Soil Surface Runoff Scheme for Improving Land-Hydrology and Surface Fluxes in Simple SiB (SSiB)

Evapotranspiration on land is hard to measure and difficult to simulate. On the scale of a GCM grid, there is large subgrid-scale variability of orography, soil moisture, and vegetation. Our hope is to be able to tune the biophysical constants of vegetation and soil parameters to get the most realistic space-averaged diurnal cycle of evaporation and its climatology. Field experiments such as First ISLSCP Field Experiment (FIFE), Boreal Ecosystem-Atmosphere Study (BOREAS), and LBA help a great deal in improving our evapotranspiration schemes. However, these improvements have to be matched with, and coupled to, consistent improvement in land-hydrology; otherwise, the runoff problems will intrinsically reflect on the soil moisture and evapotranspiration errors. Indeed, a realistic runoff simulation also ensures a reasonable evapotranspiration simulation provided the precipitation forcing is reliable. We have been working on all of the above problems to improve the simulated hydrologic cycle. Through our participation in the evaluation and intercomparison of land-models under the behest of Global Soil Wetness Project (GSWP), we identified a few problems with Simple SiB (SSIB; Xue et al., 1991) hydrology in regions of significant snowmelt. Sud and Mocko (1999) show that inclusion of a separate snowpack model, with its own energy budget and fluxes with the atmosphere aloft and soil beneath, helps to ameliorate some of the deficiencies of delayed snowmelt and excessive spring season runoff. Thus, much more realistic timing of melt water generation was simulated with the new snowpack model in the subsequent GSWP re-evaluations using 2 years of ISLSCP Initiative I forcing data for 1987 and 1988. However, we noted an overcorrection of the low meltwater infiltration of SSiB. While the improvement in snowmelt timing was found everywhere, the snowmelt infiltration has became excessive in some regions, e.g., Lena river basin. This leads to much reduced runoff in many basins as compared to observations. We believe this is a consequence of neglect of the influence of subgrid-scale variations in orography that affects the production of surface runoff

Transport Model Simulations of Projectile Fragmentation Reactions at 140 MeV/nucleon

The collisions in four different reaction systems using $^{40,48}$Ca and $^{58,64}$Ni isotope beams and a Be target have been simulated using the Heavy Ion Phase Space Exploration and the Antisymmetrized Molecular Dynamics models. The present study mainly focuses on the model predictions for the excitation energies of the hot fragments and the cross sections of the final fragments produced in these reactions. The effects of various factors influencing the final fragment cross sections, such as the choice of the statistical decay code and its parameters have been explored. The predicted fragment cross sections are compared to the projectile fragmentation cross sections measured with the A1900 mass separator. At $E/A=140$ MeV, reaction dynamics can significantly modify the detection efficiencies for the fragments and make them different from the efficiencies applied to the measured data reported in the previous work. The effects of efficiency corrections on the validation of event generator codes are discussed in the context of the two models.Comment: 28 pages, 13 figure

Snowmelt and Infiltration Deficiencies of SSiB and Their Resolution with a New Snow-Physics Scheme

A two-year 1987-1988 integration of SSiB forced with ISLSCP Initiative I surface data (as part of the Global Soil Wetness Project, GSWP, evaluation and intercomparison) produced generally realistic land surface fluxes and hydrology. Nevertheless, the evaluation also helped to identify some of the deficiencies of the current version of the Simplified Simple Biosphere (SSiB) model. The simulated snowmelt was delayed in most regions, along with excessive runoff and lack of an spring soil moisture recharge. The SSIB model had previously been noted to have a problem producing accurate soil moisture as compared to observations in the Russian snowmelt region. Similarly, various GSWP implementations of SSIB found deficiencies in this region of the simulated soil moisture and runoff as compared to other non-SSiB land-surface models (LSMs). The origin of these deficiencies was: 1) excessive cooling of the snow and ground, and 2) deep frozen soil disallowing snowmelt infiltration. The problem was most severe in regions that experience very cold winters. In SSiB, snow was treated as a unified layer with the first soil layer, causing soil and snow to cool together in the winter months, as opposed to snow cover acting as an insulator. In the spring season, a large amount of heat was required to thaw a hard frozen snow plus deep soil layers, delaying snowmelt and causing meltwater to become runoff over the frozen soil rather than infiltrate into it

An Experimental Protocol to Model Recovery of Anaerobic Work Capacity

Models of fatigue are based on physiological parameters such as Critical Power (CP) and Anaerobic Work Capacity (AWC). CP is a theoretical threshold value that a human can generate for an indefinite amount of time and AWC represents a finite expendable amount of anaerobic energy at intensities above CP. There is an increasing interest in developing mathematical models of energy expenditure and recovery for athletic training and human performance. The objective of this research is to propose and validate a model for recovery of AWC during a post exertion recovery interval of cycling. A cycling ergometer study is proposed which involves a VO2max ramp test to determine gas exchange threshold, a 3-min all-out intensity test to determine CP and AWC, and exertion-recovery interval tests to understand recovery of AWC. The results will be used to build a human in the loop control system to optimize cycling performance

Extrapolation of neutron-rich isotope cross-sections from projectile fragmentation

Using the measured fragmentation cross sections produced from the 48Ca and 64Ni beams at 140 MeV per nucleon on 9Be and 181Ta targets, we find that the cross sections of unmeasured neutron rich nuclei can be extrapolated using a systematic trend involving the average binding energy. The extrapolated cross-sections will be very useful in planning experiments with neutron rich isotopes produced from projectile fragmentation. The proposed method is general and could be applied to other fragmentation systems including those used in other radioactive ion beam facilities.Comment: accepted for publication in Europhysics Letter

A survey of mathematical models of human performance using power and energy

The ability to predict the systematic decrease of power during physical exertion gives valuable insights into health, performance, and injury. This review surveys the research of power-based models of fatigue and recovery within the area of human performance. Upon a thorough review of available literature, it is observed that the two-parameter critical power model is most popular due to its simplicity. This two-parameter model is a hyperbolic relationship between power and time with critical power as the power-asymptote and the curvature constant denoted by W′. Critical power (CP) is a theoretical power output that can be sustained indefinitely by an individual, and the curvature constant (W′) represents the amount of work that can be done above CP. Different methods and models have been validated to determine CP and W′, most of which are algebraic manipulations of the two-parameter model. The models yield different CP and W′ estimates for the same data depending on the regression fit and rounding off approximations. These estimates, at the subject level, have an inherent day-to-day variability called intra-individual variability (IIV) associated with them, which is not captured by any of the existing methods. This calls for a need for new methods to arrive at the IIV associated with CP and W′. Furthermore, existing models focus on the expenditure of W′ for efforts above CP and do not model its recovery in the sub-CP domain. Thus, there is a need for methods and models that account for (i) the IIV to measure the effectiveness of individual training prescriptions and (ii) the recovery of W′ to aid human performance optimization

Isoscaling, Symmetry Energy and Thermodynamic Models

The isoscaling parameter usually denoted by $\alpha$ depends upon both the symmetry energy coefficient and the isotopic contents of the dissociating systems. We compute $\alpha$ in theoretical models: first in a simple mean field model and then in thermodynamic models using both grand canonical and canonical ensembles. For finite systems the canonical ensemble is much more appropriate. The model values of $\alpha$ are compared with a much used standard formula. Next we turn to cases where in experiments, there are significant deviations from isoscaling. We show that in such cases, although the grand canonical model fails, the canonical model is capable of explaining the data.Comment: total 27 pages including 9 figure

Incorporating uncertainty into diagnostic analysis of mechanical systems

Analyzing systems during the conceptual stages of design for characteristics essential to the ease of fault diagnosis is important in today's mechanical systems because consumers and manufacturers are becoming increasingly concerned with cost incurred over the life cycle of the system. The increase in complexity of modem mechanical systems can often lead to systems that are difficult to diagnose, and therefore require a great deal of time and money to return the system to working condition. Mechanical systems optimized in the area of diagnosability can lead to a reduction of life cycle costs for both consumers and manufacturers and increase the useable life of the system. A methodology for completing diagnostic analysis of mechanical systems is presented. First, a diagnostic model, based on components and system indications, is constructed. Bayes formula is used in conjunction with information extracted from the Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), component reliability, and prior system knowledge to construct the diagnostic model. The diagnostic model, when presented in matrix form, is denoted as the Component-Indication Joint Probability Matrix. The Component-Indication Joint Probability Matrix presents the joint probabilities of all possible mutually exclusive diagnostic events in the system. Next, methods are developed to mathematically manipulate the Component-Indication Joint Probability Matrix into two matrices, (1) the Replacement Matrix and (2) the Replacement Probability Matrix. These matrices are used to compute a set of diagnosability metrics. The metrics are useful for comparing alternative designs and addressing diagnostic problems to the system, component and indication level, during the conceptual stages of design. Additionally, the metrics can be used to predict cost associated with fault isolation over the life cycle of the system. The methodology is applied to a hypothetical example problem for illustration, and applied to a physical system, an icemaker, for validation

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions