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Thermodynamics, environmental damage cost, exergoeconomic, life cycle, and exergoenvironmental analyses of a JP-8 fueled turbodiesel aviation engine at take-off phase

The objective of this study is to evaluate the energetic, exergetic, sustainability, economic and environmental performances of a 4-cylinder turbodiesel aviation engine (TdAE) used on unmanned aerial vehicles for the take-off operation mode to assess the system with large aspects. Energy efficiency of the system is found as 43.158%, while exergy efficiency 40.655%. Thermoeconomic analysis gives information about the costs of the inlet and outlet energy and exergy flows of the engine. Hourly levelized total cost flow of the TdAE is found as 21.036 $/h, when the hourly fuel cost flow of the engine is found as 30.328 $/h. The waste exergy cost parameter is determined as 0.0144 MJ/h/$ from exergy cost-energy-mass (EXCEM) analysis, while it is estimated as 14.043 MJ/$ from modified-EXCEM analysis. Environmental damage cost analysis evaluates the cost formation of the exhaust emissions. The total environmental damage cost of the TdAE is computed as 12.895 $/h whilst specific environmental damage cost is determined as 0.054 $/MJ for 494.145 MJ/h TdAE power production. It is assessed that the main contributors to the environmental impact rate of the TdAE are the fuel consumption and the formation pollutants of combustion reaction.

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Thermodynamics, environmental damage cost, exergoeconomic, life cycle, and exergoenvironmental analyses of a JP-8 fueled turbodiesel aviation engine at take-off phase

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Oxygen Consumption with High-Flow Nasal Oxygen versus Mechanical Ventilation—An International Multicenter Observational Study in COVID–19 Patients (PROXY–COVID)

The COVID–19 pandemic led to local oxygen shortages worldwide. To gain a better understanding of oxygen consumption with different respiratory supportive therapies, we conducted an international multicenter observational study to determine the precise amount of oxygen consumption with high-flow nasal oxygen (HFNO) and with mechanical ventilation. A retrospective observational study was conducted in three intensive care units (ICUs) in the Netherlands and Spain. Patients were classified as HFNO patients or ventilated patients, according to the mode of oxygen supplementation with which a patient started. The primary endpoint was actual oxygen consumption; secondary endpoints were hourly and total oxygen consumption during the first two full calendar days. Of 275 patients, 147 started with HFNO and 128 with mechanical ventilation. Actual oxygen use was 4.9-fold higher in patients who started with HFNO than in patients who started with ventilation (median 14.2 [8.4–18.4] versus 2.9 [1.8–4.1] L/minute; mean difference 5 11.3 [95% CI 11.0–11.6] L/minute; P, 0.01). Hourly and total oxygen consumption were 4.8-fold (P, 0.01) and 4.8-fold (P, 0.01) higher. Actual oxygen consumption, hourly oxygen consumption, and total oxygen consumption are substantially higher in patients that start with HFNO compared with patients that start with mechanical ventilation. This information may help hospitals and ICUs predicting oxygen needs during high-demand periods and could guide decisions regarding the source of distribution of medical oxygen.

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Oxygen Consumption with High-Flow Nasal Oxygen versus Mechanical Ventilation—An International Multicenter Observational Study in COVID–19 Patients (PROXY–COVID)

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Flexnode Plus Project: results of testing a hydrogen-based reversible fuel cell system

Within the Flexnode Plus project the long-term degradation characteristics of a proton exchange membrane (PEM) electrolyzer (5.5 kW, AC, 1 Nm3/h H2) and fuel cell (1.0 kW, DC, 0.9 Nm3/h) was experimentally tested. The electrolyzer unit was operated at various loads and pressures for approximately 750 hours in total, while the fuel cell was operated at a constant load of 1 Ω resistance for approximately 1120 hours in total. The efficiency of the hydrogen production in the electrolyzer and the electricity production in the fuel cell was expressed using the hourly average system efficiency and average cell efficiency. Inorder to predict the state of health and remaining lifetime of the electrolyzer cell and fuel cell, the decay of the cell voltage over time was monitored and the direct mapping from aging data method was used.The electrolyzer cell showed a stable cell voltage and cell efficiency in the studied time period, with an average cell voltage decay rate of 0.5 μV/h. The average cell voltage of the fuel cell dropped with a rate of 2 μV/h during the studied time period.

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Flexnode Plus Project: results of testing a hydrogen-based reversible fuel cell system

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The role of time, weather and Google Trends in understanding and predicting web survey response

In the literature about web survey methodology, significant eorts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.

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The role of time, weather and Google Trends in understanding and predicting web survey response

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Fault Detection and Diagnosis for Indoor Air Quality in DCV systems: Application of 4S3F method and effects of DBN probabilities.

In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632

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Fault Detection and Diagnosis for Indoor Air Quality in DCV systems: Application of 4S3F method and effects of DBN probabilities.

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Design of wind and solar energy supply, to match energy demand

Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.

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Design of wind and solar energy supply, to match energy demand

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Energy, exergy, economic, environmental, and sustainability assessments of the CFM56-3 series turbofan engine used in the aviation sector

In this study, aviation, energy, exergy, environmental, exergoeconomic, and exergoenvironmental analyses are performed on a CFM56-3 series high by-pass turbofan engine fueled with Jet-A1 fuel. Specific fuel consumption and specific thrust of the engine are found to be 0.01098 kg/kN.s and 0.3178 kN/kg/s, respectively. Engine's energy efficiency is calculated as 35.37%, while waste energy ratio is obtained as 64.63%. Exergy efficiency, waste exergy rate, and fuel exergy waste ratio are forecasted as 33.32%, 33175.03 kW, and 66.68%, respectively. Environmental effect factor and ecological effect factor are computed as 2.001 and 3.001, while ecological objective function and its index are taken into account of −16597.22 kW and −1.001, respectively. Exergetic sustainability index and sustainable efficiency factor are determined as 0.5 and 1.5 for the CFM56-3 engine, respectively. Environmental damage cost rate is determined as 519.753 $/h, while the environmental damage cost index is accounted as 0.0314 $/kWh. Specific exergy cost of the engine production is found as 40.898 $/GJ from exergoeconomic analysis, while specific product exergy cost is expressed as 49.607 $/GJ from exergoenvironmental analysis. From exergoenvironmental economic analysis, specific exergy cost of fuel is computed as 10.103 $/GJ when specific exergy cost of production is determined as 40.898 $/GJ.

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Energy, exergy, economic, environmental, and sustainability assessments of the CFM56-3 series turbofan engine used in the aviation sector

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Personalized physical activity coaching: a machine learning approach

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.

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Personalized physical activity coaching: a machine learning approach

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Building and building systems energy prediction models to enhance energy flexibility control

The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.

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Products 137

Thermodynamics, environmental damage cost, exergoeconomic, life cycle, and exergoenvironmental analyses of a JP-8 fueled turbodiesel aviation engine at take-off phase

Oxygen Consumption with High-Flow Nasal Oxygen versus Mechanical Ventilation—An International Multicenter Observational Study in COVID–19 Patients (PROXY–COVID)

Flexnode Plus Project: results of testing a hydrogen-based reversible fuel cell system

The role of time, weather and Google Trends in understanding and predicting web survey response

Fault Detection and Diagnosis for Indoor Air Quality in DCV systems: Application of 4S3F method and effects of DBN probabilities.

Design of wind and solar energy supply, to match energy demand

Energy, exergy, economic, environmental, and sustainability assessments of the CFM56-3 series turbofan engine used in the aviation sector

Personalized physical activity coaching: a machine learning approach

Building and building systems energy prediction models to enhance energy flexibility control

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Products 137

Thermodynamics, environmental damage cost, exergoeconomic, life cycle, and exergoenvironmental analyses of a JP-8 fueled turbodiesel aviation engine at take-off phase

Oxygen Consumption with High-Flow Nasal Oxygen versus Mechanical Ventilation—An International Multicenter Observational Study in COVID–19 Patients (PROXY–COVID)

Flexnode Plus Project: results of testing a hydrogen-based reversible fuel cell system

The role of time, weather and Google Trends in understanding and predicting web survey response

Fault Detection and Diagnosis for Indoor Air Quality in DCV systems: Application of 4S3F method and effects of DBN probabilities.

Design of wind and solar energy supply, to match energy demand

Energy, exergy, economic, environmental, and sustainability assessments of the CFM56-3 series turbofan engine used in the aviation sector

Personalized physical activity coaching: a machine learning approach

Building and building systems energy prediction models to enhance energy flexibility control