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It has been proven that advanced building control, like model predictive control (MPC), can notably reduce the energy use and mitigate greenhouse gas emissions. However, despite intensive research efforts, the practical applications are still in the early stages. There is a growing need for multidisciplinary education on advanced control methods in the built environment to be accessible for a broad range of researchers and practitioners with different engineering backgrounds. This paper provides a unified framework for model predictive building control technology with focus on the real-world applications. From a theoretical point of view, this paper presents an overview of MPC formulations for building control, modeling paradigms and model types, together with algorithms necessary for real-life implementation. The paper categorizes the most notable MPC problem classes, links them with corresponding solution techniques, and provides an overview of methods for mitigation of the uncertainties for increased performance and robustness of MPC. From a practical point of view, this paper delivers an elaborate classification of the most important modeling, co-simulation, optimal control design, and optimization techniques, tools, and solvers suitable to tackle the MPC problems in the context of building climate control. On top of this, the paper presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems. The paper draws practical guidelines with a generic workflow for implementation of MPC in real buildings aimed for contemporary adopters of this technology. Finally, the importance of standardized performance assessment and methodology for comparison of different building control algorithms is discussed.
Ján Drgoňa; Javier Arroyo; Iago Cupeiro Figueroa; David Blum; Krzysztof Arendt; Donghun Kim; Enric Perarnau Ollé; Juraj Oravec; Michael Wetter; Draguna L. Vrabie; Lieve Helsen. All you need to know about model predictive control for buildings. Annual Reviews in Control 2020, 50, 190 -232.
AMA StyleJán Drgoňa, Javier Arroyo, Iago Cupeiro Figueroa, David Blum, Krzysztof Arendt, Donghun Kim, Enric Perarnau Ollé, Juraj Oravec, Michael Wetter, Draguna L. Vrabie, Lieve Helsen. All you need to know about model predictive control for buildings. Annual Reviews in Control. 2020; 50 ():190-232.
Chicago/Turabian StyleJán Drgoňa; Javier Arroyo; Iago Cupeiro Figueroa; David Blum; Krzysztof Arendt; Donghun Kim; Enric Perarnau Ollé; Juraj Oravec; Michael Wetter; Draguna L. Vrabie; Lieve Helsen. 2020. "All you need to know about model predictive control for buildings." Annual Reviews in Control 50, no. : 190-232.
Muhyiddine Jradi; Na Liu; Aslak Johansen; Krzysztof Arendt; Claudio Giovanni Mattera; Mikkel Baun Kjærgaard; Christian Veje; Bo Nørregaard Jørgensen. Dynamic Energy Model-Based Automatic Building Performance Testing for Continuous Commissioning. Proceedings of Building Simulation 2019: 16th Conference of IBPSA 2020, 1 .
AMA StyleMuhyiddine Jradi, Na Liu, Aslak Johansen, Krzysztof Arendt, Claudio Giovanni Mattera, Mikkel Baun Kjærgaard, Christian Veje, Bo Nørregaard Jørgensen. Dynamic Energy Model-Based Automatic Building Performance Testing for Continuous Commissioning. Proceedings of Building Simulation 2019: 16th Conference of IBPSA. 2020; ():1.
Chicago/Turabian StyleMuhyiddine Jradi; Na Liu; Aslak Johansen; Krzysztof Arendt; Claudio Giovanni Mattera; Mikkel Baun Kjærgaard; Christian Veje; Bo Nørregaard Jørgensen. 2020. "Dynamic Energy Model-Based Automatic Building Performance Testing for Continuous Commissioning." Proceedings of Building Simulation 2019: 16th Conference of IBPSA , no. : 1.
Konstantin Filonenko; Krzysztof Arendt; Muhyiddine Jradi; Søren Andersen; Christian Veje. Modeling and Simulation of a Heating Mini-Grid for a Block of Buildings. Proceedings of Building Simulation 2019: 16th Conference of IBPSA 2020, 1 .
AMA StyleKonstantin Filonenko, Krzysztof Arendt, Muhyiddine Jradi, Søren Andersen, Christian Veje. Modeling and Simulation of a Heating Mini-Grid for a Block of Buildings. Proceedings of Building Simulation 2019: 16th Conference of IBPSA. 2020; ():1.
Chicago/Turabian StyleKonstantin Filonenko; Krzysztof Arendt; Muhyiddine Jradi; Søren Andersen; Christian Veje. 2020. "Modeling and Simulation of a Heating Mini-Grid for a Block of Buildings." Proceedings of Building Simulation 2019: 16th Conference of IBPSA , no. : 1.
One of the main challenges facing the building sector nowadays is the reported mismatch between the predicted and the actual performance throughout the building operational phase. This mismatch is referred to as the ‘building performance gap’. In this regard, the need for a systematic continuous commissioning framework to monitor, assess and evaluate the buildings performance is vital to bridge the performance gaps. In this paper, an innovative framework for building energy performance monitoring and evaluation is presented, considering a list of performance tests addressing building subsystems. The framework relies on two major pillars, actual data collected from the building site, and calibrated energy model simulations to serve as a dynamic baseline for comparison and evaluation. The framework design, development and implementation in a highly energy efficient building is presented, and findings from the initial stages of implementing the framework are highlighted considering the energy systems operation and indoor comfort perspectives.
Muhyiddine Jradi; Na Liu; Krzysztof Arendt; Claudio Giovanni Mattera. An automated framework for buildings continuous commissioning and performance testing – A university building case study. Journal of Building Engineering 2020, 31, 101464 .
AMA StyleMuhyiddine Jradi, Na Liu, Krzysztof Arendt, Claudio Giovanni Mattera. An automated framework for buildings continuous commissioning and performance testing – A university building case study. Journal of Building Engineering. 2020; 31 ():101464.
Chicago/Turabian StyleMuhyiddine Jradi; Na Liu; Krzysztof Arendt; Claudio Giovanni Mattera. 2020. "An automated framework for buildings continuous commissioning and performance testing – A university building case study." Journal of Building Engineering 31, no. : 101464.
Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To the best of our knowledge, this is the first time that this method is used for applications in smart buildings. First a dynamic model for a zone in a real-case building is developed in which faults are also introduced. Then fault diagnosability is investigated by analyzing the fault isolability of the model. Based on the investigation, it was concluded that not all the faults in the model are diagnosable. Then an approach for placing new sensors is implemented. It is observed that for two test scenarios, placing additional sensors in the model leads to full diagnosability. Since sensors placement is key for an effective FDD process, the optimal placement of such sensors is also studied in this work. A case study of campus building OU44 at the University of Southern Denmark is considered. The results show that as the system gets more complicated by introducing more faults, additional sensors should be added to achieve full diagnosability.
Max Emil S. Trothe; Hamid Reza Shaker; Muhyiddine Jradi; Krzysztof Arendt. Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings. Energies 2019, 12, 1601 .
AMA StyleMax Emil S. Trothe, Hamid Reza Shaker, Muhyiddine Jradi, Krzysztof Arendt. Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings. Energies. 2019; 12 (9):1601.
Chicago/Turabian StyleMax Emil S. Trothe; Hamid Reza Shaker; Muhyiddine Jradi; Krzysztof Arendt. 2019. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings." Energies 12, no. 9: 1601.
Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors’ impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor’s influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20% in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.
D.H. Blum; Krzysztof Arendt; L. Rivalin; M.A. Piette; Michael Wetter; Christian Veje. Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems. Applied Energy 2018, 236, 410 -425.
AMA StyleD.H. Blum, Krzysztof Arendt, L. Rivalin, M.A. Piette, Michael Wetter, Christian Veje. Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems. Applied Energy. 2018; 236 ():410-425.
Chicago/Turabian StyleD.H. Blum; Krzysztof Arendt; L. Rivalin; M.A. Piette; Michael Wetter; Christian Veje. 2018. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems." Applied Energy 236, no. : 410-425.
Fisayo Caleb Sangogboye; Krzysztof Arendt; Muhyiddine Jradi; Christian Veje; Mikkel Baun Kjærgaard; Bo Nørregaard Jørgensen. The impact of occupancy resolution on the accuracy of building energy performance simulation. Proceedings of the 5th Conference on Systems for Built Environments 2018, 103 -106.
AMA StyleFisayo Caleb Sangogboye, Krzysztof Arendt, Muhyiddine Jradi, Christian Veje, Mikkel Baun Kjærgaard, Bo Nørregaard Jørgensen. The impact of occupancy resolution on the accuracy of building energy performance simulation. Proceedings of the 5th Conference on Systems for Built Environments. 2018; ():103-106.
Chicago/Turabian StyleFisayo Caleb Sangogboye; Krzysztof Arendt; Muhyiddine Jradi; Christian Veje; Mikkel Baun Kjærgaard; Bo Nørregaard Jørgensen. 2018. "The impact of occupancy resolution on the accuracy of building energy performance simulation." Proceedings of the 5th Conference on Systems for Built Environments , no. : 103-106.
Krzysztof Arendt; Aslak Johansen; Bo Nørregaard Jørgensen; Mikkel Baun Kjærgaard; Claudio Giovanni Mattera; Fisayo Sangogboye; Jens Hjort Schwee; Christian Veje. Room-level occupant counts, airflow and CO 2 data from an office building. Proceedings of the First Workshop on Data Acquisition To Analysis 2018, 13 -14.
AMA StyleKrzysztof Arendt, Aslak Johansen, Bo Nørregaard Jørgensen, Mikkel Baun Kjærgaard, Claudio Giovanni Mattera, Fisayo Sangogboye, Jens Hjort Schwee, Christian Veje. Room-level occupant counts, airflow and CO 2 data from an office building. Proceedings of the First Workshop on Data Acquisition To Analysis. 2018; ():13-14.
Chicago/Turabian StyleKrzysztof Arendt; Aslak Johansen; Bo Nørregaard Jørgensen; Mikkel Baun Kjærgaard; Claudio Giovanni Mattera; Fisayo Sangogboye; Jens Hjort Schwee; Christian Veje. 2018. "Room-level occupant counts, airflow and CO 2 data from an office building." Proceedings of the First Workshop on Data Acquisition To Analysis , no. : 13-14.
Sensing accurately the number of occupants in the rooms of a building enables many important applications for smart building operation and energy management. A range of sensor technologies has been studied and applied to the problem. However, it is costly to achieve high accuracy by instrumenting all rooms in a building with dedicated occupant sensors. In this paper, we propose a new concept for estimating accurate room-level counts of occupants. The idea is to disaggregate accurate building-level counts via existing common sensors available at the room level. This solution is cost-effective as it scales to large buildings without requiring dedicated sensors in each room. We propose an algorithm named DCount that implements this concept. Our results document that DCount can provide room-level counts with a low normalized root mean squared error of 0.93. This is a major improvement compared to a state-of-the-art algorithm using common sensors and ventilation rate measurements resulting in a normalized root mean squared error of 1.54 on the same data set. Further more, we demonstrate how the results enable occupant-driven analysis of plug-load consumption which is one out of many applications using accurate room-level counts of occupants we hope to enable by proposing DCount.
Mikkel Baun Kjargaard; Martin Werner; Fisayo Caleb Sangogboye; Krzysztof Arendt. DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts. 2018 19th IEEE International Conference on Mobile Data Management (MDM) 2018, 46 -55.
AMA StyleMikkel Baun Kjargaard, Martin Werner, Fisayo Caleb Sangogboye, Krzysztof Arendt. DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts. 2018 19th IEEE International Conference on Mobile Data Management (MDM). 2018; ():46-55.
Chicago/Turabian StyleMikkel Baun Kjargaard; Martin Werner; Fisayo Caleb Sangogboye; Krzysztof Arendt. 2018. "DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts." 2018 19th IEEE International Conference on Mobile Data Management (MDM) , no. : 46-55.
A major challenge facing the buildings sector is the absence of continuous commissioning and the lack of performance monitoring and evaluation leading to buildings energy performance gaps between predicted and actual measured performance. Aiming to better characterize, evaluate and bridge these gaps, the paper proposes an online building energy performance monitoring and evaluation tool ObepME, serving as a basis for fault detection and diagnostics and forming a backbone for continuous commissioning. A calibrated building dynamic energy model is developed and employed to automatically run on a daily basis and simulate the building transient performance for the previous day. The simulated energy consumption results form a baseline to which the actual collected data are compared to evaluate the dynamic energy performance gap. The OU44 university building in Denmark is considered as a case study to implement the proposed framework. A holistic energy model was developed in EnergyPlus and calibrated employing data from various building meters, collected weather conditions, generated occupancy schedules and systems operational parameters and set-points. The calibrated model was employed in the ObepME tool to automatically and continuously monitor and evaluate the OU44 building energy performance, on the level of the whole building and individual energy systems consumption, throughout the period from February to mid-March 2017. The reported dynamic energy performance gap was around -2.85%, -3.47% and 5.48% for heating, total electricity and ventilation system electricity consumption. In addition, specific observations were made on a daily basis in terms of the overall electricity, heating, lighting and ventilation energy consumption as highlighted by the ObepME tool. The ObepME tool is currently running automatically as a part of the OU44 building continuous commissioning and performance evaluation aiming to identify possible discrepancies and deviations paving the way for a methodical preventive fault detection and diagnostics process on various levels in the building.
Muhyiddine Jradi; Krzysztof Arendt; Fisayo Sangogboye; C.G. Mattera; Elena Markoska; Mikkel Baun Kjærgaard; C.T. Veje; Bo Nørregaard Jørgensen. ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps. Energy and Buildings 2018, 166, 196 -209.
AMA StyleMuhyiddine Jradi, Krzysztof Arendt, Fisayo Sangogboye, C.G. Mattera, Elena Markoska, Mikkel Baun Kjærgaard, C.T. Veje, Bo Nørregaard Jørgensen. ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps. Energy and Buildings. 2018; 166 ():196-209.
Chicago/Turabian StyleMuhyiddine Jradi; Krzysztof Arendt; Fisayo Sangogboye; C.G. Mattera; Elena Markoska; Mikkel Baun Kjærgaard; C.T. Veje; Bo Nørregaard Jørgensen. 2018. "ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps." Energy and Buildings 166, no. : 196-209.
The change in the electricity supply towards solar and wind is creating new stability and balancing challenges for the electricity grid. A solution to these challenges is to change the consumption of the demand-side in particular buildings. Efforts to help change the demand-side in buildings evolves around the idea of Demand-Response. However, the impact of moving, shedding or filling loads in buildings has a large impact on building occupants. In order to further the spread of DR systems, it is necessary to consider the impact of DR on comfort. In particular to assess it to ensure compliance with both soft demands for comfort, as well as harder demands such as minimum running systems and law requirements. Furthermore, the impact on comfort needs to be calculated to an order of accuracy that is high enough to ensure proper scheduling of DR events while also meeting acceptable thresholds for the effects on the occupants. In this paper we evaluate to which degree a Model Predictive Control (MPC) system can deliver comfort compliance. We will discuss the design of a DR capable MPC system that can plan ahead and use a building's potential for DR while also providing comfort for occupants. We also present the results from a case-study utilizing MPC in an office building. We study the compliance over multiple times a day and week to consider different building states and occupancy patterns, taking into account external factors such as weather patterns and building structures. Lessons learned are summarized to inform the design of such systems and characterize their applicability. We also study the value of occupancy predictions and how these affect predictions compared to utilizing standard schedules for a building.
Peter Nelleman; Mikkel Baun Kjærgaard; Emil Holmegaard; Krzysztof Arendt; Aslak Johansen; Fisayo Sangogboye; Bo Norregaard Jorgensen. Demand response with model predictive comfort compliance in an office building. 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm) 2017, 351 -356.
AMA StylePeter Nelleman, Mikkel Baun Kjærgaard, Emil Holmegaard, Krzysztof Arendt, Aslak Johansen, Fisayo Sangogboye, Bo Norregaard Jorgensen. Demand response with model predictive comfort compliance in an office building. 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm). 2017; ():351-356.
Chicago/Turabian StylePeter Nelleman; Mikkel Baun Kjærgaard; Emil Holmegaard; Krzysztof Arendt; Aslak Johansen; Fisayo Sangogboye; Bo Norregaard Jorgensen. 2017. "Demand response with model predictive comfort compliance in an office building." 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm) , no. : 351-356.
Model predictive control is a promising approach to optimize the operation of building systems and provide demand-response functionalities without compromising indoor comfort. The performance of model predictive control relies, among other things, on the quality of weather forecasts and building occupancy predictions. The present study compares the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation “using common sensors”, (2) count prediction based on data from 3D stereovision camera. The performance of the two approaches was tested in two rooms of a case study building. The results show that the method with dedicated sensors outperforms common sensors. However, if a building is not equipped with dedicated sensors, the present study shows that the common sensor method can be a satisfactory alternative to be used in model predictive control.
Fisayo Sangogboye; Krzysztof Arendt; Ashok Singh; Christian T. Veje; Mikkel Baun Kjærgaard; Bo Nørregaard Jørgensen. Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control. Building Simulation 2017, 10, 829 -843.
AMA StyleFisayo Sangogboye, Krzysztof Arendt, Ashok Singh, Christian T. Veje, Mikkel Baun Kjærgaard, Bo Nørregaard Jørgensen. Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control. Building Simulation. 2017; 10 (6):829-843.
Chicago/Turabian StyleFisayo Sangogboye; Krzysztof Arendt; Ashok Singh; Christian T. Veje; Mikkel Baun Kjærgaard; Bo Nørregaard Jørgensen. 2017. "Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control." Building Simulation 10, no. 6: 829-843.
Electricity grids are facing challenges due to peak consumption and renewable electricity generation. In this context, demand response offers a solution to many of the challenges, by enabling the integration of consumer side flexibility in grid management. Commercial buildings are good candidates for providing flexible demand due to their volume and the stability of their loads. However, existing technologies and strategies for demand response in commercial buildings fail to enable services with an assessable impact on load changes and occupant comfort. In this paper we propose the ADRALOC system for Automated Demand Response with an Assessable impact on Loads and Occupant Comfort. This enhances the quality of demand response services from a grid management perspective, as these become predictable and trustworthy. At the same time building managers and owners can participate without worrying about the comfort of occupants. We present results from a case study in a real office building where we illustrate the advantages of the system (i.e., load sheds of 3kW within comfort limits). Presenting a better system for demand response in commercial buildings is a step towards enabling a higher penetration of intelligent smart grid solutions in commercial buildings.
Mikkel Baun Kjærgaard; Krzysztof Arendt; Anders Clausen; Aslak Johansen; Muhyiddine Jradi; Bo Norregaard Jorgensen; Peter Nelleman; Fisayo Sangogboye; Christian Veje; Morten Gill Wollsen. Demand response in commercial buildings with an Assessable impact on occupant comfort. 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm) 2016, 447 -452.
AMA StyleMikkel Baun Kjærgaard, Krzysztof Arendt, Anders Clausen, Aslak Johansen, Muhyiddine Jradi, Bo Norregaard Jorgensen, Peter Nelleman, Fisayo Sangogboye, Christian Veje, Morten Gill Wollsen. Demand response in commercial buildings with an Assessable impact on occupant comfort. 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). 2016; ():447-452.
Chicago/Turabian StyleMikkel Baun Kjærgaard; Krzysztof Arendt; Anders Clausen; Aslak Johansen; Muhyiddine Jradi; Bo Norregaard Jorgensen; Peter Nelleman; Fisayo Sangogboye; Christian Veje; Morten Gill Wollsen. 2016. "Demand response in commercial buildings with an Assessable impact on occupant comfort." 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm) , no. : 447-452.
The airflow network (AFN) modeling approach provides an attractive balance between the accuracy and computational demand for naturally ventilated buildings. Its accuracy depends on input parameters such as wind pressure and opening discharge coefficients. In most cases, these parameters are obtained from secondary sources which are solely representative for very simplified buildings, i.e. for buildings without facade details. Although studies comparing wind pressure coefficients or discharge coefficients from different sources exist, the knowledge regarding the effect of input data on AFN is still poor. In this paper, the influence of wind pressure data on the accuracy of a coupled AFN-BES model for a real building with natural wind- and stack-driven ventilation was analyzed. The results of 8 computation cases with different wind pressure data from secondary sources were compared with the measured data. Both the indoor temperatures and the airflow were taken into account. The outcomes indicated that the source of wind pressure data had a significant influence on the model performance.
Krzysztof Arendt; Marek Krzaczek; Jacek Tejchman. Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation. Building Simulation 2016, 10, 229 -238.
AMA StyleKrzysztof Arendt, Marek Krzaczek, Jacek Tejchman. Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation. Building Simulation. 2016; 10 (2):229-238.
Chicago/Turabian StyleKrzysztof Arendt; Marek Krzaczek; Jacek Tejchman. 2016. "Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation." Building Simulation 10, no. 2: 229-238.
Krzysztof Arendt; M. Krzaczek. Co-simulation strategy of transient CFD and heat transfer in building thermal envelope based on calibrated heat transfer coefficients. International Journal of Thermal Sciences 2014, 85, 1 -11.
AMA StyleKrzysztof Arendt, M. Krzaczek. Co-simulation strategy of transient CFD and heat transfer in building thermal envelope based on calibrated heat transfer coefficients. International Journal of Thermal Sciences. 2014; 85 ():1-11.
Chicago/Turabian StyleKrzysztof Arendt; M. Krzaczek. 2014. "Co-simulation strategy of transient CFD and heat transfer in building thermal envelope based on calibrated heat transfer coefficients." International Journal of Thermal Sciences 85, no. : 1-11.
Krzysztof Arendt; Marek Krzaczek; Jaroslaw Florczuk. Numerical analysis by FEM and analytical study of the dynamic thermal behavior of hollow bricks with different cavity concentration. International Journal of Thermal Sciences 2011, 50, 1543 -1553.
AMA StyleKrzysztof Arendt, Marek Krzaczek, Jaroslaw Florczuk. Numerical analysis by FEM and analytical study of the dynamic thermal behavior of hollow bricks with different cavity concentration. International Journal of Thermal Sciences. 2011; 50 (8):1543-1553.
Chicago/Turabian StyleKrzysztof Arendt; Marek Krzaczek; Jaroslaw Florczuk. 2011. "Numerical analysis by FEM and analytical study of the dynamic thermal behavior of hollow bricks with different cavity concentration." International Journal of Thermal Sciences 50, no. 8: 1543-1553.