Optimization of cold and warm workability in stainless steel type AISI 316L using instability maps

The deformation characteristics of stainless steel type AISI 316L under compression in the temperature range 20 to 600 degrees C and strain rate range 0.001 to 100 s(-1) have been studied with a view to characterizing the flow instabilities occurring in the microstructure. At temperatures lower than 100 degrees C and strain rates higher than 0.1 s(-1), 316L stainless steel exhibits flow localization whereas dynamic strain aging (DSA) occurs at intermediate temperatures and below 1 s(-1). To avoid the above flow instabilities, cold working should be carried out at strain rates less than 0.1 s(-1). Warm working of stainless steel type AISI 316L may be done in the temperature and strain rate regime of: 300 to 400 degrees C and 0.001 s(-1) 300 to 450 degrees C and 0.01 s(-1): 450 to 600 degrees C and 0.1 s(-1); 500 degrees C and 1 s(-1) since these regions are free from flow instabilities like DSA and flow localization. The continuum criterion, developed on the basis of the principles of maximum rate of entropy production and separability of the dissipation function, predicts accurately all the above instability features.

Instability map for cold and warm working of as-cast 304 stainless steel

The deformation characteristics of as-cast 304 stainless steel under compression in the temperature range 20-600 degrees C and strain rate range 0.001-100 s(-1) have been studied with a view to characterizing the flow instabilities occurring in the microstructure. Ar strain rates of less than 0.05 s(-1), as-cast 304 stainless steel exhibits flow localization in the temperature range 20-600 degrees C, whereas dynamic strain ageing occurs at intermediate temperatures and below 5 s(-1). At room temperatures and strain rates of less than 0.05 s(-1), martensite formation is observed. To avoid the above microstructural instabilities warm working should be carried out at strain rates greater than 10 s(-1) in the temperature range 400-600 degrees C and cold working could be done in the range of about 0.05-0.8 s(-1). The continuum criterion developed on the basis of the principles of maximum rate of entropy production and separability of the dissipation function, predicts accurately all of the above instability features. (C) 1997 Elsevier Science S.A.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

Use of climate information for farm-level decision making: rainfed groundnut in southern India

The production of rainfed crops in semi-arid tropics exhibits large variation in response to the variation in seasonal rainfall. There are several farm-level decisions such as the choice of cropping pattern, whether to invest in fertilizers, pesticides etc., the choice of the period for planting, plant population density etc. for which the appropriate choice (associated with maximum production or minimum risk) depends upon the nature of the rainfall variability or the prediction for a specific year. In this paper, we have addressed the problem of identifying the appropriate strategies for cultivation of rainfed groundnut in the Anantapur region in a semi-arid part of the Indian peninsula. The approach developed involves participatory research with active collaboration with farmers, so that the problems with perceived need are addressed with the modern tools and data sets available. Given the large spatial variation of climate and soil, the appropriate strategies are necessarily location specific. With the approach adopted, it is possible to tap the detailed location specific knowledge of the complex rainfed ecosystem and gain an insight into the variety of options of land use and management practices available to each category of stakeholders. We believe such a participatory approach is essential for identifying strategies that have a favourable cost-benefit ratio over the region considered and hence are associated with a high chance of acceptance by the stakeholders. (C) 2002 Elsevier Science Ltd. All rights reserved.

A cold model study of raceway hysteresis

The effect of a gas flow field on the size of raceway has been studied experimentally using a two-dimensional (2-D) cold model. It is observed that as the blast velocity from the tuyere increases, raceway size increases, and when the blast velocity is decreased from its highest value, raceway size does not change much until the velocity reaches a critical velocity. Below the critical velocity, raceway size decreases with decreasing velocity but is always larger than that for the same velocity when the velocity increased. This phenomenon is called "raceway hysteresis." Raceway hysteresis has been studied in the presence of different gas flow rates and different particle densities. Raceway hysteresis has been observed in all the experiments. The effect of liquid flow, with various superficial velocities, on raceway hysteresis has also been studied. A study of raceway size hysteresis shows that interparticle and particle-wall friction have a very large effect on raceway size. A hypothesis has been proposed to describe the hysteresis phenomenon in the packed beds. The relevance of hysteresis to blast furnace raceways has been discussed. Existing literature correlations for raceway size ignore the frictional effects. Therefore, their applicability to the ironmaking blast furnace is questionable.

Climate change and Indian forests

An assessment of the impact of projected climate change on forest ecosystems in India based on climate projections of the Regional Climate Model of the Hadley Centre (HadRM3) and the global dynamic vegetation model IBIS for A1B scenario is conducted for short-term (2021-2050) and long-term (2071-2100) periods. Based on the dynamic global vegetation modelling, vulnerable forested regions of India have been identified to assist in planning adaptation interventions. The assessment of climate impacts showed that at the national level, about 45% of the forested grids is projected to undergo change. Vulnerability assessment showed that such vulnerable forested grids are spread across India. However, their concentration is higher in the upper Himalayan stretches, parts of Central India, northern Western Ghats and the Eastern Ghats. In contrast, the northeastern forests, southern Western Ghats and the forested regions of eastern India are estimated to be the least vulnerable. Low tree density, low biodiversity status as well as higher levels of fragmentation, in addition to climate change, contribute to the vulnerability of these forests. The mountainous forests (sub-alpine and alpine forest, the Himalayan dry temperate forest and the Himalayan moist temperate forest) are susceptible to the adverse effects of climate change. This is because climate change is predicted to be larger for regions that have greater elevations.

Climate change vulnerability profiles for North East India

Climate change vulnerability profiles are developed at the district level for agriculture, water and forest sectors for the North East region of India for the current and projected future climates. An index-based approach was used where a set of indicators that represent key sectors of vulnerability (agriculture, forest, water) is selected using the statistical technique principal component analysis. The impacts of climate change on key sectors as represented by the changes in the indicators were derived from impact assessment models. These impacted indicators were utilized for the calculation of the future vulnerability to climate change. Results indicate that majority of the districts in North East India are subject to climate induced vulnerability currently and in the near future. This is a first of its kind study that exhibits ranking of districts of North East India on the basis of the vulnerability index values. The objective of such ranking is to assist in: (i) identifying and prioritizing the most vulnerable sectors and districts; (ii) identifying adaptation interventions, and (iii) mainstreaming adaptation in development programmes.

Exploring use of climate information for optimal design of water resources infrastructure

We conducted surveys of fire and fuels managers at local, regional, and national levels to gain insights into decision processes and information flows in wildfire management. Survey results in the form of fire managers’ decision calendars show how climate information needs vary seasonally, over space, and through the organizational network, and help determine optimal points for introducing climate information and forecasts into decision processes. We identified opportunities to use climate information in fire management, including seasonal to interannual climate forecasts at all organizational levels, to improve the targeting of fuels treatments and prescribed burns, the positioning and movement of initial attack resources, and staffing and budgeting decisions. Longer-term (5–10 years) outlooks also could be useful at the national level in setting budget and research priorities. We discuss these opportunities and examine the kinds of organizational changes that could facilitate effective use of existing climate information and climate forecast capabilities.

Climate forcing and response to idealized changes in surface latent and sensible heat

Land use and land cover changes affect the partitioning of latent and sensible heat, which impacts the broader climate system. Increased latent heat flux to the atmosphere has a local cooling influence known as `evaporative cooling', but this energy will be released back to the atmosphere wherever the water condenses. However, the extent to which local evaporative cooling provides a global cooling influence has not been well characterized. Here, we perform a highly idealized set of climate model simulations aimed at understanding the effects that changes in the balance between surface sensible and latent heating have on the global climate system. We find that globally adding a uniform 1 W m(-2) source of latent heat flux along with a uniform 1 W m(-2) sink of sensible heat leads to a decrease in global mean surface air temperature of 0.54 +/- 0.04 K. This occurs largely as a consequence of planetary albedo increases associated with an increase in low elevation cloudiness caused by increased evaporation. Thus, our model results indicate that, on average, when latent heating replaces sensible heating, global, and not merely local, surface temperatures decrease.

Coupled Climate Model on Grids

Community Climate System Model (CCSM) is a Multiple Program Multiple Data (MPMD) parallel global climate model comprising atmosphere, ocean, land, ice and coupler components. The simulations have a time-step of the order of tens of minutes and are typically performed for periods of the order of centuries. These climate simulations are highly computationally intensive and can take several days to weeks to complete on most of today’s multi-processor systems. ExecutingCCSM on grids could potentially lead to a significant reduction in simulation times due to the increase in number of processors. However, in order to obtain performance gains on grids, several challenges have to be met. In this work,we describe our load balancing efforts in CCSM to make it suitable for grid enabling.We also identify the various challenges in executing CCSM on grids. Since CCSM is an MPI application, we also describe our current work on building a MPI implementation for grids to grid-enable CCSM.