Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization
Not Available
Wu, Wei-Cheng (ORCID:0000000222156351)↗
SEARCH · Engineering Papers
Results for “Grid computing”
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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Efficient neutrino oscillation parameter inference using Gaussian processes
The unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics. It has gained widespread use, for instance, in measurements of neutrino oscillation parameters in long-baseline experiments. However, the approach relies on the Neyman construction of the classical confidence interval and is computationally intensive as it is typically done in a grid-based fashion over the entire parameter space. In this article, we propose an efficient algorithm for the Feldman-Cousins approach using Gaussian processes to construct confidence intervals iteratively. Further, we show that in the neutrino oscillation context, one can obtain confidence intervals fives times faster in one dimension and ten times faster in two dimensions, while maintaining an accuracy above 99.5%.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling
Not Available
97 MATHEMATICS AND COMPUTING↗
Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale
Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro- Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP).
analytics-hub↗
Dynamic Programming Method to Optimally Select Power Distribution System Reliability Upgrades
Not Available
42 ENGINEERING↗
An Evaluation of Size-Resolved Cloud Microphysics Scheme Numerics for Use with Radar Observations. Part I: Collision–Coalescence
This study evaluates some available schemes designed to solve the stochastic collection equation (SCE) for collision–coalescence of hydrometeors using a size-resolved (bin) microphysics approach and documents their numerical properties within the framework of a box model. Comparing three widely used SCE schemes, we find that all converge to almost identical solutions at sufficiently fine mass grids. However, one scheme converges far slower than the other two and shows pronounced numerical diffusion at the large-drop tail of the size distribution. One of the remaining two schemes is recommended on the basis that it is well converged on a relatively coarse mass grid, stable for large time steps, strictly mass conservative, and computationally efficient. To examine the effects of SCE scheme choice on simulating clouds and precipitation, two of the three schemes are compared in large-eddy simulations of a drizzling stratocumulus field. A forward simulator that produces Doppler spectra from the large-eddy simulation results is used to compare the model output directly with radar observations. The scheme with pronounced numerical diffusion predicts excessively large mean Doppler velocities and overly broad and negatively skewed spectra compared with observations, consistent with numerical diffusion demonstrated in the box model. Statistics obtained using the recommended scheme are closer to observations, but notable differences remain, indicating that factors other than SCE scheme accuracy are limiting simulation fidelity.
54 ENVIRONMENTAL SCIENCES↗
Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems
Complex engineering systems pose fundamental challenges in real-time operations and control because they are highly dynamic systems consisting of a large number of elements with severe nonlinearities and discontinuities. Today’s tools for real-time complex system operations are mostly based on steady state models, unable to capture the dynamic nature and too slow to prevent system failures. We developed advanced Kalman filtering techniques and the formulation of dynamic state estimation using Kalman filtering techniques to capture complex system dynamics in aiding real-time operations and control. In this work, we looked at complex system issues including severe nonlinearity of system equations, discontinuities caused by system controls and network switches, sparse measurements in space and time, and real-time requirements of power grid operations. We sought to bridge the disciplinary boundaries between Computer Science and Power Systems Engineering, by introducing methods that leverage both existing and new techniques. While our methods were developed in the context of electrical power systems, they should generalize to other large-scale scientific and engineering applications.
97 MATHEMATICS AND COMPUTING↗
Smooth Particle Hydrodynamics-based Wind Representation
As a result of the 2011 accident at the Fukushima Dai-ichi NPP and other operational NPP experience, there is an identified need to better characterize and evaluate the potential impacts of externally generated hazards on NPP safety. Due to the ubiquitous occurrence of high winds around the world and the possible extreme magnitude of the hazard that has been observed, the assessment of the impact of the high-winds hazard has been identified as an important activity by both NPP owner-operators and regulatory authorities. However, recent experience obtained from the conduct of high-winds risk assessments indicates that such activities have been both labor-intensive and expensive to perform. Additionally, the existing suite of methods and tools to conduct such assessments (which were developed decades ago) do not make use of modern computational architectures (e.g., parallel processing, object-oriented programming techniques, or simple user interfaces) or methods (e.g., efficient and robust numerical-solution schemes). As a result, the current suite of methods and tools will rapidly become obsolete. Physics-based 3D simulation methods can provide information to assist in the RISMC PRA methodology. This research is intended to determine what benefits SPH methods could bring to high-winds simulations for the purposes of assessing their potential impact on NPP safety. The initial investigation has determined that SPH can simulate key areas of high-wind events with reasonable accuracy, compared to other methods. Some problems, such as simulation voids, need to be addressed, but possible solutions have been identified and will be tested with continued work. This work also demonstrated that SPH simulations can provide a means for simulating debris movement; however, further investigations into the capability to determine the impact of high winds and the impacts of wind-driven debris that lead to SSC failures need to be done. SPH simulations alone would be limited in size and computation time. An advanced method of combing results from grid-based methods with SPH through a data-driven model is proposed. This method could allow for more accurate simulation of particle movement near rigid bodies even with larger SPH particle sizes. If successful, the data-driven model would eliminate the need for a SPH turbulence model and increase the simulation domain size. Continued research beyond the scope of this project will be needed in order to determine the viability of a data-driven model.
17 WIND ENERGY↗
Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins
Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.
97 MATHEMATICS AND COMPUTING↗
National Transmission Planning Study - Long-Term Modeling Results
The National Transmission Planning Study (NTP Study) analyzed the transformation needed to ensure the U.S. transmission system continues to reliably meet electricity demand as the power sector evolves. The study linked several long- and short-term power systems models to test numerous interregional and regional transmission buildout scenarios. The results included in this dataset are from the long-term capacity expansion modeling, completed with NREL's ReEDS model. This includes nearly 100 future transmission scenarios with a wide range of economic, reliability, and resilience conditions. The dataset includes all the results visualized in the Tableau Public site, as linked below.
Array↗
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Abstract. Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle.
Geology↗
Weather Research and Forecasting Model with Vertical Nesting Capability
The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improves WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.
Source record↗
Techno-Economic Analysis for Grid Edge Intelligence: A Preliminary Study on Smart Voltage Regulator Controls
This poster provides an overview of techno-economic analysis for grid edge intelligence.
24 POWER TRANSMISSION AND DISTRIBUTION↗
ARIES 2022
An annual report of ARIES FY 2022 research highlights and capabilities, partner impacts, engagement and outreach, and messages from DOE and an External Advisory Board member.
clean energy↗
Quantifying uncertainty in the process-structure relationship for Al–Cu solidification
Phase field method (PFM) is a simulation tool to predict microstructural evolution during solidification and is helpful to establish the process-structure relationship for alloys. The robustness of the relationship however is affected by model-form and parameter uncertainties in PFM. In this paper, the uncertainty associated with the thermodynamic and process parameters of PFM is studied and quantified. Surrogate modeling is used to interpolate four quantities of interests (QoIs), including dendritic perimeter, area, primary arm length, and solute segregation, as functions of thermodynamic and process parameters. A sparse grid approach is applied to mitigate the curse-of-dimensionality computational burden in uncertainty quantification. Polynomial chaos expansion is employed to obtain the probability density functions of the QoIs. The effect of parameter uncertainty on the Al–Cu dendritic growth during solidification simulation are investigated. Here, the results show that the dendritic morphology varies significantly with respect to the interface mobility and the initial temperature.
36 MATERIALS SCIENCE↗
Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models
Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Methods and systems for evaluating and improving distribution-grid observability
This document describes systems and techniques for evaluating and improving distribution-grid observability. These systems and techniques allow engineers to quantify the observability of a distribution grid, which represents an ability to combine actual measurements and various types of computations (e.g., analytics, estimators, forecasters), from a system model. Distribution engineers can also identify islands of observability where operating parameters, including voltages, currents, and power flows, can be determined from available sensor readings. By exclusion, distribution engineers can similarly identify areas of the distribution grid with observability deficiencies that may require additional instrumentation to maintain proper operation. Distribution engineers, using an iterative or automated process, can determine the observability of the system model with new or relocated sensors to generate a sensor allocation plan. The sensor allocation plan can indicate the number and location of sensors to either maximize observability for a fixed sensor cost or minimize sensor cost for predetermined observability.
Reiman, Anthony P.↗
Self-consistent field theory simulations of polymers on arbitrary domains
We introduce a framework for simulating the mesoscale self-assembly of block copolymers in arbitrary confined geometries subject to Neumann boundary conditions. We employ a hybrid finite difference/volume approach to discretize the mean-field equations on an irregular domain represented implicitly by a level-set function. The numerical treatment of the Neumann boundary conditions is sharp, i.e. it avoids an artificial smearing in the irregular domain boundary. This strategy enables the study of self-assembly in confined domains and enables the computation of physically meaningful quantities at the domain interface. In addition, we employ adaptive grids encoded with Quad-/Oc-trees in parallel to automatically refine the grid where the statistical fields vary rapidly as well as at the boundary of the confined domain. This approach results in a significant reduction in the number of degrees of freedom and makes the simulations in arbitrary domains using effective boundary conditions computationally efficient in terms of both speed and memory requirement. Finally, in the case of regular periodic domains, where pseudo-spectral approaches are superior to finite differences in terms of CPU time and accuracy, we use the adaptive strategy to store chain propagators, reducing the memory footprint without loss of accuracy in computed physical observables.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗