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Crushable foam plasticity models are employed to simulate material response under essentially monotonic loading. For the plastic part of the behavior, the default crushable foam model in Abaqus/Explicit is the volumetric hardening model, where the yield surface evolves by the volumetric compacting plastic strain, and the other available model is the isotropic hardening model, where the yield curve is centrally located at the origin in the pressure—the Mises stress plane. In this study, the characteristic of two models was examined by applying them to a simple 18650 lithium-ion cylindrical cell. The computation cell model consists of the shell casing and the homogenized jelly roll which represents the electrode assembly. Both crushable foam models were calibrated to represent the homogenized mechanical properties of the jellyroll, and the load–displacement relations were compared with the experimental results. Then, we examined the deformation characteristic of jellyroll for each crushable foam model. [ABSTRACT FROM AUTHOR]

This paper presents methodologies and equipment for SI-traceable inline measurements of water content (a critical quality parameter) in solid biofuels. Inline measurement systems for water content are commonly used at CHP plants, providing continuous real-time data. However, the accuracy of these systems is in most cases unsatisfactory, mainly because the systems are not calibrated representatively for the relevant material, and until now, calibrations traceable to the SI system have not been available. To provide reliable and accurate inline water content data, new procedures and equipment for calibrating measurement systems were developed. Two reference methods for the determination of water content were developed; one measures the airflow and dewpoint of desorbed water in the air passing a test sample, while the other uses a P2O₅-sensor. Additionally, a transfer standard based on a cavity resonance sensor was developed for fast onsite calibration of the inline sensor at the power plant. This new instrument allows for quick and accurate measurements. The transfer standard is made metrologically traceable to the primary measurement standards. The entire system was demonstrated by calibrating an inline microwave-based (MW) sensor at the CHP plant of VERDO in Randers, Denmark. Thus, a complete metrological traceability chain was established from an industrial to a primary standard. [ABSTRACT FROM AUTHOR]

The present paper investigates two different strategies for model-based calibration and control of tailpipe nitrogen oxide emissions in a light-duty 3.0 L diesel engine equipped with an aftertreatment system (ATS). The latter includes a diesel oxidation catalyst (DOC), a diesel particulate filter (DPF), and an underfloor selective catalytic reduction (SCR) device, in which the injection of diesel exhaust fluid (DEF), marketed as 'AdBlue', is also taken into account. The engine was modeled in the GT-SUITE environment, and a previously developed model-based combustion controller was integrated in the model, which is capable of adjusting the start of injection of the main pulse and the total injected fuel mass, in order to achieve desired targets of engine-out nitrogen oxide emissions (NOx) and brake mean effective pressure (BMEP). First, a model-based calibration strategy consisting of the minimization of an objective function that takes into account fuel consumption and AdBlue injection was developed and assessed by exploring different weight factors. Then, a direct model-based controller of tailpipe nitrogen oxide emissions was designed, which exploits the real-time value of the SCR efficiency to define engine-out NOx emission targets for the combustion controller. Both strategies exploit the model-based combustion controller and were tested through a Model-in-the-Loop (MiL) under steady-state and transient conditions. The advantages in terms of tailpipe NOx emissions, fuel consumption, and AdBlue injection were finally discussed. [ABSTRACT FROM AUTHOR]

A bias current in the power system will cause saturation of the measuring current transformer (CT), leading to an increase in measurement error. Therefore, in this paper, we first conducted measurements of the direct current component in a 10 kV distribution system. Subsequently, a reverse extraction method for the CT distorted current under direct current bias conditions based on Random Forest Classification (RFC) and Long Short-Term Memory (LSTM) was proposed. This method involves two stages for the reverse extraction of CT distorted currents under direct current bias conditions. In the offline stage, data samples were generated by changing the operating environment of the CT. The RFC classification algorithm was used to divide the saturation levels of the CT, and for each sub-class, Particle Swarm Optimization–Long Short-Term Memory Network (PSO-LSTM) models were trained to establish the mapping relationship between the secondary distorted current and the primary current fundamental component. In the online stage, the saturated data segments were extracted from the secondary current waveform using wavelet transform, and these segments were input into the offline model for current reverse extraction. The simulation results show that the proposed method exhibited strong robustness under various CT conditions, and achieved high reconstruction accuracy for the primary current. [ABSTRACT FROM AUTHOR]

This paper presents an extended calibration procedure for mode accelerometers, which makes it possible to compare the accuracy of sensors of this type from different manufacturers. This comparison involves determining the upper bound on dynamic error for a given quality criterion, i.e., the integral square error and absolute error. Therefore, this article extends the standard calibration implemented in engineering practice using tests, providing a value for the upper bound on dynamic error as an additional parameter describing the accelerometer under consideration. This paper presents the theoretical basis for this type of solution, which is partly based on measurement data obtained from a standard calibration process and on the results of parametric identification. The charge mode accelerometer is considered here because this type of sensor is commonly used in the energy industry, as it can operate over a wide range of temperatures. The calculation results presented in this paper were obtained using MathCad 5.0 software, and the tests were carried out using an accelerometer of type 357B21. In the experimental part of this article (Results of Extended Calibration section), values for the upper bound of the dynamic error were determined for two error criteria and constrained simulation signals related to these errors. The impact of interference on the results of accelerometer tests was omitted in this paper. [ABSTRACT FROM AUTHOR]

As smart grids incorporate renewable energy sources and advanced power electronics, ensuring accurate measurement systems becomes paramount due to the increased complexity and potential sources of disturbances. This article focuses on the laboratory calibration of Rogowski coils (RCs) and merging units (MUs), which are fundamental for measuring, controlling, and monitoring digital power systems. A comprehensive digital calibration system is introduced, utilizing precise, commercially available components such as a fluxgate current transducer and a National Instrument. The system assesses magnitude and phase displacement errors under various operating conditions, including abnormal scenarios. Additionally, the impact of uncertainty sources on the measurement chain analysis is discussed, with test results conforming to established standards. This research contributes to enhancing the accuracy and reliability of measurement systems in the context of evolving smart grids. [ABSTRACT FROM AUTHOR]

As the coefficients of the Ångström–Prescott model is site-dependent, the sparsity of radiation stations in regions like the Tibetan Plateau (TP) presents challenges for model calibration. Due to the unique climate and the clean air conditions over the TP, it might be feasible to calibrate the Ångström–Prescott model with short-term observations from scientific expeditions. To test this hypothesis, we used various datasets with different lengths at four stations, together with 435 daily radiations measured during a scientific expedition at Banga in the central TP from 2014 to 2015, to calibrate the Ångström–Prescott model. We found that calibration with a 1-year data length resulted in model performances comparable to those with a 20-year data length. Analysis of the expedition observations showed that the monthly average daily radiation ranged from 15.2 MJ/m2d in December 2014 to 26.5 MJ/m2d in July 2015, with an average value of 20.6 MJ/m2d. When this set of expedition data was used for calibration, the Ångström–Prescott model performed well with an NSE (Nash–Sutcliffe efficiency) of 0.820. If no data were available for calibration, the coefficients of the Ångström–Prescott model could also be directly calculated by parameterization methods established with calibrations at the other radiation stations. In this situation, the LiuJD method performed the best with the highest NSE of 0.792, followed by the LiuXY method with an NSE of 0.764. The FAO method performed poorly with an NSE of 0.578, while the Gopinathan method performed the worst with the lowest NSE of 0.218. Thus, the best strategy to calibrate the Ångström–Prescott model in the Tibetan Plateau is to use data from local observations, even if collected over short periods. When these are not available, the coefficients of the Ångström–Prescott model should be calculated using the parameterization method established with calibrations over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The article provides an analysis of the metrological properties of a 5 MN·m torque transducer. The relative electrical signal (given in electrical units, mV/V) as a function of torque measured in both the clockwise and anticlockwise directions was monitored. To fit the data, the weighted method of least squares with both a straight-line and a cubic spline curve was used. The results of the analysis indicated that the straight-line fitting method produced smaller values of expanded uncertainty than the cubic spline fitting method. Additionally, the study confirmed the assumptions that the Monte Carlo method for propagating uncorrelated distributions was more accurate than the uncertainty propagation method, regardless of the type of curve fitting used. From the estimated uncertainty coverage corridor at selected measurement points, confidence intervals–expanded uncertainties were determined. Additionally, the fuzzy sets approach to the evaluation of uncertainty was presented, and the approximate value of the expanded uncertainty was calculated. [ABSTRACT FROM AUTHOR]

The remote calibration of instrumentation transformers is theoretically possible using synchronous measurements across a transmission line with a known impedance and a local set of calibrated voltage and current transformers. In this paper, an extension of the research results presented in our paper "On the Remote Calibration of Voltage Transformers: Validation of Opportunity" published in AMPS 2018 was undertaken by reporting on how temperature could increase the measurement uncertainty contributed by voltage and current transformers. This was needed to better understand if remote calibration could be realized under real-world conditions where temperatures at different geographical locations could be significantly different. This paper describes the influence of magnetic voltage, current transformers, and capacitive voltage transformers. Based on the research in this paper, one could conclude that the influence of magnetic voltage transformers (MVTs) is low, but, especially with current measurement transformers and capacitive voltage transformers, errors should be considered when performing remote calibrations on measurement transformers over long distances with different environmental conditions. [ABSTRACT FROM AUTHOR]

The world's first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) nuclear power plant adopts an innovative reactor type and a modular structure design. Parameter estimation and model calibration are of great significance prior to the implementation of model-based control and optimization. This paper focuses on identifying the thermal hydraulic parameters of HTR-PM over the global operating domain. The process technology and model mechanism of HTR-PM are reviewed. A parameter submodel named global parameter mapping is presented to quantify the relationship between an unknown model parameter and different operating conditions in a data-driven manner. The ideal construction of such a mapping requires reliable estimates, a well-poised sample set and an appropriate global surrogate. An adaptive model calibration scheme is designed to tackle these three issues correspondingly. First, a systematic parameter estimation approach is developed to ensure reliable estimates via heuristic subset selection consisting of estimability analysis and reliability evaluation. To capture the parameter behavior among the multiple experimental conditions and meanwhile reduce the operating cost, an adaptive experimental design is employed to guide condition testing. Experimental conditions are sequentially determined by comprehensively considering the criteria of sampling density, local nonlinearity and parameter uncertainty. Support vector regression is introduced as the global surrogate due to its capability of small-sample learning. Finally, the effectiveness of the model calibration scheme and its application performance in HTR-PM are validated by the simulation results. [ABSTRACT FROM AUTHOR]

Models based on Numerical Weather Prediction (NWP) are widely used for the day-ahead forecast of solar resources. This study is focused on the calibration of the hourly global solar irradiation forecasts provided by the Global Forecast System (GFS), a model from the NWP class. Since the evaluation of GFS raw forecasts sometimes shows a high degree of uncertainty (the relative error exceeding 100%), a procedure for reducing the errors is needed as a prerequisite for engineering applications. In this study, a deep analysis of the error sources in relation to the state of the atmosphere is reported. Of special note is the use of sky imagery in the identification process. Generally, it has been found that the largest errors are determined by the underestimation of cloud coverage. For calibration, a new ensemble forecast is proposed. It combines two machine learning approaches, Support Vector Regression and Multi-Layer Perceptron. In contrast to a typical calibration, the objective function is constructed based on the absolute error instead of the traditional root mean squared error. In terms of normalized root mean squared error, the calibration roughly reduces the uncertainty in hourly global solar irradiation by 16%. The study was conducted with high-quality ground-measured data from the Solar Platform of the West University of Timisoara, Romania. To ensure high accessibility, all the parameters required to run the proposed calibration procedures are provided. [ABSTRACT FROM AUTHOR]

Calibration of the existing building simulation model is key to correctly evaluating the energy savings that are achievable through retrofit. However, calibration is a non-standard phase where different approaches can possibly lead to different models. In this study, an existing residential building is simulated in parallel by four research groups with different dynamic simulation tools. Manual/automatic methodologies and basic/detailed measurement data sets are used. The calibration is followed by a validation on two evaluation periods. Monitoring data concerning the windows opening by the occupants are used to analyze the calibration outcomes. It is found that for a good calibration of a model of a well-insulated building, the absence of data regarding the users' behavior is more critical than uncertainty on the envelope properties. The automatic approach is more effective in managing the model complexity and reaching a better performing calibration, as the RMSE relative to indoor temperature reaches 0.3 °C compared to 0.4–0.5 °C. Yet, a calibrated model's performance is often poor outside the calibration period (RMSE increases up to 10.8 times), and thus, the validation is crucial to discriminate among multiple solutions and to refine them, by improving the users' behavior modeling. [ABSTRACT FROM AUTHOR]

Intermittent forms of renewable energy destabilize electricity grids unless adequate reliable generating capacity and storage are available, while instability of hybrid electricity grids and cost fluctuations in fossil fuel prices pose further challenges for policymakers. We examine the interaction between renewable and traditional fossil-fuel energy sources in the context of the Alberta electricity grid, where policymakers seek to eliminate coal and reduce reliance on natural gas. We develop a policy model of the Alberta grid and, unlike earlier models, calibrate the cost functions of thermal generation using positive mathematical programming. Rather than employing constant average and marginal costs, calibration determines upward sloping supply (marginal cost) functions. The calibrated model is then used to determine an optimal generation mix under different assumptions regarding carbon prices and policies to eliminate coal-fired capacity. Results indicate that significant wind capacity can enter the Alberta grid if carbon prices are high, but that it remains difficult to eliminate reliable baseload capacity. Adequate baseload coal and/or natural gas capacity is required, which is the case even if battery storage is allowed into the system. Further, significant peak-load gas capacity will also be required to backstop intermittent renewables. [ABSTRACT FROM AUTHOR]

Model-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To improve the prediction accuracy of the model with limited data, a model calibration method based on error reverse correction was investigated. A traditional optimization-based calibration method was first used for preliminary model calibration to obtain simulation data and simulation errors. Then, the sources of the simulation errors were analyzed to determine the distribution characteristics of the corresponding operating conditions of the model. Finally, the performance of the model was reversely corrected by adding a correction term to the original model. The proposed calibration method was tested on a chiller plant in Xiamen, China. The results showed that the proposed calibration method improved prediction accuracy by 2.61% (the coefficient of variation of the root mean square error (CV (RMSE)) was reduced from 3.96% to 1.35%) compared to the traditional method. The maximum mean bias error (MBE) for monthly chiller energy consumption was 2.66% with the proposed calibration method, while it was 10.42% with the traditional method. Overall, in scenarios with limited data, the proposed calibration method can effectively improve the accuracy of simulation results. [ABSTRACT FROM AUTHOR]

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring. [ABSTRACT FROM AUTHOR]

This study examines the effects on combustion, engine performance and exhaust pollutant emissions of a modern Euro 6, dual-loop EGR, compression ignition engine running on regular EN590-compliant diesel and hydrotreated vegetable oil (HVO). First, the potential of HVO as a "drop-in" fuel, i.e., without changes to the original, baseline diesel-oriented calibration, was highlighted and compared to regular diesel results. This showed how the use of HVO can reduce engine-out emissions of soot (by up to 67%), HC and CO (by up to 40%), while NOx levels remain relatively unchanged. Fuel consumption was also reduced, by about 3%, and slightly lower combustion noise levels were detected, too. HVO has a lower viscosity and a higher cetane number than diesel. Since these parameters have a significant impact on mixture formation and the subsequent combustion process, an engine pre-calibrated for regular diesel fuel could not fully exploit the potential of another sustainable fuel. Therefore, the effects of the most influential calibration parameters available on the tested engine platform, i.e., high-pressure and low-pressure EGR, fuel injection pressure, main injection timing, pilot quantity and dwell-time, were analyzed along single-parameter sweeps. The substantial reduction in engine-out soot, HC and CO levels brought about by HVO could give the possibility to implement additional measures to limit NOx emissions, combustion noise and/or fuel consumption compared to diesel. For example, higher proportion of LP EGR and/or smaller pilot quantity could be exploited with HVO, at low load, to reduce NOx emissions to a greater extent than diesel, without incurring penalties in terms of incomplete combustion species. Conversely, at higher load, delayed main injection timings and reduced rail pressure could reduce combustion noise without exceeding soot levels of the baseline diesel case. [ABSTRACT FROM AUTHOR]

In order to obtain the calibration law of rock macro and meso parameters under three-dimensional conditions, based on the parallel bond model, starting with the basic theory of PFC and the qualitative relationship between macro and meso parameters, an orthogonal experimental scheme is designed. An improved BP algorithm is proposed, which has a function with gradient factor, adaptive Nesterov momentum method, and adaptive learning rate for the lightweight analysis of meso parameters. The sensitivity between macro and meso parameters is quantified, and the key meso parameters are screened out. Based on the lightweight model, the correlation and influence mechanisms between macro and meso parameters are analyzed. It was found that the elastic modulus increases linearly with the increase in equivalent modulus. The parallel bond stiffness ratio can inhibit the growth of the elastic modulus, and the elastic modulus decreases greatly when the stiffness is relatively high. There is a linear relationship between Poisson's ratio and stiffness ratio, and the increase in the friction coefficient will cause the nonlinear decrease in it. The strength parameters have an incentive effect on the peak strength. When the tensile and shear strengths of the parallel bond are at a high level, the combination has the most significant effect on the increase in the peak strength. The internal friction angle mainly has a certain influence on the postpeak strength of the rock, because it has a control effect on the particle sliding on both sides of the shear zone when the sample is loaded after the peak. Based on the central composite experimental design and response surface method, a nonlinear model of macro–meso parameters described by multiple subresponse surfaces is obtained. Finally, the mathematical model of parameter calibration is established, and the optimal solution of rock meso parameters is obtained by using optimization techniques. Through the example verification, it was found that the numerical experiment and laboratory test results are close in the stress characteristics, stress evolution, and failure mode of the sample, which proves the effectiveness and reliability of the calibration method. The research results have a certain reference value for PFC parameter calibration. [ABSTRACT FROM AUTHOR]

A variety of mathematical models are available to estimate the thermal performance of buildings. Nevertheless, mathematical models predict the thermal performance of buildings that might differ from the actual performance. The hot box is a widely-used test apparatus to assess the actual thermal performance of various building envelope components (walls, roofs, windows) in the laboratory. This paper presents the process of designing, constructing, and calibrating a unique small-scale hot box apparatus. Despite its smaller metering area (1.0 m × 1.0 m), this apparatus met the key requirements (below ±0.25 °C fluctuations in chambers' air temperature, and below 2.0% variation from the point-to-point temperature in reference to the temperature difference across the specimen) as prescribed in the ASTM C1363 and ISO 8990 standards. The walls of this apparatus are uniquely constructed using vacuum insulation panels or VIPs. The efficient and novel use of VIPs and workmanship during the construction of the apparatus are demonstrated through the temperature stability within the chambers. The achieved range of temperature steadiness below ±0.05 °C and point-to-point temperature variation below 1.0% of the temperature difference across the specimen allow for this apparatus to be considered unique among the calibrated hot box categories reported in the literature. In addition, having an affordable, simple-to-operate, and high-accuracy facility offers a great opportunity for researchers and practitioners to investigate new ideas and solutions. The apparatus was calibrated using two extruded polystyrene foam (XPS) specimens with thicknesses of 2″ and 4″. The calibration exercise indicates small differences between results obtained numerically, theoretically, and experimentally (below 3.0%). Ultimately, the apparatus was employed to measure the thermal properties of a specimen representing a lightweight steel framing (LSF) wall system, which is commonly used in cold climates. The results obtained experimentally were then compared to the ones estimated numerically using a 3D finite element modelling tool. The difference between the results obtained by both methods was below 9.0%. [ABSTRACT FROM AUTHOR]

The prediction of heat transfers in Reynolds-Averaged Navier–Stokes (RANS) simulations requires corrections for rough surfaces. The turbulence models are adapted to cope with surface roughness impacting the near-wall behaviour compared to a smooth surface. These adjustments in the models correctly predict the skin friction but create a tendency to overpredict the heat transfers compared to experiments. These overpredictions require the use of an additional thermal correction model to lower the heat transfers. Finding the correct numerical parameters to best fit the experimental results is non-trivial, since roughness patterns are often irregular. The objective of this paper is to develop a methodology to calibrate the roughness parameters for a thermal correction model for a rough curved channel test case. First, the design of the experiments allows the generation of metamodels for the prediction of the heat transfer coefficients. The polynomial chaos expansion approach is used to create the metamodels. The metamodels are then successively used with a Bayesian inversion and a genetic algorithm method to estimate the best set of roughness parameters to fit the available experimental results. Both calibrations are compared to assess their strengths and weaknesses. Starting with unknown roughness parameters, this methodology allows calibrating them and obtaining between 4.7% and 10% of average discrepancy between the calibrated RANS heat transfer prediction and the experimental results. The methodology is promising, showing the ability to finely select the roughness parameters to input in the numerical model to fit the experimental heat transfer, without an a priori knowledge of the actual roughness pattern. [ABSTRACT FROM AUTHOR]

The digital transformation of electrical substations relies on the communication protocols introduced in the IEC Std 61850. In particular, the Sampled Value data format is expected to be employed in an ever-increasing number of measurement applications and devices. In this context, National Metrological Institutes are required to develop calibration infrastructures capable of dealing with synchronous analog and digital input and output channels. In this paper, we characterize the performance of the METAS calibrator in terms of transmission latency and publishing rate stability. In particular, we discuss the feasibility of meeting the stringent requirements of meterological applications even in a non real-time operating system. To this end, an enhanced software library and measurement procedure are introduced. The proposed results confirm the quasi-deterministic behavior of the calibrator and proves its refined precision and stability with respect to commercial devices. [ABSTRACT FROM AUTHOR]

Adaptive calibration for ultrasound reflectometry methods used in the detection of lubrication film thickness is of great research interest. This is mainly due to the versatile non-destructive implementation of the technology in industrial applications, allowing for measurements of the lubrication film thickness, which directly relates to the friction, wear, and overall efficiency of the system. This study reviews and compares a curve fitting, extended Kalman filter, and resonance frequency detection adaptive calibration approach. Furthermore, the study compares two different regression models, used for the curve fitting and the extended Kalman filter approach. The study compares the methods and regression models based on both a theoretical and experimental analysis. The experimental analysis is based on data with varying lubrication film thickness. The lubrication film thickness is varied such that the resonance frequency is both detectable and non-detectable within the ultrasound transducer bandwidth. It is found that all three methods give results very similar to those achieved through manual calibration. However, since the extended Kalman filter approach is the only method that allows for continuous calibration, this method is found to have the greatest potential of the methods compared. [ABSTRACT FROM AUTHOR]

Lithium batteries for energy storage systems are a prominent solution for both stationary and mobile applications. Electro-thermal modelling of the cell is a useful tool for monitoring voltage and temperature in order to predict battery behaviour especially in cases of critical operative conditions. This paper provides a modelling approach focusing on the calibration of parameters of an electro-thermal model for large prismatic LFP lithium cells. The designed model is tuned by means of experimental tests that identify a set of parameters that are function of a cell's state-of-charge and temperature. The model outputs are voltage, cell surface, and internal temperature profiles, which are validated against experimental data referring to realistic working conditions, even providing an intense level of thermal stress. The model accuracy is marked by a voltage mean average error lower than 1% and a mean cell surface temperature deviation lower than 1 K. [ABSTRACT FROM AUTHOR]

Energy management strategies have a significant impact on the hydrogen economy of fuel cell trucks and the lifetime of battery and fuel cell systems. This contribution presents the design and optimal calibration of an energy management strategy that is adaptive to the battery and ambient temperatures. Indeed, fuel cell trucks face critical operating conditions due to high ambient temperatures or high loads on long uphill roads. However, the presented adaptive energy management strategy shifts the electric loads to the fuel cell system to limit the battery usage, avoiding accelerated degradation due to battery temperature peaks without hindering the hydrogen economy. The strategy design and calibration involves a multi-objective optimization of performance indicators related to hydrogen consumption, fuel cell degradation, battery thermal state, equivalent charge/discharge cycles, and charge control. This work uses AVL CAMEO to systematically vary the adaptive curve parameters to explore the trade-off between the key performance indicators. The calibration considers real-world driving cycles of road freight vehicles, including measured speed, road elevation, and variable vehicle mass. Moreover, the energy management design is robust because the performance indicators are evaluated over 8935 km, covering an extensive range of real-world driving scenarios. Eventually, the adaptive and predictive energy management strategy proposed in this work can meet all the performance targets thanks to the optimal calibration, and it is particularly effective in avoiding battery temperature peaks. [ABSTRACT FROM AUTHOR]

This article describes a complete characterization system for photovoltaic devices designed to acquire the current-voltage curve and to process the obtained data. The proposed system can be replicated for educational or research purposes without having wide knowledge about electronic engineering. Using standard calibrated instrumentation, commonly available in any laboratory, the accuracy of measurements is ensured. A capacitive load is used to bias the device due to its versatility and simplicity. The system includes a common part and an interchangeable part that must be designed depending on the electrical characteristics of each PV device. Control software, developed in LabVIEW, controls the equipment, performs automatic campaigns of measurements, and performs additional calculations in real time. These include different procedures to extrapolate the measurements to standard test conditions and methods to obtain the intrinsic parameters of the single diode model. A deep analysis of the uncertainty of measurement is also provided. Finally, the proposed system is validated by comparing the results obtained from some commercial photovoltaic modules to the measurements given by an independently accredited laboratory. [ABSTRACT FROM AUTHOR]

In the present work, the research derived from a wide experience on cup anemometer calibration works at IDR/UPM Institute (Instituto Universitario de Microgravedad "Ignacio Da Riva") is summarized. This research started in 2008, analyzing large series of calibrations, and is focused on two main aspects: (1) developing a procedure to predict the degradation level of these wind sensors when working on the field and (2) modeling cup anemometer performances. The wear and tear level of this sensor is evaluated studying the output signal and its main frequencies through Fourier analysis. The modeling of the cup anemometer performances is carried out analyzing first the cup aerodynamics. As a result of this process, carried out through several testing and analytical studies since 2010, a new analytical method has been developed. This methodology might represent an alternative to the classic approach used in the present standards of practice such as IEC 64000-12. [ABSTRACT FROM AUTHOR]

Cooling towers discharge waste heat from an industrial process into the atmosphere, and are essential for the functioning of large energy-producing plants, including nuclear reactors. Using a numerical simulation model of the cooling tower together with measurements of outlet air relative humidity, outlet air and water temperatures enables the quantification of the rate of thermal energy dissipation removed from the respective process. The computed quantities depend on many model parameters including correlations, boundary conditions, material properties, etc. Changes in these model parameters will induce changes in the computed quantities of interest (called "model responses"). These changes are quantified by the functional derivatives (called "sensitivities") of the model responses with respect to the model parameters. These sensitivities are computed in this work by applying the general Adjoint Sensitivity Analysis Methodology (ASAM) for nonlinear systems. These sensitivities are needed for: (i) ranking the parameters in their importance to contributing to response uncertainties; (ii) propagating the uncertainties (covariances) in these model parameters to quantify the uncertainties (covariances) in the model responses; (iii) performing predictive modeling, including assimilation of experimental measurements and calibration of model parameters to produce optimal predicted quantities (both model parameters and responses) with reduced predicted uncertainties. [ABSTRACT FROM AUTHOR]

The paper outlines a procedure for the computer-controlled calibration of the combined zero-dimensional (0D) and one-dimensional (1D) thermodynamic simulation model of a turbocharged internal combustion engine (ICE). The main purpose of the calibration is to determine input parameters of the simulation model in such a way as to achieve the smallest difference between the results of the measurements and the results of the numerical simulations with minimum consumption of the computing time. An innovative calibration methodology is based on a novel interaction between optimization methods and physically based methods of the selected ICE sub-systems. Therein physically based methods were used for steering the division of the integral ICE to several sub-models and for determining parameters of selected components considering their governing equations. Innovative multistage interaction between optimization methods and physically based methods allows, unlike the use of well-established methods that rely only on the optimization techniques, for successful calibration of a large number of input parameters with low time consumption. Therefore, the proposed method is suitable for efficient calibration of simulation models of advanced ICEs. [ABSTRACT FROM AUTHOR]

Fuel economy efficiency is one of the most important parameters for vehicle powertrains, which is of particular interest for heavy-duty powertrain calibration. Conventionally, this work relies heavily on road tests, which cost more and may lead to long duration product development cycles. The paper proposes a novel hardware-in-loop modeling and calibration method to work it out. A dSPACE hardware-based test bench was successfully established and validated, which is valuable for a more efficient and easier shift schedule in calibration. Meanwhile, a real-time dynamic powertrain model, including a diesel engine, torque converter, gear box and driver model was built. Typical driving cycles that both velocity and slope information were constructed for different road conditions. A basic economic shift schedule was initially calculated and then optimal calibrated by the test bench. The results show that there is an optimal relationship between an economic shift schedule and speed regulation. By matching the best economic shift schedule regulation to different road conditions; the fuel economy of vehicles can be improved. In a smooth driving cycle; when the powertrain applies a larger speed regulation such as 12% and the corresponding shift schedule; the fuel consumption is smaller and is reduced by 13%. In a complex driving cycle, when the powertrain applies a smaller speed regulation such as 5% along with the corresponding shift schedule; the fuel consumption is smaller and is reduced by 5%. The method thus can provide guidance for economic calibration experiments of off-road heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

Buildings do not usually perform during operation as well as predicted during the design stage. Disagreement between simulated and metered energy consumption represents a common issue in building simulation. For this reason, the calibration of building simulation models is of growing interest. Sensitivity and uncertainty analyses play an important role in building model accuracy. They can be used to identify the building model parameters most influent on the energy consumption. Given this, these analyses should be integrated within calibration methodologies and applications for tuning the parameters. This paper aims at providing a picture of the state of the art of calibration methodologies in the domain of building energy performance assessment. First, the most common methodologies for calibration are presented, emphasizing criticalities and gaps that can be faced. In particular the main issues to be addressed, when carrying out calibrated simulation, are discussed. The standard statistical criteria for considering the building models calibrated and for evaluating their goodness-of-fit are also presented. Second, the commonly used techniques for investigating uncertainties in building models are reviewed. Third, a review of the latest main studies in the calibrated simulation domain is presented. Criticalities and recommendations for new studies are finally provided. [ABSTRACT FROM AUTHOR]

A common challenge among internal combustion engine (ICE) manufacturers is shortening the development time while facing requirements and specifications that are becoming more complex and border in scope. Virtual simulation and calibration are effective instruments in the face of these demands. This article presents the development of zero-dimensional (0D)—real-time engine and exhaust after-treatment system (EAS) models and their deployment on a Virtual test bench (VTB). The models are created using a series of measurements acquired in a real test bench, carefully performed in view of ensuring the highest reliability of the models themselves. A zero-dimensional approach was chosen to guarantee that models could be run in real-time and interfaced to the real engine Electronic Control Unit (ECU). Being physically based models, they react to changes in the ECU calibration parameters. Once the models are validated, they are then integrated into a Simulink® based architecture with all the Inputs/Outputs connections to the ECU. This Simulink® model is then deployed on a Hardware in the Loop (HiL) machine for ECU testing and calibration. The results for engine and EAS performance and emissions align with both steady-state and transient measurements. Finally, two different applications of the HiL system are presented to explain the opportunities and advantages of this tool integrated within the standard engine development. Examples cited refer to altitude calibration activities and soot loading investigation on vehicle duty cycles. The cases described in this work are part of the actual development of one of the latest engines developed by Kohler Engines: the KDI 1903 TCR Stage V. The application of this methodology reveals a great potential for engine development and may become an essential tool for calibration engineers. [ABSTRACT FROM AUTHOR]

The Trombe wall is a passive system used in buildings that indirectly transfers thermal energy to the adjacent environment by radiation and convection, and directly by the thermo-circulation that arises in the air cavity delimited between a transparent and an absorbing surface. Nevertheless, the latter is painted black to increase the energy gains, but this produces a negative visual impact and promotes the overheating risk in summer. To mitigate these aspects, a hybrid Trombe wall equipped with PV panels can be employed. The PV installation results in a more pleasing wall appearance and the overheating risk reduces because part of the absorbed solar radiation is transformed into electricity. To determine the actual performance of a such system, transient simulation tools are required to consider properly the wall thermal storage features, variation of the optical properties, air thermo-circulation, and PV power production. Alternatively, regarding the traditional Trombe wall, the literature provides a simplified empirical method based on the dimensionless parameter solar load ratio (SLR) that allows for preliminary evaluations and design. In this paper, the SLR method was calibrated to determine the monthly auxiliary energy to be supplied in buildings equipped with PV-Trombe walls in heating applications. The SLR method was tuned by a multiple linear regression by data provided by TRNSYS simulation that allowed to obtain the energy performances in actual conditions of PV-Trombe walls installed on the same building but located in different localities. The comparison between the TRNSYS results and the calibrated SLR method determined average errors ranging between 0.7% and 1.4%, demonstrating the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

For effective mining, it is essential to ensure that the picks are positioned correctly on the working unit of a mining machine. This is due to the fact that the design of roadheader cutting heads/drums using computer-aided tools is based on the operating conditions of the roadheader/shearer/milling machine. The geometry of the cutting head is optimized for selected criteria by simulating the mining process using a computer. The reclaimed cutting head bodies that are utilized in production are manufactured again in the overhaul process. Ensuring that the dimensions of the cutting head bodies match the rated dimensions is labor-intensive and involves high production costs. For dimensional deviations of the cutting head bodies, it is necessary to control the position of the pick holders relative to the cutting head side surface in real time during robotic-assisted assembly. This article discusses the possibility of utilizing a stereovision system for calculating the distance between the pick holder base and the roadheader cutting head side surface at the point where the pick holder is mounted. The proposed measurement method was tested on a robotic measurement station constructed for the purpose of the study. A mathematical measurement model and procedures that allow automatic positioning of the camera system to the photographed objects, as well as acquisition and analysis of the measurement images, were developed. The proposed method was validated by using it for measuring the position of the pick holders relative to the side surface of the working unit of a mining excavating machine, focusing on its application in robotic technology. The article also includes the results observed in laboratory tests performed on the developed measurement method with an aim of determining its suitability for the metrology task under consideration. [ABSTRACT FROM AUTHOR]

One of the most important steps in the retrofitting process of a building is to understand its pre-retrofitting stage energy performance. The best choice for carrying this out is by means of a calibrated building energy simulation (BES) model. Then, the testing of different retrofitting solutions in the validated model allows for quantifying the improvements that may be obtained, in order to choose the most suitable solution. In this work, based on the available detailed building drawings, constructive details, building operational data and the data sets obtained on a minute basis (for a whole year) from a dedicated energy monitoring system, the calibration of an in-use office building energy model has been carried out. It has been possible to construct a detailed white box model based on Design Builder software. Then, comparing the model output for indoor air temperature, lighting consumption and heating consumption against the monitored data, some of the building envelope parameters and inner building inertia of the model were fine tuned to obtain fits fulfilling the ASHRAE criteria. Problems found during this fitting process and how they are solved are explained in detail. The model calibration is firstly performed on an hourly basis for a typical winter and summer week; then, the whole year results of the simulation are compared against the monitored data. The results show a good agreement for indoor temperature, lighting and heating consumption compared with the ASHRAE criteria for the mean bias error (MBE). [ABSTRACT FROM AUTHOR]

A daylighting control system that uses the closed loop proportional algorithm needs to be calibrated both during nighttime and daytime. The selection of the daytime calibration time can affect the behavior of the system and is usually performed when the ratio of the illuminance of the ceiling sensor (SD,tc) to the illuminance at a point on the working plane (ED,tc) is relatively large without sunlight patches in the ceiling photo-sensor's field of view (FOV). However, this requirement is not associated with a specific value and can be achieved under a wide range of conditions related to the sky luminance distribution. In the present work, four ceiling sensors with different field of views (FOVs) were examined in a typical north-facing office space. The effect of daytime calibration on the system's performance was estimated through the calculation of lighting energy savings and the overdimming percentage. The results show that the effect of both the FOV of the ceiling sensor and the daytime calibration period is small except for the case of the sensor without cover, especially when it is close to the opening. In an attempt to quantify the SDtc/IDtc ratio, a new magnitude (RR) is proposed by dividing the illuminance ratios of the ceiling photo-sensor by that on the working surface during daytime and nighttime calibration. Thus, the daily calibration of the sensors with cover can be performed when RR > 1. [ABSTRACT FROM AUTHOR]

Gas flaring represents a large waste of a natural resource for energy production and is a significant source of greenhouses gases to the atmosphere. The World Bank estimates annual flared gas volumes of 150 billion cubic meters, based upon a conversion of remotely sensed radiant heat data from the NOAA's VIIRS (Visible Infrared Imaging Radiometer Suite) instrument onboard the polar-orbiting Suomi NPP satellite. However, the conversion of the remotely sensed radiant heat measurements into flared gas volumes currently depends on flare operator reported volumes, which can be biased in various ways due to inconsistent reporting requirements. Here, I discuss both known and unknown biases in the datasets, using them to illustrate the current lack of accuracy in the widely discussed flaring numbers. While volume trends over time could be derived directly from the radiant heat data, absolute amounts remain questionable. Standardizing how flared gas volumes are measured and reported could dramatically improve accuracy. In addition, I suggest expanding satellite measurements of individual flares burning under controlled conditions as a major improvement to daily monitoring, alongside the potential usage of remotely sensed flare temperature to estimate combustion efficiency. [ABSTRACT FROM AUTHOR]

Consequences of failure of offshore wind turbines (OWTs) is in general lower than consequences of failure of, e.g., oil & gas platforms. It is reasonable that lower fatigue design factors can be applied for fatigue design of OWTs when compared to other fixed offshore structures. Calibration of appropriate partial safety factors/Fatigue Design Factors (FDF) for steel substructures for OWTs is the scope of this paper. A reliability-based approach is used and a probabilistic model has been developed, where design and limit state equations are established for fatigue failure. The strength and load uncertainties are described by stochastic variables. SN and fracture mechanics approaches are considered for to model the fatigue life. Further, both linear and bi-linear SN-curves are formulated and various approximations are investigated. The acceptable reliability level for fatigue failure of OWTs is discussed and results are presented for calibrated optimal fatigue design factors. Further, the influence of inspections is considered in order to extend and maintain a given target safety level. [ABSTRACT FROM AUTHOR]

The deviation of calibration coefficients from five cup anemometer models over time was analyzed. The analysis was based on a series of laboratory calibrations between January 2001 and August 2010. The analysis was performed on two different groups of anemometers: (1) anemometers not used for any industrial purpose (that is, just stored); and (2) anemometers used in different industrial applications (mainly in the field∂or outside-applications like wind farms). Results indicate a loss of performance of the studied anemometers over time. In the case of the unused anemometers the degradation shows a clear pattern. In the case of the anemometers used in the field, the data analyzed also suggest a loss of performance, yet the degradation does not show a clear trend. A recalibration schedule is proposed based on the observed performances variations. [ABSTRACT FROM AUTHOR]

The effect of air density variations on the calibration constants of several models of anemometers has been analyzed. The analysis was based on a series of calibrations between March 2003 and February 2011. Results indicate a linear behavior of both calibration constants with the air density. The effect of changes in air density on the measured wind speed by an anemometer was also studied. The results suggest that there can be an important deviation of the measured wind speed with changes in air density from the one at which the anemometer was calibrated, and therefore the need to take this effect into account when calculating wind power estimations. [ABSTRACT FROM AUTHOR]

A newly developed measurement system for current density distribution mapping has enabled a new approach for operational measurements in proton exchange membrane fuel cells (PEMFC). Taking into account previously constructed measurement systems, a method based on a multi layer printed circuit board was chosen for the development of the new system. This type of system consists of a sensor, a special electronic device and the control and visualization PC. For the acquisition of the current density distribution values, a sensor device was designed and installed within a multilayer printed circuit board with integrated shunt resistors. Varying shunt values can be taken into consideration with a newly developed and evaluated calibration method. The sensor device was integrated in a PEM fuel cell stack to prove the functionality of the whole measurement system. A software application was implemented to visualize and save the measurement values. Its functionality was verified by operational measurements within a PEMFC system. Measurement accuracy and possible negative reactions of the sensor device during PEMFC operation are discussed in detail in this paper. The developed system enables operational measurements for different operating phases of PEM fuel cells. Additionally, this can be seen as a basis for new opportunities of optimization for fuel cell design and operation modes. [ABSTRACT FROM AUTHOR]

In view of the deliberations on new Euro 7 emission standards to be introduced by 2025, original equipment manufacturers (OEMs) are already hard at work to further minimise the pollutant emissions of their vehicles. A particular challenge in this context will be compliance with new particulate number (PN) limits. It is expected that these will be tightened significantly, especially by including particulates down to 10 nm. This will lead to a substantially increased effort in the calibration of gasoline particulate filter (GPF) control systems. Therefore, it is of great interest to implement advanced methods that enable shortened and at the same time more accurate GPF calibration techniques. In this context, this study presents an innovative GPF calibration procedure that can enable a uniquely efficient development process. In doing so, some calibration work packages involving GPF soot loading and regeneration are transferred to a modern burner test bench. This approach can minimise the costly and time-consuming use of engine test benches for GPF calibration tasks. Accurate characterisation of the particulate emissions produced after a cold start by the target engine in terms of size distribution, morphology, and the following exhaust gas backpressure and burn-off rates of the soot inside the GPF provides the basis for a precise reproduction and validation process on the burner test bench. The burner test bench presented enables the generation of particulates with a geometric mean diameter (GMD) of 35 nm, exactly as they were measured in the exhaust gas of the engine. The elemental composition of the burner particulates also shows strong similarities to the particulates produced by the gasoline engine, which is further confirmed by matching burn-off rates. Furthermore, the exhaust backpressure behaviour can accurately be reproduced over the entire loading range of the GPF. By shifting GPF-related calibration tasks to the burner test bench, total filter loading times can be reduced by up to 93%. [ABSTRACT FROM AUTHOR]

The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential. An approach for such a combination is presented in this paper. Together with a Hardware-in-the-Loop (HiL) test bench, the novel methodology enables a targeted calibration process, specifically designed to address calibration challenges of hybridized powertrains. Virtual tests executed on a HiL test bench are used to efficiently generate data characterizing the behavior of the system under various conditions with a statistically based evaluation identifying white spots in measurement data, used for calibration and emission validation. In addition, critical sequences are identified in terms of emission intensity, fuel consumption or component conditions. Dedicated test scenarios are generated and applied on the HiL test bench, which take into account the state of the system and are adjusted depending on it. The example of one emission calibration use case is used to illustrate the benefits of using a HiL platform, which achieves approximately 20% reduction in calibration time by only showing differences of less than 2% for fuel consumption and emission levels compared to real vehicle tests. [ABSTRACT FROM AUTHOR]

When using the explicit dynamic relaxation approach (DRA) to model the quasi-static rock breakage, fragmentation, and flow problems, especially the top-coal caving question, introducing numerical damping into the solution equation is inevitable for reducing the vibration frequency and impact speed of mesh nodes, which is significantly affect the fidelity of the computation results. Although the DRA has been widely adopted to simulate top-coal caving, the reasonable value and calibration method of numerical damping are still open issues. In this study, the calibration process of reasonable numerical damping for modeling top-coal caving is investigated by comparing with the experimental results, in which several geometry parameters of the drawing funnel are selected as the calibration indexes. The result shows that the most reasonable numerical damping value is 0.07 for the numerical modeling of interval top-coal caving in extra-thick coal seams. Finally, the correlation between the numerical damping and the physical top-coal drawing process is discussed. The numerical damping indirectly reflects the fragmentation in multi scale of coal mass and friction interaction between coal particles during the caving process, which reduces the vibration intensity of the top-coal caving system and dissipates the kinetic energy. [ABSTRACT FROM AUTHOR]

Determining gas compositions from live well fluids on a drilling rig is critical for real time formation evaluation. Development and utilization of a reliable mass spectrometric method to accurately characterize these live well fluids are always challenging due to lack of a robust and effectively selective instrument and procedure. The methods currently utilized need better calibration for the characterization of light hydrocarbons (C1–C6) at lower concentrations. The primary goal of this research is to develop and optimize a powerful and reliable analytical method to characterize live well fluid using a quadruple mass spectrometer (MS). The mass spectrometers currently being used in the field have issues with detection, spectra deconvolution, and quantification of analytes at lower concentrations (10–500 ppm), particularly for the lighter (<30 m/z) hydrocarbons. The objectives of the present study are thus to identify the detection issues, develop and optimize a better method, calibrate and QA/QC the MS, and validate the MS method in lab settings. In this study, we used two mass spectrometers to develop a selective and precise method to quantitatively analyze low level lighter analytes (C1–C6 hydrocarbons) with masses <75 m/z at concentrations 10–500 ppm. Our results suggest that proper mass selection like using base peaks with m/z 15, 26, 41, 43, 73, and 87, respectively, for methane, ethane, propane, butane, pentane, and hexane can help detect and accurately quantify hydrocarbons from gas streams. This optimized method in quadrupole mass spectrometer (QMS) will be invaluable for early characterization of the fluid components from a live hydrocarbon well in the field in real time. [ABSTRACT FROM AUTHOR]

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model's performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-I) batteries have recently become pervasive and are used in many physical assets. For the effective management of the batteries, reliable predictions of the end-of-discharge (EOD) and end-of-life (EOL) are essential. Many detailed electrochemical models have been developed for the batteries. Their parameters are calibrated before they are taken into operation and are typically not re-calibrated during operation. However, the degradation of batteries increases the reality gap between the computational models and the physical systems and leads to inaccurate predictions of EOD/EOL. The current calibration approaches are either computationally expensive (model-based calibration) or require large amounts of ground truth data for degradation parameters (supervised data-driven calibration). This is often infeasible for many practical applications. In this paper, we introduce a reinforcement learning-based framework for reliably inferring calibration parameters of battery models in real time. Most importantly, the proposed methodology does not need any labeled data samples of observations and the ground truth parameters. The experimental results demonstrate that our framework is capable of inferring the model parameters in real time with better accuracy compared to approaches based on unscented Kalman filters. Furthermore, our results show better generalizability than supervised learning approaches even though our methodology does not rely on ground truth information during training. [ABSTRACT FROM AUTHOR]

In Thailand, Napier grass is expected to play an important role as an energy resource for future power generation. To accomplish this goal, numerous areas are required for Napier grass plantations. Before introducing crops, the land potential of the country and the impact of crops on the environment should be assessed. The soil and water assessment tool (SWAT) model is very useful in investigating crop impacts and land potential. Unfortunately, the crop growth parameters of Napier grass are yet to be identified and, thus, conducting effective analysis has not been possible. Accordingly, in this study, parameter calibration and SWAT model validation of Napier grass production in Thailand was carried out using datasets from eight sites with 93 samples. Parameter sensitivity analysis was performed prior to parameter calibration, the results of which suggest that the radiation use efficiency and potential harvested index are both highly sensitive. The crop growth parameters were calibrated in order of their sensitivity index ranking, and the final values were obtained by reducing the root mean square error from 10.77 to 1.38 t·ha−1. The validation provides satisfactory results with coefficient of determination of 0.951 and a mean error of 0.321 t·ha−1. Using the developed model and calibrated parameters, local Napier grass dry matter yield can be evaluated accurately. The results reveal that, if only abandoned area in Thailand is used, then Napier grass can provide roughly 33,600–44,900 GWh of annual electricity, and power plant carbon dioxide (CO2) emissions can be reduced by approximately 21.2–28.3 Mt-CO2. The spatial distribution of estimated yield obtained in this work can be further utilized for land suitability analysis to help identify locations for Napier grass plantations, anaerobic digesters, and biogas power plants. [ABSTRACT FROM AUTHOR]

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient ( R 2 ). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house. [ABSTRACT FROM AUTHOR]

In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach. [ABSTRACT FROM AUTHOR]

The paper describes a unique method of heat flux measurement, i.e., gradient heatmetry. Gradient heatmetry is performed using gradient heat flux sensors (GHFS) developed on the anisotropic thermoelements basis. The principle of GHFS' operation leads to the fact that their response time is about 10 ns, and the volt-watt sensitivity does not depend on the thickness. GHFS are compared with other types heat flux sensors, with the GHFS features depending on the materials being described. The theory and examples of gradient heatmetry applications in thermophysical experiment are provided. [ABSTRACT FROM AUTHOR]

The performance of wind turbines is not only dependent on the wind turbine design itself, but is also dependent on the accurate assessment of wind resources at the installation site. In this paper, the numerical site calibration (NSC) method using three-dimensional Reynolds-averaged Navier–Stokes (RANS) simulation was proposed to accurately forecast the wind flow characteristics of wind turbine sites with complex terrains, namely Methil in Scotland, and Haenam in South Korea. From NSC at the Methil and Haenam sites, it was shown that the complicated and vortical flow fields around hills and valleys were captured using the three-dimensional RANS CFD simulation in Ansys CFX software based on a high-resolution scheme with a renormalization group (RNG)-based k-ε turbulence model. It was also shown that topographically induced wind profile and turbulence intensity over a local-scale complex terrain are remarkably dominated by flow separation after passing hills. It was concluded that the proposed NSC method using three-dimensional RANS simulation with a high-resolution scheme was an economically useful method for evaluating wind flow characteristics numerically to assess wind turbine sites with complex terrains and designing the wind farm layout. [ABSTRACT FROM AUTHOR]