Search

In this paper, a modeling approach for a heat exchanger that uses a binary mixture is presented. A moving boundary method assuming a linear spatial distribution of enthalpy could result in a considerable steady state prediction error, due to the temperature glide of a binary mixture. Motivated from this, a more accurate moving boundary method that incorporates analytic enthalpy distributions is presented. The enthalpy profiles are derived by defining a specific heat capacity at each thermodynamic phase of a binary mixture and by solving crossflow heat transfer equations. The proposed approach results in accurate predictions compared with those of a detailed model using the Finite Volume Method (FVM). The corresponding computation time is two to three times slower than that of a conventional moving boundary approach, but the approach is still computationally advantageous compared with the FVM model. [ABSTRACT FROM AUTHOR]

To achieve the U.S. Department of Energys brake thermal efficiency (BTE) goal for Heavy Duty Diesel Engine (HDDE) technologies, Waste Heat Recovery (WHR) by means of Organic Rankine Cycle (ORC) systems has been selected as a suitable solution. The current relatively high return on investment period of such technology needs to be improved by significant cost reductions to realize benefits on WHR for mobile applications. The performance of the ORC system under dynamic loads relies on the choice of the working fluid, the efficiency of its components (mainly expander) as well as the control strategy that optimizes the operation. A novel ORC architecture is proposed that uses the engine coolant as the working fluid. In particular, a fraction of the engine coolant, which is a mixture of water and ethylene glycol, is employed as working fluid through the ORC to recover waste heat from EGR (Exhaust Gas Recirculation) and part of the tail pipe exhaust gases. At the inlet of the expander, the mixture has mixed-phase conditions and a fixed volume ratio expander is employed to generate power output that can be fed directly to the engine crankshaft. Heat rejection is accomplished through the spare capacity of the engine radiator, which avoids the need for a separate condenser. To evaluate the feasibility of such system architecture, a thermodynamic steady-state cycle model has been developed to predict the potential increase of BTE under different engine loads as well as to understand the ORC performance. Parametric studies are carried out by varying the system pressure ratio, the internal volume ratio of the expander and the mixture quality at the expander inlet. [ABSTRACT FROM AUTHOR]

Positive displacement expanders are proven to be cost-effective in the low to medium power range for Organic Rankine Cycle (ORC) systems. Among the different types of volumetric expanders, the screw-type presents a favorable combination of relatively high internal volume ratio (up to 7) and isentropic efficiency (up to 80%) with respect to the optimal pressure ratio at which sub-critical ORCs operate. In particular, single-screw expanders have shown some potential due to their symmetric and balanced configuration that decreases the loads on the bearings. A comprehensive characterization of this type of machine with two working fluids, i.e., SES36 and R245fa, has been carried out in a previous work [1]. Based on the experimental work, friction losses and internal leakages were found detrimental to the expander performance. As the expander requires lubrication during operation, flooded expansion can be beneficial to reduce such losses as well as to improve the expansion process toward a quasi-isothermal behavior. A thermodynamic cycle model has been developed to evaluate the potential improvements on the thermodynamic performance of organic Rankine cycle with flooded expansion and internal regeneration. A semi-empirical model of the expander is included which accounts for the effects of internal volume ratio. The results from the cycle model have used to design an ORC test setup with an independent lubricant oil loop and internal regeneration. The new test rig will be used validate the trends obtained with the cycle model and to further characterize the single-screw expander. The working fluid employed is R1233zd(E) as a replacement for R245fa. [ABSTRACT FROM AUTHOR]

Positive displacement expanders are proven to be cost-effective in the low to medium power range for Organic Rankine Cycle (ORC) systems. Among the different types of volumetric expanders, the screw-type presents a favorable combination of relatively high internal volume ratio (up to 7) and isentropic efficiency (up to 80%) with respect to the optimal pressure ratio at which sub-critical ORCs operate. In particular, single-screw expanders have shown some potential due to their symmetric and balanced configuration that decreases the loads on the bearings. A comprehensive characterization of this type of machine with two working fluids, i.e., SES36 and R245fa, has been carried out in a previous work [1]. Based on the experimental work, friction losses and internal leakages were found detrimental to the expander performance. As the expander requires lubrication during operation, flooded expansion can be beneficial to reduce such losses as well as to improve the expansion process toward a quasi-isothermal behavior. A thermodynamic cycle model has been developed to evaluate the potential improvements on the thermodynamic performance of organic Rankine cycle with flooded expansion and internal regeneration. A semi-empirical model of the expander is included which accounts for the effects of internal volume ratio. The results from the cycle model have used to design an ORC test setup with an independent lubricant oil loop and internal regeneration. The new test rig will be used validate the trends obtained with the cycle model and to further characterize the single-screw expander. The working fluid employed is R1233zd(E) as a replacement for R245fa. [ABSTRACT FROM AUTHOR]

To achieve the U.S. Department of Energys brake thermal efficiency (BTE) goal for Heavy Duty Diesel Engine (HDDE) technologies, Waste Heat Recovery (WHR) by means of Organic Rankine Cycle (ORC) systems has been selected as a suitable solution. The current relatively high return on investment period of such technology needs to be improved by significant cost reductions to realize benefits on WHR for mobile applications. The performance of the ORC system under dynamic loads relies on the choice of the working fluid, the efficiency of its components (mainly expander) as well as the control strategy that optimizes the operation. A novel ORC architecture is proposed that uses the engine coolant as the working fluid. In particular, a fraction of the engine coolant, which is a mixture of water and ethylene glycol, is employed as working fluid through the ORC to recover waste heat from EGR (Exhaust Gas Recirculation) and part of the tail pipe exhaust gases. At the inlet of the expander, the mixture has mixed-phase conditions and a fixed volume ratio expander is employed to generate power output that can be fed directly to the engine crankshaft. Heat rejection is accomplished through the spare capacity of the engine radiator, which avoids the need for a separate condenser. To evaluate the feasibility of such system architecture, a thermodynamic steady-state cycle model has been developed to predict the potential increase of BTE under different engine loads as well as to understand the ORC performance. Parametric studies are carried out by varying the system pressure ratio, the internal volume ratio of the expander and the mixture quality at the expander inlet. [ABSTRACT FROM AUTHOR]

In this paper, a modeling approach for a heat exchanger that uses a binary mixture is presented. A moving boundary method assuming a linear spatial distribution of enthalpy could result in a considerable steady state prediction error, due to the temperature glide of a binary mixture. Motivated from this, a more accurate moving boundary method that incorporates analytic enthalpy distributions is presented. The enthalpy profiles are derived by defining a specific heat capacity at each thermodynamic phase of a binary mixture and by solving crossflow heat transfer equations. The proposed approach results in accurate predictions compared with those of a detailed model using the Finite Volume Method (FVM). The corresponding computation time is two to three times slower than that of a conventional moving boundary approach, but the approach is still computationally advantageous compared with the FVM model. [ABSTRACT FROM AUTHOR]

Identification approaches applied to semi-physical thermal network structures, so called gray-box modeling approaches, are popular in building science for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. However conventional identification approaches applied to thermal networks fail when there are significant unmeasured heat gains that influence building responses. This paper presents a method to obtain improved gray-box building models from closed loop data having significant unmeasured disturbances. The method estimates both physical parameters of a building thermal network model and also a disturbance model that characterizes the unmeasured inputs. The performance of the algorithm is demonstrated using numerical and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we present a first-time cloud-based eco-feedback and gaming platform that aims to promote energy conserving thermostat-adjustment behaviors in multi-unit residential buildings. To achieve this goal, we introduce a new modeling approach for personalized eco-feedback design integrated with a collaborative social game to assist residents enhance their thermostat use while promoting community-level energy savings. Our modeling framework is integrated into a cloud-based application, MySmartE, with visual (wall-mounted tablet) and voice (Alexa) user interfaces to facilitate behavioral changes in a user-centric approach. The platform is deployed in a multi-unit residential community in Fort Wayne, IN, and data from the field study are used to investigate: (i) how occupants' thermostat behaviors changed after using the MySmartE app; (ii) how users interacted with the app during the game; and (iii) how was users' experience with the developed platform. Despite the heterogeneous characteristics of households, the results from the field study showed the positive effect of the intervention in the thermostat-adjustment behaviors, which resulted in an increase in the indoor temperature during the cooling season compared to the baseline. Findings from the user interaction analysis and post-experiment interviews also revealed the significant potential to nudge households' energy conservation behaviors with the developed platform along with the challenges that should be tackled to derive long-term behavior changes. • Presented a smart eco-feedback and gaming platform that aims to promote energy conserving thermostat-adjustment behaviors. • Introduced a new modeling approach for personalized eco-feedback design integrated with a collaborative social game. • Developed visual and voice user interfaces to facilitate behavioral changes in a user-centric approach. • Deployed the developed platform in a multi-unit residential community. • Conducted a field study to evaluate the effect of the platform on occupant behavior. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the evaluation of three different methods for determining zone temperature setpoint variations that limit peak electrical demand in buildings. The methods were developed in a companion paper [Lee K-H, Braun JE. Development of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements. Building and Environment 2007, in press, doi:10.1016/j.buildenv.2007.11.004] and are evaluated in the current paper through simulation for a small, medium, and large commercial building. Inverse models were employed for the simulation where the parameters were estimated with nonlinear regression techniques using hourly data. Two of the demand-limiting methods are based on the use of simple building models that capture dynamics of the building cooling loads in response to setpoint variations over a short time scale. The third method is data driven and only relies on load data to directly determine setpoint variations that minimize peak cooling demand. All three demand-limiting methods work well in terms of peak demand reduction for individual buildings. However, the data-driven method has slightly better performance than the other methods, is easier to implement, and is directly applicable for peak load reduction of aggregated buildings. [Copyright &y& Elsevier]

Abstract: This paper presents simple approaches for estimating building zone temperature setpoint variations that minimize peak cooling demand during critical demand periods. Three different methods were developed that are termed the semi-analytical (SA), exponential setpoint equation-based semi-analytical (ESA), and load weighted-averaging (WA) methods. The three methods are different in terms of requirements for input data. The SA and ESA methods employ simple inverse building models trained with short-term data and use analytical solutions from the models to determine setpoint trajectories. The WA method is a data-based method in which an optimal weighing factor is found that minimizes a weighted-average of two loads and then used for weighted averaging of two initial bound setpoint trajectories. The weighted-averaged setpoint trajectory is adjusted to improve the load shape and can be updated on a daily basis. A companion paper [Lee K-H, Braun JE. Evaluation of methods for determining demand-limiting setpoint trajectories in commercial buildings using short-term measurements. Building and Environment 2007, in press, doi:10.1016/j.buildenv.2007.11.003] presents evaluations of the peak load reduction potential associated with implementation of these methods. [Copyright &y& Elsevier]

Abstract: This paper describes the development and evaluation of a model-based approach for minimizing peak cooling demand using energy storage inherent in building structures. On any day where the strategy is invoked, the building is precooled with zone temperature setpoints at the low end of comfort prior to a demand-limiting (DL) period. The zone temperatures are then adjusted upwards during the demand-limiting period following a trajectory that keeps the peak cooling load below a specified target. The cooling demand target and setpoint trajectory are determined using a building model that is trained using field data. The overall approach was demonstrated for a building representative of a small commercial facility. The first step involved training the inverse model using a few weeks of hourly data. The model was then used to study the potential for peak load reduction and to determine setpoint trajectories that were implemented at the site. The demand-limiting strategy resulted in approximately 30% reductions in peak cooling loads compared to night setup control for a 5-h on-peak period of 1 PM to 6 PM. [Copyright &y& Elsevier]

Abstract: The problem of calculating transient heat transfer in concrete floor slabs is complicated due to ground coupling, which can require the numerical solution of two or three-dimensional transient conduction equations. This paper presents a simplified method for calculating transient slab-on-ground heat transfer that can be incorporated within hourly simulation programs. The method assumes that there are two primary one-dimensional paths for heat transfer from a ground-coupled floor slab: (1) one-dimensional heat transfer from the perimeter of the slab to the ambient and (2) one-dimensional heat transfer between the slab interior surface and a portion of the soil beneath the slab. The perimeter heat transfer is assumed to occur at quasi-steady state and is characterized in terms of a perimeter heat loss factor (Fp). Transient heat transfer within the slab and ground are modeled using a simple thermal circuit employing three nodes with an adiabatic boundary condition at a specified depth within the soil underneath the slab. Although some simulation models consider this type of two-path model, there appears to be no validation of this approach and there is no guidance for specifying perimeter heat loss factors and underfloor soil depths and node locations for the thermal circuit. In the current paper, results from detailed two-dimensional finite-element models for typical floor constructions and soil properties were used to identify (1) locations for nodes within the slab and soil, (2) correlations for soil depth as a function of soil properties associated with the underfloor adiabatic boundary condition, and (3) correlations for perimeter heat loss factor as a function of soil properties and edge insulation levels for different constructions. Transient heat transfer results from the simple model compared well with results from the finite-element program for different floor constructions, edge insulation, soil properties, locations, and times of year. [Copyright &y& Elsevier]

Abstract: It is necessary to know CO2 source generation rates and system flow parameters, such as supply flow rate and overall room ventilation effectiveness, in order to evaluate cost savings for demand-controlled ventilation applied to commercial buildings. This paper presents a methodology for estimating schedules for generation rates and flow parameters using short-term testing. These parameters are used within a model that predicts return air CO2 concentrations as part of an overall energy analysis model. As a first step in developing the methodology, two different parameter estimation techniques were evaluated using simulated data. Each method gave models that provide good predictions of return air CO2 concentrations, but differed in terms of the identified parameters. The preferred parameter estimation method provides estimates of both average hourly source generation rates and day-to-day variations. This technique was applied to three different types of commercial buildings using field monitored data. The sites are small commercial buildings with packaged HVAC equipment and included modular schoolrooms, children''s play areas in fast food restaurants and a pharmacy retail store. The impact of the length of model training data period on estimated CO2 generation rates was investigated. Eight weeks of data is sufficient for training. Expressed in terms of the coefficient of variation, the errors in predicted CO2 concentrations ranged from 4% to 15% depending on the sites. The predicted frequency of time that CO2 concentrations were within a given range agreed well with the field measured data. [Copyright &y& Elsevier]

Abstract: Evaluation of a building for application of demand-controlled ventilation (DCV) typically involves the use of computer simulations to predict energy use/costs for both fixed ventilation and ventilation adjusted to maintain fixed CO2 levels within the space. The simulation tools incorporate models for predicting CO2 concentrations in response to internal sources (people), infiltration/exfiltration, and ventilation. This paper presents a detailed evaluation of different modeling approaches for predicting levels of CO2 in occupied spaces for small, single-zone commercial buildings employing packaged air-conditioning equipment. Two-zone and three-zone transient models were compared with a quasi-static equilibrium model applied to three distinctly different building types. Baseline data were derived from computational fluid dynamic models that were developed for field sites. A complete building system simulation model was then used to compare the impact of the different modeling approaches on the predicted energy cost savings associated with application of DCV in each building type. The use of a transient CO2 model did not have a significant impact on model prediction accuracy and energy cost savings predictions as compared with the quasi-static model. The difference in predicted annual energy costs between the various CO2 modeling types were small and less than might result from errors introduced by factors such as CO2 sensor uncertainty. Therefore, the use of an equilibrium model is sufficient for use in evaluating DCV for small commercial buildings. [Copyright &y& Elsevier]