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2020
Robyr Jean-Luc, Bourquin Vincent, Goetschi Damien, Schroeter Nicolas, Baltensperger Richard
Journal of Aircraft, 2020, vol. 57, no. 5
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2018
Favre Ludovic, Schafer Thibaut M., Robyr Jean-Luc, Niederhäuser Elena-Lavinia
Energy and Power Engineering, 2018, vol. 12, no. 9
Résumé:
This paper presents an optimization method based on genetic algorithm for the energy management inside buildings developed in the frame of the project Smart Living Lab (SLL) in Fribourg (Switzerland). This algorithm optimizes the interaction between renewable energy production, storage systems and energy consumers. In comparison with standard algorithms, the innovative aspect of this project is the extension of the smart regulation over three simultaneous criteria: the energy self-consumption, the decrease of greenhouse gas emissions and operating costs. The genetic algorithm approach was chosen due to the large quantity of optimization variables and the non-linearity of the optimization function. The optimization process includes also real time data of the building as well as weather forecast and users habits. This information is used by a physical model of the building energy resources to predict the future energy production and needs, to select the best energetic strategy, to combine production or storage of energy in order to guarantee the demand of electrical and thermal energy. The principle of operation of the algorithm as well as typical output example of the algorithm is presented.
Fromme Paul, Pizzolato Marco, Robyr Jean-Luc, Masserey Bernard
The Journal of the Acoustical Society of America, 2018, 143, 1, pp. 287-295
Monocrystalline silicon wafers are widely used in the photovoltaic industry for solar panels with high conversion efficiency. Guided ultrasonic waves offer the potential to efficiently detect microcracks in the thin wafers. Previous studies of ultrasonic wave propagation in silicon focused on effects of material anisotropy on bulk ultrasonic waves, but the dependence of the wave propagation characteristics on the material anisotropy is not well understood for Lamb waves. The phase slowness and beam skewing of the two fundamental Lamb wave modes A0 and S0 were investigated. Experimental measurements using contact wedge transducer excitation and laser measurement were conducted. Good agreement was found between the theoretically calculated angular dependency of the phase slowness and measurements for different propagation directions relative to the crystal orientation. Significant wave skew and beam widening was observed experimentally due to the anisotropy, especially for the S0 mode. Explicit finite element simulations were conducted to visualize and quantify the guided wave beam skew. Good agreement was found for the A0 mode, but a systematic discrepancy was observed for the S0 mode. These effects need to be considered for the non-destructive testing of wafers using guided waves.
Jean-Luc Robyr, Elena-Lavinia Niederhäuser
Proceedings of 5th International Conference on Electric Power and Energy Conversion Systems (EPECS), 23-25 April 2018, Kitakyushu, Japan
The reduction of the environmental impact of buildings through better energy management could play a significant role in achieving nowadays greenhouse gas emission reduction targets. In this context and following this purpose, we developed a regulation algorithm to manage the global energy resources of buildings. The control approach optimizes the coupling between local renewable energy production systems (e.g. thermal and photovoltaic solar panels) and energy storage devices (e.g. cold and hot water storage tanks, electrical battery). The innovative aspect of this project compared to standard regulations is the simultaneous optimization of three criteria: the consumption of external energy resources, the costs and the ecological impact. In this paper we present and analyse the implementation of this regulation based on the ecological criterion. A genetic optimization is performed according to a score function evaluating the ecological impact based on the CO2 equivalent production. In order to improve the strategy, the regulation predicts the future energy demand and production. The genetic algorithm approach is used due to the large amount of optimization variables and the non-linearity of the score function. This genetic optimization algorithm uses real time data like building physical data (e.g. internal temperature) and prediction based on the user’s habits and weather information to define the best energy strategy. It insures the electrical and thermal energy demand while optimizing the ecological criterion. To demonstrate the algorithm performances, the regulation was implemented and tested with an independent simulation environment. The ecological impact of the genetic algorithm regulation over one week is then compared to the greenhouse gas emission from a standard regulation. With this setting, a reduction of 29 kg equivalent CO2 was realized, which shows the enormous potential of the new regulation approach.
Proceedings of ENERGYCON 2018 IEEE International Energy Conference, 3-7 June 2018, Cyprus
In the frame of a research project conducted at the Smart Living Lab (SLL), a research center dedicated to the building of the future, this paper presents an algorithm that optimizes the coupling of local renewable energy production systems with energy storage devices and the different consumers both at the level of the building and of its peripherals. The main goal is to improve the energy self-sufficiency of a building by combining three aspects in the same time. The optimization criteria are the renewable energy based independence and the ecological (greenhouses gases emissions) and economical (costs) aspects. The underlying approach to perform the global optimization is first presented, explaining how the algorithm combines and optimizes these three criteria. For this purpose, it takes into account the current value of the state variables (temperature, etc.) and the forecasts future values. These data represent the input of a genetic optimization algorithm that computes the best use of each element of the production and storage systems to ensure the electrical and thermal energy demand. The choice of genetic algorithm is motivated by the large amount of optimization variables and the non-linearity of the score function. The typical computation time for this kind of optimization is short enough to allow a real time regulation. The composition of the energy production and storage is flexible allowing to integrate many technologies types, thus increasing its portability.
Proceedings of 2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), 29 May-1 June 2018, Kajang, Malaysia
Better energy management systems for buildings could play a significant role in achieving nowadays greenhouse gas emission reduction targets. In this context, a regulation algorithm to manage the interaction between local renewable energy production, local energy storage devices and an external power source (power grid) was developed. The innovative aspect of this project compared to existing solution is the simultaneous optimization following three criteria: the external energy consumption, the cost and ecological impacts. The new optimization algorithm is based on the genetic algorithm method due to the large solutions space and the non-linearity of the optimization function. This method is coupled to a physical model of the building under study (a typical dwelling house) and its energetic network (production and storage). In addition, weather forecast data as well as data on the user habits are integrated. This paper shows the results of the optimization algorithm applied to a set of realistic values. The genetic algorithm is compared to a pure random optimization approach and their optimization efficiencies are analyzed. Finally, the best strategy obtained by the genetic algorithm for a realistic computation time of several minutes is presented and investigated in detailed. This results shows that the genetic algorithm can perform a 48 hours simulation with no outcome costs, a global production of 4.3 kWh of energy and a greenhouse gas production of -1.4 kg of CO2 equivalent, whereas the consumption of the building costs +1.3 CHF, consumes 7.0 kWh of energy and generates +1.3 kg of CO2 equivalent.
Jean-Luc Robyr, Bernard Masserey
Proceedings of 44th annual review of progress in quantitative nondestructive evaluation, vol. 37, Provo, Utah, USA, 16-21 July 2017
Proceedings of SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: Health Monitoring of Structural and Biological Systems XII, Denver, Colorado, United States, 4-8 March 2018
In the photovoltaic industry monocrystalline silicon wafers are employed for the manufacture of solar panels with high conversion efficiency. The cutting process induces micro-cracks on the thin wafer surface. High frequency guided ultrasonic waves are considered for the structural monitoring of the wafers and the nondestructive characterization of the micro-cracks. The material anisotropy of the monocrystalline silicon leads to variations of the wave characteristics depending on the propagation direction relative to the crystal orientation. In non-principal directions of the crystal, wave beam skewing occurs. Selective excitation of the fundamental Lamb wave modes was achieved using a custom-made angle beam transducer and holder to achieve a controlled contact pressure. The out-of-plane component of the guided wave propagation was measured using a noncontact laser interferometer. Artificial defects were introduced in the wafers using a micro indenter with varying loads. The defects were characterized from microscopy images to measure the indent size and combined crack length. The scattering of the A0 Lamb wave mode was measured experimentally and the characteristics of the scattered wave field were correlated to the defect size. The detection sensitivity is discussed.
2017
Health Monitoring of Structural and Biological Systems 2017 ; SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 25-29 March 2017, Portland, Oregon, United States
Monocrystalline silicon wafers are widely used in the photovoltaic industry for solar panels with high conversion efficiency. The cutting process can introduce micro-cracks in the thin wafers and lead to varying thickness. High frequency guided ultrasonic waves are considered for the structural monitoring of the wafers. The anisotropy of the monocrystalline silicon leads to variations of the wave characteristics, depending on the propagation direction relative to the crystal orientation. Full three-dimensional Finite Element simulations of the guided wave propagation were conducted to visualize and quantify these effects for a line source. The phase velocity (slowness) and skew angle of the two fundamental Lamb wave modes (first anti-symmetric mode A0 and first symmetric mode S0) for varying propagation directions relative to the crystal orientation were measured experimentally. Selective mode excitation was achieved using a contact piezoelectric transducer with a custom-made wedge and holder to achieve a controlled contact pressure. The out-of-plane component of the guided wave propagation was measured using a noncontact laser interferometer. Good agreement was found with the simulation results and theoretical predictions based on nominal material properties of the silicon wafer.
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