The rheological characterization of fluids using a rheometer is an essential task in food processing, materials, healthcare or even industrial engineering; in some cases, the high cost of a rheometer and the issues related to the possibility of developing both electrorheological and magnetorheological tests in the same instrument have to be overcome. With that in mind, this study designed and constructed a low-cost rotational rheometer with the capacity to adapt to electro- and magneto-rheological tests. The design team used the method of continuous improvement through Quality Function Deployment (QFD) and risk analysis tools such as Failure Mode and Effect Analysis (FMEA) and Finite Element Analysis (FEA). These analyses were prepared in order to meet the customer’s needs and engineering requirements. In addition to the above, a manufacturing control based on process sheets was used, leading to the construction of a functional rheometer with a cost of USD $1500.

The use of electric vehicles and their various configurations is seen as a major alternative in efforts towards reducing pollutant emissions from motor vehicles that continue to use fossil fuels. Electric transport technology presents more efficient means of energy conversion in vehicles: electric (EV), hybrid (VH), and hybrid electric (HEV) vehicles. For example, the energy storage system in the latter can be made up of ultracapacitors (UCs), batteries (Bs), and fuel cells. This work focuses on HEVs powered by batteries and ultracapacitors. In particular, the multiple converter configuration (C-CM) for the HEV powertrain system is analyzed using electric models of the vehicle powertrain components. To analyze the multiple converter configuration, parameters of a vehicle taken from the literature and the electrical model of the configuration were developed. With the above, the proposed configuration was evaluated before driving cycles (CITY II and ECE) and the configuration performance was compared with respect to other configurations. In the C-CM model, limitations in the choice of the number of Bs and UCs were observed in the powertrain depending on the maximum power of both energy sources and vehicle load demand. The results show that more energy is extracted from the batteries in the ECE cycle than in the CITY taking into account that the batteries are used as the main power source. C-CM results compared to other configurations show that energy extracted from batteries in the CITY is the same across all configurations. While energy consumption is lower in the ECE, C-CM results were not very significant compared to other configurations. However, the C-MC has the advantage of having better power flow control due to having two converters, thus improving HEV safety.

The wind pressure coefficient $C_P$ is a key aerodynamic parameter for describing wind-induced pressure fields on building envelopes and is widely used as an input in air flow network (AFN) models. Most CFD validation studies rely on wall-averaged $C_P$ or a limited number of center-line sensors, implicitly assuming these represent the entire surface, which can mask significant local discrepancies. In this work, Reynolds-averaged Navier–Stokes (RANS) simulations are validated against pointwise pressure-tap data from the Tokyo Polytechnic University (TPU) database for an isolated low-rise hexahedral building. Wind incidence angles of 0°, 15°, 30°, and 45° are analyzed using five turbulence closures: standard, realizable, and RNG k-$ϵ$, and standard and SST k-$\omega$. All simulations are performed in an icositetragon-shaped computational domain using OpenFOAM, allowing wind-direction changes without remeshing. While wall-averaged $C_P$ shows good agreement with experimental data for all models, local (tap-by-tap) comparisons reveal errors as high as 30–45% on leeward walls, exposing discrepancies that global metrics conceal. No single turbulence model performs best across all walls and incidence angles: RNG k-$ϵ$ and SST k-$\omega$ alternate as the top performer depending on wall orientation and wind direction, a pattern quantified here through a wall-by-angle model-selection map based on the normalized mean square error. The results and the underlying local $C_P$ dataset provides practical guidance for selecting a turbulence model according to the dominant wind direction and facade orientation of a given natural-ventilation case, as well as a reference dataset for future wind-driven ventilation studies.