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CLASS (Communications Link Analysis and Simulation System) is a software package developed for NASA to predict the communications and tracking performance of the Tracking and Data Relay Satellite System (TDRSS) services. This paper describes the capabilities of CLASS and the communications link model used and discusses typical applications for CLASS.
CLASS (Communications Link Analysis and Simulation System) is a software package developed for NASA to predict the communication and tracking performance of the Tracking and Data Relay Satellite System (TDRSS) services. The modeling techniques used in CLASS are described. The components of TDRSS and the performance parameters to be computed by CLASS are too diverse to permit the use of a single technique to evaluate all performance measures. Hence, each CLASS module applies the modeling approach best suited for a particular subsystem and/or performance parameter in terms of model accuracy and computational speed.
CLASS (Communication Link Analysis and Simulation System) is a software package developed for NASA to predict the communication and tracking performance of the Tracking and Data Relay Satellite System (TDRSS) services. The methods used to verify CLASS are described. The usefulness of a software tool such as CLASS depends strongly on the reliability and accuracy of the results it produces. For this reason, considerable attention was paid to validation throughout the CLASS development.
NASA's Exploration Technology Development and Demonstration Program has proposed plans for a series of three sub-scale flight tests at Earth for supersonic retropropulsion, a candidate decelerator technology for future, high-mass Mars missions. The first flight test in this series is intended to be a proof-of-concept test, demonstrating successful initiation and operation of supersonic retropropulsion at conditions that replicate the relevant physics of the aerodynamic-propulsive interactions expected in flight. Five sub-scale flight test article concepts, each designed for launch on sounding rockets, have been developed in consideration of this proof-of-concept flight test. Commercial, off-the-shelf components are utilized as much as possible in each concept. The design merits of the concepts are compared along with their predicted performance for a baseline trajectory. The results of a packaging study and performance-based trade studies indicate that a sounding rocket is a viable launch platform for this proof-of-concept test of supersonic retropropulsion.
This data package is associated with the publication “Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning” submitted to Remote Sensing of Environment (Zahura et al. 2023). In this research, a machine learning algorithm, random forest (RF), was utilized to examine the impact of climate and topography on post-fire vegetation recovery. We used enhanced vegetation index (EVI) to examine varying burn severity and land cover types. The data package includes the input files for RF model training, outputs from model predictions and analysis, and python scripts to run the model, analyze the results to understand model performance and interpretability, and plot manuscript figures. This data package contains three folders (Data, Scripts, and Figures), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Postfire_recovery_flmd.csv for a list of all files contained in this data package and descriptions for each. The data dictionary (Postfire_recovery_dd.csv) describes the csv column headers. The “Data” folder provides all the inputs and outputs to train the RF model, evaluate performance, and interpret predictions. The “Scripts” folder contains python scripts and jupyter notebooks for model training and result analysis. The “Figures” folder includes the figures used in the manuscript in “.png” and “.jpg” format.
Documentation of the interactive software package designed to predict the effect of certain electrical and mechanical imperfections on the performance of the SPS system is presented. The capabilities of the SOLARSIM program to quantify the spacetenna performance parameter values are described. The SOLARSIM package can compute the RMS pointing error, title effects, MPTX code tracking loop performance, MPTX carrier tracking loop performance, averaged power pattern, and the power transfer efficiency.
Individuals with rheumatoid arthritis (RA) are at increased risk of functional disability, cardiovascular disease, and obesity, all of which are influenced by dysregulated skeletal muscle. Here, this pilot study aims to identify phosphoproteomics changes in RA skeletal muscle and visualize modifications through development of a web-based app designed to promote user-friendly data interpretation and visualization. NanoLC–MS/MS analysis was performed on vastus lateralis biopsies from three women with RA and matched healthy controls. Differential analysis was performed using the Limma R package. Kinase substrate enrichment analysis (KSEA) predicted changes in kinase activity. RA muscle displayed 35 upregulated and 60 downregulated phosphosites, including the cytoskeletal proteins TTN (Ser33201, Ser33013, Ser20925), NEB (Ser2219, Thr254, Ser33013, Ser20925), FLNA (Ser1459), and LASP1 (Ser146). Compared to healthy controls, KSEA predicted decreased activity of several kinases in RA muscle, including PRKACA and CDKs. All such changes were visualized by use of our web-based app. Overall, phosphoproteome analysis reveals signaling alterations in RA skeletal muscle linked to cytoskeletal proteins, representing candidate disease biomarkers; these modifications can be explored through use of our web-based software.
This SAND report fulfills the completion requirements for the ASC Physics and Engineering Modeling Level 2 Milestone 7836 during Fiscal Year 2021. The Sandia Simplified potential energy clock (SPEC) non-linear viscoelastic constitutive model was developed to predict a whole host of polymer glass physical behaviors in order to provide a tool to assess the effects of stress on these materials over their lifecycle. Polymer glasses are used extensively in applications such as electronics packaging, where encapsulants and adhesives can be critical to device performance. In this work, the focus is on assessing the performance of the model in predicting material evolution associated with long-term physical aging, an area that the model has not been fully vetted in. These predictions are key to utilizing models to help demonstrate electronics packaging component reliability over decades long service lives, a task that is very costly and time consuming to execute experimentally. The initiating hypothesis for the work was that a model calibration process can be defined that enables confidence in physical aging predictions under ND relevant environments and timescales without sacrificing other predictive capabilities. To test the hypothesis, an extensive suite of calibration and aging data was assembled from a combination of prior work and collaborating projects (Aging and Lifetimes as well as the DoD Joint Munitions Program) for two mission relevant epoxy encapsulants, 828DGEBA/DEA and 828DGEBA/T403. Multiple model calibration processes were developed and evaluated against the entire set of data for each material. A qualitative assessment of each calibration's ability to predict the wide range of aging responses was key to ranking the calibrations against each other. During this evaluation, predictions that were identified as non-physical, i.e., demonstrated something that was qualitatively different than known material behavior, were heavily weighted against the calibration performance. Thus, unphysical predictions for one aspect of aging response could generate a lower overall rating for a calibration process even if that process generated better quantitative predictions for another aspect of aging response. This insurance that all predictions are qualitatively correct is important to the overall aim of utilizing the model to predict residual stress evolution, which will depend on the interplay amongst the different material aging responses. The DSC-focused calibration procedure generated the best all-around aging predictions for both materials, demonstrating material models that can qualitatively predict the whole host of different physical aging responses that have been measured. This step forward in predictive capability comes from an unanticipated source, utilization of calorimetry measurements to specify model parameters. The DSC-focused calibration technique performed better than compression-focused techniques that more heavily weigh measurements more closely related to the structural responses to be predicted. Indeed, the DSC-focused calibration procedure was only possible due to recent incorporation of the enthalpy and heat capacity features into SPEC that was newly verified during this L2 milestone. Fundamentally similar aspects of the two material model calibrations as well as parametric studies to assess sensitives of the aging predictions are discussed within the report. A perspective on the next steps to the overall goal of residual stress evolution predictions under stockpile conditions closes the report.
The main objective is to determine the performance of microsphere insulation in a 0-g environment and compare its performance to reference insulations such as multilayer insulation. The Lockheed Helium Extended-Life Dewar (HELD) is used to provide superfluid-helium cold sink for the experiment. The use of HELD allows the low-g dynamic properties of Passive Orbital Disconnect Struts (PODS) to be characterized and provides a flight demonstration of the PODS system. The thermal performance of microspheres in 1 and 0 g was predicted, a flight experiment was designed to determine microsphere thermal performance, and the interface was also designed between the experimental package and the shuttle through HELD and the Hitchhiker-M carrier. A single test cell was designed and fabricated. The cell was filled with uncoated glass microspheres and tested with a liquid-nitrogen cold sink. The data were found to agree with predictions of microsphere performance in 1 g.
A multiblock three-dimensional Navier-Stokes method was utilized in a two-dimensional fully implicit mode to calculate the flowfield of a single expansion ramp nozzle configuration. The code has been shown previously to be fairly accurate in predicting three-dimensional nozzle flowfields and internal performance for several axisymmetric and nonaxisymmetric geometries. A two-dimensional implementation of the method was used to reduce the resources required to obtain preliminary performance parameters of nozzle concepts. A two-dimensional description of a single-expansion ramp nozzle configuration was analyzed to verify the applicability of the Navier-Stokes code and nozzle performance package to this class of nozzles. Internal static pressure distributions, discharge coefficient and thrust ratio quantities were calculated for a range of operating conditions. Comparisons of predicted performance parameters with experimental data were within 0.5 percent for mass flow, and typically within l.5 percent for thrust ratio.
The successful operation of thermonuclear fusion reactors such as ITER, DEMO, and future commercial plants is mainly determined by the optimum choice of materials for various components. The objective of this work is to accurately and comprehensively simulate the entire device in 3D to predict pros and cons of various materials, e.g., liquid lithium in comparison to tungsten and carbon to predict future ITER-like and DEMO divertor performances. We used our comprehensive HEIGHTS simulation package to investigate ITER-like components response during transient events in exact 3D geometry. Starting from the lost hot core plasma particles through SOL, deposition on the divertor surface, and the generation of secondary plasma of divertor materials. Our simulations predicted significant reduction in the heat loading and damage to the divertor nearby and internal components in the case when lithium is used on the divertor plates. While if tungsten or carbon are used on the divertor plate, significant melting areas and vaporization spots can occur (less for carbon) on the reflector, dome, and stainless steel tubes, and even parts of the first walls can melt due to the high radiation power of the secondary divertor plasma. Lithium photon radiation deposition into the divertor and nearby surfaces was decreased by two orders of magnitude compared to tungsten and by one order of magnitude compared to carbon. This analysis showed that using liquid lithium for ITER-like surfaces and future DEMO can lead to significant enhancement in components lifetime.
VERA core simulator capabilities to predict spent fuel decay heat have been explored. The comparison with the Serpent2 and NEWT/TRITON sequence from the SCALE package, performed on the NPP Krško fuel test cases, has enabled independent verification of the successful implementation of the VERA depletion/decay calculation sequence applied on the extended nuclide set. Since the VERA is a 3-D core simulator, the focus in the rest of the paper was on the investigation of the 3-D effects and non-linearity of the soluble boron concentration averaging process, which are difficult to assess with most of available computational tools. A difference in the decay heat prediction between the 3-D and 2-D model approaches was analyzed. In addition, the impact of the Inconel grids on the decay heat was determined. Averaging the concentration of soluble boron over the fuel irradiation period has been found to be a viable approach, since it results in slight decay heat overprediction, which is considered conservative for most applications. Finally, demonstration on the Watts Bar Unit 1 cycle 1 depletion has shown formidable VERA capabilities to accurately predict spent fuel decay heat.
This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.
Predictive constitutive equations that connect easy-to-measure transport properties (e.g., viscosity and conductivity) with system performance variables (e.g., power consumption and efficiency) are needed to design advanced thermal and electrical systems. In this work, we explore the use of fluorescent particle-streak analysis to directly measure the local velocity field of a pressure-driven flow, introducing a new Python package (FSVPy) to perform the analysis. Fluorescent streak velocimetry combines high-speed imaging with highly fluorescent particles to produce images that contain fluorescent streaks, whose length and intensity can be related to the local flow velocity. By capturing images throughout the sample volume, the three-dimensional velocity field can be quantified and reconstructed. We demonstrate this technique by characterizing the channel flow profiles of several non-Newtonian fluids: micellar Cetylpyridinium Chloride solution, Carbopol 940, and Polyethylene Glycol. We then explore more complex flows, where significant acceleration is created due to microscale features encountered within the flow. We demonstrate the ability of FSVPy to process streaks of various shapes and use the variable intensity along the streak to extract position-specific velocity measurements from individual images. Thus, we demonstrate that FSVPy is a flexible tool that can be used to extract local velocimetry measurements from a wide variety of fluids and flow conditions.
The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.
Herein, this study, which has been motivated by the recent applications of the cold plate device in aviation thermal management, reports on physics-based mathematical models derived from the conservation laws of mass, momentum, and energy, and empiricism-based models. One of the objectives of the present work is to report on an elaborate and successful experimental work carried out on an additively manufactured device for the purpose of rigorously validating the numerical predictions. The excellent agreement between the numerical predictions and measured performance provides much needed confidence in the implementation, in the software package, of the offset-strip fin passage correlations, as well as in the software implementation of user-defined wavy fin correlations for aerospace heat exchangers and cold plates operating with ram air at Reynolds numbers below 8000. The contributions of this work can also be found in the development of a new and accurate thermal-hydraulic analysis procedure, referred to in this paper as plate-fin analogy. Results from this procedure are compared with those from thermal resistance network. The comparative study in this paper of the bulk and discrete enthalpy flux method is also new, as is the relative assessment of four off-set strip fin thermal-hydraulic models.
An analytical evaluation of cryogenic propellant tank insulations for liquid oxygen/liquid hydrogen low-thrust 2224N (500 lbf) propulsion systems (LTPS) was conducted. The insulation studied consisted of combinations of N2-purged foam and multilayer insulation (MLI) as well as He-purged MLI-only. Heat leak and payload performance predictions were made for three Shuttle-launched LTPS designed for Shuttle bay packaged payload densities of 56 kg/cu m, 40 kg/cu m and 24 kg/cu m. Foam/MLI insulations were found to increase LTPS payload delivery capability when compared with He-purged MLI-only. An additional benefit of foam/MLI was reduced operational complexity because Orbiter cargo bay N2 purge gas could be used for MLI purging. Maximum payload mass benefit occurred when an enhanced convection, rather than natural convection, heat transfer was specified for the insulation purge enclosure. The enhanced convection environment allowed minimum insulation thickness to be used for the foam/MLI interface temperature selected to correspond to the moisture dew point in the N2 purge gas. Experimental verification of foam/MLI benefits was recommended. A conservative program cost estimate for testing a MLI-foam insulated tank was 2.1 million dollars. It was noted this cost could be reduced significantly without increasing program risk.
Cryogenic propellant tank insulations or liquid oxygen/liquid hydrogen low-thrust 2224N (500 lbf) propulsion systems (LTPS) were assessed. The insulation studied consisted of combinations of N2-purged foam and multilayer insulation (MLI) as well as He-purged MLI-only. Heat leak and payload performance predictions were made for three shuttle-launched LTPS designed for shuttle bay packaged payload densities of 56 kg cu/m (3.5 lbm/cu ft), 40 kg/cu m (2.5 lbm/cu ft) and 24 kg/cu m (1.5 lbm/cu ft). Foam/MLI insulations were found to increase LTPS payload delivery capability when compared with He-purged MLI-only. An additional benefit of foam/MLI was reduced operational complexity because orbiter cargo bay N2 purge gas could be used for MLI purging. Maximum payload mass benefit occurred when an enhanced convection, rather than natural convection, heat transfer was specified for the insulation purge enclosure. The enhanced convection environment allowed minimum insulation thickness to be used for the foam/MLI interface temperature selected to correspond to the moisture dew point in the N2 purge gas. Experimental verification of foam/MLI benefits was recommended. A conservative program cost estimate for testing a MLI-foam insulated tank was 2.1 million dollars. This cost could be reduced significantly without increasing program risk.