Effect of expansion level on the flow development with sudden expansion at high Mach numbers

This paper reports the experimental investigation results to monitor pressure at the base and the duct’s flow development. The study aims to assess the influence of favorable and adverse pressure gradients on flow growth and control efficacy. The experimental tests were conducted at a fixed level of favorable and unfavorable pressure gradient at the nozzles for Mach 1.25 to 3.0 at various duct lengths. Only a few selected cases are considered as representative of all the possibilities. Results show that when the nozzles are under the impact of a favorable pressure gradient, they marginally affect the duct’s flow development. However, when nozzles face an adverse pressure gradient, the control acts negatively, resulting in a decline in pressure. Oscillations dominate the flow for the highest pipe length, but the flow becomes smooth for the lower duct length. In most cases, flow is not negatively affected by control. � 2021 Elsevier Ltd. All rights reserved

Influence of microjets on flow development for diameter ratio of 1.6 for correctly expanded nozzles

This paper aims to study the microjet’s efficacy as a management tool for the duct’s flow field. The nozzle was correctly expanded for a diameter ratio of 1.6 (i.e., area ratio = 2.56). The Mach numbers considered were from 1.25 to 2. The investigation shows that the development and recovery of the duct flow are smooth at lower Mach numbers. At Mach 1.48, jet noise was reduced considerably when the control is initiated. For higher Mach numbers of the study, namely Mach 1.6, 1.8, and 2.0, The flow’s oscillatory nature was noticed. This phenomenon reiterates that the nozzles flow is wave-dominated. For most of the flow, the flowing nature remains unaltered due to control. The flow remained connected with the duct for duct length twice the nozzle exit diamete

Impact of expansion level on flowfield with sudden expansion at supersonic regimes

This paper aims to assess the control mechanism’s efficiency and flow pattern in the pipe. The flow was investigated for Mach numbers M = 1.25, 1.3, 1.48, 1.6, 1.8, 2.0, 2.5, and 3.0 for a step height of 3 mm. The NPRs of the tests were from 11 to 3. The flow revealed the minimum duct requirement for a given Mach number and NPR as L = 2D. Only some selected cases where the control mechanism impacts considerably are presented. In most of the cases, the flow field was the same. There is a reversal in control in the flow field; only such cases are discussed. At low Mach numbers, the flow regulator raises the pressure, and for the rest of the Mach numbers,

Determination of non-recrystallization temperature for niobium microalloyed steel

In the present investigation, the non-recrystallization temperature (TNR) of niobium-micro alloyed steel is determined to plan rolling schedules for obtaining the desired properties of steel. The value of TNR is based on both alloying elements and deformation parameters. In the literature, TNR equations have been developed and utilized. However, each equation has certain limitations which constrain its applicability. This study was completed using laboratory-grade low-carbon Nbmicroalloyed steels designed to meet the API X-70 specification. Nbmicroalloyed steel is processed by the melting and casting process, and the composition is found by optical emission spectroscopy (OES). Multiple-hit deformation tests were carried out on a Gleeble® 3500 system in the standard pocket-jaw configuration to determine TNR. Cuboidal specimens (10 (L) * 20 (W) *20 (T) mm3) were taken for compression test (multiple-hit deformation tests) in gleeble. Microstructure evolutions were carried out by using OM (optical microscopy) and SEM (scanning electron microscopy). The value of TNR determined for 0.1 wt.% niobium bearing micro-alloyed steel is ~ 951 �C. Nb- micro-alloyed steel rolled at TNR produce partially recrystallized grain with ferrite nucleation. Hence, to verify the TNR value, a rolling process is applied with the finishing rolling temperature near TNR (~951 �C). The microstructure is also revealed in the pancake shape, which confirms TNR

Prevalence of polypharmacy and associated adverse health outcomes in adult patients with chronic kidney disease: protocol for a systematic review and meta-analysis

Background Polypharmacy, often defined as the concomitant use of ≥ 5 medications, has been identified as a significant global public health threat. Aging and multimorbidity are key drivers of polypharmacy and have been linked to a broad range of adverse health outcomes and mortality. Patients with chronic kidney disease (CKD) are particularly at high risk of polypharmacy and use of potentially inappropriate medications given the numerous risk factors and complications associated with CKD. The aim of this systematic review will be to assess the prevalence of polypharmacy among adult patients with CKD, and the potential association between polypharmacy and adverse health outcomes within this population. Methods/design We will search empirical databases such as MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science, and PsycINFO and grey literature from inception onwards (with no language restrictions) for observational studies (e.g., cross-sectional or cohort studies) reporting the prevalence of polypharmacy in adult patients with CKD (all stages including dialysis). Two reviewers will independently screen all citations, full-text articles, and extract data. Potential conflicts will be resolved through discussion. The study methodological quality will be appraised using an appropriate tool. The primary outcome will be the prevalence of polypharmacy. Secondary outcomes will include any adverse health outcomes (e.g., worsening kidney function) in association with polypharmacy. If appropriate, we will conduct random effects meta-analysis of observational data to summarize the pooled prevalence of polypharmacy and the associations between polypharmacy and adverse outcomes. Statistical heterogeneity will be estimated using Cochran’s Q and I2 index. Additional analyses will be conducted to explore the potential sources of heterogeneity (e.g., sex, kidney replacement therapy, multimorbidity). Discussion Given that polypharmacy is a major and a growing public health issue, our findings will highlight the prevalence of polypharmacy, hazards associated with it, and medication thresholds associated with adverse outcomes in patients with CKD. Our study will also draw attention to the prognostic importance of improving medication practices as a key priority area to help minimize the use of inappropriate medications in patients with CKD. Systematic review registration PROSPERO registration number: [ CRD42020206514 ]

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Microchannels Fabrication in Alumina Ceramic Using Direct Nd:YAG Laser Writing

Ceramic microchannels have important applications in different microscale systems like microreactors, microfluidic devices and microchemical systems. However, ceramics are considered difficult to manufacture owing to their wear and heat resistance capabilities. In this study, microchannels are developed in alumina ceramic using direct Nd:YAG laser writing. The laser beam with a characteristic pulse width of 10 µs and a beam spot diameter of 30 µm is used to make 200 µm width microchannels with different depths. The effects of laser beam intensity and pulse overlaps on dimensional accuracy and material removal rate have been investigated using different scanning patterns. It is found that beam intensity has a major influence on dimensional accuracy and material removal rate. Optimum parameter settings are found using grey relational grade analysis. It is concluded that low intensity and low to medium pulse overlap should be used for better dimensional accuracy. This study facilitates further understanding of laser material interaction for different process parameters and presents optimum laser process parameters for the fabrication of microchannel in alumina ceramic

Evaluation of Support Structure Removability for Additively Manufactured Ti6Al4V Overhangs via Electron Beam Melting

The addition of support structures is essential for the successful fabrication of overhang structures through additive manufacturing (AM). The support structures protect the overhang portion from distortions. They are fabricated with the functional parts and are removed later after the fabrication of the AM part. While structures bearing insufficient support result in defective overhangs, structures with excessive support result in higher material consumption, time and higher post-processing costs. The objective of this study is to investigate the effects of design and process parameters of support structures on support removability during the electron beam melting (EBM)-based additive manufacturing of the Ti6Al4V overhang part. The support design parameters include tooth parameters, no support offset, fragmentation parameters and perforation parameters. The EBM process parameters consist of beam current, beam scan speed and beam focus offset. The results show that both support design and process parameters have a significant effect on support removability. In addition, with the appropriate selection of design and process parameters, it is possible to significantly reduce the support removal time and protect the surface quality of the part

Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing

With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets

Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing

With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets