tTraditionally, in building energy modeling (BEM) programs, occupant behavior (OB) inputs are deter-ministic and less indicative of real world scenarios, contributing to discrepancies between simulated andactual energy use in buildings.... more
tTraditionally, in building energy modeling (BEM) programs, occupant behavior (OB) inputs are deter-ministic and less indicative of real world scenarios, contributing to discrepancies between simulated andactual energy use in buildings. This paper presents a new OB modeling tool, with an occupant behaviorfunctional mock-up unit (obFMU) that enables co-simulation with BEM programs implementing func-tional mock-up interface (FMI). The components detailed in the development of the obFMU include anoverview of the DNAS (drivers-needs-actions-systems) ontology and the occupant behavior eXtensibleMarkup Language (obXML) schema, in addition to details on the creation of the obFMU that containsthe co-simulation interface, the data model and solvers. To demonstrate functionality of the tool, threeexamples of occupant behaviors were simulated, including: (1) turning on and off lights, (2) opening andclosing windows, and (3) turning on and off the air conditioners. The obFMU can be used via co-simulationwith all building simulation programs that implement the FMI, thus users are not limited to a particulartool. Another advantage is the use of obXML schema to represent occupant behavior, standardize thedescription of occupant behavior enabling information exchange.
Cryptography and computational algebra designs are complex systems based on modular arithmetic and build on multi-level modules where bit-width is generally larger than 64-bit. Because of their particularity, such designs pose a real... more
Cryptography and computational algebra designs are complex systems based on modular arithmetic and build on multi-level modules where bit-width is generally larger than 64-bit. Because of their particularity, such designs pose a real challenge for verification, in part because large-integer's functions are not supported in actual hardware description languages (HDLs), therefore limiting the HDL testbench utility. In another hand, high-level verification approach proved its efficiency in the last decade over HDL testbench technique by raising the latter at a higher abstraction level. In this work, we propose a high-level platform to verify such designs, by leveraging the capabilities of a popular tool (Matlab/Simulink) to meet the requirements of a cycle accurate verification without bit-size restrictions and in multi-level inside the design architecture. The proposed high-level platform is augmented by an assertion-based verification to complete the verification coverage. The platform experimental results of the testcase provided good evidence of its performance and re-usability. Keyword: Assertion-based verification Co-simulation Cryptography Hardware description language High-level verification Large-integer Matlab/Simulink
This paper presents a VHDL design and an FPGA implementation of a direct torque controller (DTC) used to order induction machines (IM). The use of FPGA at high sampling frequency reduces the torque ripple while maintaining the classical... more
This paper presents a VHDL design and an FPGA implementation of a direct torque controller (DTC) used to order induction machines (IM). The use of FPGA at high sampling frequency reduces the torque ripple while maintaining the classical DTC control structure. We have adopted a modular approach, by dividing the global entity into a set of elementary blocks designed and implemented separately. The performances of this command are to reduce the torque ripple to 0.01 Nm and the flux ripple to 0.01 wb with a circuit implementing DTC control of 3,256 LEs of complexity and 64 latency clock cycles. To evaluate the performance of our FPGA circuit implementing DTC controller, we have performed a co-simulation platform based on MATLAB/Simulink and Modelsim programs. MATLAB/Simulink was used to simulate the dynamics of the induction machine associated with its inverter and the proposed DTC control strategy was executed under the modelsim software using the VHDL fixed point. We have operated our circuit FPGA in the loop in a speed variation platform of induction machine and we have obtained the following performances: A zero overrun, response time at speeds of 300 ms and a zero static error as required in the specifications.
This paper describes the implementation of long wave radiation (LWR) exchange as part of a co-simulation process of an urban scale simulation program, CitySim, and a building scale program, EnergyPlus. This coupling process was achieved... more
This paper describes the implementation of long wave radiation (LWR) exchange as part of a co-simulation process of an urban scale simulation program, CitySim, and a building scale program, EnergyPlus. This coupling process was achieved through the use of functional mockup units (FMU) to exchange various weather, load, and environmental information between the two simulation engines. LWR is an important factor to exchange between the programs as CitySim has more advanced capabilities for radiation exchange calculations from a set of urban buildings and EnergyPlus has a more advanced building heating and cooling load calculation engine. The LWR exchange between surfaces is computed in CitySim by a linearization of the longwave energy balance at each surface around an average between the surface and its environmental temperatures. The environmental temperature for each surface is determined using the simplified radiation algorithm neglecting inter-reflections and is aggregated into a single, global environmental radiant temperature (T env). The LWR exchange process is implemented in EnergyPlus by CitySim sharing the variables T env and h env that are then used to calculate radiation gain or loss through the envelope as well as influence on the conductances of the surfaces. This approach overrides the conventional EnergyPlus ground, sky and air radiation calculations. Solo and coupled simulations are performed on a set of four scenarios and result in up to a 36% discrepancy in heating and 11% in cooling load calculations amongst solo and coupled simulations.
