Surface Integrity Assessment Upon Electric Discharge Machining of Die Steel Using Non-Destructive Magnetic Barkhausen Noise Technique

Surface integrity characterization of manufactured component is very important as it significantly affects the in-service performance of the component. Till now, surface integrity was evaluated using conventional measurement technique like microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester. But, this technique being laboratory based cannot be used for in-service monitoring of the surface integrity. The present study focuses on the characterization of surface integrity upon electric discharge machined sample using non-destructive magnetic Barkhausen noise technique. Electric discharge machining was performed in die-sinking mode on die steel using copper–tungsten electrode (negative polarity). Experiment was performed by selecting different levels of peak current, gap voltage and pulse on time. Surface integrity characteristics like microhardness change, residual stress, microstructural alteration and surface roughness were analysed using microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester, respectively, and were then correlated with magnetic parameter like root mean square value and peak value obtained from Barkhausen noise signal. The results show a good correlation between magnetic parameter (RMS and Peak value) of Barkhausen noise with the microhardness and surface roughness of the machined sample.

     

Efficiency and effectiveness enhancement of an intercooler of two‐stage air compressor by low‐cost Al2O3/water nanofluids

The present study was aimed to utilize low‐cost alumina (Al₂O₃) nanoparticles for improving the heat transfer behavior in an intercooler of two‐stage air compressor. Experimental investigation was carried out with three different volume concentrations of 0.5%, 0.75%, and 1.0% Al₂O₃/water nanofluids to assess the performance of the intercooler, that is, counterflow heat exchanger at different loads. Thermal properties such as thermal conductivity and overall heat transfer coefficient of nanofluid increased substantially with increasing concentration of Al₂O₃ nanoparticles. Specific heat capacity of nanofluids were lower than base water. The intercooler performance parameters such as effectiveness and efficiency improved appreciably with the employment of nanofluid. The efficiency increased by about 6.1% with maximum concentration of nanofluid, that is, 1% at 3‐bar compressor load. It is concluded from the study that high concentration of Al₂O₃ nanoparticles dispersion in water would offer better heat transfer performance of the intercooler.     

Structural plasticity in RNA and its role in the regulation of protein translation in coliphage Q beta

We have analyzed both conformational and functional changes caused by two large cis-acting deletions (delta 159 and delta 549) located within the read-through domain, a 850 nucleotide hairpin, in coliphage Q beta genomic RNA. Studies in vivo show that co-translational regulation of the viral coat and replicase genes has been uncoupled in viral genomes carrying deletion delta 159. Translational regulation is restored in deletion delta 549, a naturally evolved pseudorevertant. Structural analysis by computer modeling shows that structural features within the read-through domain of delta 159 RNA are less well determined than they are in the read-through domain of wild-type RNA, whereas predicted structure in the read-through domain of evolved pseudorevertant delta 549 is unusually well determined. Structural analysis by electron microscopy of the genomic RNAs shows that several long range helices at the base of the read-through domain, that suppress translational initiation of the viral replicase gene in the wild-type genome, have been destabilized in delta 159 RNA. In addition, the structure of local hairpins within the read-through region is more variable in delta 159 RNA than in wild-type RNA. Stable RNA secondary structure is restored in the read-through domain of delta 549 RNA. Our analyses suggest that structure throughout the read-through domain affects the regulation of viral replicase expression by altering the likelihood that long-range interactions at the base of the domain will form. We discuss possible kinetic and equilibrium models that can explain this effect, and argue that observed changes in structural plasticity within the read-through domain of the mutant genomes are key in understanding the process. During the course of these studies, we became aware of the importance of the information contained in the energy dot plot produced by the RNA secondary structure prediction program mfold. As a result, we have improved the graphical representation of this information through the use of color annotation in the predicted optimal folding. The method is presented here for the first time.     

Identification of critical erosion prone areas in the small agricultural watershed using USLE,GIS and remote sensing

In the present study, Karso watershed of Hazaribagh, Jharkhand State, India was divided into 200 × 200 grid cells and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion prone areas of watershed for prioritization purpose. Average annual sediment yield data on grid basis was estimated using Universal Soil Loss Equation (USLE). In general, a major limitation in the use of hydrological models has been their inability to handle the large amounts of input data that describe the heterogeneity of the natural system. Remote sensing (RS) technology provides the vital spatial and temporal information on some of these parameters. A recent and emerging technology represented by Geographic Information System (GIS) was used as the tool to generate, manipulate and spatially organize disparate data for sediment yield modeling. Thus, the Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data to the erosion model, while the USLE was used to predict the spatial distribution of the sediment yield on grid basis. The deviation of estimated sediment yield from the observed values in the range of 1.37 to 13.85 percent indicates accurate estimation of sediment yield from the watershed.     

Snowpack response in the Assiniboine-Red River basin associated with projected global warming of 1.0 °C to 3.0 °C

This study assesses snow response in the Assiniboine-Red River basin, located in the Lake Winnipeg watershed, due to anthropogenic climate change. We use a process-based distributed snow model driven by an ensemble of eight statistically downscaled global climate models (GCMs) to project future changes under policy-relevant global mean temperature (GMT) increases of 1.0 °C to 3.0 °C above the pre-industrial period. Results indicate that basin scale seasonal warmings generally exceed the GMT increases, with greater warming in winter months. The majority of GCMs project wetter winters and springs, and drier summers, while autumn could become either drier or wetter. An analysis of snow water equivalent (SWE) responses under GMT changes reveal higher correlations of snow cover duration (SCD), snowmelt rate, maximum SWE (SWE_max) and timing of SWE_max with winter and spring temperatures compared to precipitation, implying that these variables are predominantly temperature controlled. Consequently, under the GMT increases from 1.0 °C to 3.0 °C, the basin will experience successively shorter SCD, slower snowmelt, smaller monthly SWE and SWE_max, earlier SWE_max, and a transition from snow-dominated to rain-snow hybrid regime. Further, while the winter precipitation increases for some GCMs compensate the temperature-driven changes in SWE, the increases for most GCMs occur as rainfall, thus limiting the positive contribution to snow storage. Overall, this study provides a detailed diagnosis of the snow regime changes under the policy-relevant GMT changes, and a basis for further investigations on water quantity and quality changes.     

Hybrid predictive dynamics: a new approach to simulate human motion

A new methodology, called hybrid predictive dynamics (HPD), is introduced in this work to simulate human motion. HPD is defined as an optimization-based motion prediction approach in which the joint angle control points are unknowns in the equations of motion. Some of these control points are bounded by the experimental data. The joint torque and ground reaction forces are calculated by an inverse algorithm in the optimization procedure. Therefore, the proposed method is able to incorporate motion capture data into the formulation to predict natural and subject-specific human motions. Hybrid predictive dynamics includes three procedures, and each is a sub-optimization problem. First, the motion capture data are transferred from Cartesian space into joint space by using optimization-based inverse kinematics (IK) methodology. Secondly, joint profiles obtained from IK are interpolated by B-spline control points by using an error-minimization algorithm. Third, boundaries are built on the control points to represent specific joint profiles from experiments, and these boundaries are used to guide the predicted human motion. To predict more accurate motion, the boundaries can also be built on the kinetic variables if the experimental data are available. The efficiency of the method is demonstrated by simulating a box-lifting motion. The proposed method takes advantage of both prediction and tracking capabilities simultaneously, so that HPD has more applications in human motion prediction, especially towards clinical applications.     

Predictive simulation of human walking transitions using an optimization formulation

A general optimization formulation for transition walking prediction using 3D skeletal model is presented. The formulation is based on a previously presented one-step walking formulation (Xiang et al., Int J Numer Methods Eng 79:667–695, 2009b). Two basic transitions are studied: walk-to-stand and slow-to-fast walk. The slow-to-fast transition is used to connect slow walk to fast walk by using a step-to-step transition formulation. In addition, the speed effects on the walk-to-stand motion are investigated. The joint torques and ground reaction forces (GRF) are recovered and analyzed from the simulation. For slow-to-fast walk transition, the predicted ground reaction forces in step transition is even larger than that of the fast walk. The model shows good correlation with the experimental data for the lower extremities except for the standing ankle profile. The optimal solution of transition simulation is obtained in a few minutes by using predictive dynamics method.     

Simultaneous analysis and design optimization of nonlinear response

The optimal structural design requiring nonlinear analysis and design sensitivity analysis can be an enormous computational task. It is extremely important to explore ways to reduce the computational effort so that more realistic and larger-scale structures can be optimized. The optimal design process is iterative requiring response analysis of the structure for each design improvement. A recent study has shown that up to 90 percent of the total computational effort is spent in computing the nonlinear response of the structure during the optimal design process. Thus, efficiency of the optimization process for nonlinear structures can be substantially improved if numerical effort for analyzing the structure can be reduced. This paper explores the idea of using design sensitivity coefficients (computed at each iteration to improve design) to predict displacement response of the structure at a changed design. The iterative procedure for nonlinear analysis of the structure is then started from the predicted response. This optimization procedure is called mixed and the original procedure where sensitivity information is not used is called the conventional approach. The numerical procedures for the two approaches are developed and implemented. They are compared on some truss type structures by including both geometric and material nonlinearities. Stress, strain, displacement, and buckling load constraints are imposed. The study shows the mixed method to be numerically stable and efficient.