Investigation on the changes effected by tool profile on mechanical and tribological properties of friction stir processed AZ31B magnesium alloy

The present study investigates the effect of tool shoulder profile on the mechanical and tribological properties of friction stir processed AZ31B magnesium alloy. The tool rotational speed and feed rate are the chosen process parameters. The experiments were conducted with 3 level 2 factors full factorial design. The recorded responses were tensile strength, wear losses and corrosion rate. The results were analyzed with the help microstructures of the processed samples. The study reveals that, for concave shoulder tool, the strain hardening effect was playing a major role in determining the properties of the processed materials and for the step shoulder tool, the grain size plays a major role in determining the properties of the processed materials.     

Modulation-assisted high speed machining of compacted graphite iron (CGI)

The application of controlled, low-frequency modulation (～100 Hz) superimposed onto the cutting process in the feed-direction – modulation-assisted machining (MAM) – is shown to be quite effective in reducing the wear of cubic boron nitride (CBN) tools when machining compacted graphite iron (CGI) at high machining speeds (>500 m/min). The tool life is at least 20 times greater than in conventional machining. This significant reduction in wear is a consequence of the multiple effects realized by MAM, including periodic disruption of the tool–workpiece contact, formation of discrete chips, enhanced fluid action and lower cutting temperatures. The propensity for thermochemical wear of CBN, the principal wear mode at high speeds in CGI machining, is thus reduced. The tool wear in MAM is also found to be smaller at the higher cutting speeds (730 m/min) tested. The feed-direction MAM appears feasible for implementation in industrial machining applications involving high speeds.     

A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.     

Evaluation of a Watershed Model for Estimating Daily Flow Using Limited Flow Measurements1

Abstract: The Soil and Water Assessment Tool (SWAT) model was evaluated for estimation of continuous daily flow based on limited flow measurements in the Upper Oyster Creek (UOC) watershed. SWAT was calibrated against limited measured flow data and then validated. The Nash‐Sutcliffe model Efficiency (NSE) and mean relative error values of daily flow estimations were 0.66 and 15% for calibration, and 0.56 and 4% for validation, respectively. Also, further evaluation of the model’s estimation of flow at multiple locations was conducted with parametric paired t‐test and nonparametric sign test at a 95% confidence level. Among the five main stem stations, four stations were statistically shown to have good agreement between predicted and measured flows. SWAT underestimated the flow of the fifth main stem station possibly because of the existence of complex flood control measures near to the station. SWAT estimated the daily flow at one tributary station well, but with relatively large errors for the other two tributaries. The spatial pattern of predicted flows matched the measured ones well. Overall, it was concluded from the graphical comparisons and statistical analyses of the model results that SWAT was capable of reproducing continuous daily flows based on limited flow data as is the case in the UOC watershed.     

Risk assessment tools incorporating human error probabilities in the Japanese small-sized establishment

Although it has been estimated that as many as 80% of all occupational accidents have human errors as a cause, no risk assessment tools incorporating human-related elements have been developed for small companies. Human error probability (HEP) and human error analysis (HEA) have been used for large-scale, safety-critical industries for last three decades, but these tools are not suitable for smaller, more general industries that comprise the majority of accident settings.Here, we describe and verify a risk assessment tool that includes human-related elements for small companies. The tool expands on traditional risk assessment methods, such as matrix, risk graph and numerical scoring method, by adding human-related elements. The tool is easy-to-use in occupational environments, and includes assessments of human behavior and potentially outdated machinery at work place.     

Reflections on the use of Bayesian belief networks for adaptive management

A broad range of tools are available for integrated water resource management (IWRM). In the EU research project NeWater, a hypothesis exists that IWRM cannot be realised unless current management regimes undergo a transition toward adaptive management (AM). This includes a structured process of learning, dealing with complexity, uncertainty etc. We assume that it is no longer enough for managers and tool researchers to understand the complexity and uncertainty of the outer natural system-the environment. It is just as important, to understand what goes on in the complex and uncertain participatory processes between the water managers, different stakeholders, authorities and researchers when a specific tool and process is used for environmental management. The paper revisits a case study carried out 2001-2004 where the tool Bayesian networks (BNs) was tested for groundwater management with full stakeholder involvement. With the participation of two researchers (the authors) and two water managers previously involved in the case study, a qualitative interview was prepared and carried out in June 2006. The aim of this ex-post evaluation was to capture and explore the water managers' experience with Bayesian belief networks when used for integrated and adaptive water management and provide a narrative approach for tool enhancement.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

Modeling Streamflow and Water Quality Sensitivity to Climate Change and Urban Development in 20 U.S. Watersheds

Watershed modeling in 20 large, United States (U.S.) watersheds addresses gaps in our knowledge of streamflow, nutrient (nitrogen and phosphorus), and sediment loading sensitivity to mid‐21st Century climate change and urban/residential development scenarios. Use of a consistent methodology facilitates regional scale comparisons across the study watersheds. Simulations use the Soil and Water Assessment Tool. Climate change scenarios are from the North American Regional Climate Change Assessment Program dynamically downscaled climate model output. Urban and residential development scenarios are from U.S. Environmental Protection Agency's Integrated Climate and Land Use Scenarios project. Simulations provide a plausible set of streamflow and water quality responses to mid‐21st Century climate change across the U.S. Simulated changes show a general pattern of decreasing streamflow volume in the central Rockies and Southwest, and increases on the East Coast and Northern Plains. Changes in pollutant loads follow a similar pattern but with increased variability. Ensemble mean results suggest that by the mid‐21st Century, statistically significant changes in streamflow and total suspended solids loads (relative to baseline conditions) are possible in roughly 30‐40% of study watersheds. These proportions increase to around 60% for total phosphorus and total nitrogen loads. Projected urban/residential development, and watershed responses to development, are small at the large spatial scale of modeling in this study.     

Ammonium nitrate thermal decomposition with additives

Runaway reactions present a potentially serious threat to the chemical process industry and the community; such reactions occur time and time again often with devastating consequences. The main objective of this research is to study the root causes associated with ammonium nitrate (AN) explosions during storage. The research focuses on AN fertilizers and studies the effects of different types of fertilizer compatible additives on AN thermal decomposition. Reactive Systems Screening Tool (RSST) has been used for reactivity evaluation and to better understand the mechanisms that result in explosion hazards. The results obtained from this tool have been reported in terms of parameters such as “onset” temperature, rate of temperature and pressure rise and maximum temperature. The runaway behavior of AN has been studied as a solid and solution in water. The effect of additives such as sodium sulfate (Na₂SO₄) and potassium chloride (KCl) has also been studied. Multiple tests have been conducted to determine the characteristics of AN decomposition accurately. The results show that the presence of sodium sulfate can increase the “onset” temperature of AN decomposition thus acting as AN thermal decomposition inhibitor, while potassium chloride tends to decrease the “onset” temperature thus acting as AN thermal decomposition promoter.     

A Water Allocation Decision‐Support Model and Tool for Predictions in Ungauged Basins in Northeast British Columbia,Canada

Pressures on water resources due to changing climate, increasing demands, and enhanced recognition of environmental flow needs result in the need for hydrology information to support informed water allocation decisions. However, the absence of hydrometric measurements and limited access to hydrology information in many areas impairs water allocation decision‐making. This paper describes a water balance‐based modeling approach and an innovative web‐based decision‐support hydrology tool developed to address this need. Using high‐resolution climate, vegetation, and watershed data, a simple gridded water balance model, adjusted to account for locational variability, was developed and calibrated against gauged watersheds, to model mean annual runoff. Mean monthly runoff was modeled empirically, using multivariate regression. The modeled annual runoff results are within 20% of the observed mean annual discharge for 78% of the calibration watersheds, with a mean absolute error of 16%. Modeled monthly runoff corresponds well to observed monthly runoff, with a median Nash–Sutcliffe statistic of 0.92 and a median Spearman rank correlation statistic of 0.98. Monthly and annual flow estimates produced from the model are incorporated into a map‐ and watershed‐based decision‐support system referred to as the Northeast Water Tool, to provide critical information to decision makers and others on natural water supply, existing allocations, and the needs of the environment.