| 1. |
- Donis, Daphne, et al.
(författare)
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Stratification strength and light climate explain variation in chlorophyll a at the continental scale in a European multilake survey in a heatwave summer
- 2021
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Ingår i: Limnology and Oceanography. - : John Wiley & Sons. - 0024-3590 .- 1939-5590. ; 66:12, s. 4314-4333
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Tidskriftsartikel (refereegranskat)abstract
- To determine the drivers of phytoplankton biomass, we collected standardized morphometric, physical, and biological data in 230 lakes across the Mediterranean, Continental, and Boreal climatic zones of the European continent. Multilinear regression models tested on this snapshot of mostly eutrophic lakes (median total phosphorus [TP] = 0.06 and total nitrogen [TN] = 0.7 mg L-1), and its subsets (2 depth types and 3 climatic zones), show that light climate and stratification strength were the most significant explanatory variables for chlorophyll a (Chl a) variance. TN was a significant predictor for phytoplankton biomass for shallow and continental lakes, while TP never appeared as an explanatory variable, suggesting that under high TP, light, which partially controls stratification strength, becomes limiting for phytoplankton development. Mediterranean lakes were the warmest yet most weakly stratified and had significantly less Chl a than Boreal lakes, where the temperature anomaly from the long-term average, during a summer heatwave was the highest (+4 degrees C) and showed a significant, exponential relationship with stratification strength. This European survey represents a summer snapshot of phytoplankton biomass and its drivers, and lends support that light and stratification metrics, which are both affected by climate change, are better predictors for phytoplankton biomass in nutrient-rich lakes than nutrient concentrations and surface temperature.
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| 2. |
- Galvez, Antonio, et al.
(författare)
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Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model
- 2021
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Ingår i: International Journal of Hydromechatronics. - : InderScience Publishers. - 2515-0464 .- 2515-0472. ; 4:3
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a physics-based model which is part of a hybrid model (HyM). The physics-based model is developed for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage. This model will be used to generate data for building a data-driven mode. Thus, the combination of these two models provides the hybrid model-based approach (HyMAs). The physics-based model of the HVAC system is divided into four principal parts: cooling subsystems, heating subsystems, ventilation subsystems, and vehicle thermal networking. First, the subsystems are modelled, considering the sensors embedded in the real system. Next, the model is synchronised with the real system to give better simulation results and validate the model. The cooling subsystem, heating subsystem and ventilation subsystem are validated with the acceptable sum square error (SSE) results. Second, the new virtual sensors are defined in the model, and their value to future research is suggested
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| 3. |
- Galvez, Antonio, et al.
(författare)
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Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
- 2021
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.
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| 4. |
- Gálvez, Antonio, et al.
(författare)
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Hybrid Model Development for HVAC System in Transportation
- 2021
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Ingår i: Technologies. - : MDPI. - 2227-7080. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and air conditioning system installed in the passenger vehicle of a train. The physics-based model is divided into four main parts: heating subsystems, cooling subsystems, ventilation subsystems, and cabin thermal networking subsystems. These subsystems are developed when considering the sensors that are located in the real system, so the model can be linked via the acquired sensor data and virtual sensor data to improve the detectability of failure modes. Thus, the physics-based model can be synchronized with the real system to provide better simulation results. The paper also considers diagnostics and prognostics performance. First, it looks at the current situation of the maintenance strategy for the heating, ventilation, air conditioning system, and the number of failure modes that the maintenance team can detect. Second, it determines the expected improvement using hybrid modelling to maintain the system. This improvement is based on the capabilities of detecting new failure modes. The paper concludes by suggesting the future capabilities of hybrid models.
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| 5. |
- Gálvez, Antonio, et al.
(författare)
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Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway
- 2021
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Ingår i: Simulation Notes Europe. - : ARGESIM Publisher. - 2306-0271 .- 2305-9974. ; 31:3, s. 121-126
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Tidskriftsartikel (refereegranskat)abstract
- Safety passenger transportation is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety. This manuscript presents the methodology of complete development and implementation of both hybrid model and digital twin 3.0 for an HVAC in railways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level 3.0 of digital twin will be developed for the diagnosis and prognosis of HVAC by using hybrid modeling. The description illustrated in this paper is focused on the methodology used to implement a hybrid model-based approach, and both the need and advantages of using hybrid model approaches instead of data-based approaches. The development considers the importance of safety and environmental risks, which are included in the risk quantification of failure modes. Railway’s maintainers replace critical components in early stages of degradation; thus, the use of a data-driven model loses essential information related to advanced stages of degradation which might decrease the accuracy of the maintenance instructions provided. Physics-based model can be used to generate synthetic data to overcome the lack of data in advanced stages of degradation, and then, the synthetic data can be combined with the real data, which is collected by sensor located in the real system, to build the data-driven model. The combination leads to form hybrid-model based approach with a large number of failure modes that were unpredictable. Finally, the outcome is beneficial for the proper functioning of systems; hence, safety of the passengers.
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| 6. |
- Schneider, K. M., et al.
(författare)
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Gut microbiota depletion exacerbates cholestatic liver injury via loss of FXR signalling
- 2021
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Ingår i: Nature Metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 3:9, s. 1228-1241
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Tidskriftsartikel (refereegranskat)abstract
- Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease of unknown aetiology for which there are no approved therapeutic options. Patients with PSC display changes in gut microbiota and in bile acid (BA) composition; however, the contribution of these alterations to disease pathogenesis remains controversial. Here we identify a role for microbiota-dependent changes in BA synthesis that modulates PSC pathophysiology. In a genetic mouse model of PSC, we show that loss of microbiota-mediated negative feedback control of BA synthesis results in increased hepatic BA concentrations, disruption of bile duct barrier function and, consequently, fatal liver injury. We further show that these changes are dependent on decreased BA signalling to the farnesoid X receptor, which modulates the activity of the rate-limiting enzyme in BA synthesis, CYP7A1. Moreover, patients with advanced stages of PSC show suppressed BA synthesis as measured by serum C4 levels, which is associated with poor disease prognosis. Our preclinical data highlight the microbiota-dependent dynamics of BA metabolism in cholestatic liver disease, which could be important for future therapies targeting BA and gut microbiome interactions, and identify C4 as a potential biomarker to functionally stratify patients with PSC and predict disease outcomes. Patients with primary sclerosing cholangitis (PSC), a chronic cholestatic liver disease, display changes in the gut microbiota and in bile acid composition. Schneider, Candels and colleagues identify a role for microbiota-dependent regulation of bile acid synthesis through farnesoid X receptor signalling, which is relevant for PSC disease progression.
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