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This presentation discusses the results of a MCNP user survey. Example of questions asked include: "Which MCNP particle types do you typically use?," "What sort of simulations do you run most often?," "Do you build the code?," "Which variance reduction methods do you use?," "Opinions on HDF5," with frequent discussions regarding each question.
This presentation discusses the past year and the many accomplishments of the MCNP6.3 team. It states that the MCNP6.3 release is imminent and that the approved, final documents have been making their way to the website. The code executables and source are already packaged up for distribution and the new installer is being finalized and tested now, for all platforms. The package will be sent to RSICC before the end of October 2022. Additionally, the presentation discusses some things to think about as MCNP6.3 is requested and/or used. For example, many of the recent efforts have been focused on making development, updates, and distribution of the code and documents more robust and streamlined. They will be revising/updating documents more frequently than ever before and they will be exploring avenues to distribute official patches to MCNP6.3. As they explain, this allows them to be more responsive to bugs and issues that are identified. In conclusion, the application of a patch to MCNP6.3 will require having the source code.
Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc.
The NCCS is part of the Computational and Information Sciences and Technology Office (CISTO) of Goddard Space Flight Center's (GSFC) Sciences and Exploration Directorate. The NCCS's mission is to enable scientists to increase their understanding of the Earth, the solar system, and the universe by supplying state-of-the-art high performance computing (HPC) solutions. To accomplish this mission, the NCCS (https://www.nccs.nasa.gov) provides high performance compute engines, mass storage, and network solutions to meet the specialized needs of the Earth and space science user communities
We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.
The current study provides a microstructurally-based computational framework to predict the fatigue life of additive manufactured (AM), i.e., powder bed fused–laser beam (PBF-LB), AlSi10Mg specimens using the crystal plasticity finite element method (CPFEM). The fractography analysis, electron backscatter diffraction (EBSD), uniaxial and cyclic responses, and fatigue life of specimens were used to inform the computational framework. CPFE simulation was used to compute fatigue indicator parameters (FIPs) as fatigue driving forces. A new fatigue criterion is introduced based on FIPs, which was calibrated using experimental fatigue data. The proposed fatigue measure was evaluated versus the specimens with critical defects of various sizes and locations subjected to different stress amplitudes. The results show that the developed framework can capture the fatigue life of samples with different critical defect locations and sizes along with different stress amplitudes for both high-cycle fatigue (HCF) and very high-cycle fatigue (VHCF) regimes.
A number of statistical tools have been developed over the years for assessing the risk of reentering objects to human populations. These tools make use of the characteristics (e.g., mass, shape, size) of debris that are predicted by aerothermal models to survive reentry. The statistical tools use this information to compute the probability that one or more of the surviving debris might hit a person on the ground and cause one or more casualties. The statistical portion of the analysis relies on a number of assumptions about how the debris footprint and the human population are distributed in latitude and longitude, and how to use that information to arrive at realistic risk numbers. This inevitably involves assumptions that simplify the problem and make it tractable, but it is often difficult to test the accuracy and applicability of these assumptions. This paper looks at a number of these theoretical assumptions, examining the mathematical basis for the hazard calculations, and outlining the conditions under which the simplifying assumptions hold. In addition, this paper will also outline some new tools for assessing ground hazard risk in useful ways. Also, this study is able to make use of a database of known uncontrolled reentry locations measured by the United States Department of Defense. By using data from objects that were in orbit more than 30 days before reentry, sufficient time is allowed for the orbital parameters to be randomized in the way the models are designed to compute. The predicted ground footprint distributions of these objects are based on the theory that their orbits behave basically like simple Kepler orbits. However, there are a number of factors - including the effects of gravitational harmonics, the effects of the Earth's equatorial bulge on the atmosphere, and the rotation of the Earth and atmosphere - that could cause them to diverge from simple Kepler orbit behavior and change the ground footprints. The measured latitude and longitude distributions of these objects provide data that can be directly compared with the predicted distributions, providing a fundamental empirical test of the model assumptions.
Onboard computers for pre-transmission data processing, automation of data acquisition centers, and real time data relay to control centers for advanced space information systems
Outline: Describe principle of magnetically-confined nuclear fusion; Explain the role of Poincaré maps in fusion reactor design; Describe a new method for rapidly computing Poincaré maps using physics-informed machine learning
A number of statistical tools have been developed over the years for assessing the risk of reentering object to human populations. These tools make use of the characteristics (e.g., mass, material, shape, size) of debris that are predicted by aerothermal models to survive reentry. The statistical tools use this information to compute the probability that one or more of the surviving debris might hit a person on the ground and cause one or more casualties. The statistical portion of the analysis relies on a number of assumptions about how the debris footprint and the human population are distributed in latitude and longitude, and how to use that information to arrive at realistic risk numbers. Because this information is used in making policy and engineering decisions, it is important that these assumptions be tested using empirical data. This study uses the latest database of known uncontrolled reentry locations measured by the United States Department of Defense. The predicted ground footprint distributions of these objects are based on the theory that their orbits behave basically like simple Kepler orbits. However, there are a number of factors in the final stages of reentry - including the effects of gravitational harmonics, the effects of the Earth s equatorial bulge on the atmosphere, and the rotation of the Earth and atmosphere - that could cause them to diverge from simple Kepler orbit behavior and possibly change the probability of reentering over a given location. In this paper, the measured latitude and longitude distributions of these objects are directly compared with the predicted distributions, providing a fundamental empirical test of the model assumptions.
A number of statistical tools have been developed over the years for assessing the risk of reentering objects to human populations. These tools make use of the characteristics (e.g., mass, material, shape, size) of debris that are predicted by aerothermal models to survive reentry. The statistical tools use this information to compute the probability that one or more of the surviving debris might hit a person on the ground and cause one or more casualties. The statistical portion of the analysis relies on a number of assumptions about how the debris footprint and the human population are distributed in latitude and longitude, and how to use that information to arrive at realistic risk numbers. Because this information is used in making policy and engineering decisions, it is important that these assumptions be tested using empirical data. This study uses the latest database of known uncontrolled reentry locations measured by the United States Department of Defense. The predicted ground footprint distributions of these objects are based on the theory that their orbits behave basically like simple Kepler orbits. However, there are a number of factors in the final stages of reentry - including the effects of gravitational harmonics, the effects of the Earth s equatorial bulge on the atmosphere, and the rotation of the Earth and atmosphere - that could cause them to diverge from simple Kepler orbit behavior and possibly change the probability of reentering over a given location. In this paper, the measured latitude and longitude distributions of these objects are directly compared with the predicted distributions, providing a fundamental empirical test of the model assumptions.
Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.
This paper presents results of a continuing interdisciplinary study of the potential applications of Fixed- and Broadcast-Satellites for educational information transfer in the United States for the period 1975-1985. The status of U.S. education is examined and needs, trends and issues are discussed. The existing educational telecommunications infrastructure is examined and opportunities for satellite services are defined. Potential uses include networking of educational institutions and service centers for delivery of public and instructional television, computer-aided instruction, computing and information resources to regions and groups not now adequately served. Systems alternatives and some of the organizational and economic issues inherent in the deployment of an educational satellite system are discussed.-
During the fall 2013 semester, I worked at the Kennedy Space Center as an IT Security Intern in support of the Spaceport Command and Control System under the guidance of the IT Security Lead Engineer. Some of my responsibilities included assisting with security plan documentation collection, system hardware and software inventory, and malicious code and malware scanning. Throughout the semester, I had the opportunity to work on a wide range of security related projects. However, there are three projects in particular that stand out. The first project I completed was updating a large interactive spreadsheet that details the SANS Institutes Top 20 Critical Security Controls. My task was to add in all of the new commercial of the shelf (COTS) software listed on the SANS website that can be used to meet their Top 20 controls. In total, there are 153 unique security tools listed by SANS that meet one or more of their 20 controls. My second project was the creation of a database that will allow my mentor to keep track of the work done by the contractors that report to him in a more efficient manner by recording events as they occur throughout the quarter. Lastly, I expanded upon a security assessment of the Linux machines being used on center that I began last semester. To do this, I used a vulnerability and configuration tool that scans hosts remotely through the network and presents the user with an abundance of information detailing each machines configuration. The experience I gained from working on each of these projects has been invaluable, and I look forward to returning in the spring semester to continue working with the IT Security team.
MOTIVATION: Spaceflight countermeasures mitigate the harmful effects of the space environment on astronaut health and performance. Exercise has historically been used as a countermeasure to physical deconditioning, and additional countermeasures including lower body negative pressure, blood flow occlusion and artificial gravity are being researched as countermeasures to spaceflight-induced fluid shifts. The NASA Digital Astronaut Project uses computational models of physiological systems to inform countermeasure design and to predict countermeasure efficacy.OVERVIEW: Computational modeling supports the development of the exercise devices that will be flown on NASAs new exploration crew vehicles. Biomechanical modeling is used to inform design requirements to ensure that exercises can be properly performed within the volume allocated for exercise and to determine whether the limited mass, volume and power requirements of the devices will affect biomechanical outcomes. Models of muscle atrophy and bone remodeling can predict device efficacy for protecting musculoskeletal health during long-duration missions. A lumped-parameter whole-body model of the fluids within the body, which includes the blood within the cardiovascular system, the cerebral spinal fluid, interstitial fluid and lymphatic system fluid, estimates compartmental changes in pressure and volume due to gravitational changes. These models simulate fluid shift countermeasure effects and predict the associated changes in tissue strain in areas of physiological interest to aid in predicting countermeasure effectiveness. SIGNIFICANCE: Development and testing of spaceflight countermeasure prototypes are resource-intensive efforts. Computational modeling can supplement this process by performing simulations that reduce the amount of necessary experimental testing. Outcomes of the simulations are often important for the definition of design requirements and the identification of factors essential in ensuring countermeasure efficacy.
Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.
The geothermal community is well established with long-standing events, organizations, and tools that are known across the geothermal community. But many of these tools and resources are located behind pay walls, require memberships, or are otherwise difficult to find, especially for people looking to join the geothermal community. These barriers to access can prevent outsiders from discovering valuable geothermal resources, limiting the geothermal community's potential for collaboration with other communities, such as clean energy entrepreneurs looking to expand into geothermal energy. The Department of Energy's (DOE) GeoBridge serves to bring these communities together by acting as a single, publicly accessible, searchable portal that facilitates easy access to available geothermal knowledge and information. It works to expand and diversify the pool of geothermal stakeholders by providing in-roads to geothermal information and community resources. It helps build a stronger geothermal community; one inclusive of individuals and groups from a variety of different backgrounds, including potential investors and start-up companies looking to accelerate innovation in geothermal technologies. By linking communities to geothermal information, analysis and expertise, GeoBridge serves as a launch point, directing interested parties to existing data and tools, events, educational resources, STEM programs, permitting and regulatory information, and other resources that can be used to evaluate, promote, and discover geothermal opportunities.
The overarching goal of the University of Mississippi Geoinformatics Center (UMGC) is to promote application of geospatial information technologies through technology education, research support, and infrastructure development. During the initial two- year phase of operation the UMGC has successfully met those goals and is uniquely positioned to continue operation and further expand the UMGC into additional academic programs. At the end of the first funding cycle, the goals of the UMGC have been and are being met through research and educational activities in the original four participating programs; Biology, Computer and Information Science, Geology and Geological Engineering, and Sociology and Anthropology, with the School of Business joining the UMGC in early 2001. Each of these departments is supporting graduate students conducting research, has created combination teaching and research laboratories, and supported faculty during the summer months.