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DOE OSTI2022

Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model

The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Predicting Radiation-Induced Plutonium Redox Chemistry using Multiscale Modeling Methods

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

IMAGINE BioSecurity: Mesocosm-Based Methods to Evaluate Biocontainment Strategies and Impact of Industrial Microbes Upon Native Ecosystems

Project Goals: The Integrative Modeling and Genome-scale Engineering for Biosystems Security (IMAGINE BioSecurity) SFA project seeks to establish an understanding of the behavior of engineered microbes in controlled versus environmental conditions to predictively devise new strategies for responding to biological escape. To this end, the IMAGINE Team has established a plant-soil mesocosm platform to track and quantify the fate of industrial microbes in environmental systems and assess the efficacy of biocontainment constraints upon genetically engineered microbe escape frequency and the impact of industrial microbes upon native ecological microbiomes. Abstract Text: Genetically modified industrial production microbes and their associated bioproducts have emerged as an integral component of a sustainable bioeconomy. However, the rapid development of these innovative technologies raises biosecurity concerns, namely, the risk of environmental escape. Thus, the realization of a bioeconomy hinges not only on the development and deployment of microbial production hosts, but also on the development of secure biosystems and biocontainment designs. Current laboratory-based biocontainment testing systems do not accurately reflect complexities found in natural environments, necessitating an environmentally relevant analysis pipeline that allows for the detection of rare escapees, the effect of associated bio-products, and the impact on native ecologies. To this end, we have developed an approach that utilizes soil mesocosms and integrated systems analyses to evaluate the efficacy of novel biocontainment strategies and to assess the impact of production systems upon terrestrial microbiome dynamics. We demonstrate the utility of this approach by modeling a contamination with industrial microbial chasses versus their biocontained counterparts. Here we demonstrate the broad utility of this system by highlighting findings from both strains of Saccharomyces cerevisiae that are contained with an inducible toxin anti-toxin system, and stains of Escherichia coli that are contained via genomic recoding. The resultant data demonstrate that this system has broad utility across diverse microbial chassis and biocontainment strategies, enables us to track the fate of our contaminating microbe with high sensitivity in the soil, as well as monitor broader impacts of the perturbation on the underlying soil system. The findings presented here support the use of this mesocosm-based approach to assess the environmental impact of industrial microbes and to validate biocontainment strategies.

BASIC BIOLOGICAL SCIENCES,INORGANIC, ORGANIC, PHYS↗

DOE OSTI2023

Novel method to control explosive shock sensitivity: Formulations, experiments and modeling

The microstructure of a heterogeneous high explosive (HE) affects its shock initiation sensitivity and detonation performance. Alteration of the void content and/or void structure (i.e., bulk heating or mechanical damage) therefore changes the shock initiation behavior. Controlling and predicting the change in shock sensitivity after an HE has undergone microstructural changes addresses an important and challenging goal for the design and understanding of novel energetic material formulations. In this study, we aimed to develop HE systems to precisely tune shock sensitivity by a thermal treatment prior to use. Specifically, we incorporated a small fraction (1 wt% or less) of thermally expandable microspheres (TEMs) during the formulation process of various plastic-bonded explosives (PBX). TEMs typically consist of a thermoplastic acrylonitrile shell (10-50 µm diameter), which encapsulates an inert low boiling hydrocarbon. Upon heating, the TEMs expand as the shell softens while the hydrocarbon gasifies, increasing the internal pressure and expanding the particle by as much as 120 vol% (irreversibly). Here, we present our progress on shock sensitivity comparisons of HE formulations doped with TEMs after heating and expansion. Experiments, using Composition C-4 thermally cycled to 120°C, showed an increase in shock sensitivity. Additionally, mesoscale modeling revealed that the TEM itself does not act as a hotspot, but instead has a secondary effect on the response of nearby voids. We conclude that TEMs indeed do provide a method to tune shock sensitivity. Further, our results emphasize the non-uniformity of shock waves at the mesoscale, and that upstream defects influence the reactivity of downstream defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

One-to-one aeroservoelastic validation of operational loads and performance of a 2.8 MW wind turbine model in OpenFAST

Abstract. This article presents a validation study of the popular aeroservoelastic code suite OpenFAST leveraging weeks of measurements obtained during normal operation of a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate one-to-one turbulent flow fields (i.e., comparing simulation to measurement in 10 min increments, or bins) through unconstrained and constrained assimilation methods using the kinematic turbulence generators TurbSim and PyConTurb. A total of 253 bins of 10 min of normal turbine operation were selected for analysis, and a statistical comparison in terms of performance and loads is presented. We show that successful validation of the model was not strongly dependent on the type of inflow assimilation method used for mean quantities of interest, which had median modeling errors per wind-speed interval generally within 5 %–10 % of the measurement. The type of inflow assimilation method did have a larger effect on the fatigue predictions for blade-root flapwise and tower-base fore–aft quantities, which surprisingly saw larger errors from the assumed higher-fidelity assimilation methods. Avenues for further work are discussed and include possible improvements to the aerodynamic, structural, and controller modeling that may offer insight on the origin of the up to ∼ 40 % median overprediction of fatigue for these quantities.

17 WIND ENERGY↗

DOE OSTI2023

In-Situ Detection and Prediction of WAAM Cross Feature Geometry

Abstract Wire arc additive manufacturing (WAAM) is increasingly used by manufacturers due to its relatively low cost and high deposition rate compared to other metal AM methods, but the parts produced by WAAM can be subject to localized variations in part quality. One such variation is the cross-feature defect, whereby a localized part height increase occurs due to the crossing of deposition toolpaths. Mitigation of this defect is typically achieved using manual path planning strategies, but closed-loop control is underutilized. Since the nature of this defect and of the WAAM process is such that the previous layer’s geometry influences that of the subsequent layer’s, the cross-feature defect geometry changes throughout the deposition. Therefore, any closed-loop control strategy will need to incorporate the dynamic trait of this defect. The present work seeks to implement an in-situ process modeling approach where a regression model can be continuously updated to predict the defect geometry of the subsequent deposition layer based on the historical process data. Several multi-layer cross-feature geometries are deposited and current, voltage, and optical camera data is taken for each layer. The resulting cross-feature geometries are characterized using 3D scanning and the performance and accuracy of the in-situ modeling approach is evaluated.

Thien, Austen↗

DOE OSTI2024

Role of Dynamic Polarization Interactions in the Electrical Double Layer at Calcite (104) Interfaces with Aqueous Solutions

Reactions at mineral interfaces with aqueous solutions control many geochemical and biogeochemical processes in the Earth’s critical zone. At the molecular level, insights into important properties such as the structure of the electrical double layer (EDL) at specific mineral interfaces continue to improve due to the increasing fidelity of laboratory instrumentation and computational approaches. However, molecular simulation approaches suffer from limited reach into relevant scales of time, length, and system complexity. To span this gap, a novel hybrid approach that couples first principle plane-wave density functional theory (DFT) with classical DFT (cDFT) is demonstrated and applied to calcite (104) interfaces with various electrolytes. In this approach, a region of interest described using DFT interacts with the surrounding medium described using cDFT to arrive at a self-consistent ground state. Benchmarking against experimental observations and entirely first principle DFT simulations demonstrates that this hybrid model efficiently encompasses the key short-range and collective interactions in the EDL. Simulations of calcite (104)/solution interfaces reveal the key static and dynamic polarization interactions that give rise to structuring of ions and water. Ion hydration interactions have the strongest effect on the depth of the first minimum in the density distribution of counterions at the surface, and the position and width of the first density peak is largely determined by the strength of ion-correlation forces. Finer details of ion distributions are controlled by mutual polarization of the calcite surface and interfacial electrolyte. Finally, this new ability to efficiently and rigorously predict EDL structure at mineral surfaces in contact with complex solutions paves the way to accurately modeling sorption, nucleation, dissolution, and growth in realistic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Micro on a macroscale: relating microbial-scale soil processes to global ecosystem function

ABSTRACT Soil microorganisms play a key role in driving major biogeochemical cycles and in global responses to climate change. However, understanding and predicting the behavior and function of these microorganisms remains a grand challenge for soil ecology due in part to the microscale complexity of soils. It is becoming increasingly clear that understanding the microbial perspective is vital to accurately predicting global processes. Here, we discuss the microbial perspective including the microbial habitat as it relates to measurement and modeling of ecosystem processes. We argue that clearly defining and quantifying the size, distribution and sphere of influence of microhabitats is crucial to managing microbial activity at the ecosystem scale. This can be achieved using controlled and hierarchical sampling designs. Model microbial systems can provide key data needed to integrate microhabitats into ecosystem models, while adapting soil sampling schemes and statistical methods can allow us to collect microbially-focused data. Quantifying soil processes, like biogeochemical cycles, from a microbial perspective will allow us to more accurately predict soil functions and address long-standing unknowns in soil ecology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.

Bayesian Neural Network↗

DOE OSTI2024

Predicting Radiation-Induced Plutonium Redox Chemistry using Multi-scale Modeling Methods

Over the the last 70 years plutonium (Pu) has been integral in the development of several technologies that have changed the world, yet our fundamental understanding of its chemistry is still far from complete. This is a testament to this element’s unique and complex properties, such as its ability to coexist as multiple oxidation states in aqueous solution. Careful manipulation of plutonium oxidation states is essential in the study and utilization of its rich chemistry. To achieve this level of control, a comprehensive mechanistic understanding of radiation-induced plutonium redox chemistry is critical due to the unavoidable exposure of plutonium to ionizing radiation fields, both inherent and from in-process applications. For this reason, we have developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu(IV) redox chemistry in concentrated nitric acid solutions (1.0, 3.0, and 6.0 M). Under these acidic, aqueous solution conditions, cobalt-60 gamma irradiation afforded negligible net change in the steady-state oxidation state distribution of Pu(IV). Multi-scale calculations, which are in excellent agreement with experimental data, indicate that this observation is due to radiation-induced redox cycling between Pu(IV) and Pu(III), as achieved by the reduction of Pu(IV) by radiolytic nitrous acid and hydrogen peroxide, and the oxidation of Pu(III) by nitrate and hydroxyl radicals. These radiation-induced redox processes are augmented by plutonium’s inherent disproportionation reactions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Measurement of inclusive and differential cross sections for W + W − production in proton-proton collisions at $\sqrt{s} = 13$ TeV

Measurements at $\sqrt{s} = 13$ TeV of the opposite-sign W boson pair production cross section in proton-proton collisions are presented. The data used in this study were collected with the CMS detector at the CERN LHC in 2022, and correspond to an integrated luminosity of 34.8 fb -1 . Events are selected by requiring one electron and one muon of opposite charge. A maximum likelihood fit is performed on signal- and background-enriched data categories defined by the flavor and charge of the leptons, the number of jets, and number of jets originating from b quarks. The overall sensitivity is significantly better than that of previous results with a similar integrated luminosity. The improvement comes from a more refined control of experimental uncertainties and an improved fit strategy. An inclusive W + W - production cross section of 125.7 ± 5.6 pb is measured, in agreement with standard model predictions. Cross sections are also reported in a fiducial region close to that of the detector acceptance, both inclusively and differentially, as a function of the jet multiplicity in the event. For the first time in proton-proton collisions, WW events with zero, one, and at least two jets are studied simultaneously and compared with recent theoretical predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Large area transparent refractory aerogels with high solar thermal performance

Application of transparent silica aerogels in low-temperature solar thermal systems has led to major improvements in performance. In high temperature concentrating solar thermal (CST) systems, aerogels have yet to demonstrate the necessary scalability, durability, and performance to support their widespread deployment. Here, large-area transparent refractory aerogel tiles are synthesized and shown to achieve a record-high receiver figure-of-merit (FOM) at high temperatures. The work leverages a scaled-up process for sol–gel synthesis to control the density of the aerogels for improved solar transmittance and adapts a previous atomic layer deposition (ALD) technique with the aid of predictive reaction-transport modeling. After aging for 10 days at 700 °C, the large-area tiles exhibit a solar-weighted transmittance of 95.6 % and a thermal emittance of 0.31, corresponding to a FOM of 80 % at 100 suns and 700 °C. The observed sintering rates at 700 °C are comparably low to earlier one-inch aerogels, suggesting long-term stability under relevant operating conditions. Furthermore, the study indicates that refractory aerogels are scalable materials for efficient photothermal conversion at high temperatures.

Aerogels↗

DOE OSTI2024

Soil incubation methods lead to large differences in inferred methane production temperature sensitivity

Abstract Quantifying the temperature sensitivity of methane (CH 4 ) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q 10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q 10 values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model ( ecosys ) to interpret incubation observations and investigate controls on inferred CH 4 production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH 4 production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH 4 production. CH 4 production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q 10 values (1.2–3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q 10 values of CH 4 production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q 10 values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q 10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Improvements to PVWatts for Fixed and One-Axis Tracking Systems

This work presents improvements to the widely used NREL PVWatts photovoltaic system energy model to improve modeling accuracy for typical fixed and one axis system designs. The aim is to calculate losses in the PV system assuming typical modern system design practices, while maintaining simplicity by keeping the required set of input parameters small. These improvements allow users to more credibly and quickly evaluate competing system designs in early stage feasibility. Common submodels for module cover, spectral, snow, tracker, transformer, plant controller, and self-shading losses, in addition to a bifacial gain option, are incorporated into the PVWatts model, and are shown to improve PVWatts' system performance prediction capabilities without major impact to ease of use. We anticipate including these improvements in a future release of NREL's open source PVWatts code, and some of the features may become available in the System Advisor Model (SAM) desktop software as well as the popular PVWatts web application.

14 SOLAR ENERGY↗

DOE OSTI2020

HPC4Mfg with Carbon Inc.

Continuous Liquid Interface Production (CLIP) is poised to bring additive manufacturing to multiple American manufacturing sectors owing to its unique combination of rapid print speeds and material options that resemble injection molding thermoplastics. In spite of these benefits, CLIP is still a maturing process. To improve our understanding and control of the process, this project developed a multi-physics computational model that encompasses the coupled chemical-physical processes of photopolymerization and fluid flow to predict part outcomes. A physically predictive model can enable rapid optimization of CLIP and reduce the current cycle time and waste associated with optimization by four-fold. Ultimately, this effort will help open the U.S. $400B plastic manufacturing industry to the key benefits of additive manufacturing, namely mass customization, unlimited design space, and a cost- and energy-effective path to mainstream manufacturing. In the course of the project we produced a model which coupled fluid-flow, species transport, and photochemistry to model the Carbon CLIP process. As the development proceeded, we validated the code against theory and experiments. The resulting code was transferred to Carbon, and we expect it to be a useful part of their modeling capability. Future work includes improvements to the fluid solver’s robustness and performance, along with the addition of additional physics models.

42 ENGINEERING↗

DOE OSTI2021

Development of a Multi-physics Model to Optimize Continuous Liquid Interface Production (CLIP) for Additive Manufacturing (Carbon3D HPC4Mfg Final Report)

36 MATERIALS SCIENCE↗

DOE OSTI2025

Comparing Constant and Transient Membrane Transport Parameters for Use in Wave Desalination Models

Directly pressurizing seawater for desalination with reverse osmosis membranes via wave motion is a promising and sustainable method for producing freshwater in coastal regions. However, such a system could result in significant pressure fluctuations and a departure from conventional steady-state desalination operations. This study sought to assess if membrane transport parameters (apparent water and salt permeability) should be modeled as transient or constant in solution–diffusion-based modeling efforts of dynamically operated desalination systems, such as those coupled to wave power. Two approaches were used to model membrane transport parameters: one considered each parameter to be a function of the net driving pressure of the system, and the other assumed they were constant across all conditions. A pilot-scale system was used to conduct steady-state and controlled ramping experiments. Data from steady-state experiments were used to calculate transient and constant transport parameters. Parameter combinations were used in a simulation model to predict water flux and effective permeate salinity, and simulation outcomes were compared against experimental ramping results. The transient relationships for both water and salt permeability produced the most accurate results for water flux and comparable results for effective permeate salinity. Development of such relationships would be unique to a specific system but could be valuable in modeling wave-driven desalination systems across the wide range of operating conditions they experience.

13 HYDRO ENERGY↗