Merge remote-tracking branch 'origin/develop' into crankcaseHeater

.gitignore

@@ -89,3 +89,4 @@ dist
 # if you generate sphinx docs, it builds a dummy version of the schema in the idd folder, just ignore it
 /idd/Energy+.schema.epJSON
 /idd/Energy+.schema.epJSON.in
+design/FY2023/earth_tube_solution_space_diagram.pdf

doc/engineering-reference/src/alternative-modeling-processes/airflownetwork-model.tex

@@ -1462,7 +1462,7 @@ \subsection{Duct Sizing}\label{duct-sizingl}
 
 There are two methods used to size duct diameter and cross section area by assuming round ducts. Each method is presented below.
 
-1. Maxiumum velocity
+1. Maximum velocity
 
 The cross section area (A) is calculated below
 
@@ -1520,7 +1520,7 @@ \subsection{Duct Sizing}\label{duct-sizingl}
 
 When entry and exit velocities and elevations are the same, the total pressure difference is equal to the static pressure difference. The assumption will be used for duct sizing. 
 
-The total pressure loss in either a truck or a branch can be calculated using Darcy-Weisbach Equation (Eq. 34 in Chpater 21, 2017 ASHRAE HOF)
+The total pressure loss in either a truck or a branch can be calculated using Darcy-Weisbach Equation (Eq. 34 in Chapter 21, 2017 ASHRAE HOF)
 
 \begin{equation}
 \Delta P =  \big( \frac{ f L}{D} +  \Sigma C ) *  \frac{ \rho  V^{2} }{2}  
@@ -1569,7 +1569,7 @@ \subsection{Duct Sizing}\label{duct-sizingl}
 A = \frac{D^2 * \pi}{4}
 \end{equation}
 
-When ΔP given, and the relationship between D and V is also given, there is only a single unknow D. The value can be obtained through iteration. 
+When ΔP given, and the relationship between D and V is also given, there is only a single unknown D. The value can be obtained through iteration. 
 
 \subsection{References}\label{afn-references}
 

doc/engineering-reference/src/alternative-modeling-processes/hybrid-model.tex

@@ -2,11 +2,11 @@ \section{Hybrid Model}\label{hybrid-model}
 
 \subsection{Overview}
 
-The hybrid modeling integrates physics-based and data-driven modeling methods which combines forward and inverse physics-based modeling taking advantage of easily measurable zone air temperature, humidity ratio, or CO$_2$ concentration data to solve hard-to-measure zone parameters including internal thermal mass, air infiltration rate, or zone people count. It aims to enhance the current energy retrofit practices not only offering more user-friendly energy modeling environments but also providing more accurate estimates of energy savings at the same time. Parameters such as interior thermal mass, air infiltration rates, and people count are required in physics-based models, and they have significant impacts as they are driving factors for the dynamic performance of buildings. An accurate estimate of interior thermal mass has been a difficult problem because the building usually has various amounts of furniture and changeable partitions. The air infiltration rate changes in time and dynamically interacts with indoor and outdoor climatic conditions. However the accurate estimation of the data is almost impossible to collect without a fan pressurized test, which can not be easily done by typical energy modelers (Gowri et al. 2009). The high uncertainty of occupants’ presence and behavior have significant impacts on building energy modeling (Clevenger \& Haymaker, 2001). However, people count is usually hard to measure in reality, which result in simplification of occupancy schedule assumptions in energy modeling. The hybrid model introduces an approach estimate the zone level interior thermal masss, air infiltration rate, and people count with measured zone air parameters in EnergyPlus.
+The hybrid modeling integrates physics-based and data-driven modeling methods which combines forward and inverse physics-based modeling taking advantage of easily measurable zone air temperature, humidity ratio, or CO$_2$ concentration data to solve hard-to-measure zone parameters including internal thermal mass, air infiltration rate, or zone people count. It aims to enhance the current energy retrofit practices not only offering more user-friendly energy modeling environments but also providing more accurate estimates of energy savings at the same time. Parameters such as interior thermal mass, air infiltration rates, and people count are required in physics-based models, and they have significant impacts as they are driving factors for the dynamic performance of buildings. An accurate estimate of interior thermal mass has been a difficult problem because the building usually has various amounts of furniture and changeable partitions. The air infiltration rate changes in time and dynamically interacts with indoor and outdoor climatic conditions. However the accurate estimation of the data is almost impossible to collect without a fan pressurized test, which can not be easily done by typical energy modelers (Gowri et al. 2009). The high uncertainty of occupants’ presence and behavior have significant impacts on building energy modeling (Clevenger \& Haymaker, 2001). However, people count is usually hard to measure in reality, which result in simplification of occupancy schedule assumptions in energy modeling. The hybrid model introduces an approach estimate the zone level interior thermal mass, air infiltration rate, and people count with measured zone air parameters in EnergyPlus.
 
 Solving building energy and environmental problems inversely using measured data gets more attention as more data are easily and freely available. (Yinping Zhang et al. 2015). Measurements are to supplement to reduce discrepancies or to identify model parameters, nevertheless the majority of efforts go into the derivation of the dynamic inverse modeling. Inverse modeling is a discipline that applies mathematical techniques to combine measurements and models. Inverse modeling can provide solutions when direct measurements of model parameters are not widely available, rendering the use of numerical techniques. Temperature, humidity, and CO$_2$ concentration data are easily available nowadays and are used for controls of indoor environments due to a wider use of low-cost thermostats with data loggers, which bring opportunities to inversely solve other hard-to-measure parameters.
 
-The new hybrid modeling approach uses the inverse modeling method to improve the accuracy of the building energy simulation for existing buildings, which adds measured data to solve uncertain model parameters. The hybrid modeling approach builds upon the virtue of the physics-based model taking advantage of measured data. The approach uses measured zone air temperature, humidity ratio, or CO$_2$ concentration to solve highly uncertain parameters such as internal thermal mass, infiltration airflow rate, and people count with the reformulated zone heat, moisture, or CO$_2$ balance equations. Figure~\ref{fig:hybrid-model-solution-diagram} shows the relationship among the measurements and unkonwn parameters. 
+The new hybrid modeling approach uses the inverse modeling method to improve the accuracy of the building energy simulation for existing buildings, which adds measured data to solve uncertain model parameters. The hybrid modeling approach builds upon the virtue of the physics-based model taking advantage of measured data. The approach uses measured zone air temperature, humidity ratio, or CO$_2$ concentration to solve highly uncertain parameters such as internal thermal mass, infiltration airflow rate, and people count with the reformulated zone heat, moisture, or CO$_2$ balance equations. Figure~\ref{fig:hybrid-model-solution-diagram} shows the relationship among the measurements and unknown parameters. 
 
 \begin{figure}[h]
 \begin{center}

doc/engineering-reference/src/alternative-modeling-processes/room-air-models.tex

@@ -72,7 +72,7 @@ \section{RoomAir Models}\label{roomair-models}
 
 \subsection{User Defined RoomAir Temperatures}\label{user-defined-roomair-temperatures}
 
-The input object RoomAir:TemperaturePattern:UserDefined provides a capabity for users to define the sort of air temperature pattern he or she expects in the zone. With these models, the pattern is generally set beforehand and does not respond to conditions that evolve during the simulation.~ (Exception: the pattern available through the RoomAir:TemperaturePattern:TwoGradient object will switch between two different pre-defined vertical gradients depending on the current value of certain temperatures or thermal loads. )
+The input object RoomAir:TemperaturePattern:UserDefined provides a capability for users to define the sort of air temperature pattern he or she expects in the zone. With these models, the pattern is generally set beforehand and does not respond to conditions that evolve during the simulation.~ (Exception: the pattern available through the RoomAir:TemperaturePattern:TwoGradient object will switch between two different pre-defined vertical gradients depending on the current value of certain temperatures or thermal loads. )
 
 The user-defined patterns obtain the mean air temperature, \({T_{MAT}}\), from the heat balance domain and then produce modified values for: