The Use of the Behavioral Engineering Model to Examine the Training and Delivery of Feedback

Inhalt Unscharfe Anforderungen, verschiedene Lösungs-alternativen oder eingeschränkt gültige Simulationsmodelle sind Beispiele für inhärente Unsicherheit in der Produktentwicklung. Im vorliegenden Beitrag wird ein modellbasierter Ansatz vorgestellt, der das industriell etablierte Denken in Sicherheitsfaktoren um qualitative Aspekte ergänzt. Modelle der Informationsqualität helfen, die Unsicherheit von Ent- wicklungsartefakten beschreibend zu charakterisieren. Mittels semantischer Technologien wird Unsicherheit so wirklich handhabbar – nicht im Sinne einer Berechnung, sondern im Sinne einer qualitativen Interpretation. Dadurch entsteht wertvolles Wissen für die iterative Anforderungsanalyse, die Bewertung alternativer System-Architekturen oder für die Rekonfiguration von Simulationen.

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TRACING THE EMERGENCE OF DESIGN PROBLEMS AND THEIR IMPACTS ON THE COMPLEXITY OF ENGINEERING SOLUTIONS

Proceedings of the Design Society ◽

10.1017/pds.2021.584 ◽

2021 ◽

Vol 1 ◽

pp. 3229-3238

Author(s):

Torben Beernaert ◽

Pascal Etman ◽

Maarten De Bock ◽

Ivo Classen ◽

Marco De Baar

Keyword(s):

Large Scale ◽

Fusion Reactor ◽

Design Stage ◽

Engineering Model ◽

Design Problems ◽

Nuclear Fusion Reactor ◽

Early Design Stage ◽

Iterative Design ◽

Problems And Solutions ◽

Emergent Design

AbstractThe design of ITER, a large-scale nuclear fusion reactor, is intertwined with profound research and development efforts. Tough problems call for novel solutions, but the low maturity of those solutions can lead to unexpected problems. If designers keep solving such emergent problems in iterative design cycles, the complexity of the resulting design is bound to increase. Instead, we want to show designers the sources of emergent design problems, so they may be dealt with more effectively. We propose to model the interplay between multiple problems and solutions in a problem network. Each problem and solution is then connected to a dynamically changing engineering model, a graph of physical components. By analysing the problem network and the engineering model, we can (1) derive which problem has emerged from which solution and (2) compute the contribution of each design effort to the complexity of the evolving engineering model. The method is demonstrated for a sequence of problems and solutions that characterized the early design stage of an optical subsystem of ITER.

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A Critical Note on the Engineering Model of Radiating Antenna in Homogeneous Medium: Field Regions and Transition Zone Boundaries

IEEE Electromagnetic Compatibility Magazine ◽

10.1109/memc.2021.9400993 ◽

2021 ◽

Vol 10 (1) ◽

pp. 41-50

Author(s):

Anatoly Tsaliovich ◽

Motti Haridim ◽

Jacob Gavan

Keyword(s):

Transition Zone ◽

Homogeneous Medium ◽

Engineering Model ◽

Critical Note

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Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

Sustainability ◽

10.3390/su13041595 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1595

Author(s):

Valeria Todeschi ◽

Roberto Boghetti ◽

Jérôme H. Kämpf ◽

Guglielmina Mutani

Keyword(s):

Machine Learning ◽

Energy Use ◽

Residential Buildings ◽

Building Energy ◽

Energy Performance ◽

Space Heating ◽

Engineering Model ◽

Building Energy Use ◽

The Impact ◽

The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

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