scholarly journals Building a comprehensive dataset for the validation of daylight simulation software, using complex "real architecture"

2021 ◽  
Author(s):  
◽  
Jake Osborne
Keyword(s):  
Weather Conditions ◽  
Simulation Software ◽  
Weather Data ◽  
Validation Dataset ◽  
Sensitivity Testing ◽  
Validation Data ◽  
Building Simulations ◽  
Weather Model ◽  
Single Room ◽  
Wide Range

<p>This research focused on building a comprehensive dataset for use in validation studies of daylight simulation software. The aim of the set is to add to existing validation data to better cover a wide range of complexities and weather conditions. This will allow for not only the validation of simulation software, but the comparison of multiple simulators in their general strengths and weaknesses as well as feasibility for early ‘sketch’ design stages and complete building simulations. The set can also aid in the creation of valid simulation parameter starting points for designers.  The research examined the current ‘gold standard’ validation dataset from the BRE-IDMP, and found that while it provides excellent validation opportunities for simulators that can support its detailed patch-based sky model; an equally high quality dataset is needed for simulators that support more simplified skies. This is essential as most of the weather data for annual daylighting simulations available to designers, such as the US-DOE’s collection of TMY data, can only be used in mathematical sky models such as the Perez all-weather model. It is also essential that real world, complex light-path scenarios commonly found in buildings be addressed by validation in addition to the simple single room, single opening tests which are prevalent in the daylight simulation field.  A dataset suite is proposed, similar to the BESTEST suite for energy simulation, which covers basic analytical test cases for lighting simulators, simple office scenarios and a complex shaded classroom in a tropical climate. The dataset is valuable for the testing of daylight simulators which make use of the common CIE general or Perez all-weather skies. These datasets were used in a trial validation of Autodesk’s 3ds Max Design and Radiance, which included significant sensitivity testing of the two empirical datasets included in the suite. This demonstrated the usefulness of each dataset, and any issues with their data. It also highlighted the key inputs of any simulation model where designers must take significant care.</p>

scholarly journals Building a comprehensive dataset for the validation of daylight simulation software, using complex "real architecture"

2021 ◽  
Author(s):  
◽  
Jake Osborne
Keyword(s):  
Weather Conditions ◽  
Simulation Software ◽  
Weather Data ◽  
Validation Dataset ◽  
Sensitivity Testing ◽  
Validation Data ◽  
Building Simulations ◽  
Weather Model ◽  
Single Room ◽  
Wide Range

<p>This research focused on building a comprehensive dataset for use in validation studies of daylight simulation software. The aim of the set is to add to existing validation data to better cover a wide range of complexities and weather conditions. This will allow for not only the validation of simulation software, but the comparison of multiple simulators in their general strengths and weaknesses as well as feasibility for early ‘sketch’ design stages and complete building simulations. The set can also aid in the creation of valid simulation parameter starting points for designers.  The research examined the current ‘gold standard’ validation dataset from the BRE-IDMP, and found that while it provides excellent validation opportunities for simulators that can support its detailed patch-based sky model; an equally high quality dataset is needed for simulators that support more simplified skies. This is essential as most of the weather data for annual daylighting simulations available to designers, such as the US-DOE’s collection of TMY data, can only be used in mathematical sky models such as the Perez all-weather model. It is also essential that real world, complex light-path scenarios commonly found in buildings be addressed by validation in addition to the simple single room, single opening tests which are prevalent in the daylight simulation field.  A dataset suite is proposed, similar to the BESTEST suite for energy simulation, which covers basic analytical test cases for lighting simulators, simple office scenarios and a complex shaded classroom in a tropical climate. The dataset is valuable for the testing of daylight simulators which make use of the common CIE general or Perez all-weather skies. These datasets were used in a trial validation of Autodesk’s 3ds Max Design and Radiance, which included significant sensitivity testing of the two empirical datasets included in the suite. This demonstrated the usefulness of each dataset, and any issues with their data. It also highlighted the key inputs of any simulation model where designers must take significant care.</p>

scholarly journals Assessing marine operations with a Markov-switching autoregressive metocean model

2020 ◽  
Vol 234 (4) ◽  
pp. 785-802
Author(s):  
Jack Paterson ◽  
Philipp R Thies ◽  
Roman Sueur ◽  
Jérôme Lonchampt ◽  
Federico D’Amico
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wave Height ◽  
Selection Process ◽  
Markov Switching ◽  
Transfer Task ◽  
Weather Conditions ◽  
Weather Model ◽  
Wide Range ◽  
Modelling Methodology

This article presents a metocean modelling methodology using a Markov-switching autoregressive model to produce stochastic wind speed and wave height time series, for inclusion in marine risk planning software tools. By generating a large number of stochastic weather series that resemble the variability in key metocean parameters, probabilistic outcomes can be obtained to predict the occurrence of weather windows, delays and subsequent operational durations for specific tasks or offshore construction phases. To cope with the variation in the offshore weather conditions at each project, it is vital that a stochastic weather model is adaptable to seasonal and inter-monthly fluctuations at each site, generating realistic time series to support weather risk assessments. A model selection process is presented for both weather parameters across three locations, and a personnel transfer task is used to contextualise a realistic weather window analysis. Summarising plots demonstrate the validity of the presented methodology and that a small extension improves the adaptability of the approach for sites with strong correlations between wind speed and wave height. It is concluded that the overall methodology can produce suitable wind speed and wave time series for the assessment of marine operations, yet it is recommended that the methodology is applied to other sites and operations, to determine the method’s adaptability to a wide range of offshore locations.

scholarly journals Evaluation of Multiyear Weather Data Effects on Hygrothermal Building Energy Simulations Using WUFI Plus

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7157
Author(s):  
Michele Libralato ◽  
Alessandra De Angelis ◽  
Giulia Tornello ◽  
Onorio Saro ◽  
Paola D’Agaro ◽  
...  
Keyword(s):  
Building Materials ◽  
Moisture Transfer ◽  
Weather Conditions ◽  
Building Energy ◽  
Building Envelope ◽  
Weather Data ◽  
Building Simulations ◽  
Coupled Heat And Moisture ◽  
Single Number ◽  
Energy Simulations

Transient building energy simulations are powerful design tools that are used for the estimation of HVAC demands and internal hygrothermal conditions of buildings. These calculations are commonly performed using a (often dated) typical meteorological year, generated from past weather measurements excluding extreme weather conditions. In this paper the results of multiyear building simulations performed considering coupled Heat and Moisture Transfer (HMT) in building materials are presented. A simple building is simulated in the city of Udine (Italy) using a weather record of 25 years. Performing a multiyear simulation allows to obtain a distribution of results instead of a single number for each variable. The small therm climate change is shown to influence thermal demands and internal conditions with multiyear effects. From this results it is possible to conclude that weather records used as weather files have to be periodically updated and that moisture transfer is relevant in energy and comfort calculations. Moreover, the simulations are performed using the software WUFI Plus and it is shown that using a thermal model for the building envelope could be a non negligible simplification for the comfort related calculations.

scholarly journals PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3181
Author(s):  
Petros Karvelis ◽  
Daniele Mazzei ◽  
Matteo Biviano ◽  
Chrysostomos Stylios
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Weather Data ◽  
Learning Approaches ◽  
New Era ◽  
Wide Range ◽  
Industrial Iot

Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics.

Vulnerability of sweet cherry cultivars to continuous periods of spring frosts in Plovdiv, Bulgaria

2020 ◽  
Vol 12 (4) ◽  
pp. 348-352
Author(s):  
S. Malchev ◽  
S. Savchovska
Keyword(s):  
Sweet Cherry ◽  
Weather Conditions ◽  
Frost Damage ◽  
Cold Weather ◽  
Bud Burst ◽  
Intermediate Group ◽  
Air Temperatures ◽  
Wide Range ◽  
Group Form ◽  
Sweet Cherry Cultivars

Abstract. The periods with continuous freezing air temperatures reported during the spring of 2020 (13 incidents) affected a wide range of local and introduced sweet cherry cultivars in the region of Plovdiv. They vary from -0.6°C on March 02 to -4.9°C on March 16-17. The duration of influence of the lowest temperatures is 6 and 12 hours between March 16 and 17. The inspection of fruit buds and flowers was conducted twice (on March 26 and April 08) at different phenological stages after continuous waves of cold weather conditions alternated with high temperatures. During the phenological phase ‘bud burst’ (tight cluster or BBCH 55) some of the flowers in the buds did not develop further making the damage hardly detectable. The most damaged are hybrid El.28-21 (95.00%), ‘Van’ (91.89%) and ‘Bing’ (89.41%) and from the next group ‘Lapins’ (85.98%) and ‘Rosita’ (83.33%). A larger intermediate group form ‘Kossara’ (81.67%), ‘Rozalina’ (76.00%), ‘Sunburst’ (75.00%), ‘Bigarreau Burlat’ (69.11%) and ‘Kuklenska belitza’ (66.67%). Candidate-cultivar El.17-90 ‘Asparuh’ has the lowest frost damage values of 55.00% and El.17-37 ‘Tzvetina’ with damage of 50.60%.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

2021 ◽  
pp. 204141962199349
Author(s):  
Jordan J Pannell ◽  
George Panoutsos ◽  
Sam B Cooke ◽  
Dan J Pope ◽  
Sam E Rigby
Keyword(s):  
Scaling Laws ◽  
Near Field ◽  
Specific Impulse ◽  
Gaussian Function ◽  
Data Driven ◽  
Blast Load ◽  
Structural Deformation ◽  
Load Prediction ◽  
Validation Data ◽  
Wide Range

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios.

Fully automated contrast and non-contrast cardiac view detection in echocardiography a multi-centre, multi-vendor study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Gao ◽  
D Stojanovski ◽  
A Parker ◽  
P Marques ◽  
S Heitner ◽  
...  
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Classification Model ◽  
Validation Dataset ◽  
Funding Source ◽  
Private Company ◽  
Validation Data ◽  
Independent Test ◽  
Model Training ◽  
Confusion Matrices

Abstract Background Correctly identifying views acquired in a 2D echocardiographic examination is paramount to post-processing and quantification steps often performed as part of most clinical workflows. In many exams, particularly in stress echocardiography, microbubble contrast is used which greatly affects the appearance of the cardiac views. Here we present a bespoke, fully automated convolutional neural network (CNN) which identifies apical 2, 3, and 4 chamber, and short axis (SAX) views acquired with and without contrast. The CNN was tested in a completely independent, external dataset with the data acquired in a different country than that used to train the neural network. Methods Training data comprised of 2D echocardiograms was taken from 1014 subjects from a prospective multisite, multi-vendor, UK trial with the number of frames in each view greater than 17,500. Prior to view classification model training, images were processed using standard techniques to ensure homogenous and normalised image inputs to the training pipeline. A bespoke CNN was built using the minimum number of convolutional layers required with batch normalisation, and including dropout for reducing overfitting. Before processing, the data was split into 90% for model training (211,958 frames), and 10% used as a validation dataset (23,946 frames). Image frames from different subjects were separated out entirely amongst the training and validation datasets. Further, a separate trial dataset of 240 studies acquired in the USA was used as an independent test dataset (39,401 frames). Results Figure 1 shows the confusion matrices for both validation data (left) and independent test data (right), with an overall accuracy of 96% and 95% for the validation and test datasets respectively. The accuracy for the non-contrast cardiac views of &gt;99% exceeds that seen in other works. The combined datasets included images acquired across ultrasound manufacturers and models from 12 clinical sites. Conclusion We have developed a CNN capable of automatically accurately identifying all relevant cardiac views used in “real world” echo exams, including views acquired with contrast. Use of the CNN in a routine clinical workflow could improve efficiency of quantification steps performed after image acquisition. This was tested on an independent dataset acquired in a different country to that used to train the model and was found to perform similarly thus indicating the generalisability of the model. Figure 1. Confusion matrices Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

scholarly journals Modeling of Yield Losses Caused by Potato Late Blight on Eight Cultivars with Different Levels of Resistance to Phytophthora infestans

Plant Disease ◽  
2012 ◽  
Vol 96 (7) ◽  
pp. 935-942 ◽  
Author(s):  
Toky Rakotonindraina ◽  
Jean-Éric Chauvin ◽  
Roland Pellé ◽  
Robert Faivre ◽  
Catherine Chatot ◽  
...  
Keyword(s):  
Late Blight ◽  
Phytophthora Infestans ◽  
Relative Yield ◽  
Weather Conditions ◽  
Potato Late Blight ◽  
Yield Losses ◽  
Predictive Quality ◽  
Wide Range ◽  
Cultivar Susceptibility

The Shtienberg model for predicting yield loss caused by Phytophthora infestans in potato was developed and parameterized in the 1990s in North America. The predictive quality of this model was evaluated in France for a wide range of epidemics under different soil and weather conditions and on cultivars different than those used to estimate its parameters. A field experiment was carried out in 2006, 2007, 2008, and 2009 in Brittany, western France to assess late blight severity and yield losses. The dynamics of late blight were monitored on eight cultivars with varying types and levels of resistance. The model correctly predicted relative yield losses (efficiency = 0.80, root mean square error of prediction = 13.25%, and bias = –0.36%) as a function of weather and the observed disease dynamics for a wide range of late blight epidemics. In addition to the evaluation of the predictive quality of the model, this article provides a dataset that describes the development of various late blight epidemics on potato as a function of weather conditions, fungicide regimes, and cultivar susceptibility. Following this evaluation, the Shtienberg model can be used with confidence in research and development programs to better manage potato late blight in France.

Sign in / Sign up

Export Citation Format

Share Document