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At least 487 records · Page 27

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DOE OSTI2021

Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Thermophysical Properties of Mixtures of Thorium and Uranium Nitride

Abstract The miscibility, lattice parameter, and thermophysical properties of (Th 0.2 U 0.8 )N and (Th 0.5 U 0.5 )N have been investigated. It is shown that additions of thorium nitride (ThN) to uranium nitride (UN) increases the thermophysical performance of the mixed nitride fuel form in comparison to reference UN. In the more dilute limit, additions of ThN serve as a burnable neutronic poison and reduces the change in k eff over the lifecycle of the fuel. At higher concentrations, additions of ThN serve as a significant fertile source of 233 U. Where appropriate, comparisons to previous work on UN + PuN mixtures are made, as this is a comparable fuel form for potential fast reactor concepts, and a suitable point of contrast in the possible design space afforded by mixed (Th x U 1 − x )N fuel forms. The data from this work are the input parameters for finite element modeling of the temperature distribution in a compact reactor. The results of modeling and simulation of this core design are shown for the case of steady-state operation and during double, adjacent heat pipe failure.

Parker, S. S. (ORCID:000000018597440X)↗

DOE OSTI2022

Powder Metallurgy HIP Process Study and Mechanical Property Evaluations for IN740H

The effects of hot isostatic pressing (HIP) parameters and powder size distribution (PSD) on the resulting microstructure and properties of powder metallurgy (P/M) IN740H have been investigated. Properties can be substantially tailored through the appropriate management of these parameters. Applying a thermal presoak to the input powder prior to the sintering stage of HIP can significantly increase average grain size and coarse grain fraction, enabling a trade for improved creep resistance at the expense of tensile strength. Here, powder thermal presoak was found to have a larger impact on microstructure and properties than the use of a coarser PSD alone. Additionally, substantial grain growth and grain boundary migration is observed in P/M IN740H at HIP temperatures above 1260°C. Given its inherently finer grain size, P/M HIP IN740H typically outperforms its cast and wrought counterparts in tensile response. Creep life at 700–800°C was inferior to wrought due to PPBs coincident with grain boundaries which served as cavity nucleation sites, leading to lower creep ductility. The ability to adjust the microstructure toward a more balanced creep and tensile behavior allows P/M HIP IN740H to be considered for high-temperature applications in advanced ultra-supercritical steam and supercritical carbon dioxide power cycles.

36 MATERIALS SCIENCE↗

DOE OSTI2023

On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation

Occupant behavior (OB) is one of the significant sources of uncertainty in building performance simulation. While OB modeling has received increased attention in the past decade, research on the degree of granularity or level of detail (LoD) required for representing occupants is still in the nascent stages. This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment. A medium-sized prototype office building located in Chicago, Illinois is used as the simulation case study. Ten occupant-centric attributes are adopted to develop the LoDs for OB representation. We first demonstrate the different modeling approaches required for simulating the three fidelity levels. Later, we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages. Furthermore, this study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Morphology-controlled transformation of Cu@Au core-shell nanowires into thermally stable Cu 3 Au intermetallic nanowires

Multimetallic nanowires with long-range atomic ordering hold the promise of unique physicochemical properties in many applications. Here we demonstrate the synthesis and study the stability of Cu 3 Au intermetallic nanowires. The synthesis is achieved by using Cu@Au core-shell nanowires as precursors. With appropriate Cu/Au stoichiometry, the Cu@Au core-shell nanowires are transformed into fully ordered Cu 3 Au nanowires under thermal annealing. Thermally-driven atom diffusion accounts for this transformation as revealed by X-ray diffraction and electron microscopy studies. The twin boundaries abundant in the Cu@Au core-shell nanowires facilitate the ordering process. The resulting Cu 3 Au intermetallic nanowires have uniform and accurate atomic positioning in the crystal lattice, which enhances the nobility of Cu. No obvious copper oxides are observed in fully ordered Cu 3 Au nanowires after annealing in air at 200 °C, a temperature that is much higher than those observed in Cu@Au core-shell and pure Cu nanowires. This work opens up an opportunity for further research into the development and applications of intermetallic nanowires.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

A bootstrapping approach to social media quantification

Abstract This work considers the use of classifiers in a downstream aggregation task estimating class proportions, such as estimating the percentage of reviews for a movie with positive sentiment. We derive the bias and variance of the class proportion estimator when taking classification error into account to determine how to best trade off different error types when tuning a classifier for these tasks. Additionally, we propose a method for constructing confidence intervals that correctly adjusts for classification error when estimating these statistics. We conduct experiments on four document classification tasks comparing our methods to prior approaches across classifier thresholds, sample sizes, and label distributions. Prior approaches have focused on providing the most accurate point estimate while this work focuses on the creation of correct confidence intervals that appropriately account for classifier error. Compared to the prior approaches, our methods provide lower error and more accurate confidence intervals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Evaluation of near-ambient algal biomass fractionation conditions for bioproduct development

This contribution describes an algal fractionation scheme based on cell lysing and carbohydrate hydrolysis under acidic conditions, coupled with solvent extraction, that produces algal lipids, carbohydrates, and proteinaceous solid from partially dewatered algal biomass. Here, a design of experiments analysis was employed to identify the effect of fractionation conditions on the yields of the three product streams. By selection of appropriate conditions, the process can be steered from simple lipid extraction to near complete fractionation of the biomass. Lipid purification and upgrading were respectively achieved with a low-cost adsorbent and an inexpensive Ni-based catalyst that deoxygenated the lipids via decarboxylation/decarbonylation, an approach offering several advantages over the hydrodeoxygenation-based processes typically employed to convert lipids to hydrocarbons. The proteinaceous solids obtained were found to have much lower ash content as well as higher protein content relative to the untreated algae, enhancing the suitability of this material as a feedstock for the production of bioplastics.

09 BIOMASS FUELS↗

DOE OSTI2024

Reductive catalytic fractionation of cotton stalks: catalytic strategy for tuning the selectivity of phenolic monomers

Pretreatment of lignocellulosic biomass is a primary step to delink lignin from the lignin-carbohydrate complex for bioethanol production and other value-added products. Presently, these processes yield lignin (technical) with significant structural changes compared to native lignin and make it difficult to valorize. Reductive catalytic fractionation (RCF) is an advanced pretreatment process to valorize native lignin to selective phenolic monomers before capitalizing on carbohydrates. Herein, cotton stalks (CS) were pretreated with ethanol: water mixture catalyzed by bimetallic, Ru-Ni/HY (RNY) and Ru-Fe/HY (RFY), and trimetallic, Ru-Fe-Ni/HY (RFNY), catalysts using the endogenously produced H 2 . The favorable catalytic properties of the RFNY catalyst, including high reducibility, acidity, metal dispersion, the synergistic effect of all the metals, and appropriate pore and particle size resulted in the highest catalytic activity. Maximum 92% delignification efficiency was achieved with 91% holocellulose retention. The extracted lignin after depolymerization in the same step produced a 19 wt.% yield of phenolic monomers. Increasing the duration of the reaction from 2 to 6 h increased the selectivity to saturated alkyl chain phenolics (1 to 16 wt.%) at the expense of hemicellulose (50 to 9%). The fate of carbohydrates left behind after the RCF (CS-RCF) process was analyzed through enzymatic saccharification and compared with raw CS and acid-pretreated CS. In conclusion, CS-RCF yielded maximum glucose yield (45 g/L) within an incubation period of 72 h.

09 BIOMASS FUELS↗

DOE OSTI2025

Cost-Benefit Analysis of Electricity Resilience Projects: State of the Art and Gap Analysis

Utilities and regulators must weigh the benefits of electricity resilience projects against their costs, which would be passed on to customers. Cost-benefit analysis (CBA) is an appropriate method for assessing this tradeoff. Here, in this paper we review the literature on CBA of electricity resilience projects and analyze gaps in the available methods and tools. The costs of resilience projects are typically straightforward to estimate but their benefits — particularly the avoided costs of power interruptions — are complex to quantify and monetize. A common perception among practitioners is that current CBA tools are not sufficiently mature to be adopted into real-world practice. We propose an electricity resilience CBA framework consisting of several elements: risks, physical impacts, power interruptions, economic impacts, and resilience projects. While methods for some elements are well-developed, there is a need for novel approaches to value avoided power interruptions, integrate multiple risks and benefit streams, and incorporate uncertainty.

Cost-benefit analysis↗

DOE OSTI2023

Modeling ash deposition and shedding during oxy-combustion of coal/rice husk blends at 70% inlet O2

Abstract Co-firing rice husk (RH) and coal with carbon capture using oxy-combustion presents a net carbon negative energy production opportunity. In addition, the high fusion temperature of the non-sticky, silica rich, RH can mitigate ash deposition as well as promote shedding of deposits. To identify the optimum operating conditions, fuel particle sizes, and blend ratios that minimize ash deposition, a Computational Fluid Dynamic methodology with add-on ash deposition and shedding models were employed to predict outer ash deposition and shedding rates during co-combustion of coal/RH in AIR and O 2 /CO 2 (70/30 vol%, OXY70) oxidizer compositions. After ensuring that the fly-ash particle size distributions and particle Stokes numbers near the deposition surface were accurately represented (to model impaction), appropriate models for coal ash and RH ash viscosities that were accurate in the temperature region (1200–1300 K) of interest in this study were identified. A particle viscosity and kinetic energy (PKE) based capture criterion was enforced to model the ash capture. An erosion/shedding criterion that takes the deposit melt fraction and the energy consumed during particle impact into account was also implemented. Deposition rate predictions as well as the deposition rate enhancement (OXY70/AIR) were in good agreement with measured values. While the OXY70 scenario was associated with a significant reduction (60%–70%) in flue gas velocities, it also resulted in larger fly-ash particles. As a result, the PKE distributions of the erosive RH ash were similar in both scenarios and resulted in similar shedding rates.

Energy & Fuels↗

DOE OSTI2025

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

We introduce SymbolFit (API: https://github.com/hftsoi/symbolfit), a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiring an adequate functional form to be determined before the fit can be performed. The main challenge arises when the appropriate functional forms cannot be derived from first principles, especially when there is no underlying true closed-form function for the distribution. In this work, we develop a framework that automates and streamlines the process by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without requiring a predefined functional form because the functional form itself is treated as a trainable parameter, making the process far more efficient and effortless than traditional regression methods. We demonstrate the framework in high-energy physics experiments at the CERN Large Hadron Collider (LHC) using five real proton-proton collision datasets from new physics searches, including background modeling in resonance searches for high-mass dijet, trijet, paired-dijet, diphoton, and dimuon events. We show that our framework can flexibly and efficiently generate a wide range of candidate functions that fit a nontrivial distribution well using a simple fit configuration that varies only by random seed, and that the same fit configuration, which defines a vast function space, can also be applied to distributions of different shapes, whereas achieving a comparable result with traditional methods would have required extensive manual effort.

Tsoi, Ho Fung [Univ. of Pennsylvania, Philadelphia↗

DOE OSTI2021

A Review of Local-to-Nonlocal Coupling Methods in Nonlocal Diffusion and Nonlocal Mechanics

Local-to-nonlocal (LtN) coupling refers to a class of methods aimed at combining nonlocal and local modeling descriptions of a given system into a unified coupled representation. This allows to consolidate the accuracy of nonlocal models with the computational expediency of their local counterparts, while often simultaneously removing nonlocal modeling issues such as surface effects. The number and variety of proposed LtN coupling approaches have significantly grown in recent years, yet the field of LtN coupling continues to grow and still has open challenges. This review provides an overview of the state of the art of LtN coupling in the context of nonlocal diffusion and nonlocal mechanics, specifically peridynamics. Furthermore, we present a classification of LtN coupling methods and discuss common features and challenges. The goal of this review is not to provide a preferred way to address LtN coupling but to present a broad perspective of the field, which would serve as guidance for practitioners in the selection of appropriate LtN coupling methods based on the characteristics and needs of the problem under consideration.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Continuous-variable quantum Boltzmann machine

Here, we propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution (QITE) algorithm to prepare the essential thermal state and then designed the CVQBM to proficiently generate continuous probability distributions. We applied our method to both classical and quantum data. Using real-world classical data, such as synthetic-aperture radar (SAR) images, we generated probability distributions. For quantum data, we used the output of CV quantum circuits. We obtained high fidelity and low Kullback–Leibler (KL) divergence showing that our CVQBM learns distributions from given data well and generates data sampling from that distribution efficiently. We also discussed the experimental feasibility of our proposed CVQBM. Our method can be applied to a wide range of real-world problems by choosing an appropriate target distribution (corresponding to, e.g., SAR images, medical images, and risk management in finance). Moreover, our CVQBM is versatile and could be programmed to perform tasks beyond generation, such as anomaly detection.

SAR images↗

DOE OSTI2026

Controversy and consensus: common ground and best practices for life cycle assessment of emerging technologies

This manuscript explores six controversial topics regarding LCA of emerging technologies: (1) appropriate use of LCA, (2) uncertainty assessment, (3) comparison with incumbents, (4) adopting standards, (5) system scale-up, and (6) stakeholder engagement. These topics encompass key issues vigorously debated during a series of workshop-style discussions convened by the LCA of Emerging Technologies Research Network (currently hosted by ACLCA). This paper represents the main points of support and opposition for a declarative resolution representing each topic, along with points of consensus, held amongst research network of LCA practitioners and experts. These debates and associated open questions are intended to build awareness amongst practitioners and decision-makers of the common challenges associated with assessing emerging technologies, while fostering evidence-based and context-informed discussions that are both transparent and impactful for the broader community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

In recent years, as engine control strategies have grown increasingly sophisticated in a continued drive for increasing efficiency and reducing emissions, engine operation has been pushed into regimes that are limited by combustion instabilities. These instabilities produce undesirable abnormal combustion events, which pose barriers to further improvement in engine efficiency. Many of the phenomena involved are difficult to model or control using traditional, purely physics-based models and reactive control approaches. Artificial intelligence (AI) techniques offer some promise for achieving more effective combustion stability control, especially when appropriately applied within a physics-informed framework. This chapter illustrates the current state-of-the-art in applying AI to combustion stability control and examines three case studies with application to the dilute stability limit in spark-ignition engines to illustrate the utility and limitations of AI in these applications.

Maldonado Puente, Bryan↗

DOE OSTI2023

Chapter 8 - Testing and Evaluation Methods for Protection Systems

In this chapter, we aim to present different approaches used formally and informally for the evaluation of protection devices and schemes. This chapter starts with presenting background on generalization of protection device components, followed by the resources necessary to evaluate the protection system. Further, this chapter presents the different approaches used in protection evaluation with their advantages and disadvantages. Finally, this chapter closes by presenting background on technology readiness level metrics. Technology readiness level metric is borrowed from the space industry for the purpose of comparing the maturity of the different protection technologies and using the appropriate evaluation methods from the list presented here.

power systems↗

DOE OSTI2023

Chapter 14 - Reliability of Wind Turbines

The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.

database↗

DOE OSTI2023

Study of Different Formulations for the Multiperiod Blending Problem Applied to Lithium Recovery from Produced Water

We present several formulations to solve the MPBP for the recovery of REE/CMs from produced water and propose a decomposition approach thatleverages strategies in general disjunctive programming to enhance its performance. We compare these proposed formulations/strategies via two illustrative case studies on recovering lithium from a network of produced water sources.

Ovalle, Daniel↗