Innovative Algorithm for Washing Machine: Unbalance and Inertia Detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Combination washing/drying laundry appliance having heat pump system with reversible condensing and evaporating heat exchangers.

An appliance air/water handling system includes a rotating drum, airflow and fluid paths for directing process air and fluid, respectively therethrough. First and second heat exchanger are in direct engagement with the airflow and fluid paths, respectively. A reversible refrigerant circuit delivers refrigerant through the first and second heat exchanger to alternatively define washing and drying conditions. In the washing condition the first heat exchanger cools the processed air into cooled process air, and the second heat exchanger heats the fluid to define a heated fluid that is directed to the drum. In the drying condition the first heat exchanger the process air to define heated process air that is directed through the drum and through a third heat exchanger, and the second heat exchanger cools the fluid to define a cooled fluid that is directed to the third heat exchanger intersect with the heated process air

An eDrive-Based Estimation Method of the Laundry Unbalance and Laundry Inertia for Washing Machine Applications

The estimation of the laundry unbalance and laundry inertia is fundamental in washing machine applications. On the one hand, the estimation and management of the laundry unbalance play a pivotal role in reducing mechanical stress and noise during the spinning phase. On the other hand, the laundry inertia’s estimation, performed at the beginning of the washing cycle, allows for the determination of the proper amounts of water and detergent, the water temperature, and the tumbling time. In this way, good washing performance is obtained, avoiding the waste of energy and resources. Moreover, at the end of the washing cycle, the laundry inertia’s accurate estimation is needed to properly manage the spinning phase. With the aim of optimizing the washing performance, this paper proposes a novel method to estimate the laundry unbalance and laundry inertia. The proposed approach does not require additional sensors, since it uses the already implemented motor control scheme, enhanced by a dedicated position-tracking observer. Experimental results have been carried out on a commercial horizontal-axis direct-drive washer, demonstrating the validity of the proposed solution

Measurement of Rotor Thermal Time-Constant for Permanent Magnet Synchronous Machines

Thanks to their high torque density, permanent magnet synchronous motors (PMSMs) currently represent the most competitive solution in the electrification processes involving transports and energy production. However, it is known how the torque production of such motors is strictly related to the temperature of the permanent magnet (PM), affecting control performance, and efficiency. This issue makes necessary the thermal analysis of the machine, thus requiring the determination of the PM’s thermal time-constant. In this paper, an experimental method for evaluating such a parameter is proposed, allowing high accuracy and reliability of the result. The proposed procedure can be applied to any PMSM type, without being affected by factors such as rotor lamination, shaft, PM distribution. The experimental validation has been carried out on three PMSMs, having different rotor structure, sizes, and voltage/current levels. Experimental results demonstrate the validity of the proposed method

A Test Procedure to Evaluate Magnets Thermal Time Constant of Permanent Magnet Machines

Thanks to their high torque density, permanent magnet synchronous motors (PMSMs) currently represent the most competitive solution in the electrification processes involving transports and energy production. However, it is known how the torque production of PMSMs is strictly related to the temperature of the permanent magnets (PMs) since the latter affects control performance and efficiency. This issue thus makes necessary the thermal analysis of the machine under consideration. In this scenario, the determination of the PMs thermal time constant covers a pivotal role in implementing an accurate thermal model of PMSMs. Therefore, this paper aims at proposing an experimental test procedure to evaluate the PMs thermal time constant of PMSMs. The proposed procedure can be applied to any PMSM type without being affected by factors such as rotor lamination, shaft, and PM distribution. In this way, accurate and reliable results are obtained. The experimental validation has been carried out on four PMSMs, with different rotor structures, sizes, power, and voltage/current levels. Experimental results demonstrate the validity of the proposed method

Measurement Technique for the Permanent Magnet Rotor Thermal Time Constant Determination

In this paper, an innovative measurement technique that allows the determination of the permanent magnet thermal time constant of permanent magnet synchronous machines is proposed. The novelty of the proposed procedure consists of the determination of the permanent magnet thermal time constant without the test being influenced by the other rotor parts such as lamination, shaft, etc. Therefore, it can be applied to any permanent magnet synchronous machine type, assuming general validity. The proposed experimental setup consists of controlling the currents of the machine under test while an external electric drive sets its speed. Experimental results for two permanent magnet synchronous machine types are presented, demonstrating the feasibility of the proposed procedure

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Background: Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results: Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered model

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

The International Linear Collider: Report to Snowmass 2021

The International Linear Collider (ILC) is on the table now as a new global energy-frontier accelerator laboratory taking data in the 2030s. The ILC addresses key questions for our current understanding of particle physics. It is based on a proven accelerator technology. Its experiments will challenge the Standard Model of particle physics and will provide a new window to look beyond it. This document brings the story of the ILC up to date, emphasizing its strong physics motivation, its readiness for construction, and the opportunity it presents to the US and the global particle physics community