In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as... more In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as a potential digital-twin enabler for fluid flow around an aerofoil. A wind turbine blade has its basic foundation in the aerofoil shape. A faster way of understanding dynamic flow changes around the aerofoil-shaped blade can help make quick decisions related to wind-turbine operations and lead to optimal aerodynamic performance and power production. In this direction, a case study involving the application of the NIROM methodology for flow prediction around a NACA 0015 aerofoil is considered. The Reynolds number (Re) is the varying parameter, ranging from 320 000 to 1.12 million and high-fidelity CFD simulations are performed to generate the database for developing the NIROM. The aforementioned NIROM framework employs a Grassmann manifold interpolation approach (GI) for obtaining basis functions corresponding to new values of the parameter (Reynolds number), and exploits the time series prediction capabilities of the long short-term memory (LSTM) recurrent neural network for obtaining temporal coefficients associated with the new basis functions. The methodology involves: (a) an offline training phase, where the LSTM model is trained on the modal coefficients extracted from the sampled high-resolution data using the proper orthogonal decomposition (POD), and (b) an online testing phase, where for the new parameter value, the corresponding flow field is obtained using the GI-modulated basis functions for new parameter and the LSTM-predicted temporal coefficients. The NIROM-approximated flow predictions at new parameters have been compared to the high-dimensional full-order model (FOM) solutions for the high-Re aerofoil case and for a low-Re number wake vortex merger case in order to put the performance of NIROM in perspective. The results indicate that the NIROM framework can qualitatively predict the complex flow scenario around the aerofoil for new values of Reynolds number, while it has quantitatively shown that the LSTM predictions improve with the enrichment of the training space. For the low-Re vortex merger case, NIROM works very well. Thus, it can be deduced that there is scope and potential for continued research in NIROMs as digital twin enablers in wind energy applications.
The 9th International Conference on Time Series and Forecasting
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Monitoring of Equivalent Circulating Density (ECD) may improve assessment of potential bad hole c... more Monitoring of Equivalent Circulating Density (ECD) may improve assessment of potential bad hole cleaning conditions if calculated and measured sufficiently accurately. Machine learning (ML) models can be used for predicting ECD integrating both along-string and surface drilling measurements and physics-based model (PBM) results, even though their generalization is often challenging. To remediate this generalizability issue, we present an adaptative predictive deep-learning model that is retrained with new measurements in real-time, conditionally that the new measurements are not detected as anomalies. Past ECD measurements, corresponding values predicted by a 1D PBM and other drilling measurements are used as input to a deep learning model, which is pretrained on historical drilling data without any hole cleaning problem. This model has two components: an anomaly detector, and a predictor. In this paper, both components are based on combinations of Long Short-Term Memory (LSTM) cell...
Digital twins are meant to bridge the gap between real-world physical systems and virtual represe... more Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica. Digital twin applications are required to rapidly update internal parameters with the evolution of their physical counterpart. Due to an essential need for having high-quality geometric models for accurate physical representations, the storage and bandwidth requirements for storing 3D model information can quickly exceed the available storage and bandwidth capacity. In this work, we demonstrate a novel approach to geometric change detection in a digital twin context. We address the issue through a combined solution of dynamic mode decomposition (DMD) for motion detection, YOLOv5 for object detection, and 3D machine learning for pose estimation. DMD is applied for background subtraction, enabling detection of moving foreground ...
Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, t... more Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, they may require spatial averaging to analyze flow behavior over the domain of interest. In doing so, certain important features of sub-grid scales are lost. Moreover, spatial averaging on the governing equations results in additional terms known as dispersive fluxes. These fluxes are ignored in the analysis. The aim of this paper is to identify the significance of these fluxes for accurate assessment of flow fields related to wind farm applications. The research objectives are hence twofold: 1) to quantify the impact of wind turbines on MBL characteristics. 2) to account for the magnitude of dispersive fluxes arising from spatial averaging and make a comparison against the turbulent flux values. To conduct the numerical study the NREL 5MW reference wind turbine model is employed with a RANS approach using k-ε turbulence model. The results are presented concerning spatially averaged veloc...
2021 International Conference on Applied Artificial Intelligence (ICAPAI), 2021
A methodology involving machine learning with subsequent physics-based method and a visualization... more A methodology involving machine learning with subsequent physics-based method and a visualization map has been developed for enhancing environment-friendly transport planning. This methodology is applied for enabling predictions of traffic counts of bikes in Oslo city, with the physics-based models aiding in: (a) offering causality and interpretiveness for the ML predicted correlations (or patterns), and (b) more weather data for the trained machine learning to predict the traffic counts. In this work, we compare conventional ML methods (Artificial Neural network, Support Vector Machine (SVM), Random forest (RF)) for regression along with an unconventional use of sequential Long Short term memory (LSTM-RNN) based regression-formulation for predicting traffic counts of cycles at various traffic routes for both weather related and non-weather related independent variables. Amongst the various models compared, RF and LSTM model performs the best owing to nature of data. The RF quantifies the pattern that weather related variables are influencing cycle traffic count strongly, but ML being non-interpretive, it is not able to provide causation behind these correlations. A subsequent fluid dynamics-based analysis shows that weather influences thermal comfort level of cyclist and explains the observed ML predicted travel patterns. Finally, they are used together to provide a visualization map showing bike traffic count with the expectation to enable decision on infrastructure planning for the Oslo kommune (like a network of automated kiosk location). Such methodologies are a step towards encouraging better utilization of modern sharing-concepts (like, mobility-as-a-service and bike sharing).
Cardiovascular diseases, like Carotid Artery Disease and Coronary Artery Disease (CAD) are associ... more Cardiovascular diseases, like Carotid Artery Disease and Coronary Artery Disease (CAD) are associated with the narrowing of artery due to build-up of fatty substances and cholesterol deposits (called plaque). Carotid Artery Disease increases the chances of brain stroke. Hence, the main objective of this work is to apply computational tools to help differentiate between the healthy and unhealthy artery (with 25% stenosis) using a combination of Computational Fluid Dynamics (CFD) and data mining tools. In this work, first, the CFD has been qualitatively shown to provide similar results as the experimental Phase-Contrast Magnetic Resonance Imaging (PCMRI) technique. The CFD simulation shows that wall shear stress is an ideal parameter to identify the location of plaque formation and the existence of plaque conditions in the body (due to overall higher spatially averaged wall shear stress in the clogged case at all times in the cycle). Then data mining tools like Fast Fourier Transform (FFT) and Proper Orthogonal Decomposition (POD) have been used to unearth a pattern that can be useful for diagnosis. FFT shows that the flow constriction induced by plaque leads to lesser variation in magnitudes of energy of dominant frequencies at different locations like, wake region, mid-Internal Carotid Artery (mid-ICA) and mid-Common Carotid Artery (mid-CCA) regions, while for cleaner artery, there is more variation in the magnitude of energy of these dominant frequencies when measured at wake, mid ICA and mid CCA region. POD helps by confirming the location of regions with high energy in decomposed velocity modes for both the cases. More studies are required to develop a data mining based modern 21st century cardio-vascular patient care.publishedVersio
Current work involves data-driven analysis of travel mobility. For this purpose, traffic counts o... more Current work involves data-driven analysis of travel mobility. For this purpose, traffic counts of cars and bikes at various traffic routes along with the associated weather have been collected in Oslo, Norway. Amongst the 6 machine learning algorithms compared (linear regression, random forest, decision tree, gradient boost, support vector machine and artificial neural network), the random forest model is seen to perform the best with an accuracy score of around 0.96. The results indicate : a) bike traffic is more sensitive to weather than the car traffic, b) bike traffic counts showing a stronger bimodal distribution with the hour of the day for week days than the car traffic counts, thus suggesting a wider car-usage outside the bimodal peak-times. c) Monthly bike traffic counts is influenced by the weather, while the monthly car counts is influenced by the vacation periods. PREPARED BY Mandar Tabib,Adil Rasheed SIGNATURE CHECKED BY Arne Morten Kvarving SIGNATURE APPROVED BY Trond...
A validated multi-domain 1D particle-reactor model has been developed to simulate packed bed reac... more A validated multi-domain 1D particle-reactor model has been developed to simulate packed bed reactor operation. Two main components of the model are: (1) a particle model for simulating the radial distribution of chemical species and temperature within the catalyst particles and (2) a 1D reactor model for solving mass and energy transport along the length of the reactor. The model captures the effect of intra-particle heat and mass transfer phenomena on the reactor performance. Its efficacy and usability is evaluated using a thorough verification and validation campaign. Validation has been carried out through comparisons to analytical solutions for: (a) the transient thermal response of the fixed bed to a step-change in inlet feed temperature and for (b) the maximum temperature rise during an exothermic oxidation process in a chemical looping combustion (CLC) operation. Further, its performance has been verified with two well-established solvers (a 1D Euler- Euler packed bed model developed in ANSYS FLUENT and a previously published 1D model) for simulating a realistic 500kW cyclic packed bed chemical looping combustion system involving dynamic fuel-air cycling. This successful verification demonstrates the ability of the model to simulate complex cyclic packed bed reactor processes involving stiff kinetics in an efficient manner. Further, significance of particle model is evaluated for mass transfer limiting condition and this reinforces the advantage of using the proposed 1D particlereactor model in such cases.publishedVersio
Turbulence in the wake generated by airflow over buildings may produce complex airflow patterns i... more Turbulence in the wake generated by airflow over buildings may produce complex airflow patterns in the downwind region. Examples include the recirculating flow and wind deficit areas behind airport buildings and their potential impact on flight conditions at runways. In this study a computational fluid dynamics analysis of the airflow over helicopter hangars was performed, including the turbulent wakes that were generated behind the hangars. The effects of the wakes on landing and departing aircraft on the two runways (18/36 and 11/29) at Sola were estimated, based on a number of numerical simulations. With the prevailing wind direction at Sola, the airport hangars cause no effects on aircraft landing and taking off. Strong wind perpendicular to the hangars, is a different scenario and is the most critical one when it comes to wake turbulence. Simulations were therefore performed to study the impact of building wakes under such conditions. The main findings of the analysis are that aviation turbulence at free wind speeds of 20 m/s for both runways are just light to moderate at delimited runway sections behind the hangars for each of the scenarios mentioned. However, when the free wind speed increases from 20 m/s to 30 m/s (from fresh gale to violent storm), the wake turbulence may be severe for both scenarios. However, the unfavourable impact of the NEW hangar on runway 11/29 will not be as large as the impact of the OLD hangar on runway 18/36, for similar wind conditions. Oppdragsgiver: ForsvarsbyggpublishedVersio
Artificial Neural Networks and Machine Learning – ICANN 2018, 2018
This work involves the use of combined forces of data-driven machine learning models and high fid... more This work involves the use of combined forces of data-driven machine learning models and high fidelity density functional theory for the identification of new potential thermoelectric materials. The traditional method of thermoelectric material discovery from an almost limitless search space of chemical compounds involves expensive and time consuming experiments. In the current work, the density functional theory (DFT) simulations are used to compute the descriptors (features) and thermoelectric characteristics (labels) of a set of compounds. The DFT simulations are computationally very expensive and hence the database is not very exhaustive. With an anticipation that the important features can be learned by machine learning (ML) from the limited database and the knowledge could be used to predict the behavior of any new compound, the current work adds knowledge related to (a) understanding the impact of selection of influence of training/test data, (b) influence of complexity of ML algorithms, and (c) computational efficiency of combined DFT-ML methodology.
District heating networks are commonly addressed in the literature as one of the most effective s... more District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand-outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.
ACKNOWLEDGEMENT. A POP-SEP funding from SINTEF has been used for the 1D particle-reactor model so... more ACKNOWLEDGEMENT. A POP-SEP funding from SINTEF has been used for the 1D particle-reactor model software development. The multi-scale model development has been carried out as a part of the DemoCLOCK project, which is an EU FP7 funded project under the ENERGY.2010.6.1-1 programme.
In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as... more In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as a potential digital-twin enabler for fluid flow around an aerofoil. A wind turbine blade has its basic foundation in the aerofoil shape. A faster way of understanding dynamic flow changes around the aerofoil-shaped blade can help make quick decisions related to wind-turbine operations and lead to optimal aerodynamic performance and power production. In this direction, a case study involving the application of the NIROM methodology for flow prediction around a NACA 0015 aerofoil is considered. The Reynolds number (Re) is the varying parameter, ranging from 320 000 to 1.12 million and high-fidelity CFD simulations are performed to generate the database for developing the NIROM. The aforementioned NIROM framework employs a Grassmann manifold interpolation approach (GI) for obtaining basis functions corresponding to new values of the parameter (Reynolds number), and exploits the time series prediction capabilities of the long short-term memory (LSTM) recurrent neural network for obtaining temporal coefficients associated with the new basis functions. The methodology involves: (a) an offline training phase, where the LSTM model is trained on the modal coefficients extracted from the sampled high-resolution data using the proper orthogonal decomposition (POD), and (b) an online testing phase, where for the new parameter value, the corresponding flow field is obtained using the GI-modulated basis functions for new parameter and the LSTM-predicted temporal coefficients. The NIROM-approximated flow predictions at new parameters have been compared to the high-dimensional full-order model (FOM) solutions for the high-Re aerofoil case and for a low-Re number wake vortex merger case in order to put the performance of NIROM in perspective. The results indicate that the NIROM framework can qualitatively predict the complex flow scenario around the aerofoil for new values of Reynolds number, while it has quantitatively shown that the LSTM predictions improve with the enrichment of the training space. For the low-Re vortex merger case, NIROM works very well. Thus, it can be deduced that there is scope and potential for continued research in NIROMs as digital twin enablers in wind energy applications.
The 9th International Conference on Time Series and Forecasting
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Monitoring of Equivalent Circulating Density (ECD) may improve assessment of potential bad hole c... more Monitoring of Equivalent Circulating Density (ECD) may improve assessment of potential bad hole cleaning conditions if calculated and measured sufficiently accurately. Machine learning (ML) models can be used for predicting ECD integrating both along-string and surface drilling measurements and physics-based model (PBM) results, even though their generalization is often challenging. To remediate this generalizability issue, we present an adaptative predictive deep-learning model that is retrained with new measurements in real-time, conditionally that the new measurements are not detected as anomalies. Past ECD measurements, corresponding values predicted by a 1D PBM and other drilling measurements are used as input to a deep learning model, which is pretrained on historical drilling data without any hole cleaning problem. This model has two components: an anomaly detector, and a predictor. In this paper, both components are based on combinations of Long Short-Term Memory (LSTM) cell...
Digital twins are meant to bridge the gap between real-world physical systems and virtual represe... more Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica. Digital twin applications are required to rapidly update internal parameters with the evolution of their physical counterpart. Due to an essential need for having high-quality geometric models for accurate physical representations, the storage and bandwidth requirements for storing 3D model information can quickly exceed the available storage and bandwidth capacity. In this work, we demonstrate a novel approach to geometric change detection in a digital twin context. We address the issue through a combined solution of dynamic mode decomposition (DMD) for motion detection, YOLOv5 for object detection, and 3D machine learning for pose estimation. DMD is applied for background subtraction, enabling detection of moving foreground ...
Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, t... more Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, they may require spatial averaging to analyze flow behavior over the domain of interest. In doing so, certain important features of sub-grid scales are lost. Moreover, spatial averaging on the governing equations results in additional terms known as dispersive fluxes. These fluxes are ignored in the analysis. The aim of this paper is to identify the significance of these fluxes for accurate assessment of flow fields related to wind farm applications. The research objectives are hence twofold: 1) to quantify the impact of wind turbines on MBL characteristics. 2) to account for the magnitude of dispersive fluxes arising from spatial averaging and make a comparison against the turbulent flux values. To conduct the numerical study the NREL 5MW reference wind turbine model is employed with a RANS approach using k-ε turbulence model. The results are presented concerning spatially averaged veloc...
2021 International Conference on Applied Artificial Intelligence (ICAPAI), 2021
A methodology involving machine learning with subsequent physics-based method and a visualization... more A methodology involving machine learning with subsequent physics-based method and a visualization map has been developed for enhancing environment-friendly transport planning. This methodology is applied for enabling predictions of traffic counts of bikes in Oslo city, with the physics-based models aiding in: (a) offering causality and interpretiveness for the ML predicted correlations (or patterns), and (b) more weather data for the trained machine learning to predict the traffic counts. In this work, we compare conventional ML methods (Artificial Neural network, Support Vector Machine (SVM), Random forest (RF)) for regression along with an unconventional use of sequential Long Short term memory (LSTM-RNN) based regression-formulation for predicting traffic counts of cycles at various traffic routes for both weather related and non-weather related independent variables. Amongst the various models compared, RF and LSTM model performs the best owing to nature of data. The RF quantifies the pattern that weather related variables are influencing cycle traffic count strongly, but ML being non-interpretive, it is not able to provide causation behind these correlations. A subsequent fluid dynamics-based analysis shows that weather influences thermal comfort level of cyclist and explains the observed ML predicted travel patterns. Finally, they are used together to provide a visualization map showing bike traffic count with the expectation to enable decision on infrastructure planning for the Oslo kommune (like a network of automated kiosk location). Such methodologies are a step towards encouraging better utilization of modern sharing-concepts (like, mobility-as-a-service and bike sharing).
Cardiovascular diseases, like Carotid Artery Disease and Coronary Artery Disease (CAD) are associ... more Cardiovascular diseases, like Carotid Artery Disease and Coronary Artery Disease (CAD) are associated with the narrowing of artery due to build-up of fatty substances and cholesterol deposits (called plaque). Carotid Artery Disease increases the chances of brain stroke. Hence, the main objective of this work is to apply computational tools to help differentiate between the healthy and unhealthy artery (with 25% stenosis) using a combination of Computational Fluid Dynamics (CFD) and data mining tools. In this work, first, the CFD has been qualitatively shown to provide similar results as the experimental Phase-Contrast Magnetic Resonance Imaging (PCMRI) technique. The CFD simulation shows that wall shear stress is an ideal parameter to identify the location of plaque formation and the existence of plaque conditions in the body (due to overall higher spatially averaged wall shear stress in the clogged case at all times in the cycle). Then data mining tools like Fast Fourier Transform (FFT) and Proper Orthogonal Decomposition (POD) have been used to unearth a pattern that can be useful for diagnosis. FFT shows that the flow constriction induced by plaque leads to lesser variation in magnitudes of energy of dominant frequencies at different locations like, wake region, mid-Internal Carotid Artery (mid-ICA) and mid-Common Carotid Artery (mid-CCA) regions, while for cleaner artery, there is more variation in the magnitude of energy of these dominant frequencies when measured at wake, mid ICA and mid CCA region. POD helps by confirming the location of regions with high energy in decomposed velocity modes for both the cases. More studies are required to develop a data mining based modern 21st century cardio-vascular patient care.publishedVersio
Current work involves data-driven analysis of travel mobility. For this purpose, traffic counts o... more Current work involves data-driven analysis of travel mobility. For this purpose, traffic counts of cars and bikes at various traffic routes along with the associated weather have been collected in Oslo, Norway. Amongst the 6 machine learning algorithms compared (linear regression, random forest, decision tree, gradient boost, support vector machine and artificial neural network), the random forest model is seen to perform the best with an accuracy score of around 0.96. The results indicate : a) bike traffic is more sensitive to weather than the car traffic, b) bike traffic counts showing a stronger bimodal distribution with the hour of the day for week days than the car traffic counts, thus suggesting a wider car-usage outside the bimodal peak-times. c) Monthly bike traffic counts is influenced by the weather, while the monthly car counts is influenced by the vacation periods. PREPARED BY Mandar Tabib,Adil Rasheed SIGNATURE CHECKED BY Arne Morten Kvarving SIGNATURE APPROVED BY Trond...
A validated multi-domain 1D particle-reactor model has been developed to simulate packed bed reac... more A validated multi-domain 1D particle-reactor model has been developed to simulate packed bed reactor operation. Two main components of the model are: (1) a particle model for simulating the radial distribution of chemical species and temperature within the catalyst particles and (2) a 1D reactor model for solving mass and energy transport along the length of the reactor. The model captures the effect of intra-particle heat and mass transfer phenomena on the reactor performance. Its efficacy and usability is evaluated using a thorough verification and validation campaign. Validation has been carried out through comparisons to analytical solutions for: (a) the transient thermal response of the fixed bed to a step-change in inlet feed temperature and for (b) the maximum temperature rise during an exothermic oxidation process in a chemical looping combustion (CLC) operation. Further, its performance has been verified with two well-established solvers (a 1D Euler- Euler packed bed model developed in ANSYS FLUENT and a previously published 1D model) for simulating a realistic 500kW cyclic packed bed chemical looping combustion system involving dynamic fuel-air cycling. This successful verification demonstrates the ability of the model to simulate complex cyclic packed bed reactor processes involving stiff kinetics in an efficient manner. Further, significance of particle model is evaluated for mass transfer limiting condition and this reinforces the advantage of using the proposed 1D particlereactor model in such cases.publishedVersio
Turbulence in the wake generated by airflow over buildings may produce complex airflow patterns i... more Turbulence in the wake generated by airflow over buildings may produce complex airflow patterns in the downwind region. Examples include the recirculating flow and wind deficit areas behind airport buildings and their potential impact on flight conditions at runways. In this study a computational fluid dynamics analysis of the airflow over helicopter hangars was performed, including the turbulent wakes that were generated behind the hangars. The effects of the wakes on landing and departing aircraft on the two runways (18/36 and 11/29) at Sola were estimated, based on a number of numerical simulations. With the prevailing wind direction at Sola, the airport hangars cause no effects on aircraft landing and taking off. Strong wind perpendicular to the hangars, is a different scenario and is the most critical one when it comes to wake turbulence. Simulations were therefore performed to study the impact of building wakes under such conditions. The main findings of the analysis are that aviation turbulence at free wind speeds of 20 m/s for both runways are just light to moderate at delimited runway sections behind the hangars for each of the scenarios mentioned. However, when the free wind speed increases from 20 m/s to 30 m/s (from fresh gale to violent storm), the wake turbulence may be severe for both scenarios. However, the unfavourable impact of the NEW hangar on runway 11/29 will not be as large as the impact of the OLD hangar on runway 18/36, for similar wind conditions. Oppdragsgiver: ForsvarsbyggpublishedVersio
Artificial Neural Networks and Machine Learning – ICANN 2018, 2018
This work involves the use of combined forces of data-driven machine learning models and high fid... more This work involves the use of combined forces of data-driven machine learning models and high fidelity density functional theory for the identification of new potential thermoelectric materials. The traditional method of thermoelectric material discovery from an almost limitless search space of chemical compounds involves expensive and time consuming experiments. In the current work, the density functional theory (DFT) simulations are used to compute the descriptors (features) and thermoelectric characteristics (labels) of a set of compounds. The DFT simulations are computationally very expensive and hence the database is not very exhaustive. With an anticipation that the important features can be learned by machine learning (ML) from the limited database and the knowledge could be used to predict the behavior of any new compound, the current work adds knowledge related to (a) understanding the impact of selection of influence of training/test data, (b) influence of complexity of ML algorithms, and (c) computational efficiency of combined DFT-ML methodology.
District heating networks are commonly addressed in the literature as one of the most effective s... more District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand-outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.
ACKNOWLEDGEMENT. A POP-SEP funding from SINTEF has been used for the 1D particle-reactor model so... more ACKNOWLEDGEMENT. A POP-SEP funding from SINTEF has been used for the 1D particle-reactor model software development. The multi-scale model development has been carried out as a part of the DemoCLOCK project, which is an EU FP7 funded project under the ENERGY.2010.6.1-1 programme.
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