An Analytic Model of Stratification for Liquid-Based Solar Systems

1986 ◽  
Vol 108 (2) ◽  
pp. 105-110 ◽  
Author(s):  
K. DenBraven
Keyword(s):  
Temperature Distribution ◽  
Storage Temperature ◽  
Direct Calculation ◽  
Hot Water ◽  
Forced Flow ◽  
State University ◽  
Colorado State University ◽  
Liquid Storage ◽  
System Data ◽  
Solar House

Accurate modelling of solar air or liquid heating, cooling, or domestic hot water systems with storage generally requires an accounting of the stratification within such storage. Overall system performance may be significantly affected by the storage temperature distribution. Most current stratification models utilize a finite difference scheme for solution to the general equations. An analytic method to determine the temperature distribution has been derived for liquid storage within a solar system. In liquid storage, it is assumed incoming fluid enters at the location with the temperature closest to its own. Hence, the solution requires the possibility of a region within storage where there is no forced flow. In addition, ther may be collector loop flow, load loop flow, or both concurrently. Each of these cases has different boundary conditions, and each must be solved separately. Comparisons of the resulting calculations with system data for the Colorado State University Solar House I show good agreement. This suggests that inclusion of an analytic stratification model within a system simulation may be useful by allowing direct calculation of temperatures in stratified storage.

Optimal Control of Mass Flow Rates in Flat Plate Solar Collectors

1981 ◽  
Vol 103 (2) ◽  
pp. 113-120 ◽  
Author(s):  
R. C. Winn ◽  
C. B. Winn
Keyword(s):  
Optimal Control ◽  
Flat Plate ◽  
Flow Rate ◽  
State University ◽  
Colorado State University ◽  
Collector Efficiency ◽  
The Difference ◽  
Flat Plate Collector ◽  
Solar House ◽  
Bang Bang Control

The optimal flat plate collector fluid flow rate is determined for several combinations of objective functions and system models. The method of implementing the control strategy for one of the problems considered, that which maximizes the integral of the difference between the collected solar power and fluid moving power, is described. The performance of the solar energy collection system in Solar House II at Colorado State University using this optimal controller is discussed and compared with the same system using bang-bang control. In addition, the dependence of the collector efficiency factor on flow rate is considered and its effect on the optimal control is determined.

A MODEL FOR DEVELOPING HIGH-QUALITY ONLINE COURSES: INTEGRATING A SYSTEMS APPROACH WITH LEARNING THEORY

2008 ◽  
Vol 12 (3) ◽  
Author(s):  
Maria Jean Puzziferro ◽  
Kaye Shelton
Keyword(s):  
Online Education ◽  
Design Theory ◽  
Systems Approach ◽  
Online Courses ◽  
Process Models ◽  
State University ◽  
High Quality ◽  
Colorado State University ◽  
Online Instructional Design ◽  
Online Course Development

As the demand for online education continues to increase, institutions are faced with developing process models for efficient, high-quality online course development. This paper describes a systems, team-based, approach that centers on an online instructional design theory (Active Mastery Learning) implemented at Colorado State University-Global Campus.

scholarly journals Preface: Modern Heterocycle Synthesis and Functionalization

Synlett ◽  
2021 ◽  
Vol 32 (02) ◽  
pp. 140-141
Author(s):  
Louis-Charles Campeau ◽  
Tomislav Rovis
Keyword(s):  
Active Pharmaceutical Ingredient ◽  
Associate Professor ◽  
Columbia University ◽  
State University ◽  
Heterocycle Synthesis ◽  
Colorado State University ◽  
University Of Toronto ◽  
Process Chemistry ◽  
The University ◽  
Small Molecule Therapeutics

obtained his PhD degree in 2008 with the late Professor Keith Fagnou at the University of Ottawa in Canada as an NSERC Doctoral Fellow. He then joined Merck Research Laboratories at Merck-Frosst in Montreal in 2007, making key contributions to the discovery of Doravirine (MK-1439) for which he received a Merck Special Achievement Award. In 2010, he moved from Quebec to New Jersey, where he has served in roles of increasing responsibility with Merck ever since. L.-C. is currently Executive Director and the Head of Process Chemistry and Discovery Process Chemistry organizations, leading a team of smart creative scientists developing innovative chemistry solutions in support of all discovery, pre-clinical and clinical active pharmaceutical ingredient deliveries for the entire Merck portfolio for small-molecule therapeutics. Over his tenure at Merck, L.-C. and his team have made important contributions to >40 clinical candidates and 4 commercial products to date. Tom Rovis was born in Zagreb in former Yugoslavia but was largely raised in southern Ontario, Canada. He earned his PhD degree at the University of Toronto (Canada) in 1998 under the direction of Professor Mark Lautens. From 1998–2000, he was an NSERC Postdoctoral Fellow at Harvard University (USA) with Professor David A. Evans. In 2000, he began his independent career at Colorado State University and was promoted in 2005 to Associate Professor and in 2008 to Professor. His group’s accomplishments have been recognized by a number of awards including an Arthur C. Cope Scholar, an NSF CAREER Award, a Fellow of the American Association for the Advancement of Science and a ­Katritzky Young Investigator in Heterocyclic Chemistry. In 2016, he moved to Columbia University where he is currently the Samuel Latham Mitchill Professor of Chemistry.

scholarly journals 260 Genetic prediction for first-service conception rate in Angus heifers using a random regression model

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 197-198
Author(s):  
Miguel A Sánchez-Castro ◽  
Milt Thomas ◽  
Mark Enns ◽  
Scott Speidel
Keyword(s):  
Conception Rate ◽  
Random Regression ◽  
State University ◽  
Base Function ◽  
Genetic Prediction ◽  
Colorado State University ◽  
Random Regression Model ◽  
Service Conception ◽  
Available Information ◽  
Angus Heifers

Abstract First-service conception rate (FSCR) can be defined as the probability of a heifer conceiving in response to her first artificial insemination (AI). Given the binary nature of its phenotypes, FSCR has been typically evaluated using animal threshold models (ATM). However, susceptibility of these models to the extreme-category problem (ECP) limits their ability to use all available information to calculate Expected Progeny Differences (EPD). Random regression models (RRM) represent an alternative method to evaluate binary traits, and they are not affected by ECP. Nevertheless, RRM were originally developed to analyze longitudinal traits, so their usefulness to evaluate traits with singly observed phenotypes remains unclear. Therefore, objectives herein were to evaluate the feasibility of a RRM genetic prediction for heifer FSCR by comparing its resulting EPD and genetic parameters to those obtained with a traditional ATM. Breeding and ultrasound records of 4,334 Angus heifers (progeny of 354 sires and 1,626 dams) collected between 1992 to 2019 at the Colorado State University Beef Improvement Center were utilized. Observations for FSCR (1, successful; 0, unsuccessful) were defined by fetal age at pregnancy inspections performed approximately 130 d post-AI. Traditional FSCR evaluation was performed using a univariate BLUP threshold animal model, whereas an alternative evaluation was performed by regressing FSCR on age at AI using a linear RRM with Legendre Polynomials as the base function. Heritability estimates were 0.03 ± 0.02 for the ATM and 0.005 ± 0.001 for the average age at AI with the RRM, respectively. Pearson and rank correlations between EPD obtained with each method were 0.63 and 0.60, respectively. The regression coefficient of RRM predictions on those obtained with the ATM was 0.095. In conclusion, these results suggested that although a RRM genetic prediction for FSCR was feasible, a considerable degree of re-ranking occurred between the two methodologies.

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