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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

External Release of Organic Scintillator DRiFT Software - Technology Evaluation and Demonstration Project Accomplishments [Slides]

Overview and Objectives: DRiFT post processes radiation transport output from MCNP, and generates realistic nuclear instrumentation response; Organic scintillator response has been developed, tested, and validated with measurements, these instruments are widely used in nuclear safeguards. At the beginning of this project, DRiFT was only available to LANL employees with access to yellow High Performance Computing clusters. Additionally, there are many complicated external software dependencies. Our TED project request proposed using funds to: Comment and clean up code, friendly testing, generating test suites and examples; Expansion of scintillator types natively supported by DRiFT; External release of DRiFT accompanied by documentation. We accomplished all objectives (and many others), they are highlighted in these slides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Computational Fluid Dynamics Modeling to Simulate a Combined Reforming Process for Syngas and Hydrogen Production

An Oxygen Transport Membrane (OTM) combined reforming technology for producing syngas and hydrogen integrates the advantages of multiple processes—steam methane reforming (SMR), autothermal reforming (ATR), an air separation unit (ASU)—into a single integrated technology. The OTM consists of a primary reforming tube, in which desulfurized natural gas is partially reformed by steam at high pressure in the presence of a metal catalyst. This process is followed in series by a ceramic OTM with a secondary reformer, in which residual methane reforms and O 2 - ions react with a portion of the CO and H 2 fuel to provide the heat to support both primary and secondary reforming. Although the OTM combined reformer technology for syngas and H 2 production has been substantially developed in the last decade, several challenges that affect the overall production efficiency and reliability are yet to be fully understood, addressed, and resolved. Therefore, developing Computational Fluid Dynamics (CFD) models that incorporate fluid dynamics, mass transport, kinetics, heat transport, and structural mechanics is critical to understanding and minimizing the probability of tube failures during the startup and operation. In this report, an exhaustive literature review was performed to survey the current state of technology for producing syngas and H 2 using either conventional or renewable energy sources. The feedstocks reviewed include natural gas and coal for the conventional technologies, whereas biomass, solar, wind, and nuclear energy for the renewable technologies. The existing industry-grade COMSOL multiphysics models of OTM were upgraded for the latest software release. In addition, they were improved to help achieve grid and solver independence and were successfully ported on the ORNL high-performance computing clusters to speed up their run times. A 42% reduction in the simulation run time was achieved. A new higher-fidelity CFD model of an OTM tube was developed in the StarCCM+ simulation platform. This new model was designed to simulate various physics using first principles, e.g., turbulent flow, heat transfer, and chemical reactions while avoiding unnecessary simplifications. The resulting predictions were qualitatively assessed and provided useful insights into the multiphysics complexity of an OTM tube.

08 HYDROGEN↗
DOE OSTI2023

JIRIAF: JLAB Integrated Research Infrastructure Acros Facilities

The JIRIAF project aims to combine geographically diverse computing facilities into an integrated science infrastructure. This project starts by dynamically evaluating temporarily unallocated or idled compute resources from multiple providers. These resources are integrated to handle additional workloads without affecting local running jobs. This paper describes our approach to launch best-effort batch tasks which exploit these underutilized resources. Our system measures the real-time behavior of jobs running on a machine and learns to distinguish typical performance from outliers. Unsupervised ML techniques are used to analyze hardware-level performance measures, followed by a real-time cross-correlation analysis to determine which applications cause performance degradation. We then ameliorate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. We relocate the CLAS12 data processing workflow to a remote data center for a case study, preventing file migration and temporal data persistency.

Lawrence, David↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗
DOE OSTI2024

Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks

Evaluating the mechanical response of fiber-reinforced composites can be extremely time-consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input–output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally used for linear elastic materials, and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 h to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.

Sun, Yixuan (ORCID:0000000311093380)↗
DOE OSTI2016

Task-Based Crowd Simulation for Heterogeneous Architectures

Industry trends in the coming years imply the availability of cluster computing with hundreds to thousands of cores per chip, as well as the use of accelerators. Programming presents a challenge due to this heterogeneous architecture; thus, using novel programming models that facilitate this process is necessary. In this chapter, the case of simulation and visualization of crowds is presented. The authors analyze and compare the use of two programming models: OmpSs and CUDA. OmpSs allows to take advantage of all the resources available per node by combining the CPU and GPU while automatically taking care of memory management, scheduling, communications and synchronization. Experimental results obtained from Fermi, Kepler and Maxwell GPU architectures are presented, and the different modes used for visualizing the results are described, as well.

Perez, Hugo↗
NASA NTRS1996

NASA Approach to HPCCP Support Software and Tools

The NASA HPCC Program, together with other agencies participating in the Federal HPCC Program, intends to advance technologies to enable the execution of grand challenge applications at sustained rates up to TeraFLOPS. During 1995-6 NASA undertook two major systems software efforts to improve the state of high performance support software and tools. The first of these activities was a replanning of support software and tools activities internal to the Agency. In replanning the software activities emphasis was placed on Meeting the needs of Grand Challenge Uses Few projects Near term useful results. The revised NASA plan calls for support software and tools activities in four areas: Application Creation Process Support Application Usage/Operations Support Advanced Support Software and Tools Concepts Metrics Based Monitoring and Management The second major activity undertaken was participation in a multiagency Task Force resulting from the Second Pasadena Workshop on System Software and Tools. The task force developed the Guidelines for Writing System Software and Tools Requirements for Parallel and Clustered Computers.

Blaylock, Bruce↗
NASA NTRS2002

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 km or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed-shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗
NASA NTRS2002

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 kin or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed- shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗
NASA NTRS2003

Spatial Analysis of Great Lakes Regional Icing Cloud Liquid Water Content

Abstract Clustering of cloud microphysical conditions, such as liquid water content (LWC) and drop size, can affect the rate and shape of ice accretion and the airworthiness of aircraft. Clustering may also degrade the accuracy of cloud LWC measurements from radars and microwave radiometers being developed by the government for remotely mapping icing conditions ahead of aircraft in flight. This paper evaluates spatial clustering of LWC in icing clouds using measurements collected during NASA research flights in the Great Lakes region. We used graphical and analytical approaches to describe clustering. The analytical approach involves determining the average size of clusters and computing a clustering intensity parameter. We analyzed flight data composed of 1-s-frequency LWC measurements for 12 periods ranging from 17.4 minutes (73 km) to 45.3 minutes (190 km) in duration. Graphically some flight segments showed evidence of consistency with regard to clustering patterns. Cluster intensity varied from 0.06, indicating little clustering, to a high of 2.42. Cluster lengths ranged from 0.1 minutes (0.6 km) to 4.1 minutes (17.3 km). Additional analyses will allow us to determine if clustering climatologies can be developed to characterize cluster conditions by region, time period, or weather condition. Introduction

Ryerson, Charles C.↗
NASA NTRS2003

Parallel Signal Processing and System Simulation using aCe

Recently, networked and cluster computation have become very popular for both signal processing and system simulation. A new language is ideally suited for parallel signal processing applications and system simulation since it allows the programmer to explicitly express the computations that can be performed concurrently. In addition, the new C based parallel language (ace C) for architecture-adaptive programming allows programmers to implement algorithms and system simulation applications on parallel architectures by providing them with the assurance that future parallel architectures will be able to run their applications with a minimum of modification. In this paper, we will focus on some fundamental features of ace C and present a signal processing application (FFT).

Dorband, John E.↗
NASA NTRS2004

Analysis of Regen Cooling in Rocket Combustors

The use of detailed CFD modeling for the description of cooling in rocket chambers is discussed. The overall analysis includes a complete three-dimensional analysis of the flow in the regenerative cooling passages, conjugate heat transfer in the combustor walls, and the effects of film cooling on the inside chamber. The results in the present paper omit the effects of film cooling and include only regen cooling and the companion conjugate heat transfer. The hot combustion gases are replaced by a constant temperature wall boundary condition. Load balancing for parallel cluster computations is ensured by using single-block unstructured grids for both fluids and solids, and by using a 'multiple physical zones' to account for differences in the number of equations. Validation of the method is achieved by comparing simple two-dimensional solutions with analytical results. Representative results for cooling passages are presents showing the effects of heat conduction in the copper walls with tube aspect ratios of 1.5:l.

Harper, Brent↗
NASA NTRS2001

Arthropod phylogeny based on eight molecular loci and morphology

The interrelationships of major clades within the Arthropoda remain one of the most contentious issues in systematics, which has traditionally been the domain of morphologists. A growing body of DNA sequences and other types of molecular data has revitalized study of arthropod phylogeny and has inspired new considerations of character evolution. Novel hypotheses such as a crustacean-hexapod affinity were based on analyses of single or few genes and limited taxon sampling, but have received recent support from mitochondrial gene order, and eye and brain ultrastructure and neurogenesis. Here we assess relationships within Arthropoda based on a synthesis of all well sampled molecular loci together with a comprehensive data set of morphological, developmental, ultrastructural and gene-order characters. The molecular data include sequences of three nuclear ribosomal genes, three nuclear protein-coding genes, and two mitochondrial genes (one protein coding, one ribosomal). We devised new optimization procedures and constructed a parallel computer cluster with 256 central processing units to analyse molecular data on a scale not previously possible. The optimal 'total evidence' cladogram supports the crustacean-hexapod clade, recognizes pycnogonids as sister to other euarthropods, and indicates monophyly of Myriapoda and Mandibulata.

Non-NASA Center↗
NASA NTRS2005

Three Dimensional Multi-Fluid Simulation of Comet Halley

The Cometary Atmosphere Simulator (CASIM) is designed to simulate in 2-D and 3-D the complex interaction between the cometary atmosphere and the hypersonic solar wind using a multi-fluid approach. Our simulator is based on the solution of multi-fluid equations using an efficient adaptive Cartesian mesh. It is designed to use the capabilities of highly parallel super-cluster computers.

Benna, M.↗
NASA NTRS2006

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Kim, E.↗
NASA NTRS2006

An Integrated Snow Radiance and Snow Physics Modeling Framework for Cold Land Surface Modeling

Recent developments in forward radiative transfer modeling and physical land surface modeling are converging to allow the assembly of an integrated snow/cold lands modeling framework for land surface modeling and data assimilation applications. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. Together these form a flexible framework for self-consistent remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. Each element of this framework is modular so the choice of element can be tailored to match the emphasis of a particular study. For example, within our framework, four choices of a FRTM are available to simulate the brightness temperature of snow: Two models are available to model the physical evolution of the snowpack and underlying soil, and two models are available to handle the water/energy balance at the land surface. Since the framework is modular, other models-physical or statistical--can be accommodated, too. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster at the NASA Goddard Space Flight Center. The advantages of such an integrated modular framework built on the LIS will be described through examples-e.g., studies to analyze snow field experiment observations, and simulations of future satellite missions for snow and cold land processes.

Kim, Edward J.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2007

Efficient Mosaicking of Spitzer Space Telescope Images

A parallel version of the MOPEX software, which generates mosaics of infrared astronomical images acquired by the Spitzer Space Telescope, extends the capabilities of the prior serial version. In the parallel version, both the input image space and the output mosaic space are divided among the available parallel processors. This is the only software that performs the point-source detection and the rejection of spurious imaging effects of cosmic rays required by Spitzer scientists. This software includes components that implement outlier-detection algorithms that can be fine-tuned for a particular set of image data by use of a number of adjustable parameters. This software has been used to construct a mosaic of the Spitzer Infrared Array Camera Shallow Survey, which comprises more than 17,000 exposures in four wavelength bands from 3.6 to 8 m and spans a solid angle of about 9 square degrees. When this software was executed on 32 nodes of the 1,024-processor Cosmos cluster computer at NASA s Jet Propulsion Laboratory, a speedup of 8.3 was achieved over the serial version of MOPEX. The performance is expected to improve dramatically once a true parallel file system is installed on Cosmos.

Jacob, Joseph↗