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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

A mixed optimization method for automated design of fuselage structures.

A procedure for automating the design of transport aircraft fuselage structures has been developed and implemented in the form of an operational program. The structure is designed in two stages. First, an overall distribution of structural material is obtained by means of optimality criteria to meet strength and displacement constraints. Subsequently, the detailed design of selected rings and panels consisting of skin and stringers is performed by mathematical optimization accounting for a set of realistic design constraints. The practicality and computer efficiency of the procedure is demonstrated on cylindrical and area-ruled large transport fuselages.

Sobieszczanski, J.↗
NASA NTRS2010

Simulation and Optimization Methods for Assessing the Impact of Aviation Operations on the Environment

There is increased awareness of anthropogenic factors affecting climate change and urgency to slow the negative impact. Greenhouse gases, oxides of Nitrogen and contrails resulting from aviation affect the climate in different and uncertain ways. This paper develops a flexible simulation and optimization software architecture to study the trade-offs involved in reducing emissions. The software environment is used to conduct analysis of two approaches for avoiding contrails using the concepts of contrail frequency index and optimal avoidance trajectories.

Sridhar, Banavar↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Performance Optimization Methods for a Memory-Bound, Unstructured-Grid CFD Application on Massively Parallel GPU Platforms

Computational performance of the FUN3D unstructured-grid computational fluid dynamics (CFD) application on massively parallel GPU environments is memory-bound and highly dependent upon efficient reads from and atomic updates to the irregular cell-, edge-, and node-based data structures. In this talk, we present recent efforts into optimizing select performance-critical kernels on NVIDIA Tesla V100 and A100 GPUs and AMD CDNA MI100 GPUs. A novel use of L2 cache residency controls and asynchronous loads into on-chip shared memory are explored on the A100 GPU for the sparse iterative solver, which is dominated by mixed-precision, sparse matrix vector multiplication. Demonstrations show that these methods improve global memory bandwidth utilization by 13.5% on the A100 GPU. Several techniques are also presented that use registers and/or shared memory to facilitate array transposition and aggregation which combine to reduce the frequency and increase the cache efficiency of floating-point atomic updates to the irregular data structures. These methods are demonstrated to improve the kernel throughput by nearly 500% on select kernels on the AMD MI100 over atomic updates directly to global memory. Overall, both V100 and A100 GPUs outperformed the MI100 GPU on kernels dominated by double-precision atomic updates; however, the techniques demonstrated here reduced the performance gap and improved the MI100 performance.

GPU CPU unstructured CFD memory↗
DOE OSTI2023

An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data

SUMMARY Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson’s ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth’s structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.

Geochemistry & Geophysics↗
DOE OSTI2020

Optimal methods for preparation, separation, and determination of radium isotopes in environmental and biological samples

In recent years, radium has attracted considerable attention primarily because of the rapid increase in unconventional (fracking) drilling technology in the United States and around the world. One of the major radionuclides of interest in unconventional drilling wastes is radium isotopes ( 224 Ra, 226 Ra, 228 Ra). To access long-term risks associated with radium isotopes entering into the environment, accurate measurements of radium isotopes in environmental and biological samples are crucial. This article reviews many aspects of radium chemistry, which includes recent developments in radiochemical separations methods, advancements in analytical techniques followed by a more detailed discussion on the recent trends in radium determination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

An optimized method for mouse liver sinusoidal endothelial cell isolation

The objective of the present study was to develop an accurate and reproducible method for liver sinusoidal endothelial cell (LSEC) isolation in mice. Non-parenchymal cells were isolated using a modified two-step collagenase digestion combined with Optiprep density gradient centrifugation. LSEC were further purified using two prevalent methods, short-term selective adherence and CD146+ magnetic-activated cell sorting (MACS), and compared in terms of cell yield, viability and purity to our purification technique using CD11b cell depletion combined with long-term selective adherence. LSEC purification using our technique allowed to obtain 7.07±3.80 million LSEC per liver, while CD146+ MACS and short-term selective adherence yielded 2.94±1.28 and 0.99±0.66 million LSEC, respectively. Purity of the final cell preparation reached 95.10±2.58% when using our method. In contrast, CD146+ MACS and short-term selective adherence gave purities of 86.75±3.26% and 47.95±9.82%, respectively. Similarly, contamination by non-LSEC was the lowest when purification was performed using our technique, with a proportion of contaminating macrophages of only 1.87±0.77%. Further, isolated cells analysed by scanning electron microscopy presented typical LSEC fenestrations organized in sieve plates, demonstrating that the technique allowed to isolate bona fide LSEC. In conclusion, we described a reliable and reproducible technique for the isolation of high yields of pure LSEC in mice. This protocol provides an efficient method to prepare LSEC for studying their biological functions. - Highlights: • This protocol provides an efficient method to prepare primary mouse LSEC for studying their biological functions. • The liver cell dispersion step was improved by performing a retrograde cannulation of the liver. • The cell yield and the purity obtained were higher than comparative techniques in mice. • Contaminating macrophages were removed by introducing a CD11b- magnetic –activated cell sorting step.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Optimization Method to Branch and Bound Large SBO State Spaces Under Dynamic Probabilistic Risk Assessment via use of LENDIT Scales and S2R2 Sets

Traditional Probabilistic Risk Assessment (PRA) methods have been developed and are quite effective in evaluating risk associated with complex systems, but lack the capability to evaluate complex dynamic systems. These time and energy scales associated with the transient may vary as a function of transition time to a different physical state. Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems, while complete, results in issues associated with combinatorial explosion. In order to address the combinatorial complexity arising from the number of possible state configurations and discretization of transition times, a characteristic scaling metric (LENDIT – length, energy, number, distribution, information and time) is proposed as a means to describe systems uniformly and thus provide means to describe relational constraints expected in the dynamics of a complex (coupled) systems. Thus when LENDIT is used to characterize four sets – ‘state, system, resource and response’ (S2R2) – describing reactor operations (normal and off-normal), LENDIT and S2R2 in combination have the potential to ‘branch and bound’ the state space investigated by DPRA. In this paper we introduce the concept of LENDIT scales and S2R2 sets applied to a branch-and-bound algorithm and apply the methods to a station black out transient (SBO).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

An optimized method to identify RR Lyrae stars in the SDSS×Pan-STARRS1 overlapping area using a bayesian generative technique

We present a method for selecting RR Lyrae (RRL) stars (or other types of variable stars) in the absence of a large number of multi-epoch data and light curve analyses. Our method uses color and variability selection cuts that are defined by applying a Gaussian Mixture Bayesian Generative Method (GMM) on 636 pre-identified RRL stars instead of applying the commonly used rectangular cuts. Specifically, our method selects 8115 RRL candidates (heliocentric distances < 70 kpc) using GMM color cuts from the Sloan Digital Sky Survey (SDSS) and GMM variability cuts from the Panoramic Survey Telescope and Rapid Response System 1 3π survey (PS1). Comparing our method with the Stripe 82 catalog of RRL stars shows that the efficiency and completeness levels of our method are ∼77% and ∼52%, respectively. Most contaminants are either non-variable main-sequence stars or stars in eclipsing systems. The method described here efficiently recovers known stellar halo substructures. It is expected that the current completeness and efficiency levels will further improve with the additional PS1 epochs (∼3 epochs per filter) that will be observed before the conclusion of the survey. A comparison between our efficiency and completeness levels using the GMM method to the efficiency and completeness levels using rectangular cuts that are commonly used yielded a significant increase in the efficiency level from ∼13% to ∼77% and an insignificant change in the completeness levels. Hence, we favor using the GMM technique in future studies. Although we develop it over the SDSS×PS1 footprint, the technique presented here would work well on any multi-band, multi-epoch survey for which the number of epochs is limited.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Scaling and Quality of Modularity Optimization Methods for Graph Clustering

Real-world graphs exhibit structures known as “communities” or “clusters” consisting of a group of vertices with relatively high connectivity between them, as compared to the rest of the vertices in the network. Graph clustering or community detection is a fundamental graph operation used to analyze real-world graphs occurring in the areas of computational biology, cybersecurity, electrical grids, etc. Similar to other graph algorithms, owing to irregular memory accesses and inherently sequential nature, current algorithms for community detection are challenging to parallelize. However, in order to analyze large networks, it is important to develop scalable parallel implementations of graph clustering that are capable of exploiting the architectural features of modern supercomputers. In response to the 2019 Streaming Graph Challenge, we present quality and performance analysis of our distributed- memory community detection using Vite, which is our distributed memory implementation of the popular Louvain method, on the ALCF Theta supercomputer. Clustering methods such as Louvain that rely on modularity maximization are known to suffer from the resolution limit problem, preventing identification of clusters of certain sizes. Hence, we also include quality analysis of our shared-memory implementation of the Fast-tracking Resistance method, in comparison with Louvain on the challenge datasets. Furthermore, we introduce an edge-balanced graph distribution for our distributed memory implementation, that significantly reduces communication, offering up to 80% improvement in the overall execution time. In addition to performance/quality analysis, we also include details on the power/energy consumption, and memory traffic of the distributed-memory clustering implementation using real-world graphs with over a billion edges.

Ghosh, Sayan↗