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The scale and complexity of scientific applications makes it very difficult to optimize, debug and extend them to support new capabilities. We have developed a tool that supports developers’ efforts to understand the logical flow of their applications and interactions between application components and hardware in a way that scales with application complexity and parallelism.
A full scale High Pressure Oxidizer Turbopump (HPOTP) bearing tester has been developed, and an existing test stand modified, to simulate turbopump operating conditions for Space Shuttle Main Engine HPOTP design. Following a description of test facilities and procedures, design certification and calibration tests, and tests at a moderate axial load, are discussed. Results show that a bearing life of about 4500 seconds is comparable to the life experienced in the engine pump at nominal high power level operating conditions, and that although increased coolant flow increased fluid viscous work and bearing loads, a net decrease in bearing temperature was noted. Increased coolant flow is found to improve bearing cooling for marginal conditions, and bearing temperatures are shown to become more sensitive to coolant flow as shaft speed is increased. For the 6.4 lbs/s test conditions, the heat generated in a bearing pair is determined to be about 67 percent viscous work and 33 percent contact friction.
Network analysis is now a common statistical tool for molecular biologists. Network algorithms are readily used to model gene, protein and metabolic correlations providing insight into pathways driving biological phenomenon. One output from such an analysis is a candidate gene list that can be responsible, in part, for the biological process of interest. The question remains, however, as to whether molecular network analysis can be used to inform process models at higher levels of biological organization. In our previous work, transcriptional networks derived from three plant species were constructed, interrogated for orthology and then correlated to photosynthetic inhibition at elevated temperature. One unique aspect of that study was the link from co-expression networks to net photosynthesis. In this addendum, we propose a conceptual model where traditional network analysis can be linked to whole-plant models thereby informing predictions on key processes such as photosynthesis, nutrient uptake and assimilation, and C partitioning.
This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: (1) The method used to develop multi-year continuous forcing data (Xie et al. 2004), (2) The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and (3) Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products.
The association between features from standard tokamak equilibrium reconstructions and the onset of n = 1 tearing modes (TMs) is analyzed at scale. The TM onset rate is directly modeled with a ‘hazard’ function which gives the expected number of onsets (per unit time spent) in a given equilibrium parameter region. In particular the different statistical modeling performance achieved for magnetics-only reconstructions and motional Stark effect (MSE) enhanced reconstructions is studied. It is observed that a better hazard model for the TM onset rate can be built with the MSE-enhanced equilibria compared to the matched magnetics-only situation. This advantage disappears if internal profile details are withheld from the matched analysis. Plausibility of the hazard function is further demonstrated with visualizations of global trends in the operational space, and time-traces from specific tokamak discharges. As a result, TMs typically degrade tokamak plasma performance and may lead to plasma termination, motivating this statistical study.
The ARM large-scale forcing data from the constrained variational analysis (VARANAL) version 2
Important insights into many problems that are traditionally analyzed via statistical models can be obtained by re-formulating and evaluating within a large-scale optimization framework. The theoretical underpinnings of the statistical model often shift the goal of the solution space traversal from a traditional search for a single optimal solution to a traversal with the purpose of yielding a set of high quality, independent solutions. We examine statistical frameworks with astronomical solution spaces where the independence requirement constitutes a significant additional challenge for standard optimization methodologies. We design a hybrid metaheuristic with specialized intensification and diversification protocols in the base search algorithm. We extend our algorithm to the high-performance-computing realm using the Stampede2 supercomputer. We experimentally demonstrate the effectiveness of our algorithm to utilize multiple processors to collaboratively hill climb, broadcast messages to one another regarding landscape characteristics, diversify across the solution landscape, and request aid in climbing particularly difficult peaks.
A three-dimensional (3-D) pore-scale model has been developed to construct multiscale fibrous electrodes for redox flow batteries (RFB). New designs, such as biporous electrodes modify electrode structures by creating secondary pores on single carbon fiber to reduce internal battery resistance. Existing pore-scale models only resolve electrodes to the single carbon fiber scale and cannot incorporate recent multiscale electrode designs into numerical models. Our new model aims to bridge this gap and provide a tool to rapidly screen new electrode configurations. Two multiscale electrodes, laser-perforated and biporous electrodes, were investigated at varying operation conditions with the proposed 3-D pore-scale models. The laser-perforated electrode exhibits a reduced pressure drop, but follows the same permeability correlation compared to the corresponding pristine electrode. For the biporous electrode, the added specific surface area and faster reaction kinetics from the secondary pores are the most influential factors leading to improved battery efficiency. However, operating the biporous electrode in limiting current density conditions should be avoided due to the decreased mass transfer efficiency and a more significant voltage loss. Finally, we believe that our 3-D pore-scale model can accelerate the flow battery electrode design process and provide new insights into electrode geometry optimizations.
Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.
The ultimate goal of this study is to improve representation of convective transport by cumulus parameterization for meso-scale and climate models. As Part I of the study, we perform extensive evaluations of cloud-resolving simulations of a squall line and mesoscale convective complexes in mid-latitude continent and tropical regions using the Weather Research and Forecasting (WRF) model with spectral-bin microphysics (SBM) and with two double-moment bulk microphysics schemes: a modified Morrison (MOR) and Milbrandt and Yau (MY2). Compared to observations, in general, SBM gives better simulations of precipitation, vertical velocity of convective cores, and the vertically decreasing trend of radar reflectivity than MOR and MY2, and therefore will be used for analysis of scale-dependence of eddy transport in Part II. The common features of the simulations for all convective systems are (1) the model tends to overestimate convection intensity in the middle and upper troposphere, but SBM can alleviate much of the overestimation and reproduce the observed convection intensity well; (2) the model greatly overestimates radar reflectivity in convective cores (SBM predicts smaller radar reflectivity but does not remove the large overestimation); and (3) the model performs better for mid-latitude convective systems than tropical system. The modeled mass fluxes of the mid latitude systems are not sensitive to microphysics schemes, but are very sensitive for the tropical case indicating strong microphysics modification to convection. Cloud microphysical measurements of rain, snow and graupel in convective cores will be critically important to further elucidate issues within cloud microphysics schemes.
This study develops and validates a simplified, fully solvent-free downstream processing (DSP) strategy for high-purity recovery of 3-hydroxypropionic acid (3-HP) from real fermentation broth containing 62.3 g/L of 3-HP. Optimized activated carbon treatment achieved 98% color removal, while Amberlite IRA-67 was operated at pH 4.5 and 30 °C to minimize product loss. This is the first integrated demonstration of a fully solvent-free DSP enabling recovery of bio-based 3-HP as both a solid sodium salt and a concentrated aqueous solution, supported by techno-economic analysis. At lab scale, the process achieved 77.3% recovery of sodium 3-HP with 83.2% (w/w) purity and produced a 30% (w/v) aqueous solution. Techno-economic analysis yielded minimum selling prices of $0.551/kg for the solution and $0.892/kg for the salt, both below target thresholds for cost-competitive bio-acrylic acid production. Overall, these results demonstrate an efficient, scalable, and economically viable industrial pathway for 3-HP recovery.
This paper discusses a system-level finite element model of a two-loop pressurized water reactor (PWR). Based on this model, system-level heat transfer analysis and subsequent sequentially coupled thermal-mechanical stress analysis were performed for typical thermal-mechanical fatigue cycles. The in-air fatigue lives of example components, such as the hot and cold legs, were estimated on the basis of stress analysis results, ASME in-air fatigue life estimation criteria, and fatigue design curves. Furthermore, environmental correction factors and associated PWR environment fatigue lives for the hot and cold legs were estimated by using estimated stress and strain histories and the approach described in US-NRC report: NUREG-6909.
An analysis of the acoustical and flowfield environment for the scaled 1-pound-force (lbf) thrust tabletop motor was performed. This tabletop motor from NASA Stennis Space Center Is composed of Plexiglas burning In gaseous oxygen with a graphite insert for the nozzle portion. The nozzle has a throat diameter of 0.2 inch and an exit diameter of 0.38 Inch. With a chamber pressure at 55 pounds per square Inch absolute (psia), a normal shock is formed immediately downstream of the nozzle exit plane as the combustion products exhaust into the ambient at atmospheric pressure. The jet characterization Is based on computational fluid dynamics (CFD) in conjunction with Kirchhoff surface integral formulation and compared with correlations developed for measured rocket noise and a pressure fluctuation scaling (PFS) method. Predictions and comparisons are made for the overall sound pressure levels (OASPL's) and spectral dependence of sound pressure level (SPL). The overall objective of this effort is to develop methods for scaling the acoustic and flowfield environment of rockets with a wide range of thrust (1 lbf to 1 million lbf).
Today's large-scale systems such as High Performance Computing (HPC) Systems are designed/utilized towards exascale computing, inevitably decreasing its reliability due to the increasing design complexity. HPC systems conduct extensive logging of their execution behaviour. In this paper, we leverage the inherent meaning behind the log messages and propose a novel sentiment analysis-based approach for the error detection in large-scale systems, by automatically mining the sentiments in the log messages. Our contributions are four-fold. (1) We develop a machine learning (ML) based approach to automatically build a sentiment lexicon, based on the system log message templates. (2) Using the sentiment lexicon, we develop an algorithm to detect system errors. (3) We develop an algorithm to identify the nodes and components with erroneous behaviors, based on sentiment polarity scores. (4) We evaluate our solution vs. other state-of-the-art machine/deep learning algorithms based on three representative supercomputers' system logs. Experiments show that our error detection algorithm can identify error messages with an average MCC score and f -score of 91% and 96% respectively, while state of the art ML/deep learning model (LSTM) obtains only 67% and 84%. To the best of our knowledge, this is the first work leveraging the sentiments embedded in log entries of large-scale systems for system health analysis.
Sandia National Laboratories operates the Scaled Wind Farm Technology Facility (SWiFT) on behalf of the Department of Energy Wind and Water Power Technologies Office. An analysis was performed to evaluate the hazards associated with debris thrown from one of SWiFT’s operating wind turbines, assuming a catastrophic failure. A Monte Carlo analysis was conducted to assess the complex variable space associated with debris throw hazards that included wind speed, wind direction, azimuth and pitch angles of the blade, and percentage of the blade that was separated. In addition, a set of high fidelity explicit dynamic finite element simulations were performed to determine the threshold impact energy envelope for the turbine control building located on-site. Assuming that all of the layered, independent, passive and active engineered safety systems and administrative procedures failed (a 100% failure rate of the safety systems), the likelihood of the control building being struck was calculated to be less than 5/10,000 and ballistic simulations showed that the control building would not provide passive protection for the majority of impact scenarios. Although options exist to improve the ballistic resistance of the control building, the recommendation is not to pursue them because there is a low probability of strike and there is an equal likelihood personnel could be located at similar distances in other areas of the SWiFT facility which are not passively protected, while the turbines are operating. A fenced exclusion area has been created around the turbines which restricts access to the boundary of the 1/100 strike probability. The overall recommendation is to neither relocate nor improve passive protection of the control building as the turbine safety systems have been improved to have no less than two independent, redundant, high quality engineered safety systems. Considering this, in combination with a control building strike probability of less than 5/10,000, the overall probability of turbine debris striking the control building is less than 1/1,000,000.
A numerical simulation of kinetic plasma turbulence is performed to assess the applicability of critical balance to kinetic, dissipation scale turbulence. The analysis is performed in the frequency domain to obviate complications inherent in performing a local analysis of turbulence. A theoretical model of dissipation scale critical balance is constructed and compared to simulation results, and excellent agreement is found. This result constitutes the first evidence of critical balance in a kinetic turbulence simulation and provides evidence of an anisotropic turbulence cascade extending into the dissipation range. We also perform an Eulerian frequency analysis of the simulation data and compare it to the results of a previous study of magnetohydrodynamic turbulence simulations.
The nodal admittance matrix (NAM)-based approach is well-suited for small-signal stability analysis of large-scale power electronics-based power systems (PEPSs), as it preserves the system structure through its admittance matrix. Previous studies have explored partitioning such systems into subareas and interconnections to reduce computational burden; however, they lacked a formal algorithmic procedure for determining feasible partitions. While several grid partitioning methods, such as those based on graph theory or machine learning, exist in the literature, they cannot be directly applied to NAM-based analysis due to differing objectives and constraints. Here, this paper addresses this gap by presenting a systematic, step-by-step procedure for applying a spectral partitioning algorithm that yields a division of the system into subareas suitable for NAM-based analysis. The computational complexity of the proposed method is also derived to demonstrate its efficiency and justify the practicality of the resulting subarea decomposition. The performance of the partitioning method is evaluated by applying the spectral clustering-derived subareas and interconnections to the NAM-based partitioning approach on a 140-bus system. Computational times for the full-system and partitioned NAM analyses are compared using MATLAB. Additionally, PSCAD simulations of the complete system and partitioned subareas are carried out to verify the effectiveness of the proposed method.
The Very High Temperature Critical Assembly (VHTRC) experiment represents one of the few data sets available for the validation of HTGR lattice physics. Within the framework of the IAEA Coordinated Research Project on HTGR Uncertainty Analysis in Modeling, uncertainty and sensitivity analyses of this graphite-moderated facility are performed as the validation reference to the prismatic MHTGR-350 fuel block calculations. Nominal multi-group and continuous-energy criticality calculations with the KENO-VI Monte Carlo code of the SCALE code package are compared with the continuous-energy Monte Carlo Code Serpent. Good agreement with a maximum difference of 250 pcm is obtained. The experimental data set, however, differs by several hundred pcm with the results of both codes when using the ENDF-VII.0 library. When using ENDF/B-VII.1 data, this difference is reduced to a few hundred pcm and the calculations lie within the experimental error bars. Uncertainties of the VHTRC multiplication factors due to uncertainties in nuclear data are determined with SAMPLER/KENO-VI of SCALE 6.2 and with continuous-energy TSUNAMI of SCALE 6.2. For all experimental configurations, the obtained uncertainty is found to be 0.58% when using ENDF/B-VII.0 data. The top contributor to this uncertainty is the average number of neutrons per fission event of U-235. With ENDF/B-VII.1 data, uncertainties of about 0.66% are obtained in particular due to an increased uncertainty of U-235 nu-bar in the latest ENDF release. In conclusion, when considering these nuclear data uncertainties, the obtained 1s uncertainty intervals are overlapping with the experimental error bars.