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Safety Analysis of FeCrAl Accident-Tolerant Fuels with Increased Enrichment and Extended Burnup
The U.S. nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor nuclear power plants (NPPs), and it can be achieved more economically by using a risk-informed ecosystem, such as that being developed by the Risk-Informed Systems Analysis Pathway under the U.S. Department of Energy Light Water Reactor Sustainability Program. This program is promoting a wide range of research and development activities to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering a second license renewal. The Risk-Informed Systems Analysis Pathway has two main goals: (1) The deployment of methodologies and technologies that enable a better representation of the safety margins and factors that contribute to cost and safety, and (2) The development of advanced applications that enable cost-effective plant operation. As part of this pathway, the Enhanced Resilient Plant project refers to an NPP where safety is improved by implementing various measures, such as accident-tolerant fuels, diverse and flexible coping strategies, enhancements to plant components and systems, incorporation of augmented or new passive cooling systems, and utilization of advanced battery technologies. The objective of the Enhanced Resilient Plant project is to use novel methods and computational tools to enhance existing reactors’ safety while reducing operational costs. This report documents research and development conducted in support of deployment of accident-tolerant fuels. This project performed safety analyses for the steady-state normal operation, anticipated operational occurrences, and design-basis accidents of a representative four-loop pressurized water reactor model with Zr and FeCrAl accident-tolerant fuel clad with higher enrichment and burnup supporting plant refueling cycles of 18 and 24 months. The source terms and environmental impacts were studied for a large-break loss of coolant accident, including uncertainty analyses.
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.
A Study on Surface Texture and Wettability of Femtosecond Laser Treated Aluminum Alloys
This research aims to study the effects of femtosecond laser treatment on surface texture and wettability properties. The first portion of this research studies 2024-T3 aluminum alloy concerning surface texture (e.g., roughness), wettability, and surface free energy. SEM images help aid the researchers in detecting minuscule changes in surface texture. Profilometry extracts topographical data from the surface. A contact angle goniometer measures contact angle, which follows the sessile drop method. Owens, Wendt, Rabel, and Kaelble's method calculates the surface free energy in conjunction with the contact angle. Femtosecond laser modification produces relatively uniform surface morphology at the macroscale. When looking at the microscale, surface morphology differs depending on the interplay between material melting and self-assembly, where the surface becomes rougher after laser treatment. Contact angle results show that all but the control sample and sample 7 exhibit hydrophobic behavior, i.e., contact angle >90°, with Sample 1 having the most significant angle of 115°. The effects of laser parameters on surface energy is apparent. More work is needed to establish correlations among these three quantities: contact angle, surface energy, and laser parameters. The second portion of this research studies aluminum alloy 6061 as the subject material with the primary goal of creating a superhydrophobic surface. A central composite design creates the laser surface texturing design of experiments. SEM images help aid the researchers in detecting minuscule changes in surface texture. A 3D optical profiler extracts topographical data from the surface. The sessile drop method incorporates a contact angle goniometer to measure the contact angle. The response surface method builds a second-order polynomial model for the contact angle and obtains optimized parameters to maximize the contact angle. This research shows that laser surface texturing can generate a wide range of surface profiles and roughness values with geometric features ranging from hundreds of μm to submicron. All three laser parameters (pulse energy, pulse duration, repetition rate) affect surface roughness and contact angle to some degree. To quantify the relationship between the contact angle, pulse energy, and pulse repetition rate, a response surface model for Al 6061 is identified and used to find the optimal conditions of E=214 μJ, tp=10 ps, and fp=2427 Hz with a predicted maximum contact angle of 161°. A confirmation experiment produces a contact angle of 168°, in good agreement with the predicted value.
The Effects of Compounded Model Size Reductions on Adversarial Robustness
Recent advances in Edge AI and Tiny Machine Learning (TinyML) have enabled the deployment of machine learning models on resource-constrained environments. However, deploying these models on edge devices, such as micro-controllers, requires significant model footprint reduction through a variety of techniques such as quantization, pruning, and clustering. While these optimization methods offer considerable advantages, they potentially introduce AI-related security vulnerabilities, particularly concerning model robustness with respect to adversarial AI attacks. Prior research has extensively examined the impact of quantization on adversarial robustness; however, the effects of alternative reduction techniques and their combinations remain understudied. This paper investigates the impact of model size reduction techniques on adversarial robustness, when applied individually and combined. We utilized Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks to generate adversarial perturbations for both training and testing data, and then evaluated the models' accuracy under adversarial training conditions. Our findings revealed that reduction techniques generally diminished robustness; although, combining techniques was not found to make robustness any worse than when applied individually. Moreover, specific techniques can potentially enhance resistance to small size perturbations. This research provides insights into the trade-offs between model size reduction and security, establishing a foundation for future investigations into improving adversarial training techniques and methodologies for maintaining robustness while preserving memory footprint benefits.
Batteries Included: Top 10 Findings from Berkeley Lab Research on the Growth of Hybrid Power Plants in the United States
One of the most important electric power system trends of the 2010s was the rapid deployment of wind turbines and photovoltaic arrays, but a twist for the 2020s may be the rapid deployment of ‘hybrid’ generation resources. Hybrid power plants typically combine solar or wind (or other energy sources) with co-located storage. While hybridization helps to ease the challenge of balancing variable supply and demand, its relative novelty means that research is needed to facilitate integration and promote innovation. Combining the characteristics of multiple energy, storage, and conversion technologies poses complex questions for grid operations and economics. Project developers, system operators, planners, and regulators would benefit from better data, methods, and tools to estimate the costs, values, and system impacts of hybrid projects. This publication showcases some of Berkeley Lab’s robust research program intended to support private- and public-sector decision-making about hybrid plants in the United States. Our short briefing summarizes articles that we published between 2020 and 2022, links to the in-depth reports, and provides contact details for further engagement on the specific research topics: Growth: Developer interest in hybrid power plants is strong and growing Price vs. Value: PV+storage hybrids have low PPA prices and high value in some regions Market Drivers: Solar hybridization is driven by tax credits and other benefits Configuration Choices: Market prices have incentivized shorter duration batteries with PV Capacity Value: The capacity contribution of a hybrid is less than the sum of its parts Ancillary Services: AS markets are a valuable yet fleeting option for hybrids Market Participation: Hybrids can more flexibly engage with electricity markets Operations: The power system value of hybrids depends on how they are operated Distributed Hybrids: Growth of customer-sited PV+storage hybrids offers new opportunities Future Research: Where next? Priority areas for hybrid power research.
Demystifying Piecewise and Localized Scatter Correction Methods
Multiplicative scatter is a common source of noise in near-infrared spectroscopy and other related instrumental techniques. A wide variety of methods are commonly used for the correction of multiplicative scatter. However, the majority of such methods assume that the parameters that describe the scatter are constant throughout the measured spectrum, which is often not the case. This work investigates a family of methods that perform scatter correction using local regions of neighboring wavelengths in order to better account for wavelength-dependent scattering. The methods in question are piecewise standard normal variate, localized standard normal variate, piecewise multiplicative scatter correction, and localized multiplicative scatter correction. This work describes the theoretical and algorithmic foundations of the family of local region-based scatter correction methods and compares their application and optimization at a qualitative and quantitative level using several datasets.
Remote Home Energy Score Assessments (Feasibility Study)
The delivery of Home Energy Scores (HES) has been significantly impacted over the last year by the recent pandemic and ongoing limitations to person-to-person interactions. There are also historic challenges in providing HES to more remote geographies where assessor infrastructure is not in place. Alternative approaches to in-person, on-site home energy assessments could help address these current restrictions, while also potentially providing opportunities for the efficient delivery of HES in certain use cases after COVID-related restrictions are lifted. From the program administrator’s perspective, there needs to be confidence that the remote scores closely match what they would have been if the assessment had been performed on-site. The recommendations shown on a report generated from a remote score should closely match those of an onsite assessment. A remote assessment should be an option for all HES Partners and have direction on how to deliver those scores. From the HES partner’s and assessor’s perspective, there needs to be understanding of what level of experience is needed, what system infrastructure is required, the time expenditure of the remote assessment process, and the limitations of a remote assessment. From the homeowner’s perspective, there needs to be an awareness and comfort with the time commitment, the level of knowledge of the home, the equipment needed, and any physical requirements of the remote assessment process. During this study, Earth Advantage assessed methods for delivering Home Energy Score assessments remotely through web-based technology platforms and resident interaction in a diverse array of home types in different geographic locations. Earth Advantage developed remote assessment test protocols and integrated those testing protocols into existing Home Energy Score programs being overseen by USDOE Home Energy Score partners New York State Energy Research & Development Authority (NYSERDA), the Oregon Department of Energy (ODOE), and the City of Portland (PDX). Earth Advantage staff acted as the Remote assessor and gathered HES data remotely during the video sessions with participants. Authorized Home Energy Score assessors performed the onsite assessments that were then used to compare with the remote assessment data. This allowed the research to effectively test the efficacy of various remote assessment approaches in a diverse array of home-types. Earth advantage identified the key components of a remote assessment infrastructure which included Remote Home Energy Score Assessments Page vi processes, tools, services, methods and mechanisms that would better enable remote assessments. This report details methods, results, and findings of the HES remote assessment research. In addition, the report provides recommendations and best practices that could be used as guidance for the Home Energy Score administrator and the HES partners seeking to effectively conduct remote assessments.
Calculation of the First Moment of Energy Using D-T Reactivity Formalisms Under the Maxwell-Boltzmann Distribution (Pt. 1)
Nuclear fusion science is an example of a scientific field with a rich history of expert involvement and scientific publications, which together, form an expert-knowledge base. One example of this history is the utilization of published reaction rates from a variety of authors. Investigators for Deuterium-Tritium (D-T) ion fusion can choose from using frequently cited methods: the Bosch and Hale reactivity, thermonuclear reaction rates from Caughlan and Fowler, and the reactivity evaluation from Miley, Towner & Ivich which forms the basis of the Naval Research Lab (NRL) formulary. There are other choices available. Each of the reactivity formulations considered here, are based upon the Maxwell- Boltzmann velocity distribution for D-T fusion ion reactants. Numerical methods for computer codes simulating hot, energetic plasmas, include tabulations of the reactivity, and the first moment of energy. The purpose of this study is to answer the question: what is the first moment of energy, and how has it been formulated? This report is part of a series of reports by the authors on D-T fusion-reaction formalism. The present focus is on defining the mathematical relationship of the first moment of D-T fusion ion kinetic energy, $\langle$E$\rangle$, with the fusion crosssection, fusion reactivity and its derivative with respect to ion-temperature. Three variants of the first moment $\langle$E$\rangle$ are analytically developed and explored: 1) constant cross-section, 2) a normalized first moment, and 3) a particular function of the first moment. Much of the mathematical development is relegated to the Appendices, for a concise presentation of topics and results. A research component of this discussion is our comparison of some of the reactivity and its derivative results between evaluations (1) versus (2) and (3), which the authors have not found in any other publication to date. This investigation is useful for validation, verification (V&V) and uncertainty quantification (UQ) as it compares the work of several authors.
Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track
Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.
On the characterization of biases arising from methods and approximations used for sensitivity analyses
The preliminary assessment of the performance and safety of advanced reactors, as well as the identification of research and development needs, rely on computer simulations due to the lack of operational experience. Those simulations should be based on verified and validated computational tools, calculation schemes, and nuclear data libraries, and should be accompanied of a proper characterization of the involved uncertainties, providing confidence in the computational-based predictions. Thus, sensitivity and uncertainty studies, together with integral experiments, play an essential role in that process. Sensitivities are then a critical element and guidelines about how to produce accurate enough sensitivities depending on the subsequent analyses to be performed are of interest. The present study addresses the sensitivities biases arising from the use of different nuclear data libraries, computational methods and the assumption of modelling simplifications. Not only sensitivities for multiplication factor but also for safety-relevant reactivity responses have been analyzed. In the study, propagated uncertainties are also included for the considered parameters to illustrate the impact of the sensitivity profiles obtained for each case on the final uncertainty. Then, relevant recommendations are given for sensitivity analyses of highly complex systems. (authors)
Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings
As wind energy plays a growing role in the energy sector, new methods for controlling wind turbines and wind farms to maximize performance are garnering industry interest. A developing body of research treats the entire wind farm as a control system, with individual turbines acting as agents in a network, allowing farm-level objectives to be considered. Two promising developments in this research are wake steering control, which seeks to increase the power generated at a wind farm by directing the wakes of upstream turbines away from downstream ones, and communication-based spatial filtering, which seeks to improve the quality of information used by turbine- and farm-level controllers by combining measurements of the wind field collected at the individual turbines. The latter method has been shown to improve the estimates of wind direction at the turbines; however, the resulting potential for increased power capture warrants further investigation. With this paper, we begin to address this gap by combining wake steering with wind direction spatial filtering. To do so, we present a preliminary method for assessing the power capture of dynamic controllers using wind farm codes designed for time-averaged simulation. This allows us to generate results much more rapidly than would be possible using high-fidelity wind farm simulators and may be useful in many wind farm control design applications.
The moving target of visualization software for an increasingly complex world
Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.
American-Made Challenges: Acoustic Cleaving of Gallium Arsenide for Next Generation Photovoltaic: Cooperative Research and Development Final Report, CRADA Number CRD-19-00831
To evaluate the potential of Sonic Wafering(TM), a new manufacturing method that may lower the cost of high-efficiency photovoltaic materials. This work is to be conducted in support of the Department of Energy’s American Made Challenges Solar Prize.
In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics
Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone ( n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.
Advanced Thermoluminescence Spectroscopy as a Research Tool for Semiconductor and Photonic Materials: A Review and Perspective
Thermoluminescence (TL) or thermally stimulated luminescence (TSL) spectroscopy is based on liberating charge carriers from traps in the bandgap by providing enough thermal energy to overcome the potential barrier of the traps. It provides a powerful tool to measure the positions of the localized states/traps in the bandgap. Despite that, its applications in semiconductors are very limited. Herein, the basics of TL spectroscopy and the recent advances in the technique with focus on cryogenic thermally stimulated photoemission spectroscopy (C‐TSPS) which extends TL measurements to cryogenic regime and allows the detection of very low concentrations of shallow and deep localized states is discussed. One goal herein is to introduce the reader to the use of TL and C‐TSPS in the characterization of semiconductors, explaining how it can be applied and demonstrating its advantages as a powerful tool for measuring shallow donor/acceptor ionization energies in semiconductors and as a method for characterizing compensating defects. The article also discusses interesting potential applications of C‐TSPS in new research areas such as corrosion and formation of oxide layers on metal surfaces.
Photoelectron spectroscopy in molecular physical chemistry
Photoelectron spectroscopy has long been a powerful method in the toolbox of experimental physical chemistry and molecular physics. Recent improvements in coincidence methods, charged-particle imaging, and electron energy resolution have greatly expanded the variety of environments in which photoelectron spectroscopy can be applied, as well as the range of questions that can now be addressed. In this Perspectives Article, we focus on selected recent studies that highlight these advances and research areas. Furthermore, the topics include reactive intermediates and new thermochemical data, high-resolution comparisons of experiment and theory using methods based on pulsed-field ionisation (PFI), and the application of photoelectron spectroscopy as an analytical tool to monitor chemical reactions in complex environments, like model flames, catalytic or high-temperature reactors.
Chapter 3: Nanostructured Iron Oxide Hybrid Composites as Heterogeneous Fenton-Like Catalyst for Remediation of Persistent Organic Pollutants
In water purification research and technologies, heterogeneous Fenton catalysts are evolving as an excellent material recently. A summary of present research on heterogeneous Fenton/Fenton-like processes is essential for developing novel and high-efficiency organic wastewater treatment methods. In this chapter, we discussed the basics of Fenton reactions taking the example of iron oxides and their different composites, such as bimetallic composites, composites with carbon compounds, and Fe metal-organic frameworks (MOFs). The important operating parameters (solution pH, H 2 O 2 dosage, amount of catalyst used, and reaction temperature) in heterogeneous Fenton-like reactions are discussed. Strategies to increase the rate and activities of heterogeneous Fenton catalysts by using energy sources are presented. The mechanistic aspects of heterogeneous Fenton processes and reusability of catalysts along with their application in real wastewater plants are summarized and discussed. Finally, we concluded with the scope and future prospects of heterogeneous Fenton catalysis research to motivate the readers to construct novel and efficient heterogeneous Fenton/Fenton-like systems for industrial applications.