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An analysis tool to aid in preliminary design of re-entry aeroshells for interplanetary exploration is presented. The computational tool addresses a common feature of design analysis, the need to make use of results from several analysis regimes and from several sources.
Stochastic simulation and modeling play an important role to elucidate the fundamental mechanisms in complex biochemical networks. The parametric sensitivity analysis of reaction networks becomes a powerful mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, due to overwhelming computational cost, parametric sensitivity analysis is a extremely challenging problem for stochastic models with a high-dimensional parameter space and for which existing approaches are very slow. Here we present an information-theoretic sensitivity analysis in path-space (ISAP) MATLAB package that simulates stochastic processes with various algorithms and most importantly implements a gradient-free approach to quantify the parameter sensitivities of stochastic chemical reaction network dynamics using the pathwise Fisher information matrix (PFIM; Pantazis, Katsoulakis, and Vlachos 2013). The sparse, block-diagonal structure of the PFIM makes its computational complexity scale linearly with the number of model parameters. As a result of the gradientfree and the sparse nature of the PFIM, it is highly suitable for the sensitivity analysis of stochastic reaction networks with a very large number of model parameters, which are typical in the modeling and simulation of complex biochemical phenomena. Finally, the PFIM provides a fast sensitivity screening method (Arampatzis, Katsoulakis, and Pantazis 2015) which allows it to be combined with any existing sensitivity analysis software.
Uranium dioxide (UO 2 ) fuel is used as fuel in light water reactors (LWRs). While the fuel pellet is technically the first engineering barrier for radionuclide release, pellet fracturing at intermediate- to high-burnup values releases fission gases into the fuel rod plenum [1, 2]. Therefore, the true engineering barrier is the fuel cladding, which performs very well in LWR environments [3]. The extreme temperature gradients generated by fission energy and the low thermal conductivity of UO 2 quickly induce radial cracking in UO 2 during operation [4]. Cracks in the fuel provide opportunities for fuel relocation, increased fission gas release, and pellet-cladding mechanical interaction (PCMI) [5]. The ability to predict and engineer the fracture of UO 2 fuel pellets using modern computational tools is therefore a key engineering goal that has been the focus of ongoing experimental and computational efforts [6, 7]. Accurate predictions of fuel pellet cracking during operation requires knowledge of more complex phenomena, but improved understanding of the fundamental fracture behavior of unirradiated UO 2 is first necessary.
Dynamic analysis and controls design for flexible spacecraft involves high-order dynamic systems with multiple inputs and outputs. Computer tools are essential for such analysis. The controls/structures interaction analysis capability of an inter-disciplinary computer software system, called the Integrated Analysis Capability (IAC), is described. An overview of IAC components and procedures is presented, and an example of a preliminary space station controls design is shown.
Commercial digital signal processors (DSPs) for use in high-speed satellite computers are challenged by the damaging effects of space radiation, mainly single event upsets (SEUs) and single event functional interrupts (SEFIs). Innovations have been developed for mitigating the effects of SEUs and SEFIs, enabling the use of very-highspeed commercial DSPs with improved SEU tolerances. Time-triple modular redundancy (TTMR) is a method of applying traditional triple modular redundancy on a single processor, exploiting the VLIW (very long instruction word) class of parallel processors. TTMR improves SEU rates substantially. SEFIs are solved by a SEFI-hardened core circuit, external to the microprocessor. It monitors the health of the processor, and if a SEFI occurs, forces the processor to return to performance through a series of escalating events. TTMR and hardened-core solutions were developed for both DSPs and reconfigurable field-programmable gate arrays (FPGAs). This includes advancement of TTMR algorithms for DSPs and reconfigurable FPGAs, plus a rad-hard, hardened-core integrated circuit that services both the DSP and FPGA. Additionally, a combined DSP and FPGA board architecture was fully developed into a rad-hard engineering product. This technology enables use of commercial off-the-shelf (COTS) DSPs in computers for satellite and other space applications, allowing rapid deployment at a much lower cost. Traditional rad-hard space computers are very expensive and typically have long lead times. These computers are either based on traditional rad-hard processors, which have extremely low computational performance, or triple modular redundant (TMR) FPGA arrays, which suffer from power and complexity issues. Even more frustrating is that the TMR arrays of FPGAs require a fixed, external rad-hard voting element, thereby causing them to lose much of their reconfiguration capability and in some cases significant speed reduction. The benefits of COTS high-performance signal processing include significant increase in onboard science data processing, enabling orders of magnitude reduction in required communication bandwidth for science data return, orders of magnitude improvement in onboard mission planning and critical decision making, and the ability to rapidly respond to changing mission environments, thus enabling opportunistic science and orders of magnitude reduction in the cost of mission operations through reduction of required staff. Additional benefits of COTS-based, high-performance signal processing include the ability to leverage considerable commercial and academic investments in advanced computing tools, techniques, and infra structure, and the familiarity of the science and IT community with these computing environments.
In my research I have addressed several fundamental issues of dynamic fracture: 1. established the role of heat conduction as a natural regularizing mechanism in shearband problems (i.e. using the correct physics for this problem), 2. developed a unified model to incorporate fracture through a modified phase field formulation, 3. developed stability analysis tools to determine the onset of localization, 4. developed discretization methods and solvers to solve this problem (on parallel computers) that lead to mesh insensitive results and fast convergence. For such difficult problems, the physics and numerics are strongly coupled. To accurately model the physics, one must develop reliable numerics but reliable numerics depends on correct description of the physics. To this end, we have already made significant progress in the field showing that a fully-coupled implicit solution with heat conduction is the key to obtain reliable results for shear bands (see the next sections for more details). This research fits well within the mission of DOE ASCR program on all aspects: from governing equations to discretization to solvers to parallel computers to applications problems. It also relies on the computational tools developed through the SciDAC institutes FASTMath and SDAV, such as PETSc (and SLEPc), ParaView, Trilinos and Masquite.
Unlike terrestrial applications where gravity positions liquid at the "bottom" of the tank, the location of liquid propellant in spacecraft tanks is uncertain unless specific actions are taken or special features are built into the tank. Some mission events require knowledge of liquid position prior to a particular action: liquid must be positioned over the tank outlet prior to starting the main engines and must be moved away from the tank vent before vapor can be released overboard to reduce pressure. It may also be desirable to positively position liquid to improve propulsion system performance: moving liquid away from the tank walls will dramatically decrease the rate of heat transfer to the propellant, suppressing the boil-off rate, thereby reducing overall mission propellant requirements. The process of moving propellant to a desired position is referred to as propellant orientation or reorientation. Propulsive reorientation relies on small auxiliary thrusters to accelerate the tank. The inertia of the liquid causes it to collect in the aft-end of the tank if the acceleration is forward. Liquid Acquisition Devices (LAD's) rely on surface tension to hold the liquid within special geometries, (i.e. vanes, wire-mesh channels, start-baskets), to positively position propellants. Both of these technologies add significant weight and complexity to the spacecraft and can be limiting systems for long duration missions. The subject of the present research is an alternate technique for positively positioning liquid within spacecraft propellant tanks: magnetic fields. LOX is paramagnetic (attracted toward a magnet) and LH2 is diamagnetic (repelled from a magnet). Order-of-magnitude analyses, performed in the 1960's to determine required magnet size, concluded that the magnets would be prohibitively massive and this option has remained dormant during the intervening years. Recent advances in high-temperature superconducting materials hold the promise of electromagnets with sufficient performance to support cryogenic propellant management tasks. In late 1992, NASA MSFC began a new investigation in this technology commencing with the design of the Magnetically-Actuated Propellant Orientation (MAPO) experiment. A mixture of ferrofluid and water is used to simulate the paramagnetic properties of LOX and the experiment is being flown on the KC-135 aircraft to provide a reduced gravity environment. The influence of a 0.4 Tesla ring magnet on flow into and out of a subscale Plexiglas tank is being recorded on video tape. The most efficient approach to evaluating the feasibility of MAPO is to compliment the experimental program with development of a computational tool to model the process of interest. The goal of the present research is to develop such a tool. Once confidence in its fidelity is established by comparison to data from the MAPO experiment, it can be used to assist in the design of future experiments and to study the parameter space of the process. Ultimately, it is hoped that the computational model can serve as a design tool for full-scale spacecraft applications.
The NASA Langley and Ames Research Centers have teamed together for comparisons of computational predictions of the Boeing Mach 0.80Transonic Truss-Braced Wing (TTBW) configuration with a high-speed experimental dataset. The Mach 0.80 TTBW vehicle is a high wing, high aspect ratio configuration, designed for a high lift-to-drag ratio. System studies have predicted significant fuel burn and emissions benefits with the TTBW technology moving toward meeting NASA Subsonic Transport Systems-Level-Metrics. A 4.5% scale Mach 0.80 design TTBW model was recently tested at the NASA Ames Research Center 11-by 11-Foot Transonic Wind Tunnel(11-Ft TWT),providing a valuable dataset to validate computational tools and investigate best practices as risk reduction efforts continue for the development of the advanced TTBW vehicle. The NASA Computational Fluid Dynamics (CFD) team has computed free-air flow solutions on the Mach 0.80 design flight configuration and two wind tunnel configuration variants using the USM3D and LAVA flow solvers. Accurate modeling of the configuration tested in the wind tunnel environment is critical to validating the CFD tools, thus the team has included the internal cavity region and sting in their modeling of the configuration, similar to that tested in the 11-Ft TWT. Overall, the CFD simulations compared well and show similar trends as the corrected experimental data for lift and drag polars. The CFD predicted lift curve is shifted in angle of attack from what was observed in the experiment. The shift in lift also was seen in the pitching moment comparison plots. CFD solutions were computed at constant CL test point values and showed overall very good agreement when comparing constant spanwise cuts of pressure coefficient data on the wing and strut with experimental data. CFD cavity corrections were also investigated using the 11-Ft TWT cavity correction method, similar to that used to correct the wind tunnel data. Results showed some improvement in pitching moment coefficient predictions, and an increase in drag, shifting the data to the right in drag polars, further from the experimental data at lower lift conditions, good agreement near the design CL, and slight improvement at the higher lift conditions.
Probabilistic failure metrics such as leak frequency and rupture frequency are commonly used to characterize piping reliability. The methodologies for calculating the failure metrics rely on a complex set of input parameters. Operating experience data and experimental data play an important role in informing the different input parameters. The paper describes results and conclusions of a coordinated research project to benchmark three different reliability models using a four-step procedure: reference case definition of relevance to advanced reactor designs, input parameter calibration, validation of results, and application of different methodologies upon completion of the calibration and validation steps. The reference case is a weld consisting of nickel-base alloy 152/52 and located within a primary pressure boundary of an advanced reactor. This alloy is a class of structural materials known to be highly resistant to stress corrosion cracking. Synergies between the different methods are noted and the importance of a multi-disciplinary approach to input parameter development is underscored. A key conclusion is that the three methods are equally suitable for estimating failure frequencies. In any specific application, a selection of the most practical or effective computational tool can be considered. The comparison of alternative models confirms and helps to gain confidence in the computed failure frequency estimates. The study was part of a coordinated research project organized by the International Atomic Energy Agency.
Here, additive manufacturing offers unprecedented opportunities to design complex structures optimized for performance envelopes inaccessible under conventional manufacturing constraints. Additive processes also promote realization of engineered materials with microstructures and properties that are impossible via traditional synthesis techniques. Enthused by these capabilities, optimization design tools have experienced a recent revival. The current capabilities of additive processes and optimization tools are summarized briefly, while an emerging opportunity is discussed to achieve a holistic design paradigm whereby computational tools are integrated with stochastic process and material awareness to enable the concurrent optimization of design topologies, material constructs and fabrication processes.
The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.
The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.
Pronghorn is a thermal-hydraulics computational tool developed using the Idaho National Laboratory's Multiphysics Object-Oriented Simulation Environment (MOOSE). It is designed to support Computational Fluid Dynamics (CFD) modeling, ranging from subchannel and porous media analysis to Reynolds Averaged Navier-Stokes (RANS) turbulence modeling. As an integral part of the MOOSE-based suite of tools, Pronghorn seamlessly couples with other MOOSE-based applications to simulate a variety of physical phenomena. This article highlights recent significant enhancements to Pronghorn's CFD modeling capabilities and demonstrates their application to advanced nuclear reactor designs. The recent improvements in Pronghorn primarily focus on modifications to its turbulence modeling capabilities, near-wall corrections and numerical schemes. In terms of turbulence modeling, the two-equation $k-\epsilon$ and $k-\omega$ SST models have been implemented and validated with both equilibrium and non-equilibrium wall treatments. Additionally, corrections for wall roughness, and curvature, and wall-channeling in pebble beds have been introduced in the near-wall modeling. These developments enable more accurate simulations of advanced nuclear reactors. Two case studies are presented in this work: a pool-type Molten Chloride Reactor and a salt-cooled Pebble-Bed High Temperature Reactor. In both cases, the previous models in Pronghorn are compared with the new implementations, demonstrating the improved accuracy achieved with the updated models.
This project was a multi-lab, multi-partner CRADA involving LLNL, Los Alamos National Laboratory, Sandia National Laboratories, Oak Ridge National Laboratory, Martin Marietta Energy Systems and the industrial partner, The National Center of Manufacturing Sciences (NCMS). A number of member companies of NCMS participated including General Motors Corporation, Ford Motor Company, The Torrington Company, Gear Research, the Illinois Institute of Technology Research Institute, and Deformation Control Technology •. LLNL was the lead laboratory for metrology technology used for validation of the computational tool/methodology. LLNL was also the lead laboratory for the development of the software user interface , for the computational tool. This report focuses on the participation of LLNL and NCMS. The purpose of the project was to develop a computational tool/methodology that engineers would use to predict the effects of heat treatment on the _size and shape of industrial parts made of quench hardenable alloys. Initially, the target application of the tool was gears for automotive power trains.
Recent flight test projects and NASA missions have highlighted the challenges associated with accurately and efficiently modeling the behavior of parachute deployment systems needed for parachute design. Moreover, parachute deployment has been identified as one of the higher risk components for such missions. The analysis of textile fabrics used for atmospheric entry is inherently complex due to the multiple scales present in the fabric structure, including individual fiber filaments at the microscale, yarn bundles of fibers at the mesoscale, and the overall woven fabric at the macroscale. Computational tools for simulating fabric behavior must be able to account for the different mechanisms present at each scale without sacrificing computational efficiency. This work examines the generalized multiscale method of cells micromechanics theory, which has previously been used for the analysis of reinforced composite structures, to unreinforced textile fabrics. Modifications to the existing composite multiscale framework, implemented in NASA’s Multiscale Analysis Tool (NASMAT), include the specific mechanics unique to unreinforced textile fabrics, and overcoming the assumptions of a fixed fiber angle. It looks to assess the feasibility of using the NASMAT tool for efficient prediction of the response of unreinforced fabrics to loading such that it can ultimately be applied to fluid structure interaction tools for the prediction of parachute deployment systems. In this work, fabric behavior is simulated in NASMAT through homogenization of a triply periodic repeating unit cell, where the geometry of the subcells can change as a function of loading to represent the relative rotation and uncrimping that can occur in fabric tows. Predictions from the amended NASMAT code are compared to experimental data for uniaxial and off-axis tension to verify the ability of the code to incorporate lower-scale mechanics in prediction of unreinforced fabrics under loading.
This document presents the technical approach for developing a set of tools to perform a cumulative impact evaluation (CIE) on the effects of cleanup decisions regarding groundwater quality in the Hanford Site Central Plateau. Due to the complexity and large number of waste sites in noncontiguous source operable units (OUs), the computational tools used for the CIE must be capable of representing a range of site conditions and source terms in the vadose zone while also efficiently computing the impact that cleanup decisions have on the underlying aquifer. These tools will support remedial and closure decisions across the Central Plateau by providing cumulative information, but without replacing or displacing any part of those decision processes. The objective of this document is to describe the major components required for the CIE, the approach for developing these tools, and the plan for implementation. The Central Plateau has more than 1,300 waste sites, active and inactive burial grounds, and active and inactive waste processing facilities that are regulated under a combination of the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA); the Resource Conservation and Recovery Act of 1976 (RCRA); and other waste disposal regulatory frameworks. Figure 1-1 shows the source OUs on the Central Plateau and illustrates the geographical intermixing of sites within different source OUs. Groundwater OUs have several overlying source OUs, and actions taken within source OUs may affect groundwater across the entire Central Plateau. Waste site proximity between and within source OUs has resulted in contaminants commingling in the vadose and saturated zones in complex ways (Figure 1-2). Plume commingling requires cleanup decisions to be evaluated considering the surrounding waste sites and existing groundwater contamination, therefore demonstrating the need to evaluate cumulative impacts from the vadose zone to groundwater. Soil (vadose zone) and groundwater (saturated zone) contamination across the Central Plateau is undergoing investigation and remediation, and decisions need to be made regarding how to most efficiently disposition and remediate sites. Because contaminants intermingle among the OUs, decisions should be evaluated considering the context of the surrounding sites and facilities and not in isolation. Thus, the CIE of Central Plateau groundwater contamination should consider the integrated effects of numerous contaminant sources and prospective remedies. For this CIE, cumulative impacts of an action (e.g., groundwater remediation or placement of an evapotranspiration barrier over a waste site) will be viewed as the total effects on groundwater from that action, considering all other activities or likely future activities for source or groundwater OUs.
We report that one suggested approach to managing the risk of geologic CO 2 storage projects is to perform risk-based subsurface monitoring targeting early leak detection. The challenge in risk-based monitoring is that leakage impacts are expected to be small in comparison with the footprint of the stored CO 2 plume and could be at depths ranging from the depth of storage (often ~1 km bgs) up to surficial aquifers. Addressing this challenge requires a multidisciplinary effort capable of characterizing potential subsurface leakage in terms of likelihood, location, magnitude, impacts to overlying aquifers, and uncertainty as well as innovative monitoring technology design and implementation. The National Risk Assessment Partnership (NRAP) was established to develop related risk assessment tools to provide guidance on such assessments. Here we demonstrate the application workflow of two of the NRAP computational tools, WLAT (Wellbore Leakage Analysis Tool) and DREAM (Designs for Risk Evaluation and Management), to a hypothetical CO 2 storage site based on a study area in the Midwestern United States. This workflow shows how CO 2 leakage may be estimated, evaluated, and monitored in terms of risk. Results provide guidance on monitoring configurations and technologies which minimize the time to leak detection, maximize the probability detection, and provide cost considerations.
Metabolic engineering is a critical biotechnological approach in addressing global energy and environment challenges. Most engineering efforts, however, consist of laborious and inefficient trial-and-error of target pathways, due in part to the lack of methodologies that can comprehensively assess pathway properties in thermodynamics and kinetics. Metabolic engineering can benefit from computational tools that evaluate feasibility, expense and stability of non-natural metabolic pathways. Such tools can also help us understand natural pathways and their regulation at systems level. Here we introduce a computational toolbox, PathParser, which, for the first time, integrates multiple important functions for pathway analysis including thermodynamics analysis, kinetics-based protein cost optimization and robustness analysis. Specifically, PathParser enables optimization of the driving force of a pathway by minimizing the Gibbs free energy of least thermodynamically favorable reaction. In addition, based on reaction thermodynamics and enzyme kinetics, it can compute the minimal enzyme protein cost that supports metabolic flux, and evaluate pathway stability and flux in response to enzyme concentration perturbations. In a demo analysis of the Calvin-Benson-Bassham cycle and photorespiration pathway in the model cyanobacterium Synechocystis PCC 6803, the computation results are corroborated by experimental proteomics data as well as metabolic engineering outcomes. This toolbox may have broad application in metabolic engineering and systems biology in other microbial systems.