2/3/2024 0 Comments Principal data feed instacalThese studies were used to quantify the inhibitive effect of noncondensables on steam condensation heat transfer. The UCB facility simulated expected containment accident conditions with pressures that ranged from 1 to 4 atm. One of the key objectives of the UCB program was to observe scaled steady-state operation to simulate energy removal for proposed PCCS designs. A series of tests for a scaled PCCS facility have been carried out at the UCB. Several relevant experimental or theoretical investigations have been conducted in the past few years to provide improved heat transfer correlations for steam in the presence of noncondensable gases. To make reliable design decisions about PCCS operation, basic questions must be answered as to how steam mixed with a variety of noncondensable gases will transfer energy to its surroundings. The more » PCCS heat exchangers remove core decay power by free convection and transfer this energy to an external pool of water located above containment. The SBWR is an advanced design that relies on a passive containment cooling system (PCCS) to remove thermal loads from the dry well. This paper presents work that has been used to support the US Nuclear Regulatory Commission's evaluation of General Electric's simplified boiling water reactor (SBWR). This preliminary assessment was made using data from the University of California, Berkeley (UCB), natural circulation loop test facility. « lessĪs part of a research effort to better understand passive heat removal dynamics, a series of numerical steady-state simulations in the presence of noncondensable gases was performed to evaluate RELAP5/MOD3 against test data. The cell length term in the condensation heat transfer correlation implemented in the code must be removed to allow for accurate calculations with smaller cell sizes. Hence, the UCB correlation predicts condensation heat transfer in the presence of noncondensable gases with only a coarse mesh. The three-node model has a large cell in the entrance region which smeared out the entrance effects on the heat transfer, which tend to overpredict the condensation. Homals, plot.= 5% of the data with a three-node model. Multivariate Analysis with Optimal Scaling. New York: Wiley.ĭe Leeuw, J., Mair, P., Groenen, P. The existing plugins are: Eigenspine This plugin computes principal components of the (angles between adjacent segments of the) spine. Optimally scaled data matrix (first dimension) For more advanced transformations such as polynomial or more flexible splines, the knots and ordinal arguments need to be specified instead of levels.Įigenvalues of induced correlation matrix If all scale level transformations are the same, ordinal can be a single value. The corresponding spline transformations (unrestricted, monotone, and linear) are then created internally. If the user only needs simple scale levels like nominal, ordinal, and metric, a corresponding vector can be specified in the levels argument without setting knots and ordinal. The measurement (or scale) levels of the variables are incorporated via spline transformations. If TRUE, object scores are z-scores, if FALSE, they are restricted to SS of 1. Which variables should be active or inactive (also as vector of length m) How missing values should be handled: multiple ( "m"), single ( "s"), or average ( "a") If copies is a scalar the function creates a copies vector internally with a value of 2 for each nominal variable. Number of copies for each variables (also as vector of length m). If different degrees should be used across variables, a vector of length m can be specified. How ties should be handled: primary ( "p"), secondary ( "s"), or tertiary ( "t") If knots is set, this overrides level (see details) Scale levels can be specified manually using splines (see knotsGifi). If knots are specified manually, a boolean vector of length m denotes which variables should be ordinally restricted or not (see details) Input data frame: n observations, m variablesĪ vector of length m denoting basic scale levels ( "nominal", "ordinal", "metric" see details Usage princals(data, ndim = 2, levels = "ordinal", ordinal, knots, ties = "s",ĭegrees = 1, copies = 1, missing = "s", normobj.z = TRUE, active = TRUE, Through a proper spline specification various continuous transformation functions can be specified: linear, polynomials, and (monotone) splines. The default is to take each input variable as ordinal but it works for mixed scale levels (incl. Title: Views: Last Modified: Using a USB-TEMP-AI module and DAQami to measure temperature. Home » Knowledgebase » Out of the Box Software » InstaCal. Categorical principal component analysis (PRINCALS).įits a categorical PCA. Measurement Computing Data Acquisition Knowledgebase.
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