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DOE OSTI2021

Iron-based nanoparticles and grains

Example nanoparticles may include an iron-based core, and a shell. The shell may include a non-magnetic, anti-ferromagnetic, or ferrimagnetic material. Example alloy compositions may include an iron-based grain, and a grain boundary. The grain boundary may include a non-magnetic, anti-ferromagnetic, or ferrimagnetic material. Example techniques for forming iron-based core-shell nanoparticles may include depositing a shell on an iron-based core. The depositing may include immersing the iron-based core in a salt composition for a predetermined period of time. The depositing may include milling the iron-based core with a salt composition for a predetermined period of time. Example techniques for treating a composition comprising core-shell nanoparticles may include nitriding the composition.

Wang, Jian-Ping↗

DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Bio-based polymers from raw lignocellulosic biomass

Disclosed herein is a method of making polymerizable bio-based monomers containing one phenolic hydroxyl group which has been derivatized to provide at least one polymerizable functional group which is an ethylenically unsaturated functional group (such as a [meth]acrylate group), where the precursors of the polymerizable bio-based monomers are derived from raw lignin-containing biomass. Also disclosed herein are bio-based copolymers prepared from such bio-based monomers and a co-monomer, and methods of making and using such bio-based copolymers. In particular, the bio-based copolymers can be used as pressure sensitive adhesives, binders, and polymer electrolytes.

Epps, III, Thomas H.↗

DOE OSTI2022

Application of Margin-Based Methods to Assess System Health

Health management of complex systems such as nuclear power plants is an essential task to guarantee system reliability. This task can be greatly enhanced by constantly monitoring asset status and performances and process such data (through anomaly detection, diagnostic, and prognostic computational algorithms) to identify asset degradation trends and faulty states. While such information and data are typically available for many of the assets, they are not propagated from the asset to the system level in order to identify the most critical assets and prioritize maintenance and surveillance activities. The main reason is driven by the fact that current reliability modeling techniques are inadequate to process such information/data. This is due to the nature of these techniques which are based on the concept of failure rate/probability that do not serve an operational context where quantitative asset health information is available. Simply stated, current reliability techniques serve a run-to-failure operational setting and not a predictive maintenance one where the goal is to perform maintenance and surveillance activities only when they are needed based on asset health. The risk informed asset management (RIAM) project is focusing on the development of a different kind of reliability modeling techniques designed to adequately serve a predictive operational setting. Such reliability techniques move aways from a failure rate/probability to a margin-based mindset where margin is here used as a metric to quantify asset health based only on current and past operational experience of the asset under consideration. In addition, margin-based reliability techniques are able to propagate asset health information from the component to system level and provide importance measure to each asset. This report summarizes a recent activity performed in collaboration with plant modernization pathway designed to integrate monitoring data into margin-based reliability models. Such activity focuses on a specific system of an existing nuclear power plant where large amount of historic monitoring data is used to monitor asset and system health.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Modular base for an antenna array

A base configured to be joined with other bases to form a substrate for an antenna array comprises a body, a plurality of male interconnecting features, and a plurality of female interconnecting features. The body includes a front surface and a rear surface and a plurality of edges positioned therebetween. The front surface or the rear surface is configured to retain an antenna. The male interconnecting features of a first base connect with the female interconnecting features of a second base when the first base is joined with the second base to form the substrate or a portion of the substrate.

Furman, Nicholas J.↗

DOE OSTI2023

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, Seyed Mohammad↗

DOE OSTI2023

Iron-based nanoparticles and grains

Wang, Jian-Ping↗

DOE OSTI2024

Graph Neural Network-based Tracking as a Service

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Use of Lignin-Based Admixture as Water Reducer (WRA) for Tailoring the Rheological Properties of Mortars for 3D-Printing

Efforts towards decarbonizing construction materials and industrial processes related to cement and concrete can be aided via multifaceted approaches that target alternative admixtures as well as precision control of fabrication. Chemical admixtures for water reduction have played a crucial role in the development of advanced mortar and concrete mixtures. Newer biomass processing techniques developed for aviation fuel production from corn stover biomass produce a highly reactive lignin byproduct that is suitable for chemical modifications to mimic the properties of commonly used polycarboxylate ethers (PCEs) with a smaller carbon footprint. These lignin-based plasticizers developed at NREL can be used in place of petrochemical-derived superplasticizers to tailor the rheological properties of cement-based systems for applications such as additive manufacturing while reducing the carbon intensity of the concrete mix. Lignosulfonates derived from paper pulping were historically used as water reducers only to be displaced with the rise of PCEs. The present study examines the use of a NREL produced lignin-based water reducing admixture (WRA), in cement pastes and mortar mixtures for 3D-printing at small- (mm) scale. The experimental program consisted of different formulations of oxidized lignin-based WRAs added in variable dosages to cement pastes with a fixed water-to-cement ratio. The objective is to achieve an appropriate workability, extrudability and buildability of mortar mixtures to produce 3D-printed specimens. The rheological characterization was performed to compare the initial yield stress and viscosity of the different mixtures with lignin-based admixture with respect to conventional PCE superplasticizers. The rheological characterization shows that the proposed material act as an effective water-reducer in cement systems, affecting the yield stress, plastic viscosity, and structuration rate of the mixtures evaluated. On the other hand, the effect on early hydration process of the cement pastes containing lignin-based admixtures is comparable to conventional superplasticizers.

3D-printing↗

DOE OSTI2021

A physics-based parametric regression approach for feedwater pump system diagnosis

Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Machine Learning–Based Condition Monitoring of a Circulating Water System of a Canadian Nuclear Plant

With the need to maintain long-term reliable energy using nuclear power plants, there is an underlying demand to ensure that the maintenance of plant components and systems is also done in an efficient and cost-effective manner. One way to achieve this is by moving from time-based maintenance to condition-based maintenance. The research presented in this paper focuses on applying statistical and machine-learning-based methods to capture anomalies within data for fault detection to further develop into condition monitoring. This paper focuses on system data for a circulating water system (CWS) of a pressurized heavy-water reactor for detecting anomalies. The different methodologies used for detecting and capturing anomalies in the CWS data are matrix profile, density-based spatial clustering of applications with noise (DBSCAN), and support vector machines (SVMs). Matrix profile and DBSCAN are used to distinguish between normal data and anomalous data. This paper presents a hybrid method using DBSCAN and SVM when a portion of the data is used for DBSCAN to generate clusters. This portion of data is then used to train the SVM along with the clusters generated by DBSCAN as output. SVM is then tested on unseen data as a predictive tool, which can work in real time to categorize data points as either normal or anomalous. This paper presents results that show the high accuracies of DBSCAN and SVM in capturing anomalies within the data for a CWS for fault detection. Thus, the maintenance plan would be focused on component condition rather than a time-based schedule by switching to an automated system to identify and predict faults within a CWS.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2019

Accrued Delay Application in Trajectory-Based Operations

The air traffic management system lacks integration among its elements often due to using inconsistent information, models, and metrics about the traffic. Transitioning to trajectory-based operations, whereby flights are managed by full trajectories in space and time, will enable more integration, with the help of increased automation. Building on trajectory-based operations, an "accrued delay" metric is proposed, which continuously measures the amount of delay that a flight has accumulated up to the current time, including delays incurred during the current flight and inherited from previous flights through the turnaround process. Through a time-based metering and scheduling example, we show how using accrued delay as a metric can help integrate the decision-making across multiple decision horizons, leading to more efficient and balanced access to airspace services. We show that when prioritizing flights that have already accrued high delay because of a constrained runway resource, significant gains are achieved in terms of reducing total delay and its variance. We studied the sensitivity of these gains to numerous factors, such as time-based versus distance-based horizons, horizon size, and errors in conformance to scheduled times.

delay; trajectory-based operations; time-based flo↗

DOE OSTI2021

Nanoscale Design of Pd-Based Electrocatalysts for Oxygen Reduction Reaction Enhancement in Alkaline Media

Palladium (Pd)-based electrocatalysts have recently emerged as one class of the foremost promising candidates for the oxygen reduction reaction (ORR) in alkaline media due to their excellent ORR activity and durability and lower costs compared with platinum. Insightful design of Pd-based nano-architectures with optimized active surface sites and maximal intrinsic performance is central to promoting the ORR applications. To further accelerate the sluggish ORR kinetics at the cathode of fuel cells and substantially decrease the overall cost of the electrocatalysts, various strategies, including controlled sizes and shapes with selected crystallographic facets, crystal-phase engineering, heteroatom doping, tailored surface strains, and surface engineering by de-alloying, have been extensively developed in the past decade. Here in this review, a brief introduction to the fundamental ORR mechanisms of Pd-based electrocatalysts in alkaline media is presented, followed by a thorough discussion on various strategies for delicately designing high-performance Pd-based catalysts with corresponding examples. Thereafter, the perspectives and new insights into the challenges are outlined, and some emerging research directions related to the rational design and controlled synthesis of Pd-based ORR electrocatalysts are also proposed.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices

Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

A level-of-details framework for representing occupant behavior in agent-based models

We report agent-based modeling is an advanced computational technique capable of representing complex and dynamic processes of human behavior in building performance simulation. Though the agent-based approach supports diverse applications concerning human behavior modeling within the built environment, there is no consensus on the optimal amount of information or level of granularity needed for occupant information representation. This paper attempts to formalize the level of details (LoD) needed for occupant behavior representation in agent-based environments. A novel framework, grounded on the concept of LoD, is proposed to select the required details in representing occupants in agent-based models. Ten attributes related to occupants' presence, movement, behavioral processes, and repertoire are considered to define the LoD. The framework identifies use case parameters as the guiding principle and allows a hybrid approach for selecting varying degrees of occupant attributes to serve the purpose of simulation. A discussion on the pertinence of different occupant behavior LoDs in relation to the desired objective and simulation context is also presented. The study intends to support the occupant behavior research by advancing agent-based occupant modeling in building performance simulation.

42 ENGINEERING↗

DOE OSTI2020

Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration

Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Polythiophene-based terpolymers with modulated aggregation behaviors for high-performance organic solar cells with 16.6% efficiency

Polythiophenes (PTs) are an attractive class of polymer donors (PDs) for organic solar cells (OSCs) owing to their relatively simple structures and scalable synthesis. Herein, a series of chlorinated thiazole-incorporated PT terpolymers are designed and high-performance OSCs with a power conversion efficiency (PCE) of 16.6% are demonstrated. By incorporating two different units, 3,3'-difluoro-2,2'-bithiophene (T2F2) and thieno[3,2-b]thiophene (TT), the aggregation properties of the terpolymers (PTz-FX; X = 0, 30, 50, 70, and 100, where X represents the mole percentage of T2F2 to total T2F2 +TT) are modulated. Among the PTz-FX series, PTz-F70 is found to be the optimal P D because its suitably tuned aggregation property leads to an optimized blend morphology with well-developed crystalline structures and donor–acceptor intermixed domains. The balanced morphology not only promotes charge generation/transport but also suppresses charge recombination in OSC devices. Thus, the PTz-F70-based OSCs achieve the highest PCE (16.6%), outperforming the OSCs based on PTz-FX with extremely strong (PTz-F100, PCE = 14.7%) or weak (PTz-F0, PCE = 12.0%) aggregation properties. The PCE of the PTz-F70-based OSCs is one of the highest performances among PT-based binary OSCs. Finally, this study highlights the importance of controlling the aggregation property of PTs for achieving high-performance PT-based OSCs.

14 SOLAR ENERGY↗

DOE OSTI2020

A large-scale, agent-based simulation of metropolitan freight movements with passenger and freight market interactions

This study summarizes the first stage in the implementation of an agent-based freight modeling system that has a global representation of agents and detailed modeling of a large-scale transportation network. The model is used to evaluate the transportation and energy impacts of goods movement across urban and national scales. The framework is implemented within POLARIS, a C++-based Planning and Operations Language for Agent-based Regional Integrated Simulation, which consists of an activity-based modeling (ABM) and dynamic traffic assignment (DTA) system that has robust features for passenger travel. This platform provides a tool to model interactions among consumers, producers, and the transportation system. The main objective of this initial implementation is to implement a freight model within POLARIS following an agent-based paradigm with behavioral and simulation methods. This paper presents the initial framework and illustrates the application of the model. Building upon earlier works, a parcel location assignment algorithm for business establishments in the population is documented, along with a method for estimating establishment production and consumption volumes. In addition to population generation, other features of the model include push-pull supply chains, multimodal path choice, choice of transportation logistics node, and dynamic traffic assignment. A module with e-commerce supply and demand was also developed to analyze the effects of e-commerce delivery on last-mile energy use and congestion.

agent-based freight↗