Jacobo Porteiro

This paper presents an integrated simulation methodology that combines fire hazard modeling with pedestrian evacuation analysis to evaluate how tenability conditions affect occupant egress. In the proposed approach, fire scenarios representative of leisure and spa resorts are modeled using the Fire Dynamics Simulator (FDS). The most critical hazards—visibility reduction, oxygen depletion, and toxic gas concentrations—were integrated as dynamic 3D, time‐dependent data into evacuation simulations performed with Pathfinder. The stochastic nature of the simulations arises from variability in occupant premovement times, demographic characteristics, and walking speeds, which reflect realistic heterogeneity in evacuation behavior. The results show that poor visibility conditions reduce the available safe egress time (ASET), the time before conditions become untenable, by 55%, increase required safe egress time (RSET) by up to 26.2%, and consequently decrease the safe egress margin (the difference between ASET and RSET) by 702%. Conversely, safety systems such as sprinklers and fire doors significantly enhance egress performance, increasing ASET by 133%, reducing RSET by 3.5%, and improving the safe egress margin by 90.1% compared with scenarios without these systems. These findings demonstrate that the integrated framework not only improves the accuracy of evacuation performance assessment but also provides practical insights for real‐world performance‐based design. In particular, the methodology supports safer exit allocation, enhanced emergency planning, and the identification of architectural modifications that reduce evacuation risks.

Uncontrolled wear in complex multiphysics systems can cause catastrophic failures, prompting the development of empirical methods and numerical prediction models for managing system lifetimes. This study introduces a novel approach for predicting wear on lubricated sliding surfaces by integrating rough contact mechanics into a nonlocal function with a non-uniform distribution. The model considers the sliding speed, contact area, fluid pressures, lubricant properties, and surface roughness. It employs a mixed elastohydrodynamic lubrication (mixed EHL) model to simulate lubrication and wear, using a multiscale roughness model to adjust the parameters based on the wear evolution. Validated against journal bearing data, the model accurately predicted wear rates and depths, revealing distinct roughness variations depending on the lubricant viscosity.

This article addresses the comprehensive experimental characterization and numerical modelling of an axial flux permanent magnet synchronous Electric Vehicle Drivetrain System (AFPMSM). A methodology based on studies from various procedures, both from research and the industrial sector (Original Equipment Manufacturer (OEM) & Tier1), is employed. A Design of Experiments (DoE) approach was employed to guide the experimental procedures, enabling the precise determination of critical parameters, magnitudes, and their interrelationships. Most studies on characterization focus on a single aspect, idealizing or simplifying other influences and nonlinearities, which limits the analysis and does not include the complete drivetrain. An analysis of Field Oriented Control (FOC) and its performance was conducted. This included experimentally quantification of factors such as losses, inertias, phase resistances, temperatures, back electromotive force (BEMF), nominal and peak torque and power, efficiencies, inductances and magnetic flux. Correlations were established through equations or Look Up Tables (LUT), to capture the nonlinearities, determining their effects on other variables. A numerical model of the powertrain was generated and validated in AVL CRUISETM M. The prediction error for torque and power of the model is less than 3%, and the mean absolute percentage error (MAPE) for the phase (id) and quadrature (iq) currents are less than 4.74% and 2.51%, respectively. The model reduces the need for experimental testing in the development of electric powertrains. The experimental procedure described in this work enables a comprehensive characterization of the drivetrain, specifically addressing the nonlinearities and associated effects such as magnetic circuit saturation, inductance variation, and temperature impact, thereby providing a more accurate representation of the SPMSM motor's behavior.

Background: The development of new large-scale saliva pooling detection strategies can significantly enhance testing capacity and frequency for asymptomatic individuals, which is crucial for containing SARS-CoV-2. Objective: This study aims to implement and scale-up a SARS-CoV-2 screening method using pooled saliva samples to control the virus in critical areas and assess its effectiveness in detecting asymptomatic infections. Methods: Between August 2020 and February 2022, our laboratory received a total of 928,357 samples. Participants collected at least 1 mL of saliva using a self-sampling kit and registered their samples via a smartphone app. All samples were directly processed using AutoMate 2550 for preanalytical steps and then transferred to Microlab STAR, managed with the HAMILTON Pooling software for pooling. The standard pool preset size was 20 samples but was adjusted to 5 when the prevalence exceeded 2% in any group. Real-time polymerase chain reaction (RT-PCR) was conducted using the Allplex SARS-CoV-2 Assay until July 2021, followed by the Allplex SARS-CoV-2 FluA/FluB/RSV assay for the remainder of the study period. Results: Of the 928,357 samples received, 887,926 (95.64%) were fully processed into 56,126 pools. Of these pools, 4863 tested positive, detecting 5720 asymptomatic infections. This allowed for a comprehensive analysis of pooling’s impact on RT-PCR sensitivity and false-negative rate (FNR), including data on positive samples per pool (PPP). We defined Ctref as the minimum cycle threshold (Ct) of each data set from a sample or pool and compared these Ctref results from pooled samples with those of the individual tests (ΔCtP). We then examined their deviation from the expected offset due to dilution [ΔΔCtP = ΔCtP – log2]. In this work, the ΔCtP and ΔΔCtP were 2.23 versus 3.33 and –0.89 versus 0.23, respectively, comparing global results with results for pools with 1 positive sample per pool. Therefore, depending on the number of genes used in the test and the size of the pool, we can evaluate the FNR and effective sensitivity (1 – FNR) of the test configuration. In our scenario, with a maximum of 20 samples per pool and 3 target genes, statistical observations indicated an effective sensitivity exceeding 99%. From an economic perspective, the focus is on pooling efficiency, measured by the effective number of persons that can be tested with 1 test, referred to as persons per test (PPT). In this study, the global PPT was 8.66, reflecting savings of over 20 million euros (US $22 million) based on our reagent prices. Conclusions: Our results demonstrate that, as expected, pooling reduces the sensitivity of RT-PCR. However, with the appropriate pool size and the use of multiple target genes, effective sensitivity can remain above 99%. Saliva pooling may be a valuable tool for screening and surveillance in asymptomatic individuals and can aid in controlling SARS-CoV-2 transmission. Further studies are needed to assess the effectiveness of these strategies for SARS-CoV-2 and their application to other microorganisms or biomarkers detected by PCR.