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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

SIM_EXPLORE: Software for Directed Exploration of Complex Systems

Physics-based numerical simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. While such codes may provide the highest- fidelity representation of system behavior, they are often so slow to run that insight into the system is limited. Trying to understand the effects of inputs on outputs by conducting an exhaustive grid-based sweep over the input parameter space is simply too time-consuming. An alternative approach called "directed exploration" has been developed to harvest information from numerical simulators more efficiently. The basic idea is to employ active learning and supervised machine learning to choose cleverly at each step which simulation trials to run next based on the results of previous trials. SIM_EXPLORE is a new computer program that uses directed exploration to explore efficiently complex systems represented by numerical simulations. The software sequentially identifies and runs simulation trials that it believes will be most informative given the results of previous trials. The results of new trials are incorporated into the software's model of the system behavior. The updated model is then used to pick the next round of new trials. This process, implemented as a closed-loop system wrapped around existing simulation code, provides a means to improve the speed and efficiency with which a set of simulations can yield scientifically useful results. The software focuses on the case in which the feedback from the simulation trials is binary-valued, i.e., the learner is only informed of the success or failure of the simulation trial to produce a desired output. The software offers a number of choices for the supervised learning algorithm (the method used to model the system behavior given the results so far) and a number of choices for the active learning strategy (the method used to choose which new simulation trials to run given the current behavior model). The software also makes use of the LEGION distributed computing framework to leverage the power of a set of compute nodes. The approach has been demonstrated on a planetary science application in which numerical simulations are used to study the formation of asteroid families.

Burl, Michael↗
NASA NTRS1999

Multidisciplinary Design Technology Development: A Comparative Investigation of Integrated Aerospace Vehicle Design Tools

This research effort is a joint program between the Departments of Aerospace and Mechanical Engineering and the Computer Science and Engineering Department at the University of Notre Dame. The purpose of the project was to develop a framework and systematic methodology to facilitate the application of Multidisciplinary Design Optimization (MDO) to a diverse class of system design problems. For all practical aerospace systems, the design of a systems is a complex sequence of events which integrates the activities of a variety of discipline "experts" and their associated "tools". The development, archiving and exchange of information between these individual experts is central to the design task and it is this information which provides the basis for these experts to make coordinated design decisions (i.e., compromises and trade-offs) - resulting in the final product design. Grant efforts focused on developing and evaluating frameworks for effective design coordination within a MDO environment. Central to these research efforts was the concept that the individual discipline "expert", using the most appropriate "tools" available and the most complete description of the system should be empowered to have the greatest impact on the design decisions and final design. This means that the overall process must be highly interactive and efficiently conducted if the resulting design is to be developed in a manner consistent with cost and time requirements. The methods developed as part of this research effort include; extensions to a sensitivity based Concurrent Subspace Optimization (CSSO) NMO algorithm; the development of a neural network response surface based CSSO-MDO algorithm; and the integration of distributed computing and process scheduling into the MDO environment. This report overviews research efforts in each of these focus. A complete bibliography of research produced with support of this grant is attached.

Renaud, John E.↗
NASA NTRS1998

Multidisciplinary Design Technology Development: A Comparative Investigation of Integrated Aerospace Vehicle Design Tools

This research effort is a joint program between the Departments of Aerospace and Mechanical Engineering and the Computer Science and Engineering Department at the University of Notre Dame. Three Principal Investigators; Drs. Renaud, Brockman and Batill directed this effort. During the four and a half year grant period, six Aerospace and Mechanical Engineering Ph.D. students and one Masters student received full or partial support, while four Computer Science and Engineering Ph.D. students and one Masters student were supported. During each of the summers up to four undergraduate students were involved in related research activities. The purpose of the project was to develop a framework and systematic methodology to facilitate the application of Multidisciplinary Design Optimization (N4DO) to a diverse class of system design problems. For all practical aerospace systems, the design of a systems is a complex sequence of events which integrates the activities of a variety of discipline "experts" and their associated "tools". The development, archiving and exchange of information between these individual experts is central to the design task and it is this information which provides the basis for these experts to make coordinated design decisions (i.e., compromises and trade-offs) - resulting in the final product design. Grant efforts focused on developing and evaluating frameworks for effective design coordination within a MDO environment. Central to these research efforts was the concept that the individual discipline "expert", using the most appropriate "tools" available and the most complete description of the system should be empowered to have the greatest impact on the design decisions and final design. This means that the overall process must be highly interactive and efficiently conducted if the resulting design is to be developed in a manner consistent with cost and time requirements. The methods developed as part of this research effort include; extensions to a sensitivity based Concurrent Subspace Optimization (CSSO) MDO algorithm; the development of a neural network response surface based CSSO-MDO algorithm; and the integration of distributed computing and process scheduling into the MDO environment. This report overviews research efforts in each of these focus. A complete bibliography of research produced with support of this grant is attached.

Renaud, John E.↗
DOE OSTI2016

SU-F-J-123: CT-Based Determination of DIBH Variability and Its Dosimetric Impact On Post-Mastectomy Plus Regional Nodal Radiation Therapy

Purpose: Breast cancer radiotherapy delivered using voluntary deep inspiration breath-hold (DIBH) requires reproducible breath holds, particularly when matching supraclavicular fields to tangential fields. We studied the impact of variation in DIBHs on CTV and OAR dose metrics by comparing the dose distribution computed on two DIBH CT scans taken at the time of simulation. Methods: Ten patients receiving 50Gy in 25 fractions to the left chestwall and regional lymph nodes were studied. Two simulation CT scans were taken during separate DIBHs along with a free-breathing (FB) scan. The treatment was planned using one DIBH CT. The dose was recomputed on the other two scans using adaptive planning (Pinnacle 9.10) in which the scans are registered using a cross-correlation algorithm. The chestwall, lymph nodes and OARs were contoured on the scans following the RTOG consensus guidelines. The overall translational and rotational variation between the DIBH scans was used to estimate positional variation between breath-holds. Dose metrics between plans were compared using paired t-tests (p < 0.05) and means and standard deviations were reported. Results: The registration parameters were sub-millimeter and sub-degree. Although DIBH significantly reduced mean heart dose by 2.4Gy compared to FB (p < 0.01), no significant changes in dose were observed for targets or OARs between the two DIBH scans. Nodal coverage as assessed by V90% was 90%±8% and 89%±8% for supraclavicular and 99%±2% and 97%±22% for IM nodes. Though a significant decrease (10.5%±12.4%) in lung volume in the second DIBH CT was observed, the lung V20Gy was unchanged (14±2% and 14±3%) between the two DIBH scans. Conclusion: While the lung volume often varied between DIBHs, the CTV and OAR dose metrics were largely unchanged. This indicates that manual DIBH has the potential to provide consistent dose delivery to the chestwall and regional nodes targets when using matched fields.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Proximity Portability and in Transit , M-to-N Data Partitioning and Movement in SENSEI [Book Chapter]

In high-performance parallel in situ processing, the term in transit processing refers to those configurations where data must move from a producer to a consumer that runs on separate resources. In the context of parallel and distributed computing on an HPC platform one of the central challenges is to determine a mapping of data from producer ranks to consumer ranks. This problem is complicated by the heterogeneity that arises in producer-consumer pairs, such as when producer and consumer codes have different levels of concurrency, different scaling characteristics, or different data models. The resulting mapping and movement of data from M producer to N consumer ranks can have a significant impact on aggregate application performance, particularly when the data consumer requires only a subset of the overall data for its task. This chapter focuses on the design considerations that underlie SENSEI’s implementation to this challenging problem. These design considerations extend the core SENSEI architecture and include ideas like the need to accommodate flexibility in the choice of different partitioning methods, the ability for a data consumer to request and receive only the subset of data needed for its particular operation, and the ability to leverage any of several different data transport tools. The idea of proximity portability, being able to use different data transport methods as part of an in transit workflow, is illustrated through the use of three different transport layers where switching from one transport tool to another is accomplished with only a configuration file change. Here, the chapter also includes a performance analysis summary showing the performance gains that are possible in terms of multiple metrics, such as memory footprint, time to solution, and amount of data moved, when using optimized partitioners in an in transit setting, gains that are made possible by the implementation shaped by specific design considerations.

Bethel, E. Wes↗
DOE OSTI2016

HPC Node Performance and Energy Modeling with the Co-Location of Applications

Multicore processors have become an integral part of modern large-scale and high-performance parallel and distributed computing systems. This advance became necessary as the demand for increased system performance has exceeded the limits that single core processors can provide. Unfortunately, applications co-located on multicore processors can suffer from decreased performance and increased dynamic energy use as a result of access to shared resources, such as memory. Consequently, it is increasingly important to characterize the performance of applications that execute on these architectures. This work investigates some of the disadvantages of co-location, and presents a methodology for building models capable of utilizing varying amounts of information about a target application and its co-located applications to make predictions about the target application’s execution time and the system’s energy use under arbitrary co-locations of a wide range of application types. The proposed methodology is validated on three different server class Intel Xeon multicore processors using eleven applications from two scientific benchmark suites. The model’s utility for scheduling is also demonstrated in a simulated large-scale high-performance computing environment through the creation of a co-location aware scheduling heuristic. This heuristic demonstrates that scheduling using information generated with the proposed modeling methodology is capable of making significant improvements over a scheduling heuristic that is naive to co-location interference.

performance modeling, energy modeling, resource ma↗
DOE OSTI2018

Fractal Dimension Calculation for Big Data Using Box Locality Index

The box-counting approach for fractal dimension calculation is scaled up for big data using a data structure named box locality index (BLI). The BLI is constructed as key-value pair style with the key (an integer array) indexing the location of a “box” (i.e., a gird cell on the multi-dimensional space embedding a given dataset) and the value counting the number of data points inside the box (called “box occupancy”). Compared with the traditionally used E-dim tree, the BLI avoids complex hierarchical structure and encodes only necessary information required by box-counting approach for fractal dimension calculation. Moreover, the key-value pair nature of BLI, together with the fact that the box occupancy is aggregatable, grants box-counting approach the needed scalability for fractal dimension calculation of big data using distributed computing techniques (e.g., MapReduce and Spark). Taking the advantage of the BLI, MapReduce and Spark methods for fractal dimension calculation of big data are developed, which conduct box-counting for each grid level as a cascade of MapReduce/Spark jobs in a bottom-up fashion. In an empirical validation, the MapReduce and Spark methods demonstrated good effectiveness and efficiency in fractal calculation of a big synthetic dataset. In summary, this work provides an efficient solution for the estimation of intrinsic dimension of big data that is essential for many machine learning methods and data analytics.

Fractal dimension, Intrinsic dimension, Box-counti↗
DOE OSTI2024

Parallel quantum computing simulations via quantum accelerator platform virtualization

Quantum circuit execution is a central task in quantum computation. Due to inherent quantum-mechanical constraints, quantum computing workflows often involve a considerable number of independent measurements over a large set of slightly different quantum circuits. Here we discuss a simple model for parallelizing such quantum circuit executions that is based on introducing a large array of virtual quantum processing units (mapped to HPC nodes in our case) as a parallel quantum computing platform. Implemented within the XACC framework, the model can readily take advantage of its backend-agnostic features, enabling parallel quantum computing/simulation over any target backend supported by XACC. We illustrate the performance of this approach by demonstrating strong scaling in two pertinent domain science problems, namely in computing the gradients for the multi-contracted variational quantum eigensolver and in data-driven quantum circuit learning, where we vary the number of qubits and the number of circuit layers. Here, the latter simulation leverages the cuQuantum library to run efficiently on GPU-accelerated HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Mesh-free data transfer algorithms for partitioned multiphysics problems: Conservation, accuracy, and parallelism

In this study we analyze and extend mesh-free algorithms for three-dimensional data transfer problems in partitioned multiphysics simulations. We first provide a direct comparison between a mesh-based weighted residual method using the common-refinement scheme and two mesh-free algorithms leveraging compactly supported radial basis functions: one using a spline interpolation and one using a moving least square reconstruction. Through the comparison we assess both the conservation and accuracy of the data transfer obtained from each of the methods. We do so for a varying set of geometries with and without curvature and sharp features and for functions with and without smoothness and with varying gradients. Our results show that the mesh-based and mesh-free algorithms are complementary with cases where each was demonstrated to perform better than the other. We then focus on the mesh-free methods by developing a set of algorithms to parallelize them based on sparse linear algebra techniques. This includes a discussion of fast parallel radius searching in point clouds and restructuring the interpolation algorithms to leverage data structures and linear algebra services designed for large distributed computing environments. The scalability of our new algorithms is demonstrated on a leadership class computing facility using a set of basic scaling studies. Finally, these scaling studies show that for problems with reasonable load balance, our new algorithms for both spline interpolation and moving least square reconstruction demonstrate both strong and weak scalability using more than 100,000 MPI processes with billions of degrees of freedom in the data transfer operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints

We approach the problem of parallelizing state-of-the-art nonlinear programming optimization algorithms.Specifically, we focus on parallelizing quasi-Newton interior-point methods that use limited-memory secant Hessian approximations. Such interior-point methods are known to have better convergence properties and to be more effective on large-scale problems than gradient-based and derivative-free optimization algorithms. We target nonlinear and potentially nonconvex optimization problems with an arbitrary number of bound constraints and a small number of general equality and inequality constraints on the optimization variables. These problems occur for example in the form of optimal control, optimal design, and inverse problems governed by ordinary or partial differential equations, whenever they are expressed in a “reduced-space” optimization approach. We introduce and analyze the time and space complexity of a decomposition method for solving the quasi-Newton linear systems that leverages the fact that the quasi-Newton Hessian matrix has a small number of dense blocks that border a low-rank update of a diagonal matrix. This enables an efficient parallelization on memory-distributed computers of the iterations of the optimization algorithm, a state-of-the-art filter line-search interior-point algorithm by Wächter et. al. We illustrate the efficiency of the proposed method by solving structural topology optimization problems on up to 4608 cores on a parallel machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A non-destructive method for crack quantification in photovoltaic backsheets under accelerated and real-world exposures

The long-term durability of photovoltaic modules is paramount for the continued growth of the industry. Polymer backsheets are of particular concern since they provide electrical insulation and an environmental barrier. In this paper, 23 freestanding, multilayer backsheets with nine unique material combinations underwent four different weathering exposures under accelerated and real-world conditions. Besides changes in color and gloss, the induced degradation included parallel or mudflat cracks on 11 backsheets, sometimes in combination with delamination or blistering. Similar degradation has been observed in previous studies and is concerning since cracks compromise the mechanical integrity and electrical safety of backsheets. Quantitative parameters are desirable to reliably classify categories of cracks and supply unbiased features for statistical analysis in predictive lifetime models. We developed an analysis technique that utilizes surface profilometry data to quantify the depth, width, area, spacing, and number of cracks. Parameters are automatically extracted from the raw data by an algorithm running on a high performance distributed computing cluster. Our algorithm excelled at characterizing parallel cracks with minimal de-adhesion, and only an estimated 4% of crack detections were false positives. The addition of humidity and temperature variation formed up to three times as many cracks on a photodose basis compared to dry, constant temperature exposures. Cracks in real-world and accelerated exposures propagated to similar depths with equivalent photodoses; however, the number of cracks formed in accelerated exposures was far greater on a photodose basis. Of samples that cracked, the best performing backsheet configuration was polyvinyl fluoride/poly (ethylene-terephthalate)/polyethylene (PVF/PET/PE) while the least durable was PET/PET/ethylene-vinyl acetate. Finally, none of the six PVF/PET/PVF backsheets cracked in any of the exposures.

14 SOLAR ENERGY↗
DOE OSTI2019

Vehicle energy consumption estimation using large scale simulations and machine learning methods

Vehicle energy consumption and powertrain operations for future vehicle powertrain technologies are predicted through full-vehicle simulations using Autonomie, a simulation tool developed at Argonne National Laboratory. Over 1 million vehicles, with different powertrain types (conventional and numerous electrified powertrains) and component technologies (advanced engines, batteries, electrical machines, light weighting) are simulated for outputs including vehicle fuel economy and cost. Despite the ability of Autonomic to predict fuel economy given various combination of vehicle inputs, the full-vehicle simulation is very time-consuming. It takes approximately 84 h to simulate for 33,060 vehicles even using distributed computing on a cluster of 128 worker nodes. Moreover, Autonomic is unable to populate a continuous output space through simulation since it only yields outputs with discrete values instead of an input-output relation. This paper proposes a novel large-scale learning and prediction process (LSLPP) via machine learning approaches to answer the need for a continuous space of vehicle fuel economy and a more efficient simulation process. By learning in a supervised fashion to discover structures in data, the machine learning approaches seek to train a model that can explain the data with which we can perform prediction over unseen data. As such, we are able to accomplish analytic continuation on the current discrete space of simulation results. In addition, we present a random sampling -based numerosity reduction algorithm incorporated in LSLPP that reduces simulation runs and improves efficiency of data procurement. We analytically derive a theoretical bound in order to guarantee the effectiveness of our algorithm. We also compare random sampling to stratified sampling and suggest sampling strategies. A user-friendly tool is developed to support the process. Our experimental results confirm that the proposed LSLPP is able to greatly accelerate prediction and analysis of vehicle fuel economy. The output of this study is used by the US government to evaluate the impact of its R&D funding on energy security and CO 2 emission as well as for the setting of Corporate Average Fuel Economy (CAFE) standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Uptake Mechanism for Iodine Species to Black Carbon

Natural organic matter (NOM) plays an important role in determining the fate and transport of iodine species such as iodide (I-) and iodate (IO3-) in groundwater system. Although NOM exists as diverse forms in environments, prior iodine studies have mainly focused on uptake processes of iodide and iodate to humic materials. This study was conducted to determine the iodide and iodate uptake potential for a particulate NOM (i.e., black carbon [BC]). A laboratory-produced BC and commercial humic acid were used for batch experiments to compare their iodine uptake properties. The BC exhibited >100 times greater uptake capability for iodide than iodate at low pH~3, while iodide uptake was negligible for the humic acid. The uptake properties of both solids strongly depend on the initial iodine aqueous concentrations. After uptake reaction of iodide to the BC, X-ray Absorption Fine Structure spectroscopy results indicated that the iodide was converted to electrophilic species, and iodine was covalently bound to carbon atom in polycyclic aromatic hydrocarbons present in the BC. The computed distribution coefficients (i.e., Kd values) suggest that the BC materials retard significantly the transport of iodide at low pH in environmental systems containing even a small amount of BC.

black carbon↗
DOE OSTI2019

Understanding the evolution of conditions data access through Frontier for the ATLAS Experiment

The ATLAS Distributed Computing system uses the Frontier system to access the Conditions, Trigger, and Geometry database data stored in the Oracle Offline Database at CERN by means of the HTTP protocol. All ATLAS computing sites use Squid web proxies to cache the data, greatly reducing the load on the Frontier servers and the databases. One feature of the Frontier client is that in the event of failure, it retries with different services. While this allows transient errors and scheduled maintenance to happen transparently, it does open the system up to cascading failures if the load is high enough. Throughout LHC Run 2 there has been an ever increasing demand on the Frontier service. There have been multiple incidents where parts of the service failed due to high load. A significant improvement in the monitoring of the Frontier service was required. The monitoring was needed to identify both problematic tasks, which could then be killed or throttled, and to identify failing site services as the consequence of a cascading failure is much higher. This presentation describes the implementation and features of the monitoring system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2020

Evolution of the CMS Global Submission Infrastructure for the HL-LHC Era

Efforts in distributed computing of the CMS experiment at the LHC at CERN are now focusing on the functionality required to fulfill the projected needs for the HL-LHC era. Cloud and HPC resources are expected to be dominant relative to resources provided by traditional Grid sites, being also much more diverse and heterogeneous. Handling their special capabilities or limitations and maintaining global flexibility and efficiency, while also operating at scales much higher than the current capacity, are the major challenges being addressed by the CMS Submission Infrastructure team. These proceedings discuss the risks to the stability and scalability of the CMS HTCondor infrastructure extrapolated to such a scenario, thought to be derived mostly from its growing complexity, with multiple Negotiators and schedulers flocking work to multiple federated pools. New mechanisms for enhanced customization and control over resource allocation and usage, mandatory in this future scenario, are also described.

Yzquierdo, Antonio Pérez-Calero↗