RESUMO
A radiator is used to remove a portion of the heat generated by a vehicle engine. It is challenging to efficiently maintain the heat transfer in an automotive cooling system even though both internal and external systems need enough time to keep pace with catching up with evolving engine technology advancements. The effectiveness of a unique hybrid's heat transfer nanofluid was investigated in this study. The hybrid nanofluid was mainly composed of graphene nanoplatelets (GnP), and cellulose nanocrystals (CNC) nanoparticles suspended in a 40:60 ratio of distilled water and ethylene glycol. A counterflow radiator equipped with a test rig setup was used to evaluate the hybrid nano fluid's thermal performance. According to the findings, the proposed GNP/CNC hybrid nanofluid performs better in relation to improving the efficiency of heat transfer of a vehicle radiator. The suggested hybrid nanofluid enhanced convective heat transfer coefficient by 51.91%, overall heat transfer coefficient by 46.72%, and pressure drop by 34.06% with respect to distilled water base fluid. Additionally, the radiator could reach a better CHTC with 0.01% hybrid nanofluid in the optimized radiator tube by the size reduction assessment using computational fluid analysis. In addition to downsizing the radiator tube and increasing cooling capacity over typical coolants, the radiator takes up less space and helps to lower the weight of a vehicle engine. As a result, the suggested unique hybrid graphene nanoplatelets/cellulose nanocrystal-based nanofluids perform better in heat transfer enhancement in automobiles.
RESUMO
This report on rehospitalization in chronic schizophrenia is based on a 1-year study of the postdischarge experiences of 119 chronic schizophrenics in New York City. The life table method of analysis identified the important role of discharge planning, community treatment compliance, and interpersonal stress in the patient's living environment in determining the number of days postdischarge that the patient remained in the community without further inpatient care. A mathematical model to predict days in hospital over the follow-up period, based on three specific components of time in hospital defined in numerical terms (PR, NR, LSR), was devised and tested. The first component, the experience of rehospitalization (PR), was determined by interpersonal stress, social supports, and aftercare treatment compliance. Adequacy of discharge planning, an intervention designed to link the patient to community treatment services, has its greatest impact in identifying number of rehospitalizations for the rehospitalized group (NR). Aftercare treatment compliance has its greatest effect in relation to length of subsequent rehospitalization episodes (LSR). Test of the model revealed that it can predict time in hospital within less than one half of a standard deviation of observed hospital days in approximately 50% of cases.
