chain Multiple Access Site Selection - Rapid Solution with DC Capacity Constraint (Source Code) The introduction shared the problem of supply chain multiple access site selection with DC capacity constraint conditions. A friend tried using genetic algorithm to solve it, but believed that the timeliness was not high. Some friends also mentioned that the site selection result is in a mountain ditch, hoping to be closer to the city. This article provides a high-speed solution version, which uses a method with constraint k-means to solve the problem, and then selects the city. It must be noted that directly selecting DC within the city is another issue (discrete site selection), which is not within the scope of this article. This is the curved national salvation route: first select sites consecutively, and then select sites nearby. Project review: For multi site selection, simulate the allocation of 1000 customers to 8 DCs and add requirements: the number of

pd meters|orifice meter
customers for some DC services cannot be lower than a certain value (such as 40), otherwise it is not worth setting up a DC. Specify alternative cities, here as a simple demo demonstration, choose provincial capitals as alternatives. Main_cities=all_cities-d f [all_cities-d f. sort-values (by="administrative code") ["administrative code"]. type ("str"). str. ind ("0100")>0] main_cities-columns=["id", "name", "lon", "lat"]. Main_cities starts modeling and solves using k_means_straight, defining 8 clusters with a minimum capacity of 50 and a maximum capacity of 200 for each cluster. Record the time consumption and display the results. from k_means_constrained import KMeansConstrainedfrom datetime import datetimenow = datetime.now()clf = KMeansConstrained(n_clusters=8,size_min=50,size_max=200, random_state=0)X = customer_df[["lat","lon"]].valuescustomer_df["dc"]= clf.fit_predict(X)cons = datetime.now()-nowprint(f"time usage is {cons} s")customer_df["dc"].value_counts() The solution took only 1.7 seconds to complete and met the preset conditions. Choose a nearby major city to calculate the distance between 8 DCs and each provincial capital, and select the provincial capital with the smallest distance as the new DC location. def geo_distance(p1: pd.Series,p2:list): dis = haversine_distances([[radians(_) for _ in p1.values], [radians(_) for _ in p2]])[0][1]* 6371000/1000 # multiply by Earth radius to get kilometers return disfor i, dc_pos in enumerate(main_cities[["name","lat","lon"]].values): dc_scipy_df[tuple(dc_pos)]=dc_scipy_df[["lat","lon"]].apply(geo_distance,p2=dc_pos[1:],axis=1)dc_scipy_df["dis_min"] = dc_scipy_df[[tuple(dc_pos) for dc_pos in main_cities[["name","lat","lon"]].values]].min(axis=1)dc_scipy_df["

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