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A Monte Carlo test particle model was developed that simulates the charge evolution of micron and sub-micron sized dust grains ejected upon low-energy impact of a moderate-size object onto a lunar polar crater floor. Our analog is the LCROSS impact into Cabeus crater. Our primary objective is to model grain discharging as the plume propagates upwards from shadowed crater into sunlight.
The objective of this paper is to develop a nonlinear viscoelastic–viscoplastic constitutive model for epoxy polymers. The classic nonlinear viscoelasticity model is reformulated to yield a closed-form incremental constitutive relation, which relates the stress increments to the viscoelastic strain increments. A viscoplasticity model, which consists of the Drucker–Prager yield function, nonlinear isotropic and kinematic hardening laws, and the Perzyna viscosity function, is subsequently developed. The nonlinear viscoelasticity and the vis-coplasticity models are then implemented in a radial return algorithm. A closed-form algorithmic tangent operator is derived to facilitate model implementation. The nonlinear viscoelasticity model is validated by reproducing the test data for polymethyl methacrylate(PMMA). The present constitutive model’s capabilities are demonstrated through modeling viscoelastic–viscoplastic PMMA loaded at different strain rates. The mechanics of structure genome-based micromechanics approach, embedded with the present constitutive model, is used to homogenize a unidirectional fiber-reinforced composite with a PMMA matrix, subjected to uniaxial and shear loading at different strain rates. The viscoelastic characterization of PMT-F7 epoxy is evaluated by comparing the predicted creep–recovery responses by the present constitutive model with the experimental ones.
We describe NIR imaging observations of embedded young stars in the Taurus-Auriga molecular cloud. We find a large range in J-K and H-K colors for these class I sources. The bluest objects have colors similar to the reddest T Tauri stars in the cloud; redder objects lie slightly above the reddening line for standard ISM dust and have apparent K extinctions of up to 5 mag. Most of these sources also show extended NIR emission on scales of 10-20 arcsec which corresponds to linear sizes of 1500-3000 AU. The NIR colors and nebular morphologies for this sample and the magnitude of linear polarization in several sources suggest scattered light produces most of the NIR emission in these objects. We present modeling results that suggest mass infall rates that agree with predictions for cold clouds and are generally consistent with rates estimated from radiative equilibrium models. For reasonable dust grain parameters, the range of colors and extinctions require flattened density distributions with polar cavities evacuated by bipolar outflows. These results support the idea that infall and outflow occur simultaneously in deeply embedded bipolar outflow sources. The data also indicate fairly large centrifugal radii and large inclinations to the rotational axis for a typical source.
Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.
Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.
Procedures for dealiasing Seasat data and developing global surface wind and latent and sensible heat flux fields are discussed. Seasat data from September 20, 1978 was dealiased using the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system. The wind data obtained with the objective GLA forecast model are compared to the data subjectively dealiased by Peteherych et al. (1984) and Hoffman (1982, 1984). The GLA procedure is also verified using simulated Seasat data. The areas of high and low heat fluxes and cyclonic and anticyclonic wind stresses detected in the generated fields are analyzed and compared to climatological fields. It is observed that there is good correlation between the time-averaged analyses of wind stress obtained subjectively and objectively, and the monthly mean wind stress and latent fluxes agree with climatological fields and atmospheric and oceanic features.
The 45th Weather Squadron Launch Weather Officers use the 12-km resolution North American Mesoscale model (MesoNAM) forecasts to support launch weather operations. In Phase I, the performance of the model at KSC/CCAFS was measured objectively by conducting a detailed statistical analysis of model output compared to observed values. The objective analysis compared the MesoNAM forecast winds, temperature, and dew point to the observed values from the sensors in the KSC/CCAFS wind tower network. In Phase II, the AMU modified the current tool by adding an additional 15 months of model output to the database and recalculating the verification statistics. The bias, standard deviation of bias, Root Mean Square Error, and Hypothesis test for bias were calculated to verify the performance of the model. The results indicated that the accuracy decreased as the forecast progressed, there was a diurnal signal in temperature with a cool bias during the late night and a warm bias during the afternoon, and there was a diurnal signal in dewpoint temperature with a low bias during the afternoon and a high bias during the late night.
An object is an abstract software model of a problem domain entity. Objects are packages of both data and operations of that data (Goldberg 83, Booch 83). The Ada (tm) package construct is representative of this general notion of an object. Object-oriented design is the technique of using objects as the basic unit of modularity in systems design. The Software Engineering Laboratory at the Goddard Space Flight Center is currently involved in a pilot program to develop a flight dynamics simulator in Ada (approximately 40,000 statements) using object-oriented methods. Several authors have applied object-oriented concepts to Ada (e.g., Booch 83, Cherry 85). It was found that these methodologies are limited. As a result a more general approach was synthesized with allows a designer to apply powerful object-oriented principles to a wide range of applications and at all stages of design. An overview is provided of this approach. Further, how object-oriented design fits into the overall software life-cycle is considered.
Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows , achieves a test accuracy of 92.0%, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of 87.6%. Finally, these results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
The achievement of quality dynamic performance in manipulator systems is difficult using conventional control methods because of both the inherent geometric nonlinearities of these systems and the dependence of the system dynamics on the characteristics of manipulated objects. A model-referenced adaptive control law is developed for maintaining uniformly good performance over a wide range of motions and payloads. The effectiveness of the approach is demonstrated in several simulations and the system stability as a function of input is investigated. Also developed is a 'learning signal' approach designed to minimize initial transients arising from abrupt changes in the inertial payload.
After decades of debate, new methods for characterizing color rendition have been formalized, providing an improved system of tools to aid technology developers and lighting specifiers. Solid-state lighting, previously burdened by legacy measures based on outdated science, is positioned to take advantage of these new tools to provide tailored, efficient lighting that better meets the needs of specific architectural lighting applications. This transition requires new guidance, however, presenting an opportunity for researchers. There are several key methodological considerations specific to experiments investigating color rendition that will help build a base of guidance that can be widely applied. The importance of these ideas mostly stems from new knowledge gained in the last five years, but their origins date back much further. 1.The spectral power distribution must be adequately varied to stress the measure(s) of color rendition being evaluated. Often, this means varying the gamut shape—the pattern of shift across different hues—to probe weaknesses of hue-averaged characterizations, avoiding misleading conclusions resulting from limited variation in the stimuli. 2.Evaluating the role of chromaticity on color quality evaluations requires careful accommodation of chromatic adaptation to prevent a centering bias. Asking for delayed responses may be insufficient if chromaticity changes are apparent to participants, just as side-by-side viewing of different chromaticities may not produce results that can be reliably applied in the field. It is also critical that color rendition and chromaticity conditions are varied independently. 3.The stimulus being viewed is the interaction of light and objects’ spectral reflectances. This interaction should be quantified in order to assess how derived guidelines will apply measures using standardized color sample sets. Although light source characterizations are typically used to model responses, the objects and their presentation may strongly influence evaluations. 4.Context is important. Many factors—some investigated, others hypothesized—can influence subjective evaluations of color quality, including illuminance level, type of scene, object familiarity, color composition, viewing duration, and viewer traits. Although their influence on research results is unavoidable, the effects of these contextual factors must be carefully considered in developing guidance. Appropriate experimental design can often mitigate negative consequences arising from these considerations. At a minimum, it is crucial to understand how each of these considerations can influence experimental outcomes, as well as the interpretation and application of the results in practice. Consulting other experts prior to conducting an experiment can help ensure appropriate, up-to-date research methods will be used, so that the results will be novel and useful—and not rejected during peer review. There is an interesting feedback loop: technology development provides new research tools, which allow for refined research methods and new results that lead to more technology development. It is time to make a concerted, collective effort to improve color rendition research so that the full potential of solid-state lighting and new characterization methods can be realized, ensuring that high-efficiency, high-utility, high-appeal lighting systems become standard.
In a trend toward clean energy alternatives, recent years have seen great strides in the marine energy space. Consequently, there is a pressing need for the design, development, and validation of novel energy harvesting technologies such as hydrokinetic devices, which capture kinetic energy from waves, tides, and currents. However, these devices span numerous concepts and designs that often lack solid benchmark research that can be freely referenced throughout their development. This work focuses on the design process of an open-source composite hydrokinetic turbine blade for a three-bladed marine turbine rotor assembly with a diameter of 2.5 m. The proposed blade consists of two structural composite skins that are bonded with an adhesive and filled with a foam core. This study also explores and contrasts the efficiency and resolution of low-fidelity rapid design methodologies and comprehensive high-fidelity approaches in the context of blade design, modeling, and analysis efforts, a key objective in this research. Blade hydrodynamic loads were modeled and applied to finite-element blade models to study deformations and potential failure. Ongoing and upcoming efforts will result in blade manufacture and structural testing at the National Renewable Energy Laboratory. In future work, multiple blades will be deployed at the Living Bridge site at the University of New Hampshire and will be compared to rigid aluminum blades of the same geometry, developed by Sandia National Laboratories. Ultimately, this research will lay foundational groundwork for researchers and manufacturers, establishing a baseline composite blade design that will serve as a benchmark in the development of future hydrokinetic turbine blades.
The nonlinear mathematical programming method (formal optimization) has had many applications in engineering design. A figure illustrates the use of optimization techniques in the design process. The design process begins with the design problem, such as the classic example of the two-bar truss designed for minimum weight as seen in the leftmost part of the figure. If formal optimization is to be applied, the design problem must be recast in the form of an optimization problem consisting of an objective function, design variables, and constraint function relations. The middle part of the figure shows the two-bar truss design posed as an optimization problem. The total truss weight is the objective function, the tube diameter and truss height are design variables, with stress and Euler buckling considered as constraint function relations. Lastly, the designer develops or obtains analysis software containing a mathematical model of the object being optimized, and then interfaces the analysis routine with existing optimization software such as CONMIN, ADS, or NPSOL. This final state of software development can be both tedious and error-prone. The Sizing and Optimization Language (SOL), a special-purpose computer language whose goal is to make the software implementation phase of optimum design easier and less error-prone, is presented.
The objective of this study was to determine whether a defect in transport of calcium in the duodenum was related to decreased bone formation in the suspended rat. Rats were suspended by the tail at a 40 deg angle for up to 15 days. Ca-45 was injected into the ligated duodenum in situ 15 minutes prior to sacrific. Blood, tibia, vertebra and humerus were obtained for total calcium and Ca-45 analyses. Intestinal calcium transport did not appear to be significantly altered by suspension. However, by 5 days of suspension a significant decrease in accumulation of Ca-45 into tibia and vertebra was observed. A trend of decreasing bone mineral and mass was established in tibia and vertebra by the fifth day of suspension. The humerus failed to demonstrate a significant weight decrease or change in Ca-45 accumulation after 15 days of suspension. Results from this simulated weightlessness model suggest that transport of calcium from intestine into bone is decreased within 5 days of suspension. This deficiency appears to be associated with a progressive decrease in total mass of non-weightbearing bones.
Graph theory provides a convenient framework for modeling and solving structured optimization problems. Under this framework, the modeler can arrange/assemble the components of an optimization model (variables, constraints, objective functions, and data) within nodes and edges of a graph, and this representation can be used to visualize, manipulate, and solve the problem. In this work, we present a Julia framework for modeling and solving graph-structured nonlinear optimization problems. Our framework integrates the modeling package Plasmo.jl (which facilitates the construction and manipulation of graph models) and the nonlinear optimization solver MadNLP.jl (which provides capabilities for exploiting graph structures to accelerate solution). We illustrate with a simple example how model construction and manipulation can be performed in an intuitive manner using Plasmo.jl and how the model structure can be exploited by MadNLP.jl. We also demonstrate the scalability of the framework by targeting a large-scale, stochastic gas network problem that contains over 1.7 million variables.
Experiences in using composite skin material on an aeroelastic research wing used in flight flutter testing are described. Significant variations in skin shear modulus due to stress and temperature were encountered with the original fiberglass laminate skin designed to minimize wing torsional stiffness. These variations along with the sensitivity of wing torsional stiffness to the skin-to-frame attachment method complicated the structural model vibration mode predictions. A wing skin redesign with different fiber orientation and a reduction in the amount of skin-to-frame bonding resulted in more predictable modal characteristics without sacrificing design objectives. Design and modeling considerations for future applications are discussed.
The joint NASA Global Precipitation Measurement (GPM) -- DOE Atmospheric Radiation Measurement (ARM) Midlatitude Continental Convective Clouds Experiment (MC3E) was conducted from April 22-June 6, 2011, centered on the DOE-ARM Southern Great Plains Central Facility site in northern Oklahoma. GPM field campaign objectives focused on the collection of airborne and ground-based measurements of warm-season continental precipitation processes to support refinement of GPM retrieval algorithm physics over land, and to improve the fidelity of coupled cloud resolving and land-surface satellite simulator models. DOE ARM objectives were synergistically focused on relating observations of cloud microphysics and the surrounding environment to feedbacks on convective system dynamics, an effort driven by the need to better represent those interactions in numerical modeling frameworks. More specific topics addressed by MC3E include ice processes and ice characteristics as coupled to precipitation at the surface and radiometer signals measured in space, the correlation properties of rainfall and drop size distributions and impacts on dual-frequency radar retrieval algorithms, the transition of cloud water to rain water (e.g., autoconversion processes) and the vertical distribution of cloud water in precipitating clouds, and vertical draft structure statistics in cumulus convection. The MC3E observational strategy relied on NASA ER-2 high-altitude airborne multi-frequency radar (HIWRAP Ka-Ku band) and radiometer (AMPR, CoSMIR; 10-183 GHz) sampling (a GPM "proxy") over an atmospheric column being simultaneously profiled in situ by the University of North Dakota Citation microphysics aircraft, an array of ground-based multi-frequency scanning polarimetric radars (DOE Ka-W, X and C-band; NASA D3R Ka-Ku and NPOL S-bands) and wind-profilers (S/UHF bands), supported by a dense network of over 20 disdrometers and rain gauges, all nested in the coverage of a six-station mesoscale rawinsonde network. As an exploratory effort to examine land-surface emissivity impacts on retrieval algorithms, and to demonstrate airborne soil moisture retrieval capabilities, the University of Tennessee Space Institute Piper aircraft carrying the MAPIR L-band radiometer was also flown during the latter half of the experiment in coordination with the ER-2. The observational strategy provided a means to sample the atmospheric column in a redundant framework that enables inter-calibration and constraint of measured and retrieved precipitation characteristics such as particle size distributions, or water contents- all within the umbrella of "proxy" satellite measurements (i.e., the ER-2). Complimenting the precipitation sampling framework, frequent and coincident launches of atmospheric soundings (e.g., 4-8/day) then provided a much larger mesoscale view of the thermodynamic and winds environment, a data set useful for initializing cloud models. The datasets collected represent a variety cloud and precipitation types including isolated cumulus clouds, severe thunderstorms, mesoscale convective systems, and widespread regions of light to moderate stratiform precipitation. We will present the MC3E experiment design, an overview of operations, and a summary of preliminary results.
The objective here is to test the Dynamic Localization (DL) model in a wall-bounded channel flow for numerical stability and accuracy of results. Algebraic stress models suggest that the model for the residual subgrid-scale (SGS) Reynolds stress and scalar flux should generally have terms comprising most of the unique products of the resolved strain (S) and rotation (R) tensors with S and the resolved scalar gradient. The standard dynamic SGS model uses a simple (Smagorinsky) base model for the residual Reynolds stress, which is made proportional to S, and down-gradient base models for residual scalar fluxes; these correspond to the lowest, 'first-order' terms in algebraic stress models. Temporal scaling terms in these base models are formed from the magnitude of the resolved strain rate. While this is appropriate for simple shear flows, it may not be appropriate for more complicated flows (relevant to geophysical and astrophysical problems) that include any combination of shear, rotation, buoyancy, etc. On the other hand, the coefficient in the dynamic SGS model readily adjusts itself to different flow conditions and may adequately take account of these effects without the need for more complicated base models. Cabot (1993) has begun to test the dynamic SGS model in buoyant flows (Rayleigh-Benard and internally heated convection) with and without buoyancy terms explicitly included in the scaling terms of the base model; no great differences were found in large eddy simulation (LES) results for the different base model scalings. The second objective in this work is to test base models with additional, 'second-order' terms (e.g., S(sup 2) and RS for the residual Reynolds stress). These terms have been found to improve large-scale flow predictions by kappa-epsilon models in the presence of rotation and shear. Second-order base models will be tested here in the LES of channel flow with and without solid-body rotation and compared with results from the standard first-order base models to determine if there are significant differences or improvements in results that would warrant the added complexity of the second-order base models.