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Computational method estimates weight of aircraft wings from theoretical relationships and empirical data. Permits comparison of alternative materials, methods of construction, and design philosophies. Method used to make tradeoffs in preliminary design phases on basis of simple input data and for more accurate calculations in later phases when more data are available.
A laser based method was employed to measure and numerically describe the architecture of a corn canopy. Use of the method provides an interception coefficient for classes of vegetation in layers of the canopy viewed in various directions. Architectural data obtained for similar corn canopies but measured by differing methods are comparable to the results obtained using the laser based method. These results provide input data to mathematical models employed in remote sensing for describing the radiation environment in a plant canopy and predicting its reflectance.
A comprehensive description of user problem definition for the PAN AIR (Panel Aerodynamics) system is given. PAN AIR solves the 3-D linear integral equations of subsonic and supersonic flow. Influence coefficient methods are used which employ source and doublet panels as boundary surfaces. Both analysis and design boundary conditions can be used. This User's Manual describes the information needed to use the PAN AIR system. The structure and organization of PAN AIR are described, including the job control and module execution control languages for execution of the program system. The engineering input data are described, including the mathematical and physical modeling requirements. Version 3.0 strictly applies only to PAN AIR version 3.0. The major revisions include: (1) inputs and guidelines for the new FDP module (which calculates streamlines and offbody points); (2) nine new class 1 and class 2 boundary conditions to cover commonly used modeling practices, in particular the vorticity matching Kutta condition; (3) use of the CRAY solid state Storage Device (SSD); and (4) incorporation of errata and typo's together with additional explanation and guidelines.
ICESat/GLAS waveform data are used to estimate biomass and carbon on a 1.27 million sq km study area. the Province of Quebec, Canada, below treeline. The same input data sets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include nonstratified and stratified versions of a multiple linear model where either biomass or (square root of) biomass serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial biomass estimates of up to 0.35 Gt (range 4.942+/-0.28 Gt to 5.29+/-0.36 Gt). The results suggest that if different predictive models are used to estimate regional carbon stocks in different epochs, e.g., y2005, y2015, one might mistakenly infer an apparent aboveground carbon "change" of, in this case, 0.18 Gt, or approximately 7% of the aboveground carbon in Quebec, due solely to the use of different predictive models. These findings argue for model consistency in future, LiDAR-based carbon monitoring programs. Regional biomass estimates from the four GLAS models are compared to ground estimates derived from an extensive network of 16,814 ground plots located in southern Quebec. Stratified models proved to be more accurate and precise than either of the two nonstratified models tested.
This study was the performance of the benchmark calculation for the Hatch Unit 1 cycles 1–3, to validate the SCALE 6.3/Polaris–PARCS v3.4.2 with the ENDF/B-VII.1 AMPX 56-group library by comparing the simulated results with the measured data. The benchmark results will be used in evaluating uncertainties of the SCALE/Polaris–PARCS code package for boiling water reactor (BWR) physics analysis for key nuclear parameters such as reactivity and assembly power peaking factors. This report details plant and fuel design specifications and input data for SCALE/Polaris, GenPMAXS and PARCS, and additionally, detailed information is provided for all the input and output files produced for the benchmark calculations. The benchmark results were summarized such that they can be used in evaluating uncertainties for key nuclear parameters with other BWR benchmark results.
Axis position and sequence number indicator system is designed to work in conjunction with numerical tape controller. Visual display of coordinate-axis position to nearest 0.0001 inch and sequential blocks of input data up to maximum of 999 are provided.
Modular interface unit transfers data at high speeds along four channels. Device expedites variable-word-length communication between computers. Operation eases exchange of bit information by automatically reformatting coded input data and status information to match requirements of output.
The Shuttle Imaging Radar-C (SIR-C) is the third in a series of space shuttle based synthetic aperture radars (SAR) sponsored by the National Aeronautics and Space Administration (NASA). The SIR-C ground data processing system is to process the playbacked SAR signal data into a variety of data products for distribution to the science community. An overview of the end-to-end ground data processing system with emphasis on the unique characteristics involved in the system design is presented. Included in the discussion are science requirements, radar system specifications, input data format specifications, system operations design, data products design, processing algorithm design, hardware architecture design, and software design.
To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.
A global run of a process-based methane model [Walter et al., this issue] is performed using high-frequency atmospheric forcing fields from ECMWF reanalyses of the period from 1982 to 1993. We calculate global annual methane emissions to be 260 Tg/ yr. 25% of methane emissions originate from wetlands north of 30 deg. N. Only 60% of the produced methane is emitted, while the rest is re-oxidized. A comparison of zonal integrals of simulated global wetland emissions and results obtained by an inverse modeling approach shows good agreement. In a test with data from two wetlands, the seasonality of simulated and observed methane emissions agrees well. The effects of sub-grid scale variations in model parameters and input data are examined. Modeled methane emissions show high regional, seasonal and interannual variability. Seasonal cycles of methane emissions are dominated by temperature in high latitude wetlands, and by changes in the water table in tropical wetlands. Sensitivity tests show that +/- 1 C changes in temperature lead to +/- 20 % changes in methane emissions from wetlands. Uniform changes of +/- 20% in precipitation alter methane emissions by about +/- 18%. Limitations in the model are analyzed. Simulated interannual variations in methane emissions from wetlands are compared to observed atmospheric growth rate anomalies. Our model simulation results suggest that contributions from other sources than wetlands and/or the sinks are more important in the tropics than north-of 30 deg. N. In higher northern latitudes, it seems that a large part, of the observed interannual variations can be explained by variations in wetland emissions. Our results also suggest that reduced wetland emissions played an important role in the observed negative methane growth rate anomaly in 1992.
Magnetic fields measured by the satellite Lunar Prospector show large scale features resulting from remanently magnetized crust. Vector data synthesized at satellite altitude from a spherical harmonic model of the lunar crustal field, and the radial component of the magnetometer data, have been used to produce spatially continuous global magnetization models for the lunar crust. The magnetization is expressed in terms of localized basis functions, with a magnetization solution selected having the smallest root-mean square magnetization for a given fit to the data, controlled by a damping parameter. Suites of magnetization models for layers with thicknesses between 10 and 50 km are able to reproduce much of the input data, with global misfits of less than 0.5 nT (within the uncertainties of the data), and some surface field estimates. The magnetization distributions show robust magnitudes for a range of model thicknesses and damping parameters, however the magnetization direction is unconstrained. These global models suggest that magnetized sources of the lunar crust can be represented by a 30 km thick magnetized layer. Average magnetization values in magnetized regions are 30-40 mA/m, similar to the measured magnetizations of the Apollo samples and significantly weaker than crustal magnetizations for Mars and the Earth. These are the first global magnetization models for the Moon, providing lower bounds on the magnitude of lunar crustal magnetization in the absence of multiple sample returns, and can be used to predict the crustal contribution to the lunar magnetic field at a particular location.
Monte Carlo event generators are a critical tool for the interpretation of data obtained by neutrino experiments. Several modern event generators are available which are well-suited to the GeV energy scale used in studies of accelerator neutrinos. However, theoretical modeling differences make their immediate application to lower energies difficult. In this paper, I present a new event generator, MARLEY, which is designed to better address the simulation needs of the low-energy (tens of MeV and below) neutrino community. The code is written in C++14 with an optional interface to the popular ROOT data analysis framework. The current release of MARLEY (version 1.2.0) emphasizes simulations of the reaction 40 Ar ( ν e , e − ) 40 K ⁎ but is extensible to other channels with suitable user input. This paper provides detailed documentation of MARLEY's implementation and usage, including guidance on how generated events may be analyzed and how MARLEY may be interfaced with external codes such as Geant4. Further information about MARLEY is available on the official website at http://www.marleygen.org. Program title:MARLEY 1.2.0 CPC Library link to program files:https://doi.org/10.17632/4v7zxnc8j3.1 Developer's respository link:http://github.com/MARLEY-MC/marley Code Ocean capsule:https://codeocean.com/capsule/9868179 Licensing provisions: GNU General Public License 3.0 Programming language: C++14 External routines/libraries used: GNU Scientific Library [1,2] (required), ROOT [3,4] (optional) Nature of problem: Simulation of neutrino-nucleus scattering events at energies of tens-of-MeV and below Solution method: Initial two-to-two scattering kinematics are sampled using the allowed approximation differential cross section and tables of precomputed nuclear matrix elements. Subsequent de-excitations of the remnant nucleus are simulated using a Monte Carlo implementation of the Hauser-Feshbach statistical model and tabulated γ-ray decay schemes for discrete nuclear levels. Additional comments including restrictions and unusual features: Input data are provided with the code that are suitable for producing simulations of the charged-current reaction 40 Ar ( ν e , e − ) 40 K ⁎ , coherent elastic neutrino-nucleus scattering on spin-zero target nuclei, and neutrino-electron elastic scattering on any atomic target. Preparation of new reaction input files (whose format is documented in Appendix B) would enable other reaction channels and nuclear targets to be handled by the existing code framework. Although there is no maximum neutrino energy enforced by the code itself, realistic neutrino-nucleus scattering events may be generated up to roughly 50 MeV. Above this energy, the effects of forbidden nuclear transitions, which are neglected in the current treatment of the cross sections (see section 2.1), become increasingly important.
Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.
This dataset will contain the following files: - RectifHydV2.zip – the RectifHydV2 dataset, split into two files: o RectifHydV2_Actual_MWh.csv: Estimated actual monthly net generation from 590 Hydropower Plants (>10MW nameplate) in CONUS, 1980 – 2019 o RectifHydV2_Counterfactual_MWh.csv: Counterfactual (climate-only) monthly net generation from 590 Hydropower Plants (>10MW nameplate) in CONUS, 1980 – 2019 - RectifHydV2_code.zip: Full data processing pipeline, coded using R {targets} framework. This is a snapshot release (v1.0) of the code repository stored at https://code.ornl.gov/turnersw/rectifhydv2 - RectifHydV2_inputs.zip: Complete set of input data used to create RectifHydV2, organized for direct entry into “/data/” directory of the RectifHydV2 reproducible data pipeline - RectifHydV2_misc.zip: - RectifHydV2_dailyRelease.csv
We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.
A general simulation program is presented (GSP) involving nonlinear state estimation for space vehicle flight navigation systems. A complete explanation of the iterative guidance mode guidance law, derivation of the dynamics, coordinate frames, and state estimation routines are given so as to fully clarify the assumptions and approximations involved so that simulation results can be placed in their proper perspective. A complete set of computer acronyms and their definitions as well as explanations of the subroutines used in the GSP simulator are included. To facilitate input/output, a complete set of compatable numbers, with units, are included to aid in data development. Format specifications, output data phrase meanings and purposes, and computer card data input are clearly spelled out. A large number of simulation and analytical studies were used to determine the validity of the simulator itself as well as various data runs.
In the finite difference technique, the thermal network is represented by an analogous electrical network. The development of this network model, which is used to describe a physical system, often requires tedious and mental data preparation and checkout by the analyst which can be greatly reduced through the use of the computer programs to develop automatically the mathematical model and associated input data and graphically display the analytical model to facilitate model verification. Three separate programs are involved which are linked through common mass storage files and data card formats. These programs are SPAR, CINGEN and GEOMPLT, and are used to (1) develop thermal models for the MITAS II thermal analyzer program; (2) produce geometry plots of the thermal network; and (3) produce temperature distribution and time history plots.
The values of inner and outer race temperature, cage speed, and heat transferred to the lubricant or bearing power loss, calculated using the computer programs Shaberth and Cybean, with the corresponding experimental data for the large bore ball and roller bearings were compared. After the development of computer program, it is important that values calculated using such program are compared with actual bearing performance data to assess the programs predictive capability. Several comprehensive computer programs currently in use are capable of predicting rolling bearing operating and performance characteristics. These programs accept input data of bearing internal geometry, bearing material and lubricant properties, and bearing operating conditions. The programs solve several sets of equations that characterize rolling element bearings. The output produced typically consists of rolling element loads and Hertz stresses, operating contact angles, component speed, heat generation, local temperatures, bearing fatigue life, and power loss. Two of these programs, Shaberth and Cybean were developed.