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DOE OSTI2018

Insensitivity of the Cloud Response to Surface Warming Under Radical Changes to Boundary Layer Turbulence and Cloud Microphysics: Results From the Ultraparameterized CAM

We study the cloud response to a +4K surface warming in a new multi- scale climate model that uses enough interior resolution to begin explicitly resolving boundary layer turbulence (i.e. Ultra-Parameterization or UP), and the associated low clouds. UP's predictions are compared against those from standard Super-Parameterization (SP). The mean global cloud feedback turns out to be remarkably neutral across all of our simulations, despite some radical changes in both cloud microphysical parameter settings and cloud resolving model grid resolution. The overall cloud response to warming is the positive low-cloud feedbacks over land, negative feedbacks at high latitudes, and weak feedbacks over the low-latitude oceans. The most distinct effects of UP appear to be the result of tuning decisions impacting high latitude cloud feedback. UP's microphysics are tuned to optimize the model present-day, top-of-atmosphere radiation fluxes against CERES observations, by lowering the cloud ice-liquid phase-shift temperature ramp, adjusting the ice/liquid autoconversion rate, and increasing the ice fall speed. This reduces baseline high-latitude low cloud amounts, damping the phase change cloud feedback at high latitudes, leading to a slightly more positive global cloud feedback compared to SP. A sensitivity test that isolates these microphysical impacts from UP's grid resolution conrms that the microphysical settings are mostly responsible for the dierences between SP and UP cloud feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

THE MASS AND SIZE DISTRIBUTION OF PLANETESIMALS FORMED BY THE STREAMING INSTABILITY. I. THE ROLE OF SELF-GRAVITY

We study the formation of planetesimals in protoplanetary disks from the gravitational collapse of solid over-densities generated via the streaming instability. To carry out these studies, we implement and test a particle-mesh self-gravity module for the Athena code that enables the simulation of aerodynamically coupled systems of gas and collisionless self-gravitating solid particles. Upon employment of our algorithm to planetesimal formation simulations, we find that (when a direct comparison is possible) the Athena simulations yield predicted planetesimal properties that agree well with those found in prior work using different numerical techniques. In particular, the gravitational collapse of streaming-initiated clumps leads to an initial planetesimal mass function that is well-represented by a power law, dN/dM{sub p}∝M{sub p}{sup −p}, with p≃1.6±0.1, which equates to a differential size distribution of dN/dR{sub p}∝R{sub p}{sup −q}, with q≃2.8±0.1. We find no significant trends with resolution from a convergence study of up to 512{sup 3} grid zones and N{sub par}≈1.5×10{sup 8} particles. Likewise, the power-law slope appears indifferent to changes in the relative strength of self-gravity and tidal shear, and to the time when (for reasons of numerical economy) self-gravity is turned on, though the strength of these claims is limited by small number statistics. For a typically assumed radial distribution of minimum mass solar nebula solids (assumed here to have dimensionless stopping time τ=0.3), our results support the hypothesis that bodies on the scale of large asteroids or Kuiper Belt Objects could have formed as the high-mass tail of a primordial planetesimal population.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Evaluation of Aggregated EV Flexibility With TSO-DSO Coordination

Electric vehicles (EVs) are becoming a promising source of grid ancillary services due to the temporal and spatial charging flexibility, quick response and storage capability. Such advantages are increasing with government policy promotion and technology improvement. However, the exploration of EV flexibility requires the coordination of both transmission system operators (TSOs) and distribution system operators (DSOs), to ensure the safe and reliable operation of power network. In this paper, we propose a coordinated evaluation method that determines the optimal utilization of EV temporal flexibility without compromising EV owners’ usage. At the distribution level, DSOs first evaluate EV aggregators’ operational boundaries to exploit distribution level services. At the transmission level, TSOs then determine EV charging schedules and ancillary service capacity simultaneously, taking into account the requirement from DSOs. Here, we validate the model in a case study using the IEEE 123 node test feeder and EV charging sessions obtained from a transportation simulation tool that uses real-world data.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

Evaluation of the Impact of Slab Foundation Heat Transfer on Heating and Cooling in Florida

During the last three decades of energy-efficiency research, there has been limited study of heat transfer to slab-on-grade foundations in cooling-dominated climates. Most experimental research has focused on the impact of slab-on-grade foundations and insulation schemes on heat losses in heating-dominated climates. This is surprising because the floor area in single-family homes is generally equal to wall area, window area, or attic area, all of which have been extensively evaluated for heat-transfer properties. Moreover, slab foundations are the most common foundation type in cooling-dominated climates. Slab-on-grade construction is very popular in southern states, accounting for 77% of new home floors according to 2014 U.S. Census data. There is a widespread perception that tile flooring, as opposed to carpet, provides a cooler home interior in warm climates. Empirical research is needed because building energy simulation software programs running DOE-2 and EnergyPlus engines often rely on simplified models to evaluate the influence of flooring on interior temperature, even though in some cases more detailed models exist. The U.S. Department of Energy Building America Partnership for Improved Residential Construction (BA-PIRC) performed experiments in the Florida Solar Energy Center’s Flexible Residential Test Facility intended to assess for the first time (1) how slab-on-grade construction influences interior cooling in a cooling-dominated climate and (2) how the difference in a carpeted versus uncarpeted building might influence heating and cooling energy use. Two nominally identical side-by-side residential buildings were evaluated during the course of 1 year, from 2014 to 2015: the east building with a pad and carpet floor and the west building with a bare slab floor. A detailed grid shows temperature measurements taken on the slab surface at various locations as well as at depths of 1.0 ft, 2 ft, 5.0 ft, 10.0 ft, and 20.0 ft below the surface. Temperature measurements were taken at both buildings for more than 3 years prior to the experiments to ensure that the ground and foundation temperatures had fully come into equilibrium.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Evaluation of the Impact of Slab Foundation Heat Transfer on Heating and Cooling in Florida

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1986

Direct simulation of a turbulent boundary layer up to R(sub)(theta)= 1410

The turbulent boundary layer on a flat plate, with zero pressure gradient, is simulated numerically at four stations between R sub theta = 225 and R sub theta = 1410. The three-dimensional time-dependent Navier-Stokes equations are solved using a spectra method with up to about 10 to the 7th power grid points. Periodic spanwise and stream-wise conditions are applied, and a multiple-scale procedure is applied to approximate the slow streamwise growth of the boundary layer. The flow is studied, primarily, from a statistical point of view. The solutions are compared with experimental results. The scaling of the mean and turbulent quantities with Reynolds number is examined and compared with accepted laws, and the significant deviations are documented. The turbulence at the highest Reynolds number is studied in detail. The spectra are compared with various theoretical models. Reynolds-stress budget data are provided for turbulence-model testing.

Spalart, Philippe R.↗

NASA NTRS2015

Modeling of Non-Isothermal Cryogenic Fluid Sloshing

A computational fluid dynamic model was used to simulate the thermal destratification in an upright self-pressurized cryostat approximately half-filled with liquid nitrogen and subjected to forced sinusoidal lateral shaking. A full three-dimensional computational grid was used to model the tank dynamics, fluid flow and thermodynamics using the ANSYS Fluent code. A non-inertial grid was used which required the addition of momentum and energy source terms to account for the inertial forces, energy transfer and wall reaction forces produced by the shaken tank. The kinetics-based Schrage mass transfer model provided the interfacial mass transfer due to evaporation and condensation at the sloshing interface. The dynamic behavior of the sloshing interface, its amplitude and transition to different wave modes, provided insight into the fluid process at the interface. The tank pressure evolution and temperature profiles compared relatively well with the shaken cryostat experimental test data provided by the Centre National D'Etudes Spatiales.

Cryogenics↗

DOE OSTI2020

Overset meshes for incompressible flows: On preserving accuracy of underlying discretizations

This study on overset meshes for incompressible-flow simulations is motivated by accurate prediction of wind farm aerodynamics involving large motions and deformations of components with complex geometry. Specifically, using first-order hyperbolic and elliptic equation proxies for the incompressible Navier-Stokes (NS) equations, we investigate the influence of information exchange between overset meshes on numerical performance where the underlying discretization is second-order accurate. The first aspect of information exchange surrounds interpolation of solution where we examine Lagrange and point-cloud-based interpolation for creating constraint equations between overset meshes. To maintain overall second-order accuracy, higher-order interpolation is required for elliptic problems, but linear interpolation is sufficient for hyperbolic problems in first-order form. Higher-order point-cloud-based interpolation provides a pathway to maintaining accuracy in unstructured meshes, but at higher complexity. The second aspect of information exchange focuses on comparing the approaches of overset single system (OSS) and overset Additive Schwarz (OAS) for coupling the linear systems of the overlapping meshes. While the former involves a single linear system, in the latter the discrete linear systems are solved separately, and solving the global system is accomplished through outer iterations and sequential information exchange in a Jacobi fashion. For the test cases studied, accuracy for hyperbolic problems is maintained by performing two outer iterations, whereas many outer iterations are required for elliptic systems. The order-of-accuracy studies explored here are critical for verifying the overset-mesh coupling algorithms used in engineering simulations. Accuracy of these simulations themselves is, however, quantified using engineering quantities of interest such as drag, power, etc. Consequently, we conclude with numerical experiments using NS equations for incompressible flows where we show that linear interpolation and few outer iterations are sufficient for achieving asymptotic convergence of engineering quantities of interest.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Mapping Turbulent Flow around a Rod Bundle Induced by a 5 x 5 PWR Mixing Vane Grid using Particle Tracking Velocimetry

This study presents an experimental visualization technique developed for the measurement of the lateral liquid velocity components within a vertical up-flow 5 x 5 rod bundle. The experimental technique uses refractive index matching (RIM) and time resolved particle tracking velocimetry (PTV) to obtain full field measurements at different axial locations. Relevant features of the technique are first, the multi-scale capability, which allows measurements either on a single subchannel or measurements to include the full bundle accounting for the 25 rods and corresponding liquid subchannels. Secondly, the use of FEP tubes to match the refractive index of water allows a uniform illumination which in turn reduces the velocity measurements uncertainty, and third, the use of a high energy continuous laser allows for the temporal evaluation of fluctuating structures within the bundle. This study was conducted with the aim to quantify the effect of the mixing vanes on the development of the liquid turbulence and to test the motivation feasibility of the technique to serve as a benchmark for advanced CFD simulations. In this context, there have been multiple experimental studies with the objective to improve the database for CFD benchmarking purposes. Several experimental techniques have been used to this end; for instance, laser doppler velocimetry (LDV) is a preferred tool to measure the axial and lateral velocity components through bundles due to its accuracy. Although LDV is a mature technique, it only provides point measurements which do not allow for the instantaneous investigation of the momentum exchange among neighboring subchannels; moreover, the small spaces between the rods and within the subchannels makes LDV measurements difficult. In the other hand, flow visualization techniques can provide a 2D map of the instantaneous and average structure of the liquid velocity. Among the used visualization techniques, PIV has gained a lot of attention due to its high accuracy, relative simple application, and non-intrusiveness. Several studies had applied PIV to study the flow structures within bundles sub-channels with some success; however, in these studies there exist an increased uncertainty due to reflections and shadows from the rods. Shadows and undesired reflections appear when the light passes through materials with different refractive indexes. This issue was addressed in other studies on which the refractive index of the rod bundle (or sections of it) was matched with that of water. These studies used particle tracking velocimetry (PTV) and obtained comparable results with LDV measurements. This success indicated that RIM-PTV experiments can be successfully implemented to gather information for simulation benchmarking. The present work is an extension of previous efforts on which measurements were performed only on vertical planes but lacked of the important flow structures that can only be observed through a horizontal cross section of the bundle at different axial height. The focus of the present study was to fill this information gap by measuring the lateral velocity in horizontal planes at different heights. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Testbed and Simulation-based Evaluation of Privacy-preserving Algorithms for Smart Grid AMI Networks

Although smart meters are deployed in a lot of countries as a result of aiming to make the power grid more resilient and efficient, the data collection process from smart meters in Smart Grid (SG) has still challenges that relate to consumer privacy. Referred to as Advanced Metering Infrastructure (AMI), the data collected and transmitted through this AMI can leak sensitive information about the consumers if it is sent as plaintext. While a number of solutions have been proposed in the past, the deployment of these solutions in real-life was not possible since the actual AMIs were not accessible to researchers. Therefore, a lot of solutions relied on simulations which may not be able to capture the performance of these solutions. In this paper, we pick a widely used homomorphic-based aggregation and implement it in a realistic testbed so that it can be compared with a simulation-based solution. Specifically, we develop a system that provides privacy with the Paillier cryptosystem and twofactor authentication with ECDSA and OpenSSL certificates. In order to test the system, an IEEE 802.11s-based SG AMI network testbed is constructed with Beaglebone Black boards that imitate the behavior of smart meters. The same network is also simulated in widely used ns-3. The results showed that ns-3 simulation and testbed results are parallel in most cases and the proposed system can perform effectively. However, there are still many differences that need to be taken into account in deploying real systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗

NASA NTRS1994

Calculation of clear-sky outgoing longwave radiation using ECMWF gridded fields and ISCCP C1 cloud data

Clear-Sky OLR (outgoing longwave radiation) has a variety of definitions. Clear-sky OLR can be measured from satellites by examining the data to detect clouds, eliminating those observations that are contaminated with clouds, and averaging the remaining data. The disadvantage of this method is that satellites do not provide time continuous coverage, requiring many observations and careful analysis to remove sampling biases. Alternatively, clear-sky OLR can be found continuously using a radiative flux code, combined with analyzed fields of temperature, humidity and clouds, either from assimilated observations or a GCM simulation. The disadvantage is that the cloud field is typically very low resolution, on the order of 200 km, so they lack important small scale cloud detail. Even if fractional clouds data is available, it is not obvious how GCM and ERBE results should be compared. The purpose of this study is to assess clear-sky OLR calculated from these methods using available data. Cloud data from ISCCP provides the fractional cloud coverage data necessary to test these methods against calculations based on observed data. Clear-sky OLR is calculated using the ECMWF/TOGA archive. Monthly averages are made in the manner of the several methods listed above and compared to each other and to ERBE.

Ramsey, Perry G.↗

DOE OSTI2022

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scattered spatio-temporal points (called residual points). The location and distribution of these residual points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling for PINNs: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling methods, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. Here, we show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points for both forward and inverse problems. Furthermore, the results obtained in this study can also be used as a practical guideline in choosing sampling methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Spatiotemporal predictions of toxic urban plumes using deep learning

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.

Civil and Environmental Engineering↗

DOE OSTI2020

Reduced-Order Parameterized Short-Circuit Current Model of Inverter-Interfaced Distributed Generators

The short-circuit response of inverter-interfaced distributed generators (IIDGs) is not adequately represented in many conventional protection studies. This paper presents an in-depth analysis of IIDG behavior during grid faults and proposes a more accurate reduced-order parameterized short-circuit current (RPSC) model of inverters. Typical inverter components are thoroughly investigated to identify those that play dominant roles during faults. The paper shows that the current limiter is the dominant factor for the steady-state fault-current of inverter; while the inverter filter along with the severity of the voltage disturbance largely determine the initial transient spike of inverter fault current. The proposed model is low-order and can be used in large scale simulations. The parameters of the proposed RPSC model can be extracted from laboratory experiments without requiring proprietary manufacturer information. The proposed fault-current model is analogous to the well-known synchronous machine model that segregates the inverter fault current into subtransient, transient, and steady-state fault currents. Finally, experimental and simulation tests are presented to validate the model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1991

Studies of the charging of a thin dust layer in a plasma

The present study is designed to extend Wilson's model to include refinements in the actual operational design of the simulation code itself and to include additional physics in order to assess the resulting electric field structure and grain charges. Most of the initial phases of the work have focussed on the fact that the code, though in principle reliable, suffers from severe time constraints, requiring as must as 10-50 hours of CPU time for typical runs on a VAX computer. It is likely that by staging the code into steps in which early steps are carried out crudely and later steps successively fine tuned the total CPU time can be cut significantly. Preliminary results indicate the possibility to cut CPU time to as much as 1/3 or 1/5 the former time. The original Wilson code has been carefully examined, tested and documented. Shortcuts and staging systems have been introduced and tested which should make the code produce more accurate results with a finer grid system than would have been feasible heretofor. Continued testing and implementation of these refinements will be carried out at the author's home institution in the near future. In addition to the effort at reducing CPU time to a manageable amount, work is in progress to include a dust-size distribution instead of assuming all dust grains have the same size.

Peterson, Lennart R.↗

DOE OSTI2024

Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

The multiport autonomous reconfigurable solar (MARS) power plant is a promising solution to integrate renewable resources and energy storage systems into the alternating current (ac) power grid and an high-voltage direct current (HVdc) link. In the MARS system, various input power sources are connected to the individual submodules (SMs) through direct current (dc)–dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This article aims to address the gap by accurately determining the stability boundary of the MARS system. As such, a novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. Further, in conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both power systems computer aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criterion, efficient energy management is achieved for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high-efficiency standards.

14 SOLAR ENERGY↗