Flight determination of the aerodynamic stability and control characteristics of the NASA SGS 1-36 sailplane in the conventional and deep stall angles-of-attack of between -5 and 75 degrees

The flight test procedure and the preliminary analysis of the results obtained from twenty manned flights of the SGS 1-36 in the high angles of attack Deep Stall region are discussed. A comparison of the flight determined stability and control derivatives, those of the wind tunnel, and the estimated aerodynamic data is also presented. Furthermore, deep stall dynamics response of the SGS 1-36 is discussed briefly to explain some of the unexpected flight observations

On the utility of occupants’ behavioural diversity information for building performance simulation: An exploratory case study

The present study aims at investigating the potential advantages of integrating inter-occupant diversity information into occupant behaviour models used in building performance simulation. To this end, the authors model the operation of windows by occupants in a monitored open-plan office at aggregate and individual levels. The models use indoor and outdoor temperature as well as the interaction of these variables to estimate the probability of opening and closing windows in the building located in Vienna, Austria. Subsequently, a number of existing and novel metrics serve to compare the predictive performance of the aggregate and individual models. In addition, a calibrated energy model of the office area incorporates the window operation models to evaluate their potential contribution to the reliability of building performance assessments. The results of this exploratory case study suggest that individual window operation models outperform the aggregate model in capturing the peak and variations of window operation across occupants. This resulted in a more reliable thermal comfort assessment in the free-running season. The individual models, however, overestimated peak heating demand, as compared with the benchmark value resulting from the actual window operations in a single year

Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks

The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most favorable results

Multi-stage calibration of the simulation model of a school building through short-term monitoring

The increasing attention on the improvement of new and existing buildings' performance is emphasizing the importance of the reliability of the simulation models in predicting the complexity of the building behaviour and, consequently, in some advanced applications of building simulation, such as the optimization of the choice of different Energy Efficiency Measures (EEMs) or the adoption of model predictive control strategies. The reliability of the energy model does not depend only on the quality and details of the model itself, but also on the uncertainty related to many input values, such as the physical properties of materials and components, the information on the building management and occupation, and the boundary conditions considered for the simulation. Especially for the existing buildings, this kind of data is often missing or characterized by high uncertainty, and only very simplified behavioural models of occupancy are available. This could compromise the optimization process and undermine the potential of building simulation. In this context, the calibration of the simulation model by means of on-site monitoring is of crucial importance to increase the reliability of the predictions, and to take better decisions, even though this process can be time consuming. This work presents a multi-stage methodology to calibrate the building energy simulation by means of low-cost monitoring and short-term measurements. This approach is applied to a Primary School in the North-East of Italy, which has been monitored from December 2012 to April 2014. Four monitoring periods have been selected to calibrate different sets of variables at a time, while the validation has been carried out on two different periods. The results show that even if less than 8 weeks have been considered in the proposed calibration approach, the maximum error in the estimation of the temperature is less than ±0.5 in 77.3% of the timesteps in the validation period

Monitored data on occupants’ presence and actions in an office building

Within a study, an open plan area and one closed office in a university building with a floor area of around 200 m2 were monitored. The present data set covers a period of one year (from 2013-01-01 to 2013-12-31). The collected data pertains to indoor environmental conditions (temperature, humidity) as well as plug loads and external factors (temperature, humidity, wind speed, and global irradiance) along with occupants’ presence and operation of windows and lights. The monitored data can be used for multiple purposes, including the development and validation of occupancy-related models

Overheating mitigation in buildings: a computational exploration of the potential of phase change materials

Phase change materials (PCMs) can store and release thermal energy. The energy is stored when the material goes through a solid-toliquid phase change, and released in the reverse process. Such materials can contribute to the mitigation of overheating in buildings, if their melting and solidification temperatures are in a suitable range. The present contribution entails a computational examination of this potential as relevant to overheating mitigation in typical residential units in the Central European context of Vienna, Austria. Thereby, multiple variations of PCM application (size, thickness, location, and application thickness) under different contextual settings (fenestration and insulation, boundary conditions in terms of weather) were simulated and comparatively evaluated. Results indicate that certain PCM application configurations can significantly influence indoor thermal condition. For instance, PCM elements with larger surface areas displayed a more pronounced effect as compared to bulkier elements with smaller surface areas. Likewise, ceilingintegrated PCM application was found to be more effective that those involving other room surfaces. The results also highlight the importance of rooms ventilation regime if the PCM application potential toward overheating mitigation is to be effectively harvested

Essential fish habitats (EFH) of small pelagic fishes in the north of the Persian Gulf and Oman Sea, Iran

Small pelagic fishes particularly anchovy (Encrasicholina punctifer) and sardine (Sardinella sindensis) have an important role to support the Iranian fisheries and are distributed along the coastal waters of the Persian Gulf and Oman Sea. Using a logbook on small pelagic fisheries, a GIS-based environmental modeling approach was applied to investigate the presence and abundance of anchovy and sardine in relation to environmental variables. Redundancy analysis (RDA) was applied to provide a preliminary view of the relationships between fish presence/absence and environmental variables. Generalized Additive Models (GAMs) indicated the presence/absence of fish was related to distance from the nearest coast, depth, sea surface Chlorophyll-a, and SST. Results of the EFH showed that sardine is concentrated in specific areas of more favorable conditions, such as north of the Persian Gulf and all areas of the northwest of the Oman Sea. However, EFH of anchovy showed a more widespread distribution, occupying most of the north-west of the Oman Sea coastal waters, south of Qeshm Island in the Strait of Hormoz as well as the Parsian district in the north of the Persian Gulf. In this study, it seems that the anchovy showed the probability of presence in the areas with more distance from the coastal waters. However, the EFH probability presences of sardine were predicted for near coastal water and obviously, shallower waters. Due to the development of small pelagic fisheries, it is highly recommended to investigate anchovy and sardine fishing possibility in areas with high EFH prediction probability

An inquiry into the reliability of window operation models in building performance simulation

Given the impact of inhabitants’ control actions on indoor environment and the complex nature of such interactions, sophisticated models of occupants’ presence and behavior are increasingly deployed to enhance the reliability of building performance simulations. However, the use of occupant behavior models in building simulation efforts and their predictive performance in different contexts involves potentially detrimental uncertainties. To address this issue, the present study deploys long-term monitored data from an office area and its calibrated simulation model to conduct an external evaluation of a number of stochastic and non-stochastic window operation models in view of their a) potential in predicting occupants’ operation of windows, and b) effectiveness to enhance the reliability of building performance simulation efforts. The results suggest that, while stochastic models can emulate the seemingly random character of occupant behavior and provide probabilistic distributions of performance indicators, their use does not guarantee more reliable predictions. Leaving aside the large errors resulted from using such models without the necessary adjustments, stochastic window operation models overestimated the occupants’ operation of windows in heating season and thus the annual and peak heating demands. However, as compared with rule-based models, the stochastic models displayed a better performance in predicting window operations and thermal comfort assessment in the free-running season

On the quality evaluation of behavioural models for building performance applications

Building performance assessment applications require multiple categories of input information. These include, aside from building construction and systems and external conditions, representations of inhabitants. It has been suggested that the representation of people as passive and static entities is unlikely to yield reliable building performance assessment and building operation planning. Rather, adequate representations of building inhabitants should account for dynamics of inhabitants’ presence in buildings and their control-oriented actions (e.g. interactions with buildings indoor environmental control devices and systems). To address these requirements, many recent model development efforts have explored the potential of advanced mathematical formalisms. However, the resulting occupancy-related behavioural models have rarely gone through a rigorous evaluation process. The present contribution is indeed motivated primarily by the lack of explicit procedures and guidelines for the evaluation of proposed user-related behavioural models. Specifically, we formulate a number of conditions that are necessary for systematic and dependable quality assessment of buildings’ inhabitants. Towards this end, we discuss both general model evaluation requirements and specific circumstances pertaining to behavioural models of building inhabitants. By using specific instances of such models, we intend to identify the requirements of a rigorous quality assurance process with regard to behavioural models in building performance assessment applications