NOPR Collection:

Water-containing i-propanol-n-butanol-ethanol (IBE) as a next-generation biofuel of n-butanol for diesel engine

2021-09-10T05:07:32Z

Title: Water-containing i-propanol-n-butanol-ethanol (IBE) as a next-generation biofuel of n-butanol for diesel engine Authors: Abubakar, Shitu; Hu, Junping; Li, Yuqiang; Narayan, Sunny Abstract: The high emission level of diesel engines has been an issue of global concern and the sophisticated means of controlling the emissions were not cost-effective. In this work, effects of water addition in a bio-derived fuel to mitigate engine emissions and enhances the brake thermal efficiency have been investigated. Four test samples including IBE10, IBE30, IBE29.5W0.5 and IBE29W1 have been prepared and tested in a diesel engine. The engine combustion characteristics, performance and emissions have been observed. It has been established that the water containing blends improve the BTE, BSFC and further reduces emissions at varying loads. In comparison with IBE30, IBE29W1 (29 vol. % IBE, 1 vol. % water and 90 vol. % diesel) has shown decreasing peak in-cylinder pressure and increases ignition delay and combustion duration by 0.13% – 4.8 %, 0.5% – 12.4 % and 0.26% – 3.8 % respectively. As for the engine performance, BTE has been increased by 2.6 % – 14.1% and BSFC decreased by 0.1% – 15 %, respectively, and the emissions of UHC, smoke, CO and NOx emissions was decreased by 21% – 42.6%, 0% – 21.7%, 5.4% –11%, and 0.64% – 9%, at varying loading conditions respectively. Page(s): 223-233

Establish time-temperature-transformation diagram based on dilatometry results and microstructural evolutions in an AISI 1010 steel

2021-09-10T05:04:38Z

Title: Establish time-temperature-transformation diagram based on dilatometry results and microstructural evolutions in an AISI 1010 steel Authors: Mehta, Yashwant; Rajput, Sunil Kumar; Kumar, Sanjeev Abstract: A time-temperature-transformation (TTT) diagram has been constructed using dilation results. The phase transformations have been confirmed through microstructural changes of AISI 1010 steel. The strength of this steel has been improved for various applications using heat treatments. Experiments for a series of steel samples have been conducted between 310- 730°C isothermal temperature range using a thermo-mechanical simulator Gleeble^®3800. The austenitised microstructure of the steel has been transformed into a combination of ferrite, pearlite, widmanstätten ferrite, massive ferrite, upper and lower bainite, and martensite, etc., when held at different isothermal temperatures. Ferrite along with pearlite have been observed at higher isothermal temperatures while bainite with martensite have been observed at lower isothermal temperatures. Two C-curves have been observed in pearlite and bainite transformation regions. Page(s): 234-239

Implementation of machine learning model-based decision support system for healthcare professionals to predict T2DM risk using heart rate variability features

2021-10-08T09:47:59Z

Title: Implementation of machine learning model-based decision support system for healthcare professionals to predict T2DM risk using heart rate variability features Authors: Rathod, Shashikant Rajaram; Chaskar, Uttam; Phadke, Leena; Patil, Chetan Kumar Abstract: Non-invasive early diabetes prediction has been gaining much premarkable over the last decade. Heart rate variability (HRV) is the only non-invasive technique that can predict the future occurrence of the disease. Early prediction of diabetes can help doctors start an early intervention. To this end, the authors have developed a computational machine learning model to predict type 2 diabetes mellitus (T2DM) risk using heart rate variability features and have evaluated its robustness against the HRV of 50 patients data. The electrocardiogram (ECG) signal of the control population (n=40) and T2DM population (n=120) have been recorded in the supine position for 5 minutes, and HRV signals have been obtained. The time domain, frequency domain, and non-linear features have been extracted from the HRV signal. A decision support system has been developed based on a machine learning algorithm. Finally, the decision support system has been validated using the HRV features of 50 patients (Control n=10 and T2DM n=40). HRV features are selected for the prediction of T2DM. The decision support system has been designed using three machine learning models: Gradient boosting decision tree (GBDT), Extreme Gradient boosting (XGBoost), Categorical boosting (CatBoost), and their performance have been evaluated based on the Accuracy (ACC), Sensitivity (SEN), Specificity (SPC), Positive predicted value (PPV), Negative predicted value (NPV), False-positive rate (FPR), False-negative rate (FNR), F1 score, and Area under the receiver operating characteristic curve (AUC) metrics. The CatBoost model offers the best performance outcomes, and its results have been validated on 50 patients. Thus the CatBoost model can be use as a decision support system in hospitals to predict the risk of T2DM. Page(s): 240-249

Influence processing parameters of FDM 3D printer on the mechanical properties of ABS Parts

2021-09-10T04:57:20Z

Title: Influence processing parameters of FDM 3D printer on the mechanical properties of ABS Parts Authors: Shafaat, Amir; Ashtiani, Hamidreza Rezaei Abstract: 3D printing has been a type of additive manufacturing (AM) that creates parts by adding or printing thin layers of material on top of each other using computer-aided design (CAD) models. Fused deposition modeling (FDM) is a 3D printing process that produces parts by heating, extruding, and depositing the thermoplastic polymers. FDM-fabricated products have been becoming increasingly popular in various industries such as medical, electronics, automobile, pharmaceutical, etc. This study has been carried out on a set of standard samples out of acrylonitrile butadiene styrene (ABS) which have been produced using the FDM process. A comprehensive mechanical property evaluation has been performed to determine the influence of the infill density and layer thickness of ABS (FDM-fabricated) on the ultimate tensile strength, elastic modulus, yield strength, fracture strain, and toughness (energy absorption) using a tensile test. From the result analysis, it has been found that infill density and layer thickness haveimportant effects on the tensile properties. The behavior investigation of ABS-filament freeform fabrication has shown that infill density of 100% and a layer height of 0.1 mm achieve optimized process parameters values. Page(s): 250-257