Model Development. Thermal transfer limits were calculated for summer peak load conditions without and with the SPS. The cases without the SPS (Case 1) and with the SPS (Case 2) are described in Section 3.4.
Model Development. Voltage transfer limits were calculated for summer peak load conditions without and with the SPS. The cases without the Project (Case 1) and with the Project (Case 2) are described in Section 3.4.
Model Development. This section describes the methods used to compile data for the hydrologic restoration evaluation. The goal of the hydrologic report is to conduct an evaluation on pre and post hydrologic conditions that will occur during the rehydration of the Breakfast Point mitigation area. The Breakfast Point primary stormwater management system (PSWMS) consists of connected series of natural creeks and ditched canals. Characteristic data was obtained from a new field survey, site visits, interviews, topographic and aerial maps. Survey locations and data are illustrated in Exhibits 3-1 and 3-2. For this study, the existing Breakfast Point PSWMS was represented with sixteen (16) hydrologic basins (storage) connected by thirteen (13) link (conveyance/structure) nodes. A nodal diagram is included in Exhibit 3-3. The nodes identified in the Breakfast Point PSWMS can be classified as either conveyance or storage elements. Conveyance elements include closed conduits, open channels, bridge crossings and road overflows that collect and route runoff through the system. Storage elements (basin nodes) include closed basins, natural depression areas that store and attenuate runoff within the system. Sixteen basins were delineated for this study and are identified in Exhibit 3-4 and represented with the symbols N-10 through N-160. Link structures (culverts, bridges, low areas) at basin outflows are also represented in Exhibit 3-4 and are labeled similar to L-071.
Model Development. The consultant shall utilize the FDOT approved computer-based tools are to calculate and evaluate signal timing. Since many of these tools assume the presence of under-saturated conditions, it is important to recognize their capabilities and limitations. The requirements for developing timings for saturated and under-saturated conditions should be considered as the model is developed. The consultant should consider the following elements: • Establish a “standards and conventions” document (i.e., file naming, map settings, base data parameters, analysis settings) that provides the user with consistency through the retiming process; • Review the plan development in levels or stages to ensure efficiency; • Coordinate with the respective signal maintaining agencies; and • Include quality assurance and quality control measures.
Model Development determine cell number and growth kinetics for use in efficacy model (do we need to create a new luciferized model)
Model Development. Xxxxxxxxx developed a new TransCAD-based travel demand model for the KYTC District 9 region. The model covers eight counties (Bath, Carter, Elliott, Fleming, Greenup, Xxxxx, Xxxxx and Xxxxx) in northeastern Kentucky and three counties (Xxxxx, Brown and Scioto) in southern Ohio. The District 9 Model was developed using KYTC’s standardized modeling procedure. Xxxxxxxxx developed TAZ boundaries, zonal data and roadway network for 2015 base year and 2040 future year. This time-of-day (XXX) model includes four dayparts (AM, midday, PM and night) and integrates an enhanced truck model (by single-unit truck and combination truck) in the model stream, using Quick Response Freight Manual II and origin-destination matrix estimation (ODME) techniques. The modeling process incorporated Big Data (AirSage) for trip generation, trip distribution, XXX distribution and directional pattern estimations. In particular, Xxxxxxxxx conducted a research to assess trip generation rates by purpose in terms of area types, based on AirSage OD data. The derived area type factors were embedded into the trip generation forecasting procedure to improve the model. The 2015 Base Model was validated to KYTC’s validation targets. Xxxxxxxxx enhanced the model’s reporting function to provide further detailed validation results for auto and trucks. A new separate reporting capability was also added into the model to summarize system performance, such as vehicle- miles traveled (VMT), vehicle-hours traveled (VHT), and speed by vehicle class, functional classification and county. KYTC On-Call Modeling. Since the early 2000s Xxxxxxxxx has served the Kentucky Transportation Cabinet as an on-call modeling consultant. This work has covered: • Update and enhancement of the Kentucky Statewide Traffic Model (KYSTM). • Development of a TransCAD model for Ashland, Paducah, Versailles, Owensboro/Xxxxxxxxx, District 9, Lexington, Xxxxxx County and Elizabethtown, KT. • Development of design traffic forecasts for widening I-71 in Louisville, KT. • Development of project planning traffic for KY-101 in Xxxxx’x Grove, KT. • Modeling for the SHIFT program. • Development of the Traffic Forecasting Tool (TFT). • TransModeler microsimulation for Frankfort. • Development of the TREDIS data support tool. • KYTC staff training. Xxxxxxxxx has been selected to serve in this capacity for nine 2-year terms, most recently in 2020.
Model Development. Descriptive statistics were analyzed for outliers and the final model was run with and without cases identified as outliers. Although the removal of outliers produced no changes to the interpretation, an extreme outlier based on UVS was removed (the participant reported 100 more sex acts than the next highest participant). There was minimal missing data (N=13) for the economic index variable and all cases were retained in the analysis. The descriptive statistics for the calibration sample are available by request. The measurement model provided a good fit to the data (CFI=.94 and RMSEA=.05). Paths described by the hypothesized model that demonstrated significant bivariate associations in the measurement model were retained in model testing. The structural model demonstrated a good fit, with a CFI of .94 and an RMSEA of .05. However, it was noted that the latent variable of knowledge did not demonstrate a hypothesized association at the level of p < .10 and it was dropped from the model in the interest of parsimony. The final, more parsimonious model afforded slightly better fit statistics (CFI = .95, RMSEA = .05).
Model Development. The model is based on the following principles: the fall in reservoir pressure over a period of time is directly related to the cumulative production over that period the cumulative production is equal to the integrated flow rate over time the flow rate is equal to the rate of change of reservoir pressure. Two principal sets of equations were developed. The first describes the behaviour of Xxxxx’s reservoir pressure in the Stand-Alone case: P' P P et P D D (1) Where, P ’ Stand-alone Alpha reservoir pressure (at time t). (The dash ’ denotes the Stand-Alone case and is not the differential operator) P ° Initial Alpha reservoir pressure PD Delivery pressure (at gas plant) t time μ is a constant determined by several system parameters; see Section 6 for its definition. The second describes Alpha’s reservoir pressure in the Shared Production case: P C e1t C e2t P 1 2 D (2) Where, P Shared Production Alpha reservoir pressure (at time t) C1, C2, 1 and 2 are all constants determined by the system parameters; see Section 6 for their definition. Hence, at any point in time these equations provide the reservoir pressures for the two scenarios. Two analogous equations provide the flow rate at any point in time. Alpha’s flow rate in the Stand-Alone case is given by: Q' P P et k D (3) Where, Q ’ Stand-alone Alpha flow rate (at time t) k Constant relating Alpha’s reservoir pressure to its gas volume And for the Shared Production case:
Model Development. During the past three years, we have added several new and significant capabilities to UTCHEM to make it into a general-purpose NAPL simulator. These new features are discussed below. The simulator is now capable of modeling transient and steady-state three-dimensional flow and mass transport in the groundwater (saturated) and vadose (unsaturated) zones of aquifers. The model allows for changes in fluid properties as a site is remediated; heterogeneous aquifer properties; the flow and transport of remedial fluids whose density, viscosity and temperature are variable, including surfactants, cosolvents and other enhancement agents; the dissolution and/or mobilization of NAPLs by nondilute remedial fluids; and chemical and microbiological transformations. Appropriate physical, chemical and biological process models important in describing the fate and transport of NAPLs in contaminated aquifers have been incorporated into the simulator, such as multiple organic NAPL phase, nonequilibrium interphase mass transfer, sorption, microbiological and geochemical reactions, and the temperature dependence of pertinent chemical and physical properties. The biodegradation model includes inhibition, sequential use of electron acceptors, and cometabolism and can be used to model a very general class of bioremediation processes. The model can be used to simulate the actual field operation of remediation activities such as surfactant remediation or bioremediation as well as laboratory experiments with large-scale aquifer models.
