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DOE OSTI2017

Filters for Improvement of Multiscale Data from Atomistic Simulations

Multiscale computational models strive to produce accurate and efficient numerical simulations of systems involving interactions across multiple spatial and temporal scales that typically differ by several orders of magnitude. Some such models utilize a hybrid continuum-atomistic approach combining continuum approximations with first-principles-based atomistic models to capture multiscale behavior. By following the heterogeneous multiscale method framework for developing multiscale computational models, unknown continuum scale data can be computed from an atomistic model. Concurrently coupling the two models requires performing numerous atomistic simulations which can dominate the computational cost of the method. Furthermore, when the resulting continuum data is noisy due to sampling error, stochasticity in the model, or randomness in the initial conditions, filtering can result in significant accuracy gains in the computed multiscale data without increasing the size or duration of the atomistic simulations. In this work, we demonstrate the effectiveness of spectral filtering for increasing the accuracy of noisy multiscale data obtained from atomistic simulations. Moreover, we present a robust and automatic method for closely approximating the optimum level of filtering in the case of additive white noise. By improving the accuracy of this filtered simulation data, it leads to a dramatic computational savings by allowing for shorter and smaller atomistic simulations to achieve the same desired multiscale simulation precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Development of a Control Platform for a Building-Scale Hybrid Solar PV-Natural Gas Microgrid

Building-scale microgrids are a type of behind-the-meter microgrids where the building operator has control of the distributed energy resources, including, in this case, a natural gas-fired microturbine in addition to solar PV and battery energy storage systems. There is a growing trend in deploying behind-the-meter microgrids due to their benefits including the resiliency of serving critical loads, especially in regions with abundant natural gas. In order to ensure distributed energy resources are dispatched optimally for the desired mode of operation, a hierarchical control platform including a centralized controller was developed and installed. The platform includes communication and control infrastructure that interface with controllers for distributed energy resources and the building automation system of a recently built energy efficient commercial building. Based on desirable outcomes under different grid and building conditions, operational modes were defined for the microgrid controller. The controller is programmed to map each mode to respective operational modes for distributed energy resources controllers. Experimental data for test runs corresponding to two operational modes confirm the communication and control infrastructure can execute hierarchical control commands. Finally, dispatch optimization for a year-long simulation of system operation is presented and the benefits of the hybrid solar PV-natural gas setup are evaluated.

Energy & Fuels↗
DOE OSTI2022

Trust-Region Approximation of Extreme Trajectories in Power System Dynamics

In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given a bounded set that represents parametric uncertainty. Furthermore, we show how this method, while remaining computationally efficient compared with sampling-based techniques, overcomes the limitations of previous sensitivity-based techniques to approximate the bounds of the trajectories when the local approximation loses validity because of the nonlinearity. We present several numerical experiments that showcase the accuracy and scalability of the technique, including a demonstration on the IEEE New England test system.

42 ENGINEERING↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach

Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is considered a promising tool to model complex two-phase flow systems. MCFD simulation can predict local flow features without resolving interfacial information. As a result, the MCFD solver relies on closure relations to describe the interaction between the two phases. Those empirical or semi-mechanistic closure relations constitute a major source of uncertainty for MCFD predictions. In this paper, we leverage a physics-informed uncertainty quantification (UQ) approach to inversely quantify the closure relations’ model form uncertainty in a physically consistent manner. This proposed approach considers the model form uncertainty terms as stochastic fields that are additive to the closure relation outputs. Combining dimensionality reduction and Gaussian processes, the posterior distribution of the stochastic fields can be effectively quantified within the Bayesian framework with the support of experimental measurements. As this UQ approach is fully integrated into the MCFD solving process, the physical constraints of the system can be naturally preserved in the UQ results. Here, in a case study of adiabatic bubbly flow, we demonstrate that this UQ approach can quantify the model form uncertainty of the MCFD interfacial force closure relations, thus effectively improving the simulation results with relatively sparse data support.

42 ENGINEERING↗
DOE OSTI2025

Testing the parametric model for self-interacting dark matter using matched halos in cosmological simulations

Here, we systemically evaluate the performance of the self-interacting dark matter (SIDM) halo model proposed in Ref.Yang et al. (2023) with matched halos from high-resolution cosmological CDM and SIDM simulations. The model incorporates SIDM effects along mass evolution histories of CDM halos and it is applicable to both isolated halos and subhalos. We focus on the accuracy of the model in predicting halo density profiles at z = 0 and the evolution of maximum circular velocity. We find the model predictions agree with the simulations within 10%–50% for most of the simulated (sub)halos, 50%–100% for extreme cases. This indicates that the model effectively captures the gravothermal evolution of the halos with very strong, velocity-dependent self-interactions. For an example application, we apply the model to study the impact of various SIDM scenarios on strong lensing perturber systems, demonstrating its utility in predicting SIDM effects for small-scale structure analyses. Our findings confirm that the model is an effective tool for mapping CDM halos into their SIDM counterparts.

Yang, Daneng [Chinese Academy of Sciences, Nanjing↗
DOE OSTI2020

Modeling of Grid-Forming and Grid-Following Inverters for Dynamic Simulation of Large-Scale Distribution Systems

Historically, distribution system planning studies mainly focused on steady state and quasi-steady state analysis, with limited attention paid to dynamic analysis. This paper develops three-phase, electromechanical models for both grid-forming and grid-following inverters, and integrates them into an open source, three-phase distribution network solver, thereby enabling dynamic simulation of large-scale, three-phase unbalanced distribution systems with high penetration of inverter-based DERs. The proposed inverter models are validated against electromagnetic simulation and field test data from the CERTS/AEP microgrid testbed, and simulated in an islanded 5252 node distribution system in the GridLAB-D simulation environment. Simulation verifies the effectiveness of the proposed inverter models for large-scale distribution system analysis. Finally, study results show that compared to traditional grid-following inverters, the high penetration of grid-forming inverters can improve the voltage and frequency stability of islanded distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Representations of Precipitation Diurnal Cycle in the Amazon as Simulated by Observationally Constrained Cloud‐System Resolving and Global Climate Models

Abstract The ability of an observationally‐constrained cloud‐system resolving model (Weather Research and Forecasting; WRF, 4‐km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1‐degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The WRF model coupled with a 3‐D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea‐breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations despite the nudging of large‐scale winds toward global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. The role of propagating MCS on the environment are discussed by using a multivariate perturbation analysis. We also find that the advection of moisture perturbations from ocean to inland regions have a higher correlation with the occurrence of MCSs in the Amazon than the intensity of colder air intrusion associated with sea breezes along the coast. Moreover, the presence of large cold pools over the central Amazon basin are responsible for the maintenance of propagating deep convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Transactive energy systems for distributed blackstart and service recovery

Abstract Current transactive controls use marginal benefits and marginal costs to achieve an economic market efficiency during normal grid operations. However, the transactive mechanisms designed for normal economic operations cannot be applied directly for the contingencies because the grid operations during contingencies are often dictated by technical needs rather than purely economic criteria. For instance, one of the key technical requirements for the blackstart is to have at least one blackstart capable resource cleared which cannot be ensured by the transactive mechanism designed for normal economic operations because they work primarily based on the marginal benefit and marginal cost of the participants. This article presents one of the first attempts to develop a transactive mechanism to be used during grid contingencies. A distributed blackstart and service recovery is used as an example contingency to evaluate the performance of the proposed transactive mechanism. The performance of the proposed transactive mechanism is demonstrated for various use cases using a modified IEEE‐123 node test system. The simulation results demonstrated the proof of concept of applying a transactive mechanism to enable distributed blackstart and service recovery by engaging the mix of blackstart capable and non‐capable distributed energy resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Simultaneous Estimation of Electromechanical Modes and Forced Oscillations

Over the past several years, great strides have been made in the effort to monitor the small-signal stability of power systems. These efforts focus on estimating electromechanical modes, which are a property of the system that dictate how generators in different parts of the system exchange energy. Though the algorithms designed for this task are powerful and important for reliable operation of the power system, they are susceptible to severe bias when forced oscillations are present in the system. Forced oscillations are fundamentally different from electromechanical oscillations in that they are the result of a rogue input to the system, rather than a property of the system itself. To address the presence of forced oscillations, the frequently used AutoRegressive Moving Average (ARMA) model is adapted to include sinusoidal inputs, resulting in the AutoRegressive Moving Average plus Sinusoid (ARMA+S) model. From this model, a new Two-Stage Least Squares algorithm is derived to incorporate the forced oscillations, thereby enabling the simultaneous estimation of the electromechanical modes and the amplitude and phase of the forced oscillations. The method is validated using simulated power system data as well as data obtained from the western North American power system (wNAPS) and Eastern Interconnection (EI).

Electromechanical modes↗
DOE OSTI2018

Power Take-off System for Marine Renewable Devices

The Power Take-off (PTO) System for Marine Renewable Devices (Project) envisioned the design, manufacture and testing of a generator that would ultimately be suitable for fully flooded seawater operation. This would require a generator design and bearing system capable of operating in the presence of seawater without corrosion. The original generator concept design focused on a switched reluctance electromagnetic design for the generator with the understanding that the switched reluctance design would be more robust and tolerant of a seawater medium. A trade study was conducted to prove that hypothesis, but the results of the study indicated that there was no benefit to adopting a switched reluctance design over a more conventional synchronous permanent magnet design. In addition, the novelty of the switched reluctance design added more technical risk to the Project than was warranted. ORPC’s cost of energy analyses pointed to reliability as being the most critical factor for achieving Project systems performance advancement (SPA) metrics. Consequently, the choice of a permanent magnet (PM) machine design over a potentially less reliable switched reluctance design was necessary to achieve the Project results. As a result, ORPC determined that a PM design was a more appropriate design to achieve the Project goals and ORPC selected a highly qualified PM generator firm for the design, manufacture and factory testing of the generator. The factory acceptance test (FAT) of the generator took place on March 20-22, 2017 and all tests were successfully passed. Developing a design for a driveline as part of the PTO system required a full understanding of the loads and operating conditions expected. Detailed analyses for developing turbine loads and subsequent definition of bearing specifications were performed using various analytical methods. Two candidate bearing designs were proposed as part of the Project. After initial design work had been completed, ORPC designed a representative full-scale driveline to validate the detailed design of the advanced driveline components. Full-scale components were manufactured, instrumented and tested, simulating actual system parameters. This full-scale bearing test was conducted at the University of Maine Advanced Structures and Composites Laboratory. The purpose of this test was to characterize the frictional losses associated with bearings and test the suitability and functionality of some commercially available shaft couplings and expansion bushings in cross-flow turbine drivelines. In parallel with component testing and dynamometer testing of the PTO system, ORPC performed design work to demonstrate how the advanced PTO system will be integrated with all ORPC turbine generator units (TGUs). The work would focus primarily on the TidGen® TGU and assess how the advanced PTO would be integrated into TidGen® 001 or other units, then field-deployed and tested in an open water environment under actual operational conditions. In addition to the benefits provided to the MHK industry in general, and ORPC in particular, the Project provided benefits to the general public, including the following: • Increased general awareness of the potential for generation of electricity from ocean energy resources • Demonstrated continuing advancements in the commercialization of MHK technologies, making them more likely to contribute to the electricity generation supply in America in the coming years • Continued awareness of the local economic benefits from the MHK industry through job creation and local spending • Confirmation of a path to improvements in availability, power-to-weight ratio (PWR) and reduction in levelized cost of energy (LCOE). All Project tasks were completed, and the Project objectives were accomplished. All project milestones and deliverables were met. The designs, methodologies, practices, testing, data, analysis and lessons learned from the Project are a step forward in the development of the U.S. MHK industry and provide a sound basis for ultimate commercialization of ORPC’s power systems. Based on the what was learned and demonstrated during the Project, ORPC has determined that the ORPC power systems have the potential of accelerating the commercialization of tidal and ocean current MHK power systems.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Exploring Bioeconomy Growth through the Public Release of the Biomass Scenario Model

The Biomass Scenario Model (BSM) is an important tool for exploring vibrant future bioeconomy scenarios that leverage domestic resources. Developed by NREL and BETO, this model of the domestic biofuels supply chain has been used to explore success strategies for BETO's activities towards bioeconomy growth. The BSM offers a robust test bed for detailed exploration of effects of BETO activities within the complex context of resource availability; physical, technological, and economic constraints; behavior; and policy. The public release of the model in 2017 will allow broad engagement with the theme of the conference as model users can analyze bioeconomy growth, domestic biomass resource use, and associated effects. The BSM is a carefully validated, state-of-the-art, dynamic model of the biomass to biofuels supply chain. Using a system dynamics simulation modeling approach, the model tracks long-term deployment of biofuels given technology development and investment, considering land availability, the competing oil market, consumer demand, and government policies over time. Sample outputs include biofuels production, feedstock use, capital investment, incentives, and costs of feedstocks and fuels. BSM scenarios reveal technological, economic, and policy challenges, as well as opportunities for dynamic growth of the bioeconomy with strategic public and private investment at key points in the system. The model logic and results have been reviewed extensively, through collaborative analysis, expert reviews and external publications (https://www.zotero.org/groups/bsm_publications/).

09 BIOMASS FUELS↗
DOE OSTI2024

Machine learning for reducing noise in RF control signals at industrial accelerators

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2014

Greenland Surface Mass Balance as Simulated by the Community Earth System Model. Part II: Twenty-First-Century Changes

This study presents the first twenty-first-century projections of surface mass balance (SMB) changes for the Greenland Ice Sheet (GIS) with the Community Earth System Model (CESM), which includes a new ice sheet component. For glaciated surfaces, CESM includes a sophisticated calculation of energy fluxes, surface albedo, and snowpack hydrology (melt, percolation, refreezing, etc.). To efficiently resolve the high SMB gradients at the ice sheet margins and provide surface forcing at the scale needed by ice sheet models, the SMB is calculated at multiple elevations and interpolated to a finer 5-km ice sheet grid. During a twenty-first-century simulation driven by representative concentration pathway 8.5 (RCP8.5) forcing, the SMB decreases from 372 ± 100 Gt yr -1 in 1980–99 to -78 ± 143 Gt yr -1 in 2080–99. The 2080–99 near-surface temperatures over the GIS increase by 4.7 K (annual mean) with respect to 1980–99, only 1.3 times the global increase (+3.7 K). Snowfall increases by 18%, while surface melt doubles. The ablation area increases from 9% of the GIS in 1980–99 to 28% in 2080–99. Over the ablation areas, summer downward longwave radiation and turbulent fluxes increase, while incoming shortwave radiation decreases owing to increased cloud cover. The reduction in GIS-averaged July albedo from 0.78 in 1980–99 to 0.75 in 2080–99 increases the absorbed solar radiation in this month by 12%. Summer warming is strongest in the north and east of Greenland owing to reduced sea ice cover. In the ablation area, summer temperature increases are smaller due to frequent periods of surface melt.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

First measurements of beam backgrounds at SuperKEKB

The high design luminosity of the SuperKEKB electron-positron collider is expected to result in challenging levels of beam-induced backgrounds in the interaction region. Properly simulating and mitigating these backgrounds is critical to the success of the Belle~II experiment. We report on measurements performed with a suite of dedicated beam background detectors, collectively known as BEAST II, during the so-called Phase 1 commissioning run of SuperKEKB in 2016, which involved operation of both the high energy ring (HER) of 7 GeV electrons as well as the low energy ring (LER) of 4 GeV positrons. We describe the BEAST II detector systems, the simulation of beam backgrounds, and the measurements performed. The measurements include standard ones of dose rates versus accelerator conditions, and more novel investigations, such as bunch-by-bunch measurements of injection backgrounds and measurements sensitive to the energy spectrum and angular distribution of fast neutrons. We observe beam-gas, Touschek, beam-dust, and injection backgrounds. We do not observe significant synchrotron radiation, as expected. Measured LER beam-gas backgrounds and Touschek backgrounds in both rings are slightly elevated, on average three times larger than the levels predicted by simulation. HER beam-gas backgrounds are on on average two orders of magnitude larger than predicted. Systematic uncertainties and channel-to-channel variations are large, so that these excesses constitute only 1-2 sigma level effects. Neutron background rates are higher than predicted and should be studied further. We will measure the remaining beam background processes, due to colliding beams, in the imminent commissioning Phase 2. These backgrounds are expected to be the most critical for Belle II, to the point of necessitating replacement of detector components during the Phase 3 (full-luminosity) operation of SuperKEB

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Effects of Scavenging, Entrainment, and Aqueous Chemistry on Peroxides and Formaldehyde in Deep Convective Outflow Over the Central and Southeast United States

Deep convective transport of gaseous precursors to ozone (O3) and aerosols to the upper troposphere is affected by liquid phase and mixed-phase scavenging, entrainment of free tropospheric air and aqueous chemistry. The contributions of these processes are examined using aircraft measurements obtained in storm inflow and outflow during the 2012 Deep Convective Clouds and Chemistry (DC3) experiment combined with high-resolution (dx = 3 km) WRF-Chem simulations of a severe storm, an air mass storm, and a mesoscale convective system (MCS). The simulation results for the MCS suggest that formaldehyde (CH2O) is not retained in ice when cloud water freezes, in agreement with previous studies of the severe storm. By analyzing WRF-Chem trajectories, the effects of scavenging, entrainment, and aqueous chemistry on outflow mixing ratios of CH2O, methyl hydroperoxide (CH3OOH), and hydrogen peroxide (H2O2) are quantified. Liquid phase microphysical scavenging was the dominant process reducing CH2O and H2O2 outflow mixing ratios in all three storms. Aqueous chemistry did not significantly affect outflow mixing ratios of all three species. In the severe storm and MCS, the higher than expected reductions in CH3OOH mixing ratios in the storm cores were primarily due to entrainment of low-background CH3OOH. In the air mass storm, lower CH3OOH and H2O2 scavenging efficiencies (SEs) than in the MCS were partly due to entrainment of higher background CH3OOH and H2O2. Overestimated rain and hail production in WRF-Chem reduces the confidence in ice retention fraction values determined for the peroxides and CH2O.

Bela, Megan↗
DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
DOE OSTI2024

Enhanced Interfacial Electron Transfer in Photocatalyst-Natural Enzyme Coupled Artificial Photosynthesis System: Tuning Strategies and Molecular Simulations

Laccase is capable of catalyzing a vast array of reactions, but its low redox potential limits its potential applications. The use of photocatalytic materials offers a solution to this problem by converting absorbed visible light into electrons to facilitate enzyme catalysis. Herein, MIL-53(Fe) and NH 2 -MIL-53(Fe) serve as both light absorbers and enzyme immobilization carriers, and laccase is employed for solar-driven chemical conversion. Electron spin resonance spectroscopy results confirm that visible light irradiation causes rapid transfer of photogenerated electrons from MOF excitation to T1 Cu(II) of laccase, significantly increasing the degradation rate constant of tetracycline (TC) from 0.0062 to 0.0127 min -1 . Conversely, there is only minimal or no electron transfer between MOF and laccase in the physical mixture state. Theoretical calculations demonstrate that the immobilization of laccase's active site and its covalent binding to the metal-organic framework surface augment the coupled system's activity, reducing the active site accessible from 27.8 to 18.1 Å. In conclusion, the constructed photo-enzyme coupled system successfully combines enzyme catalysis’ selectivity with photocatalysis's high reactivity, providing a promising solution for solar energy use.

36 MATERIALS SCIENCE↗