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DOE OSTI2017

Event generator tuning using Bayesian optimization

Monte Carlo event generators contain a large number of parameters that must be determined by comparing the output of the generator with experimental data. Generating enough events with a fixed set of parameter values to enable making such a comparison is extremely CPU intensive, which prohibits performing a simple brute-force grid-based tuning of the parameters. Bayesian optimization is a powerful method designed for such black-box tuning applications. In this article, we show that Monte Carlo event generator parameters can be accurately obtained using Bayesian optimization and minimal expert-level physics knowledge. A tune of the PYTHIA 8 event generator using $e^+e^-$ events, where 20 parameters are optimized, can be run on a modern laptop in just two days. Combining the Bayesian optimization approach with expert knowledge should enable producing better tunes in the future, by making it faster and easier to study discrepancies between Monte Carlo and experimental data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Heuristic methods and performance bounds for photonic design

In the photonic design problem, a scientist or engineer chooses the physical parameters of a device to best match some desired device behavior. Many instances of the photonic design problem can be naturally stated as a mathematical optimization problem that is computationally difficult to solve globally. Because of this, several heuristic methods have been developed to approximately solve such problems. These methods often produce very good designs, and, in many practical applications, easily outperform ‘traditional’ designs that rely on human intuition. Yet, because these heuristic methods do not guarantee that the approximate solution found is globally optimal, the question remains of just how much better a designer might hope to do. This question is addressed by performance bounds or impossibility results, which determine a performance level that no design can achieve. We focus on algorithmic performance bounds, which involve substantial computation to determine. We illustrate a variety of both heuristic methods and performance bounds on two examples. In these examples (and many others not reported here) the performance bounds show that the heuristic designs are nearly optimal, and can be considered globally optimal in practice. This review serves to clearly set up the photonic design problem and unify existing approaches for calculating performance bounds, while also providing some natural generalizations and properties.

Angeris, Guillermo (ORCID:0000000249503990)↗

DOE OSTI2026

Cooperative Agreement To Analyze variabiLity, change and predictabilitY in the earth SysTem (CATALYST)

CATALYST proposes to perform foundational coordinated research in a team-oriented collaborative effort aimed at advancing a robust understanding of modes of Earth system variability and change using models, observations and process studies. The proposed research will address the DOE/BER mission by exploring the limits to predictability, identifying fundamental underlying mechanisms, quantifying interactions among modes of variability, and discovering tipping points in the Earth system to understand the current and future impacts of these phenomena on regional and global climate. Four fundamental gaps are identified in our knowledge of the Earth system: 1) What are the limits to predictability on various timescales? 2) What are the interactions among modes of Earth system variability? 3) How may modes of Earth system variability change in response to changes in external forcing, and what are the tipping points involved with those changes? 4) How are high impact events connected to modes of Earth system variability and how may they change in the future? Related to those gaps in our knowledge, we formulate four research objectives to address those gaps using a combination of Earth system models (ESMs) and machine learning (ML) methods. Research Objective 1 (RO1) addresses the first gap above and proposes to understand modes of variability and their limits of predictability on subseasonal to decadal timescales using ESMs and ML. Research Objective 2 (RO2) addresses the second gap and proposes to use a hierarchy of models to understand relevant processes and feedbacks related to how modes of variability interact with each other. Research Objective 3 (RO3) is designed to study the third gap and proposes to examine the role of external forcings in changes of modes of Earth system variability and their interactions, and the likelihood and predictability of tipping points and irreversible changes. Research Objective 4 (RO4) will address the fourth gap and proposes to use high resolution ESMs, regionally refined models (RRMs), and ML methods to investigate the relationships between high impact events (e.g. flash droughts and precipitation extremes, atmospheric rivers (ARs), tropical cyclones (TCs), storm surge/sea level rise), the synoptic systems that produce them, and their changes related to modes of Earth system variability. The research will involve the use of the Community Earth System Model (CESM), Energy Exascale Earth System Model (E3SM), CMIP multi-model data sets, a hierarchy of simpler models, and numerous observational data sets. In the course of the proposed research, CATALYST will contribute to metrics and diagnostics that will be integrated in Coordinated Model Evaluation Capabilities (CMEC), particularly with regards to the Quasi-biennial Oscillation (QBO) and its interactions with the Madden-Julian Oscillation (MJO), high atmospheric pressure blocking, and new precipitation metrics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Data-Driven Computation of Probabilistic Marching Cubes for Efficient Visualization of Level-Set Uncertainty

Uncertainty visualization is an important emerging research area. Being able to visualize data uncertainty can help scientists improve trust in analysis and decision-making. However, visualizing uncertainty can add computational overhead, which can hinder the efficiency of analysis. In this paper, we propose novel data-driven techniques to reduce the computational requirements of the probabilistic marching cubes (PMC) algorithm. PMC is an uncertainty visualization technique that studies how uncertainty in data affects level-set positions. However, the algorithm relies on expensive Monte Carlo (MC) sampling for the multivariate Gaussian uncertainty model because no closed-form solution exists for the integration of multivariate Gaussian. In this work, we propose the eigenvalue decomposition and adaptive probability model techniques that reduce the amount of MC sampling in the original PMC algorithm and hence speed up the computations. Our proposed methods produce results that show negligible differences compared with the original PMC algorithm demonstrated through metrics, including root mean squared error, maximum error, and difference images. We demonstrate the performance and accuracy evaluations of our data-driven methods through experiments on synthetic and real datasets.

Athawale, Tushar↗

DOE OSTI2019

Thermonuclear fusion rates for tritium + deuterium using Bayesian methods

The 3 H(d, n) 4 He reaction has a large low-energy cross section and will likely be utilized in future commercial fusion reactors. This reaction also takes place during Big Bang nucleosynthesis. Studies of both scenarios require accurate and precise fusion rates. To this end, we implement a one-level, two-channel R-matrix approximation into a Bayesian model. Our main goals are to predict reliable astrophysical S-factors and to estimate R-matrix parameters using the Bayesian approach. All relevant parameters are sampled in our study, including the channel radii, boundary condition parameters, and data set normalization factors. In addition, we take uncertainties in both measured bombarding energies and S-factors rigorously into account. Thermonuclear rates and reactivities of the 3 H(d, n) 4 He reaction are derived by numerically integrating the Bayesian S-factor samples. The present reaction rate uncertainties at temperatures between 1.0 MK and 1.0 GK are in the range of 0.2% to 0.6%. Our reaction rates differ from previous results by 2.9% near 1.0 GK.Our reactivities are smaller than previous results, with a maximum deviation of 2.9% near a thermal energy of 4 keV. The present rate or reactivity uncertainties are more reliable compared to previous studies that did not include the channel radii, boundary condition parameters, and data set normalization factors in the fitting. Finally, we investigate previous claims of electron screening effects in the published 3 H(d, n) 4 He data. No such effects are evident and only an upper limit for the electron screening potential can be obtained.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Complete collision data set for electrons scattering on molecular hydrogen and its isotopologues: IV. Vibrationally-resolved ionization of the ground and excited electronic states

Here, we present a comprehensive set of vibrationally-resolved cross sections for electron-impact ionization of molecular hydrogen and its isotopologues (H 2 , D 2 , T 2 , HD, HT, and DT) in both the ground and excited electronic states. We apply the adiabatic-nuclei molecular convergent close-coupling (MCCC) method to calculate cross sections from threshold to 1000 eV for ionization of the ground and excited vibrational levels of the X 1 Σ$^{+}_{g}$, B 1 Σ$^{+}_{u}$, C 1 π u , EF 1 Σ$^{+}_{g}$, a 3 Σ$^{+}_{g}$, and c 3 π u electronic states, representing all states with united-atoms-limit principle quantum number n=1–2. The cross sections are presented in graphical form and provided as both numerical values and analytic fit functions in supplementary data files. The data can also be downloaded from the MCCC database at mccc-db.org.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2018

Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach

The significantly increasing deployment of wind power necessitates that system operation considers the spatial-temporal correlation of power forecast from different wind power plants. How to model this spatial-temporal correlation in the actual system dispatch is challenging. In this paper, a sparse correlation matrix is utilized to efficiently model the spatial-temporal correlation of wind power forecast in the generation dispatch model. A novel, adjustable, and distributionally-robust chance-constrained multi-interval optimal power flow (ADRCC-MIOPF) model is proposed to obtain reliable economic dispatch (ED) solutions. The spatial-temporal correlation of wind power plants power forecasts is represented by the sparse correlation covariance matrix obtained from historical, time series wind power forecast error data. The chance constraints in the ADRCC-MIOPF model are transformed into a set of second-order-cone (SOC) constraints in which an adjustable coefficient in the chance constraints controls the robustness of the ADRCC-MIOPF model to the wind power forecast errors distribution. Case studies performed on the PJM 5-bus system and IEEE 118-bus system verify the proposed method to improve the system security and reduce the cost especially under the high wind penetration levels. All the cases can be solved within several minutes for both the small and large cases which validates the efficiency of the proposed sparse matrix model. In addition, considering the spatial-temporal correlation of wind power forecast and the distributional robustness of wind power forecast error leads to a more reliable economic dispatch with lower system violations.

17 WIND ENERGY↗

DOE OSTI2018

Two-dimensional simulations of mixing in classical novae: The effect of white dwarf composition and mass

Context. Classical novae are explosive phenomena that take place in stellar binary systems. They are powered by mass transfer from a low-mass main sequence star onto either a CO or ONe white dwarf. The material accumulates for 104–105 yr until ignition under degenerate conditions, resulting in a thermonuclear runaway. The nuclear energy released produces peak temperatures of ~0.1–0.4 GK. During these events, 10 –7 –10 –3 M ⊙ enriched in intermediate-mass elements, with respect to solar abundances, are ejected into the interstellar medium. Yet, the origin of the large metallicity enhancements and the inhomogeneous distribution of chemical species observed in high-resolution spectra of ejected nova shells is not fully understood. Aims. Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz instabilities that operate at the core-envelope interface can naturally produce self-enrichment of the accreted envelope with material from the underlying white dwarf at levels that agree with observations. Yet, such multidimensional simulations have been performed for a small number of cases and much of the parameter space remains unexplored. Methods. We explored the dredge-up, driven by Kelvin-Helmholtz instabilities, for white dwarf masses in the range 0.8–1.25 M ⊙ and different core compositions, that is, CO-rich and ONe-rich substrates. We present a set of five numerical simulations performed in two dimensions aimed at analyzing the possible impact of the white dwarf mass, and composition, on the metallicity enhancement and explosion characteristics. Results. At the time we stop the simulations, we observe greater mixing (~30% higher when measured in the same conditions) and more energetic outbursts for ONe-rich substrates than for CO-rich substrates and more massive white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

A New Method for Generating Probability Tables in the Unresolved Resonance Region

One new method for constructing probability tables in the unresolved resonance region (URR) has been developed. This new methodology is an extensive modification of the single-level Breit-Wigner (SLBW) pseudo-resonance pair sequence method commonly used to generate probability tables in the URR. The new method uses a Monte Carlo process to generate many pseudo-resonance sequences by first sampling the average resonance parameter data in the URR and then converting the sampled resonance parameters to the more robust R-matrix limited (RML) format. Furthermore, for each sampled set of pseudo-resonance sequences, the temperature-dependent cross sections are reconstructed on a small grid around the energy of reference using the Reich-Moore formalism and the Leal-Hwang Doppler broadening methodology. We then use the effective cross sections calculated at the energies of reference to construct probability tables in the URR. The RML cross-section reconstruction algorithm has been rigorously tested for a variety of isotopes, including 16 O, 19 F, 35 Cl, 56 Fe, 63 Cu, and 65 Cu. The new URR method also produced normalized cross-section factor probability tables for 238 U that were found to be in agreement with current standards. The modified 238 U probability tables were shown to produce results in excellent agreement with several standard benchmarks, including the IEU-MET-FAST-007 (BIG TEN), IEU-MET-FAST-003, and IEU-COMP-FAST-004 benchmarks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Erratum: First-Principles Approach to Calculating Energy Level Alignment at Aqueous Semiconductor Interfaces [Phys. Rev. Lett. 113, 176802 (2014)]

We note, while extending our approach in our Letter to calculate energy level alignment at aqueous TiO 2 interfaces, we discovered two problems with our application of the GW methodology. Briefly, in our approach, we used a DFT + U approach for the underlying structure determination and as an input to the GW calculations. Then, GW calculations were carried out at the full-frequency spectrum-self-consistent level. All calculations were performed with the projector augmented wave (PAW) method as it is implemented in VASP. The specific semiconductors considered were GaN and ZnO. The first problem was caused by improper handling of the U parameter when initializing the follow-up GW calculations, leading to a final calculated band gap for GaN and ZnO that was too large. Second, we used an older set of pseudopotentials for the PAW method. In a paper that was published while our original manuscript was under review, the impact of the completeness of the partial waves representing empty states in the PAW method was analyzed, showing significant issues with the convergence of the GW results for certain materials, particularly ZnO. Specifically, those issues included use of PAW pseudopotentials that deviate too much from norm conservation, an unduly restricted basis set size that constrains the number of empty states available, and use of a small cutoff for the dielectric matrix size that can result in a false convergence with respect to the number of empty states included. Our GW calculations for GaN and ZnO were affected by these problems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Transmission Interface Limits for High-Spatial Resolution Capacity Expansion Modeling

Large-scale capacity expansion models typically rely on estimates of the power transfer limits between modeled zones. Accurate estimation of these interface transfer limits (ITLs) requires modeling the underlying transmission network. Here we expand on a maximum flow optimization method that uses linearized power flow to estimate transfer limits. We apply this method to a data set of the U.S. transmission network to estimate ITLs between U.S. counties. By calculating ITLs using different subsets of the network, we evaluate how the size of the network used in the estimation affects the results. The results show diminishing returns to ITL accuracy after six hops, suggesting that a network subset can reasonably be used to approximate ITLs. The county-level estimates produced in this study will support more spatially resolved capacity expansion modeling and will help inform policy making at local and national levels.

capacity planning↗

DOE OSTI2021

A Bilevel Voltage Regulation Operation for Distribution Systems With Self-Operated Microgrids

The emerging of microgrids in distribution systems has significantly enhanced the resilience of power grids. However, the operators of a distribution system and microgrids therein can be different and have accessibility to different devices. To model the operation of such a grid, this work proposes a bilevel formulation and probes into the voltage regulation operation, considering the interaction between different systems. The proposed bilevel formulation considers the cooperation of active energy resources (AER), transformer tap-changers, and capacitor banks that are controlled by different operators. To facilitate the solution time of the target bilevel optimization, the lower-level problems with different objectives are modeled using deep neural networks (DNNs) which are then converted into a set of constraints. Hence, the bilevel problem can be reformed to a single-level problem. Lastly, the proposed solution procedures are validated using a customized joint system constructed by the IEEE 123-bus system and a real distribution system in Iowa. According to the numerical validation results, the solution time of the proposed nonlinear activation function based DNN model is 69 times faster than other methods in solving voltage regulation with a bilevel structure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Data‐driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates

Summary Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well‐studied major crops. Using 727 maize ( Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre‐ and postanthesis maximum temperature, mid‐season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well‐studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.

Kusmec, Aaron↗

DOE OSTI2023

Study on Electrostatic Separation of Quinoline Insolubles from Coal Tar Pitch

The feasibility of electrical separation in the removal of quinoline insoluble (QI) particles from coal tar pitch (CTP) was experimentally investigated. QI particle involvement prohibits the effective fabrication of high-quality value-added products, such as carbon fibers, from CTP. A substantial and sustainable CTP market exists around the world; therefore, a strong incentive exists to develop a viable technical approach to effectively and economically remove QI particles from CTP. The electrical separation method shows promise to achieve this technical goal (Cao et al., 2012). Even with the given setup (wire- cylinder adapted), critical issues remain to be addressed for this method to be applicable to the CTP: (1) identifying the wash oil used in the original QI separation, (2) understanding the mechanism of QI separation, (3) characterizing the deposit, and (4) identifying the QI. This study integrated a set of experimental and analysis techniques into the electrical separation tests to address the aforementioned issues. Key findings are as follows: 1. Electric current responses: • The electric current level of the CTP–wash oil solution reported by Cao et al. (2012) can be attained by using a mixture of 25% quinoline and 75% toluene for the CTPs examined in this study under the same electrical load condition. • The electric current tends to decrease during the test period because of the decreasing number of charged particles. • The reversed field corresponded to the configuration of electrostatic precipitation for positive corona discharge. The high electric field can result in dielectric breakdown and lead to an abrupt current surge. 2 Deposit response and solvent candidates: • Deposit of particles in CTP mixture can be effectively implemented in a wire-cylinder configuration as proposed and examined in this study. • Deposit depends on the solvents. Among the solvents tested, two- and three-part solvents that included quinoline and ethanol (i.e., 25% quinoline and 75% toluene; 50% quinoline and 50% toluene; and 33% wash oil, 33% BTX, and 34% ethanol) produced the highest deposit weight. • The deposit process examined in this study is derived from the charged particles. An appreciable relation exists between deposit weight and electric charge. 3. QI removal efficiency (RE): • The RE of the electrostatic separation can reach as high as 76.2% for Carbores, and 61.6% for Koppers. • The RE can be further enhanced if the field level increases from 0.16 kV/mm that was used in the current study to 0.23 kV/mm, according to the relation established between the RE and the electric field. 4. Testing of the modeling system: • The motion of particles is originally driven by the charge-based electric force in the cases tested. The solid particle separation mechanism is similar to that of QI deposition in CTP. • The mechanical movement of solid particles can be strongly affected by the gravitational and viscous forces in the electric field. 5. EDS analysis and chemical compositions: • The main chemical composition of deposit QI matches that of as-prepared CTP QI. The deposit QI is derived from the same group of the CTP QI. • In addition to carbon, the QI contains oxygen, sodium, aluminum, silicon, iron, and sulfur. The work for the near future is also discussed.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Transmission Interface Limits for High-Spatial Resolution Capacity Expansion Modeling

capacity planning↗

DOE OSTI2022

Multi-Pressure Rail System Design with Variable Pressure Control Strategy

This paper presents the design and the control method of a Multiple Pressure Rail system (MPR) with a variable pressure control logic that allows minimization of throttling loss, therefore, increasing the overall fluid power actuation efficiency. An MPR system allows controlling hydraulic actuators with a pressure control logic, as opposed to traditional flow control logics which are well established in mobile applications. A proper setting of the number of pressure rails and the pressure level in each rail permits optimizing the system efficiency of an MPR solution. MPR systems have been recently proposed with different purposes, but seldom adopt variable pressure control strategies to manage the pressure variations in the rails. Moreover, the related past work attempts to maximize the energy recovery during overrunning load conditions. This paper, instead, addresses the case of an MPR system that handles a multitude of hydraulic functions without significant overrunning loads, as it occurs in many mobile applications such as in agriculture. The paper first presents the design alternatives considered for the MPR system, including the supply, the control valves, and the controller. Second, a dedicated test rig is developed to support experimental activities and validations of a lumped parameter model developed within this research to develop the MPR control strategy. The results describe the operating features of the proposed MPR solution. By considering the case of a traditional LS system, the paper results also show how system efficiency can be highly improved, with energy savings in the order of 49%

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Integrating Multi-Source Data for Bi-Level Traffic Simulator Calibration: A Literature Review and Highway Case Study

Traffic simulation serves as a powerful tool for pre-evaluating policies and technologies. In this context, simulation-based Dynamic traffic assignment (DTA) models are capable of capturing traffic dynamics. They are well-known as critical tools in controlling and predicting traffic situations. The reliability of simulation results heavily depends on the calibration process. Most studies in the literature formulate and calibrate simulators based on a single source of collected data or multiple data sets with the same spatiotemporal characteristics. However, in practice, traffic data is collected by various tools with usually different spatial and temporal resolutions. This study introduces a novel approach to taking into account diverse input data from a variety of sources. An iterative bi-level solution is proposed. to equally treat traffic flow and speed data. The upper level solves flow calibration with the exact solution method, and the lower level calibrates the speed with the simultaneous perturbation stochastic approximation (SPSA) algorithm. Subsequently, the effectiveness of the proposed model is investigated using data from a six-mile section of Nashville's I-24 highway in Tennessee. The results demonstrate that our proposed model creates an effective feedback loop between the optimizer and the simulator for calibrating flow and speed to reduce the error between simulated and real data.

42 ENGINEERING↗

DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗