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DOE OSTI2021

Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation: Preprint

This paper presents a cyber-physical algorithm for grid interactive DER control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, we propose a cyber-physical grid-interactive DER control algorithm using the previous message strategy. The tracking and regulation capabilities of this proposed algorithm can be made immune to the asynchrony resulting from the communications network traffic. We carry out simulations to show possible numerical instabilities and sensitivities of the tracking and regulation capabilities on the proposed strategy. Our results exhibit that the uncertainties of the underlying communications infrastructure must be considered in the control algorithm for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS).

co-simulation↗

DOE OSTI2021

Real time trajectory optimization for hybrid energy management utilizing connected information technologies

The disclosure is directed to solving a full trajectory optimization problem in real-time for a hybrid electric vehicle (HEV) such that future driving conditions and energy usage may be fully considered in determining optimal engine energy usage and battery energy usage in real-time during a trip. An electronic control unit of the HEV may be configured to: receive route information for a route to be driven by the HEV; and after receiving the route information, iterating the operations of: measuring a current state of charge (SOC) of the battery; using at least the measured SOC and an initial co-state value stored in a memory, performing a process to iteratively update the co-state value to obtain an updated co-state value; using at least the updated co-state value, computing an updated control value; and applying the updated control value to control a usage of the battery and the internal combustion engine.

Huang, Mike X.↗

DOE OSTI2024

SIMULATION CLONING FOR DIGITAL TWINS: A SCALABLE APPROACH

Digital Twin (DT) methods represent an important technology in which a simulated model of the operations of a physical system uses real-time sensor data to simulate, monitor, and consequently, improve its operations. One of the primary objectives of such a DT is to inform the physical system of measures to be taken in response to one or multiple intervening events that can change the state of the physical system. As such, a capability that is able to carry out multiple scenario assessments in real time in readiness for such events is a very effective tool in the use of simulations as DTs. However, continuous evaluation with highly probable event simulation scenarios are challenging due to the constraints of finite memory and a large exploration space. This paper reports a novel methodology for the continuous evaluation of $k$ probabilistic \textit{what-if} event scenarios under finite resource constraints and demonstrates its use as a digital-twin for a real-world application.

Yoginath, Srikanth↗

DOE OSTI2025

High Spatial Resolution Mapping of Retained Fission Gas

Fission gas isotopic analysis provides quantitative high precision determination of irradiated nuclear fuel burnup, offers diagnostic value, and informs fuel performance models. A measurement capability has been developed at Idaho National Laboratory (INL) for the release of retained fission gas using a focused laser and static noble gas mass spectrometry (MS) analysis. This high resolution (10s microns spot size) capability was demonstrated using Xe implanted metal foils.

07 - ISOTOPES AND RADIATION SOURCES↗

DOE OSTI2021

Fast radio burst dispersion measure distribution as a probe of helium reionization

Fast radio burst (FRB) discoveries are occurring rapidly, with thousands expected from upcoming surveys. The dispersion measures (DM) observed for FRB include important information on cosmological distances and the ionization state of the universe from the redshift of emission until today. Rather than considering the DM–redshift relation, we investigate the statistical ensemble of the distribution of dispersion measures. Here, we explore the use of this abundance information, with and without redshift information, to probe helium reionization through simulated data to redshift z = 6. Carrying out Monte Carlo simulations of FRB survey samples, we examine the effect of different source redshift distributions, host galaxy models, sudden vs gradual reionization, and covariance with cosmological parameters on determination of helium reionization properties. We find that a fluence limited survey with 10 4 FRBs can discriminate different helium reionization histories at ~6σ using the DM-distribution of bursts, without redshift information (and ~10σ with redshifts).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Jaynesian Analysis of Environmental Chemistry: Systems Model Component Integration via the Arctic Aquatic Carbon Cycle

Focal Areas: All three are touched upon from the OBER call. We will consider formal logical inference and entropic information analysis for sparse but intensive chemical measurements, as optimal drivers for managing process complexity. As a centerpiece for the discussion we take soil-to-sea organic structural evolution within the Arctic hydro-geochemosphere, assessing detrital negentropy then algorithmic greed to generate ranked, reduced mechanisms for system integration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

25 Siβ + -decay spectroscopy

Background: β-decay spectroscopy provides valuable information on exotic nuclei and a stringent test for nuclear theories beyond the stability line. Purpose: To search for new β-delayed protons and γ rays of 25 Si to investigate the properties of 25 Al excited states. Method: 25 Si β decays were measured by using the Gaseous Detector with Germanium Tagging system at the National Superconducting Cyclotron Laboratory. The protons and γ rays emitted in the decay were detected simultaneously. Here, a Monte Carlo method was used to model the Doppler broadening of 24 Mg γ-ray lines caused by nuclear recoil from proton emission. Shell-model calculations using two newly developed universal sd-shell Hamiltonians were performed. Results: The most precise 25Si half-life to date has been determined. A new proton branch at 724(4) keV and new proton-γ-ray coincidences have been identified. Three 24 Mg γ-ray lines and eight 25 Al γ-ray lines are observed for the first time in 25 Si decay. The first measurement of the 25 Si β-delayed γ-ray intensities through the 25 Al unbound states is reported. All the bound states of 25 Al are observed to be populated in the β decay of 25 Si. Several inconsistencies between the previous measurements have been resolved, and new information on the 25 Al level scheme is provided. An enhanced decay scheme has been constructed and compared to the mirror decay of 25 Na and the shell-model calculations. Conclusions: The measured excitation energies, γ-ray and proton branchings, log $ft$ values, and Gamow-Teller transition strengths for the states of 25 Al populated in the β decay of 25 Si are in good agreement with the shell model calculations, offering gratifyingly consistent insights into the fine nuclear structure of 25 Al.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

EXCESS workshop: Descriptions of rising low-energy spectra

Many low-threshold experiments observe sharply rising event rates of yet unknown origins below a few hundred eV, and larger than expected from known backgrounds. Due to the significant impact of this excess on the dark matter or neutrino sensitivity of these experiments, a collective effort has been started to share the knowledge about the individual observations. For this, the EXCESS Workshop was initiated. In its first iteration in June 2021, ten rare event search collaborations contributed to this initiative via talks and discussions. The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER, NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about their observed energy spectra and known backgrounds together with details about the respective measurements. In this paper, we summarize the presented information and give a comprehensive overview of the similarities and differences between the distinct measurements. The provided data is furthermore publicly available on the workshop's data repository together with a plotting tool for visualization.

Adari, Prakruth↗

DOE OSTI2022

EXCESS workshop: Descriptions of rising low-energy spectra

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

R-matrix Resolved Resonance Region Evaluation of 140,142 Ce

Oak Ridge National Laboratory completed the resolved resonance region (RRR) evaluation of the two most abundant cerium isotopes, 140 Ce (88.45%) and 142 Ce (11.11%), as requested by the US Nuclear Criticality Safety Program. These evaluations are based on recent high-resolution transmission and capture measurements performed on nat Ce and highly enriched 142 Ce samples at the JRC-Geel Linear Accelerator facility, as well as measured thermal constants available from the EXFOR database. Starting from the resonance parameters of the ENDF/B-VIII.0 library followed by a preliminary R-matrix analysis, an updated set of resonance parameters and corresponding covariance information were derived by fitting these measured data using the Reich–Moore approximation of the R -matrix theory, as implemented in the SAMMY code system. The 140 Ce RRR upper energy limit was kept at 200 keV, whereas the 142 Ce resonance region was extended from 13 to 26 keV. Updated statistical properties were obtained for the new evaluations and compared to those derived from the ENDF/B-VIII.0 nuclear data library. The new evaluation work improved some of the discrepancies found in previous work, such as the capture resonance integral and stellar Maxwellian-averaged cross sections. These integral quantities were mainly derived from the fit of the latest measured data, especially the neutron capture yield data for 142 Ce isotope.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Tunable Infrared Laser Absorption Spectroscopy of Aluminum Monoxide $A^2\Pi_i $–$X^2\Sigma^+$.

We report the details of an infrared, laser absorption diagnostic capable of quantifying aluminum monoxide temperature and column density at 100 kHz repetition rate. This novel technique employs a near infrared MEMS-VCSEL to measure rotationally resolved optical absorption spectra of aluminum monoxide $A^2\Pi_i$ - $X^2\Sigma^+$ from approximately 7400 –7900 cm -1 . Temperatures and column densities are extracted from model regressions to provide temporally resolved thermochemical information on aluminum oxidation reactions. The measurement capability is demonstrated by performing 100 kHz measurement in the plume of an exploding bridgewire with measured temperatures of 3450–3100 K and column densities of 1– 11 x 10 16 cm -2 . To the authors knowledge, this is the first use of the AlO $A^2\Pi_i$ - $X^2\Sigma^+$ transition to characterize aluminum combustion environments. Details regarding signal extraction and calibration of MEMS-VCSEL spectra are also included. Although unsuccessful, efforts to extract kinetic temperature and column density from simultaneously measured, atomic aluminum 2 P 3/2,1/2 - 2 S 1/2 transitions at 7618 cm -1 and 7602 cm -1 are also described.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Density reconstruction in convergent high-energy-density systems using x-ray radiography and Bayesian inference

X-ray radiography is a technique frequently used to diagnose convergent high-energy-density (HED) systems, such as inertial confinement fusion implosion, and provides unique information that is not available through self-emission measurements. In this work, we investigate the scope and limits of that information using a radiography simulation combined with the Bayesian inference workflow. The accuracy of density reconstruction from simulated radiographs of spherical implosions driven with 27 kJ laser energy is assessed, including the increase or decrease in accuracy due to the addition of Lagrangian marker layers, Poisson noise, and improved prior information. This work is the first to present the full uncertainty distributions inferred from radiography analysis in HED systems and demonstrates the importance of constructing the full posterior probability density, as opposed to a point estimate, due to the modal structure of the likelihood surface introduced by typical experimental noise sources. This general methodology can be used both for robust analysis of radiographic data and for an improved design of radiography experiments by modeling the full experimental system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Improve Learning from Crowds via Generative Augmentation

Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typically annotated by a small number of annotators. This creates a sparsity issue and limits the quality of machine learning models trained on such data. In this paper, we study how to handle sparsity in crowdsourced data using data augmentation. Specifically, we propose to directly learn a classifier by augmenting the raw sparse annotations. We implement two principles of high-quality augmentation using Generative Adversarial Networks: 1) the generated annotations should follow the distribution of authentic ones, which is measured by a discriminator; 2) the generated annotations should have high mutual information with the ground-truth labels, which is measured by an auxiliary network. Extensive experiments and comparisons against an array of state-of-the-art learning from crowds methods on three real-world datasets proved the effectiveness of our data augmentation framework. It shows the potential of our algorithm for low-budget crowdsourcing in general.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗

DOE OSTI2024

Proceedings for the Workshop on Applied Nuclear Data Activities 2024

The Workshop for Applied Nuclear Data Activities (WANDA) is designed to increase communication among nuclear data (ND) users in multidisciplinary federal programs, ND producers, ND funders, and other ND experts. It also presents an opportunity to cross-pollinate ideas as well as introduce ND gaps identified by federal programs to ND experts and ND capabilities to the various federal ND users. WANDA 2024 included five technical sessions, three of which focused on Fusion Energy Sciences (FES)—FES Fusion Neutronics, FES Tritium Production, and FES Material Damage—and two stand-alone sessions—Isotopes and Targetry for Nuclear Data and Uncertainty Quantification. The FES sessions successfully brought new voices to the WANDA discussions, expanding the application space in which nuclear data are critical. FES programs need accurate nuclear data with realistic uncertainty quantification to properly estimate, for example, shielding, activation, tritium production, helium production, structural material integrity, and superconducting magnet operation. This includes a variety of projectile (neutrons, photons, charged particles) and target atoms. One of the action items common to all the FES sessions was a need to perform sensitivity studies to identify the prioritization of nuclear data needs. The Isotopes and Targetry session highlighted the many capabilities available to produce high-quality targets for nuclear data measurements, including 3D printing with spherical powders, combustion synthesis coupled with spin coating & electrospraying, inkjet printing, and isotopic doping. These new methods open doors for more accurate measurement, but it was also stressed that sample characterization following any method of fabrication is of the highest importance to accurately interpret nuclear data measurement results that used that sample. The Uncertainty Quantification (UQ) session was broken into two categories: nuclear data uncertainty quantification and the use of that uncertainty quantification. Thematic to the UQ session was the loss of information when going from nuclear data measurement, to evaluation, to evaluated file, and finally to neutron transport calculations. Current evaluated ND libraries typically only contain covariances, which assume that the probability distributions are Gaussian. Beyond being a simplified assumption for many evaluations, this can lead to negative values on many observables when attempting to sample the covariance. The covariance format, however, is very efficient in that a simple set of linear equations can transform uncertainty from parameters or cross sections to the application of interest. Focused collaboration is needed between nuclear data evaluators and nuclear data users to ensure that needs are being met.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Quantifying and Understanding the Access Time to Dockless Micromobility: A Case Study in Washington, D.C.

Micromobility has been widely deployed in many cities. Similar as how access time/distance affects the travel demand to use public transit and informs transit system design, access time/distance to micromobility service measures its service efficiency and also serves as an equity indicator to inform city agencies from a regulation perspective. Though there is an increasing need to understand it, access to micromobility has not been sufficiently studied. This paper developed a framework to quantity the access time to dockless micromobility service (i.e., the minimum time needed to walk to reach the closest dockless micromobility vehicles). Based on the real-time vehicle location data collected from Washington, DC, this research quantified the access time to dockless micromobility, analyzed its spatial and temporal variation patterns and investigated its relationship with socio-demographic variables (i.e., population density, employment density and low-income population). The results revealed that the access time to dockless micromobility ranges between 0 to 4 minutes with the most frequently observed range of 0.5 to 1 minutes, and the city center area tends to have shorter access time than the outskirts areas. Results also indicate a quite stable access time level in DC with access time standard deviation of 0.2 to 0.5 minutes. After correlating the access time at census-block-group level with socio-demographic data, it was discovered that shorter access time usually aligns with larger population and employment density, and the proportion of low-income population was found not helpful with explaining the access time variation, which indicating a relatively equitable micromobility program.

access time↗

DOE OSTI2020

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. In this work, to overcome this limitation, we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

FolpsD: combining EFT and phenomenological approaches for joint power spectrum and bispectrum analyses

We present a theoretical model for the power spectrum and bispectrum of galaxy clustering that exploits the complementarity between small-scale power spectrum information and large-scale bispectrum measurements. We extend the FOLPS code by combining its one-loop EFT galaxy power spectrum with a tree-level galaxy bispectrum projected onto the tripolar spherical harmonics (Sugiyama) basis. To access additional small-scale information, we also consider a line-of-sight damping factor in both statistics, mirroring approaches commonly used in studies of redshift-space distortions. We test the model using DESI DR2 galaxy mocks. Even without damping, the joint analysis of the EFT power spectrum and bispectrum significantly improves constraints and reduces parameter degeneracies relative to power spectrum analyses alone. For LRG-like samples, including the damping further extends the range beyond $k\sim 0.3 \,h \text{Mpc}^{-1}$ in the power spectrum and $k \sim 0.24 \,h \text{Mpc}^{-1}$ in the bispectrum without introducing statistically significant parameter biases. This leads to up to $\sim 30\%$ tighter constraints on $A_s$ and $ω_{cdm}$. For low signal-to-noise tracers such as QSOs, however, the damping parameters are weakly constrained and can absorb noise fluctuations, leading to shifts in inferred parameters. Similar limitations may arise in models where cosmological information is encoded in power-spectrum shape features degenerate with the damping, such as scenarios with massive neutrinos. In contrast, for $w_0w_a$CDM we obtain $15\%$ and $21\%$ tighter constraints on $w_0$ and $w_a$, respectively, yielding a deviation from constant dark energy at slightly more than the $1σ$ level using full-shape information alone. The code is publicly available at https://github.com/cosmodesi/FolpsD

Bansal, P. [Michigan U., MCTP; Michigan U.] (ORCID↗