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At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Parallel CFD Algorithms for Aerodynamical Flow Solvers on Unstructured Meshes

The Advisory Group for Aerospace Research and Development (AGARD) has requested my participation in the lecture series entitled Parallel Computing in Computational Fluid Dynamics to be held at the von Karman Institute in Brussels, Belgium on May 15-19, 1995. In addition, a request has been made from the US Coordinator for AGARD at the Pentagon for NASA Ames to hold a repetition of the lecture series on October 16-20, 1995. I have been asked to be a local coordinator for the Ames event. All AGARD lecture series events have attendance limited to NATO allied countries. A brief of the lecture series is provided in the attached enclosure. Specifically, I have been asked to give two lectures of approximately 75 minutes each on the subject of parallel solution techniques for the fluid flow equations on unstructured meshes. The title of my lectures is "Parallel CFD Algorithms for Aerodynamical Flow Solvers on Unstructured Meshes" (Parts I-II). The contents of these lectures will be largely review in nature and will draw upon previously published work in this area. Topics of my lectures will include: (1) Mesh partitioning algorithms. Recursive techniques based on coordinate bisection, Cuthill-McKee level structures, and spectral bisection. (2) Newton's method for large scale CFD problems. Size and complexity estimates for Newton's method, modifications for insuring global convergence. (3) Techniques for constructing the Jacobian matrix. Analytic and numerical techniques for Jacobian matrix-vector products, constructing the transposed matrix, extensions to optimization and homotopy theories. (4) Iterative solution algorithms. Practical experience with GIVIRES and BICG-STAB matrix solvers. (5) Parallel matrix preconditioning. Incomplete Lower-Upper (ILU) factorization, domain-decomposed ILU, approximate Schur complement strategies.

Barth, Timothy J.↗
NASA NTRS2004

Characteristics and Trade-Offs of Doppler Lidar Global Wind Profiling

Accurate, global profiling of wind velocity is highly desired by NASA, NOAA, the DOD/DOC/NASA Integrated Program Office (IPO)/NPOESS, DOD, and others for many applications such as validation and improvement of climate models, and improved weather prediction. The most promising technology to deliver this measurement from space is Doppler Wind Lidar (DWL). The NASA/NOAA Global Tropospheric Wind Sounder (GTWS) program is currently in the process of generating the science requirements for a space-based sensor. In order to optimize the process of defining science requirements, it is important for the scientific and user community to understand the nature of the wind measurements that DWL can make. These measurements are very different from those made by passive imaging sensors or by active radar sensors. The purpose of this paper is to convey the sampling characteristics and data product trade-offs of an orbiting DWL.

Kavaya, Michael J.↗
NASA NTRS2019

Retrieval of Vegetation Water Content Using Brightness Temperatures from the Soil Moisture Active Passive (SMAP) Mission

In this paper, we explore a time series approach to using the tau-omega (τ-ω) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor α and an offset factor β. By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, α and β can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of α and β can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) ofSMAP brightness temperature observations.

radiative transfer↗
DOE OSTI2013

Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

First-principles based plasma profile predictions for optimized stellarators

In the present Letter, first-of-its-kind computer simulations predicting plasma profiles for modern optimized stellarators—while self-consistently retaining neoclassical transport, turbulent transport with 3D effects, and external physical sources—are presented. These simulations exploit a newly developed coupling framework involving the global gyrokinetic turbulence code GENE-3D, the neoclassical transport code KNOSOS, and the 1D transport solver TANGO. This framework is used to analyze the recently observed degradation of energy confinement in electron-heated plasmas in the Wendelstein 7-X stellarator, where the central ion temperature was 'clamped' to $T_\textrm i \approx 1.5$ keV regardless of the external heating power. By performing first-principles based simulations, we provide key evidence to understand this effect, namely the inefficient thermal coupling between electrons and ions in a turbulence-dominated regime, which is exacerbated by the large $T_\textrm e/T_\textrm i$ ratios, and show that a more efficient ion heat source, such as direct ion heating, will increase the on-axis ion temperature. This work paves the way towards the use of high-fidelity models for the development of the next generation of stellarators, in which neoclassical and turbulent transport are optimized simultaneously.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm

The trading of fidelity for scale enables approximate classical simulators such as matrix product states (MPSs) to run quantum circuits beyond exact methods. A control parameter, the so-called bond dimension $\mathcal{χ}$ for MPSs, governs the allocated computational resources and the output fidelity. Here, we characterize the fidelity for the quantum approximate optimization algorithm by the expectation value of the cost function that it seeks to minimize and find that it follows a scaling law $\mathscr{F}$(ln $\mathcal{χ}$/N), where N is the number of qubits. With ln $\mathcal{χ}$ amounting to the entanglement that a MPS can encode, we show that the relevant variable for investigating the fidelity is the entanglement per qubit. Importantly, our results calibrate the classical computational power required to achieve the desired fidelity and benchmark the performance of quantum hardware in a realistic setup. For instance, we quantify the hardness of performing better classically than a noisy superconducting quantum processor by readily matching its output to the scaling function. Moreover, we relate the global fidelity to that of individual operations and establish its relationship with $\mathcal{χ}$ and N. We sharpen the requirements for noisy quantum computers to outperform classical techniques at running a quantum optimization algorithm in speed, size, and fidelity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2023

GA-Based Voltage Optimization of Distribution Feeder with High-Penetration of DERs Using Megawatt-Scale Units

In this paper, genetic algorithm (GA)-based voltage optimization of a modified IEEE-34 node distribution feeder with high penetration of distributed energy resources (DERs) is proposed using two megawatt-scale reactive power sources. Traditional voltage support units present in distribution grids are not suitable for DER-rich feeders, while voltage support using small-scale DERs present in the feeder requires considerable communication effort to reach a global solution. In this work, two megawatt-scale units are placed to improve the voltage profile across the IEEE 34-node feeder, which has been modified to include several PV units and an energy storage unit. The megawatt-scale units are optimized using GA for fast and accurate operation. The performance of the proposed scheme is verified using simulation results with a multi-platform setup where the modified IEEE-34 node feeder is modeled in OpenDSS while the GA optimization scheme is programmed in MATLAB.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

A new modified BFGS method for unconstrained optimization problems

Using chain rule, we propose a modified secant equation to get a more accurate approximation of the second curvature of the objective function. Then, based on this modified secant equation we present a new BFGS method for solving unconstrained optimization problems. The proposed method makes use of both gradient and function values, and utilizes information from two most recent steps, while the usual secant relation uses only the latest step information. Under appropriate conditions, we show that the proposed method is globally convergent without convexity assumption on the objective function. Comparative numerical results show computational efficiency of the proposed method in the sense of the Dolan–Moré performance profiles.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

A new look at the simultaneous analysis and design of structures

The minimum weight optimization of structural systems, subject to strength and displacement constraints as well as size side constraints, was investigated by the Simultaneous ANalysis and Design (SAND) approach. As an optimizer, the code NPSOL was used which is based on a sequential quadratic programming (SQP) algorithm. The structures were modeled by the finite element method. The finite element related input to NPSOL was automatically generated from the input decks of such standard FEM/optimization codes as NASTRAN or ASTROS, with the stiffness matrices, at present, extracted from the FEM code ANALYZE. In order to avoid ill-conditioned matrices that can be encountered when the global stiffness equations are used as additional nonlinear equality constraints in the SAND approach (with the displacements as additional variables), the matrix displacement method was applied. In this approach, the element stiffness equations are used as constraints instead of the global stiffness equations, in conjunction with the nodal force equilibrium equations. This approach adds the element forces as variables to the system. Since, for complex structures and the associated large and very sparce matrices, the execution times of the optimization code became excessive due to the large number of required constraint gradient evaluations, the Kreisselmeier-Steinhauser function approach was used to decrease the computational effort by reducing the nonlinear equality constraint system to essentially a single combined constraint equation. As the linear equality and inequality constraints require much less computational effort to evaluate, they were kept in their previous form to limit the complexity of the KS function evaluation. To date, the standard three-bar, ten-bar, and 72-bar trusses have been tested. For the standard SAND approach, correct results were obtained for all three trusses although convergence became slower for the 72-bar truss. When the matrix displacement method was used, correct results were still obtained, but the execution times became excessive due to the large number of constraint gradient evaluations required. Using the KS function, the computational effort dropped, but the optimization seemed to become less robust. The investigation of this phenomenon is continuing. As an alternate approach, the code MINOS for the optimization of sparse matrices can be applied to the problem in lieu of the Kreisselmeier-Steinhauser function. This investigation is underway.

Striz, Alfred G.↗
NASA NTRS1997

Satellite Sensed Skin Sea Surface Temperature

Quantitative predictions of spatial and temporal changes the global climate rely heavily on the use of computer models. Unfortunately, such models cannot provide the basis for climate prediction because key physical processes are inadequately treated. Consequently, fine tuning procedures are often used to optimize the fit between model output and observational data and the validation of climate models using observations is essential if model based predictions of climate change are to be treated with any degree of confidence. Satellite Sea Surface Temperature (SST) observations provide high spatial and temporal resolution data which is extremely well suited to the initialization, definition of boundary conditions and, validation of climate models. In the case of coupled ocean-atmosphere models, the SST (or more correctly the 'Skin' SST (SSST)) is a fundamental diagnostic variable to consider in the validation process. Daily global SST maps derived from satellite sensors also provide adequate data for the detection of global patterns of change which, unlike any other SST data set, repeatedly extend into the southern hemisphere extra-tropical regions. Such data are essential to the success of the spatial 'fingerprint' technique, which seeks to establish a north-south asymmetry where warming is suppressed in the high latitude Southern Ocean. Some estimates suggest that there is a greater than 80% chance of directly detecting significant change (97.5 % confidence level) after 10-12 years of consistent global observations of mean sea surface temperature. However, these latter statements should be qualified with the assumption that a negligible drift in the observing system exists and that biases between individual instruments required to derive a long term data set are small. Given that current estimates for the magnitude of global warming of 0.015 K yr(sup -1) - 0.025 K yr(sup -1), satellite SST data sets need to be both accurate and stable if such a warming trend is to be confidently detected. Some of these activities are focussed to develop and deploy instrumentation suitable for the collection of precise in situ measurements of the SSST which can be used to improve the accuracy of satellite measurements, while others develop techniques to generate improved global analyses of sea surface temperature using historical data.

Donlon, Craig↗
DOE OSTI2012

Ocean Thermal Extractable Energy Visualization (Final Technical Report)

The Ocean Thermal Extractable Energy Visualization (OTEEV) project focuses on assessing the Maximum Practicably Extractable Energy (MPEE) from the world's ocean thermal resources. MPEE is defined as being sustainable and technically feasible, given today's state-of-the-art ocean energy technology. Under this project the OTEEV team developed a comprehensive Geospatial Information System (GIS) dataset and software tool, and used the tool to provide a meaningful assessment of MPEE from the global and domestic U.S. ocean thermal resources. The OTEEV project leverages existing NREL renewable energy GIS technologies and integrates extractable energy estimated from quality-controlled data and projected optimal achievable energy conversion rates. Input data are synthesized from a broad range of existing in-situ measurements and ground-truthed numerical models with temporal and spatial resolutions sufficient to reflect the local resource. Energy production rates are calculated for regions based on conversion rates estimated for current technology, local energy density of the resource, and sustainable resource extraction. Plant spacing and maximum production rates are then estimated based on a default plant size and transmission mechanisms. The resulting data are organized, displayed, and accessed using a multi-layered GIS mapping tool, http://maps.nrel.gov/mhk_atlas with a user-friendly graphical user interface.

14 SOLAR ENERGY↗
DOE OSTI2015

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the control of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Numerical study and test of the APS linac transverse deflecting cavity

The transverse deflecting cavity can be used to transform particle distributions in the 6D phase space, which makes it a promising component in phase space beam diagnostics and beam manipulations. In the Advanced Photon Source (APS) Linac, a LOLA-type traveling wave deflecting cavity was installed for the diagnostics of beam characteristics, such as the bunch length, bunch temporal profile, time-dependent energy spread and slice (time-correlated) transverse emittance. In this paper, this deflecting cavity is modeled and analyzed with the numerical method. The effects of the center cell and coupler cell dimensions on the performance of the whole structure are studied, which shows the coupler cell radius has a dominant effect over the coupler cell length and slot width on the global reflection coefficient and field flatness. Important RF parameters, such as S-11, field flatness and phase advance etc., of the LOLA-II cavity in tuning are calculated and discussed. After optimization, the field flatness of the cavity is 2.1%, the phase advance is 119.66 degrees degrees with a standard deviation smaller than 0.5 degrees and the bandwidth is 15.738 MHz when VSWR < 1.1 (i.e. the S-11, < -26 dB). Bead-pull measurements and RF conditioning have been also performed and discussed. Finally, the initial cavity commissioning with electron beam of 375 MeV is reported, and compared with simulation.

coupler↗
DOE OSTI2018

A class of customized proximal point algorithms for linearly constrained convex optimization

In this paper, we propose a class of customized proximal point algorithms for linearly constrained convex optimization problems. The algorithms are implementable, provided that the proximal operator of the objective function is easy to evaluate. We show that, with special setting of the algorithmic scalar, our algorithms contain the customized proximal point algorithm (He et al., Optim Appl 56:559–572, 2013), the linearized augmented Lagrangian method (Yang and Yuan, Math Comput 82:301–329, 2013), the Bregman Operator Splitting algorithm (Zhang et al., SIAM J Imaging Sci 3:253–276, 2010) as special cases. The global convergence and worst-case convergence rate measured by the iteration complexity are established for the proposed algorithms. Numerical results demonstrate that the algorithms work well for a wide range of the scalar.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2023

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Agriculture↗
NASA NTRS2016

Infrared Observations of the Orion Capsule During EFT-1 Hypersonic Reentry

High-resolution infrared observations of the Orion capsule during its atmospheric reentry on December 5, 2015 were made from a US Navy NP-3D. This aircraft, equipped with a long-range optical sensor system, tracked the capsule from Mach 10 to 7 from a distance of approximately 60 nmi. Global surface temperatures of the capsule's thermal heatshield were derived from near infrared intensity measurements. The global surface temperature measurements complemented onboard instrumentation and were invaluable to the interpretation of the in-depth thermocouple measurements which rely on inverse heat transfer methods and material response codes to infer the desired surface temperature from the sub-surface measurements. The full paper will address the motivations behind the NASA Engineering Safety Center sponsored observation and highlight premission planning processes with an emphasis on aircraft placement, optimal instrument configuration and sensor calibrations. Critical aspects of mission operations coordinated from the NASA Johnson Spaceflight Center and integration with the JSC Flight Test Management Office will be discussed. A summary of the imagery that was obtained and processed to global surface temperature will be presented. At the capsule's point of closest approach relative to the imaging system, the spatial resolution was estimated to be approximately 15-inches per pixel and was sufficient to identify localized temperature increases associated with compression pad support hardware on the heatshield. The full paper will discuss the synergy of the quantitative imagery derived temperature maps with in-situ thermocouple measurements. Comparison of limited onboard surface thermocouple data to the image derived surface temperature will be presented. The two complimentary measurements serve as an example of the effective leveraging of resources to advance the understanding of high Mach number environments associated with an ablated heatshield and provide unique data for the validation of design tools and numerical flight simulation techniques. Collaborative opportunities and technology investments in support of planned observations of NASA's next Orion flight test in 2018 will be explored in the full manuscript.

Horvath, Thomas J.↗
DOE OSTI2018

Design of high-strength refractory complex solid-solution alloys

Nickel-based superalloys and near-equiatomic high-entropy alloys containing molybdenum are known for higher temperature strength and corrosion resistance. Yet, complex solid-solution alloys offer a huge design space to tune for optimal properties at slightly reduced entropy. For refractory Mo-W-Ta-Ti-Zr, we showcase KKR electronic structure methods via the coherent-potential approximation to identify alloys over five-dimensional design space with improved mechanical properties and necessary global (formation enthalpy) and local (short-range order) stability. Deformation is modeled with classical molecular dynamic simulations, validated from our first-principle data. We predict complex solid-solution alloys of improved stability with greatly enhanced modulus of elasticity (3× at 300 K) over near-equiatomic cases, as validated experimentally, and with higher moduli above 500 K over commercial alloys (2.3× at 2000 K). We also show that optimal complex solid-solution alloys are not described well by classical potentials due to critical electronic effects.

36 MATERIALS SCIENCE↗