Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Operator learning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

A preliminary user-friendly, digital console for the control room parameters supervision in old-generation Nuclear Plants

Improvements in the awareness of a system status is an essential requirement to achieve safety in every kind of plant. In particular, in the case of Nuclear Power Plants (NPPs), a progress is crucial to enhance the Human Machine Interface (HMI) in order to optimize monitoring and analyzing processes of NPP operational states. Firstly, as old-fashioned plants are concerned, an upgrading of the whole console instrumentation is desirable in order to replace an analog visualization with a full-digital system. In this work, we present a novel instrument able to interface the control console of a nuclear reactor, developed by using CompactRio, a National Instruments embedded architecture and its dedicated programming language. This real-time industrial controller composed by a real-time processor and FPGA modules has been programmed to visualize the parameters coming from the reactor, and to storage and reproduce significant conditions anytime. This choice has been made on the basis of the FPGA properties: high reliability, determinism, true parallelism and re-configurability, achieved by a simple programming method, based on LabVIEW real-time environment. The system architecture exploits the FPGA capabilities of implementing custom timing and triggering, hardware-based analysis and co-processing, and highest performance control algorithms. Data stored during the supervisory phase can be reproduced by loading data from a measurement file, re-enacting worthwhile operations or conditions. The system has been thought to be used in three different modes, namely Log File Mode, Supervisory Mode and Simulation Mode. The proposed system can be considered as a first step to develop a more complete Decision Support System (DSS): indeed this work is part of a wider project that includes the elaboration of intelligent agents and meta-theory approaches. A synoptic has been created to monitor every kind of action on the plant through an intuitive sight. Furthermore, another important aim of this work is the possibility to have a front panel available on a web interface: CompactRio acts as a remote server and it is accessible on a dedicated LAN. This supervisory system has been tested and validated on the basis of the real control console for the 1-MW TRIGA reactor RC-1 at the ENEA, Casaccia Research Center. In this paper we show some results obtained by recording each variable as the reactor reaches its maximum level of power. The choice of a research reactor for testing the developed system relies on its training and didactic importance for the education of plant operators: in this context a digital instrument can offer a better user-friendly tool for learning and training. It is worthwhile to remark that such a system does not interfere with the console instrumentation, the latter continuing to preserve the total control. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Mega salt-stone Disposal Units at the Savannah River site - 15322

The Liquid Waste (LW) System at Savannah River Site (SRS) is a highly integrated operation involving safely storing radioactive liquid waste in underground storage tanks; vitrifying the higher activity waste; and storing the vitrified waste in stainless steel canisters until permanent disposition is provided; and removing, treating, and dispositioning the low-level waste (LLW) fraction in concrete with final disposition being the salt-stone Disposal Units (SDUs) at the SRS salt-stone Disposal Facility (SDF). Treatment and disposition of salt waste is the critical path to completion of the SRS Liquid Waste (LW) Disposition Program. Successful and timely salt waste removal and disposal is integral to efforts by SRS to proceed with all aspects of tank cleanup and removal from service. The construction and implementation of the first-of-a-kind Mega SDU (MSDU) is underway to provide the most environmentally safe and cost-effective solution for salt processing and long term storage of the LLW at SRS. The MSDU design is a duplicate of the Water Treatment Tanks located in Syracuse, New York. This commercial design, developed by an affiliate of Savannah River Remediation LLC (SRR), was slightly modified to meet the requirements of the nuclear industry. The MSDU is a critical link in salt disposition. In comparison to the current SDU design, approximately 46 meters (150 feet) in diameter and 7 meters (22 feet) tall with a capacity of approximately 11,000 m{sup 3} (3.0 million gallons), the MSDU is 114 meters (375 feet) in diameter and 13 meters (43 feet) tall which will provide a capacity of approximately 121,000 m{sup 3} (32.0 million gallons) to receive the LLW fraction embedded in a grout-like form. The MSDU will provide the taxpayers an estimated $300 M in life cycle savings at SRS by reducing the number of SDUs from the estimated 72 smaller vessels to 7 of the new larger vessels. Additionally, based on the reduced number of SDUs, the environmental footprint is significantly reduced as well. Due to the similarities between the existing SDU design and the MSDU design, efforts to integrate the MSDU into the LW system are minimal. Required infrastructure modifications will consist of tying into the existing grout and drain lines, as well as the installation of new electrical and instrumentation services for the MSDU. Grout flow testing has been successfully completed, with results showing the MSDU will be a safe and cost-effective replacement for the existing SDUs. The MSDU design is complete, Critical Decision (CD)-3 was approved on June 17, 2014, and the first MSDU, salt-stone Disposal Unit n.6 (SDU 6), is being constructed in the existing LW SDF operated by SRR as a Capital Line Item project. As an added benefit, once constructed, lessons learned will be available for future MSDUs at SRS and throughout the complex as necessary. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

The Ambivalent Role of Participants in Nuclear Waste Decisions - 16360

Today's global nuclear sector has entered a critical juncture. Waste management solutions have the potential to influence the prospects of whether new plants will be built and when aging plants will be decommissioned. Even in countries without nuclear power plants, the siting of nuclear waste can present communication challenges and opportunities for public involvement. As new nuclear programs withstand scrutiny, local communities share their want for solutions to nuclear waste before new plant construction commences. Developing nations have begun to explore the potential of adding nuclear power production to their energy mix. They carefully watch established nuclear players to accelerate their plans with lessons learned elsewhere. Some newcomers to nuclear energy will make decisions on new construction based on other operators' abilities to reconcile public concerns regarding radioactive waste. Distinct forms of government, cultural norms and means of public discourse present innovative approaches and changing expectations worldwide. This paper explores how practitioners are evaluating ways to activate stakeholders on nuclear waste siting in nations embarking on new nuclear programs. It investigates the incentives that people have to participate and legitimize the formation of laws, regulations and standards for nuclear materials management. Finally, it highlights examples of public participation techniques from established operating environments and posits that overly prescriptive requirements for stakeholder engagement can reduce the authenticity of public involvement and weaken confidence among participants in the decision making process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Autonomous Utility Pole Identification

The implementation of small unmanned aerial systems (sUAS) for the purpose of powerline inspection is an emerging concept among utility companies. Such operations can produce a significant amount of useful data for the purpose of training different machine learning models or keep track of the integrity of electrical infrastructure. As such, this system is developed for the purpose of properly cataloging and leveraging this collected data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Augmented cognition tool for rapid military decision making.

This report describes the laboratory directed research and development work to model relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism to support rapid decision making. Using the biology and functionality of the hippocampus as an analogy or inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations form. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a novel approach to data mining for knowledge discovery. This report describes the neurobiological inspiration, architecture, and operational characteristics of our model, and also provides a real world terrorist network example to illustrate the model's functionality.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2015

Crystallization in high level waste (HLW) glass melters: Savannah River Site operational experience

This paper provides a review of the scaled melter testing that was completed for design input to the Defense Waste Processing Facility (DWPF) melter. Testing with prototype melters provided the data to define the DWPF operating limits to avoid bulk (volume) crystallization in the un-agitated DWPF melter and provided the data to distinguish between spinels generated by refractory corrosion versus spinels that precipitated from the HLW glass melt pool. A review of the crystallization observed with the prototype melters and the full-scale DWPF melters (DWPF Melter 1 and DWPF Melter 2) is included. Examples of actual DWPF melter attainment with Melter 2 are given. The intent is to provide an overview of lessons learned, including some example data, that can be used to advance the development and implementation of an empirical model and operating limit for crystal accumulation for a waste treatment and immobilization plant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Intelligent Optimization of the Digital Low Level RF Control System for LANSCE LINAC

The LINAC at the Los Alamos Neutron Science Center (LANSCE) accelerates protons from 750 keV to its final energy at 800 MeV via 48 radio frequency (RF) modules. However, the startup and recovery process of the low-level RF (LLRF) systems, the primary controls for the RF modules, cost significant time for the beam operation, while the process itself is highly prone to human errors. With the new conversion from the analog LLRF (aLLRF) to digital LLRF (dLLRF) system under the recent LANSCE Modernization Project, new approaches with the new dLLRF capabilities can be achieved to address this issue. We propose to develop an intelligent optimization scheme that can significantly lower the downtime caused by the LLRF systems. This directly address the MFR problem statement that asks for “innovative engineering improvements to ancillary systems such as RF and pulsed power that improve reliability, maintainability, and/or performance.”

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Symplectic machine learning model for fast simulation of space-charge effects

Symplectic simulation of space-charge effects is crucial for the design and operation of high-intensity particle accelerators. Traditional methods for simulating these effects are often computationally expensive, resulting in significant overhead. In this work, we introduce a generative model based on a U-Net architecture within a generative adversarial network framework to efficiently simulate space-charge effects. The model is trained to predict the transverse multiparticle space-charge Hamiltonian, which can be physically computed using a gridless spectral method. The one-step symplectic transverse transfer map for the particles is then obtained by differentiating the predicted Hamiltonian. Benchmarking results demonstrate that this generative model achieves an order of magnitude higher computational efficiency compared to the spectral method, providing a highly efficient alternative for simulating space-charge effects with a large number of particles. By maintaining symplecticity, the model effectively preserves the phase-space structure and mitigates nonphysical errors in long-term simulations. This model has been integrated into jutrack, a novel autodifferentiable accelerator modeling code developed in the julia programming language.

Beam code development & simulation techniques↗
DOE OSTI2018

The Operation of the Tevatron Vacuum System

Outline: Tevatron Overview and Some History; Vacuum Upgrades; Cryogenic Upgrades; Maintenance and Records; Vacuum Diagnostics; Failures; Lessons Learned

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

System Deterioration Detection and Root Cause Learning on Time Series Graphs

System deterioration detection and root cause analysis is crucial for today's industrial society. However, the design and operation of mechanic system is getting more and more complex, which makes it hard at identifying deterioration with noisy data. Our research focus on solving such problem on time-evolving sensor graphs in a streaming setting. Given a sequence of graphs, the ability to identify 1) any gradual and stable structured change and 2) the root cause components is of importance for early warning and system diagnosis. Existing methods either raise too many false alerts on instant changes or are too sensitive to noise. To address these problems, we propose Robust Failure Detection and Diagnosis (RoFaD). RoFaD can capture failure propagation given a time series of graph. By optimizing a matrix based Taylor expansion, RoFaD can identify system deterioration in the presence of noise and immediate changes, and diagnose the root cause components. Experiments on both synthetic and real world datasets demonstrate that RoFaD is more effective than the popular baselines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Electric Power Generation from Low to Intermediate Temperature Resources

This project was designed to test the concept on the Eland-Lodgepole Field near Dickinson, North Dakota in the Williston Basin. The field is in secondary-recovery water-flood and consists of 12 producing oil wells, 5 water injection wells and one disposal well. Water production at the site averages approximately 320 gallons per minute (20.2 l s-1) and the temperature is 100 ⁰C. Engineers at Ormat estimated power production potential with the existing resource to be approximately 350 kWh. Unfortunately, ownership of the field was transferred from Encore, Inc., to Denbury, Inc., within the first week of the project. After two years of discussion and planning, Denbury decided not to pursue this project due to complications with the site location and its proximity to Patterson Lake. Attempts to find other partners operating in the Williston Basin were unsuccessful. Consequently, we were unable to pursue the primary objective of the project. However, during negations with Denbury and subsequent time spent contacting other potential partners, we focused on objectives 2 and 3 and developed a clear understanding of the potential for co-produced production in the Williston Basin and the best practices for developing similar projects. At least nine water bearing formations with temperatures greater than 90 ⁰C extend over areas of several 10s of km2. The total energy contained in the rock volume of those geothermal aquifers is 283.6 EJ (1 EJ = 1018 J). The total energy contained in the water volume, determined from porosities which range from 2 percent to 8 percent, is 6.8 EJ. The aquifers grouped by 10 ⁰C temperature bins (Table 1) include one or more formations due to the bowl-shape structure of the basin. Table 1. Summary of energy available in geothermal aquifers in the Williston Basin Analysis of overall fluid production from active wells, units, fields and formations in North Dakota showed that few sites co-produce sufficient fluid for significant power production with ORC technology. Average co-produced water for 10,480 wells is 3.2 gallons per minute (gpm). Even excluding the tight formations, Bakken and Three Forks, average co-produced water for the remaining 3,337 is only 5 gpm. The output of the highest producing well is 184 gpm and the average of the top 100 wells is 52 gpm. Due to the depth of the oil producing formations in the Williston Basin, typically 3 km or greater, pumps are operated slowly to prevent watering out thus total fluid production is purposefully maintained at low volumes. There remain potential possibilities for development of geothermal fluids in the Williston Basin. Unitized fields in which water production from several tens of wells is collected at a single site are good possibilities for development. Water production in the unitized fields is greater than 1000 gpm is several areas. A similar possibility occurs where infill-drilling between Bakken and Three Forks horizontal wells has created areas where large volumes of geothermal fluids are available on multi-well pads and in unitized fields. Although the Bakken produces small amounts of water, the water/oil ration is typically less than 1, the oil and water mix produced at the well head can be sent through the heat exchanger on an ORC. It is estimated that several tens of MWh of power could be generated by a distributed system of ORC engines in the areas of high-density drilling in the Bakken Formation. Finally, horizontal drilling in water bearing formations is the other possibility. Several secondary recovery water-flood projects in the basin are producing water above 100 ⁰C at rates of 300 gpm to 850 gpm. Those systems also could produce several tens of MWh of power with ORC technology. Objective 3 of the project was highly successful. The program has produced 5 PhDs, 7 MS, and 3 BS students with theses in geothermal energy. The team has involved 7 faculty in 4 different engineering and science disciplines, ChE, EE, GE, and Geol. The team has produced 26 peer-reviewed papers and 62 presentations at professional meetings. Faculty involved in the program developed five graduate level courses covering different elements in heat flow and geothermal energy that are now offered in the Harold Hamm School of Geology and Geological Engineering. Lessons learned – Keys to developing a successful project;1. Determine target formations; a. Data from oil and gas operators, state oil and gas regulatory agencies, and state geological surveys help to identify producing formations and their properties; 2. Determine the quantity of energy available in the target formations; a. A complete thermal analysis of the basin or region yields the most useful information; b. Critical data include: BHT, heat flow, stratigraphy, lithology, lithological properties, and thermal conductivity, subsurface structure; 3. Determine fluid production potential; a. State oil and gas regulatory agencies, and state geological surveys have data on oil, gas and water production. State Water Commission/Agencies have data on water quality, aquifers, and regulations; b. Consider single horizontal wells, multiple conventional wells, and unitized fields; 4. Calculate energy production capacity of each formation based on different well combination and power plant scenarios. This is a broad overview rather than a site specific analysis; 5. Research and understand the local electrical power industry. Obtain the PPA before committing to the project; 6. Work with the high-level personnel in the oil company partner. Obtain an MOU that addresses all issues in the project including what to expect if the company goes out of business, is bought out, changes management, etc; and 7. Be prepared for project delays.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Crystallization In High Level Waste (HLW) Glass Melters: Operational Experience From The Savannah River Site

processing strategy for the Hanford Tank Waste Treatment and Immobilization Plant (WTP). The basis of this alternative approach is an empirical model predicting the crystal accumulation in the WTP glass discharge riser and melter bottom as a function of glass composition, time, and temperature. When coupled with an associated operating limit (e.g., the maximum tolerable thickness of an accumulated layer of crystals), this model could then be integrated into the process control algorithms to formulate crystal tolerant high level waste (HLW) glasses targeting higher waste loadings while still meeting process related limits and melter lifetime expectancies. This report provides a review of the scaled melter testing that was completed in support of the Defense Waste Processing Facility (DWPF) melter. Testing with scaled melters provided the data to define the DWPF operating limits to avoid bulk (volume) crystallization in the un-agitated DWPF melter and provided the data to distinguish between spinels generated by K-3 refractory corrosion versus spinels that precipitated from the HLW glass melt pool. This report includes a review of the crystallization observed with the scaled melters and the full scale DWPF melters (DWPF Melter 1 and DWPF Melter 2). Examples of actual DWPF melter attainment with Melter 2 are given. The intent is to provide an overview of lessons learned, including some example data, that can be used to advance the development and implementation of an empirical model and operating limit for crystal accumulation for WTP. Operation of the first and second (current) DWPF melters has demonstrated that the strategy of using a liquidus temperature predictive model combined with a 100 °C offset from the normal melter operating temperature of 1150 °C (i.e., the predicted liquidus temperature (TL) of the glass must be 1050 °C or less) has been successful in preventing any detrimental accumulation of spinel in the DWPF melt pool, and spinel has not been observed in any of the pour stream glass samples. Spinel was observed at the bottom of DWPF Melter 1 as a result of K-3 refractory corrosion. Issues have occurred with accumulation of spinel in the pour spout during periods of operation at higher waste loadings. Given that both DWPF melters were or have been in operation for greater than 8 years, the service life of the melters has far exceeded design expectations. It is possible that the DWPF liquidus temperature approach is conservative, in that it may be possible to successfully operate the melter with a small degree of allowable crystallization in the glass. This could be a viable approach to increasing waste loading in the glass assuming that the crystals are suspended in the melt and swept out through the riser and pour spout. Additional study is needed, and development work for WTP might be leveraged to support a different operating limit for the DWPF. Several recommendations are made regarding considerations that need to be included as part of the WTP crystal tolerant strategy based on the DWPF development work and operational data reviewed here. These include: Identify and consider the impacts of potential heat sinks in the WTP melter and glass pouring system; Consider the contributions of refractory corrosion products, which may serve to nucleate additional crystals leading to further accumulation; Consider volatilization of components from the melt (e.g., boron, alkali, halides, etc.) and determine their impacts on glass crystallization behavior; Evaluate the impacts of glass REDuction/OXidation (REDOX) conditions and the distribution of temperature within the WTP melt pool and melter pour chamber on crystal accumulation rate; Consider the impact of precipitated crystals on glass viscosity; Consider the impact of an accumulated crystalline layer on thermal convection currents and bubbler effectiveness within the melt pool; Evaluate the impact of spinel accumulation on Joule heating of the WTP melt pool; and Include noble metals in glass melt experiments because of their potential to act as nucleation sites for spinel crystallization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Industrial Cavity Production: Lessons Learned to Push the Boundaries of Nitrogen-Doping

Nitrogen doping has been proven now in several labs to enhance Q0 values of 1.3 GHz cavities in the gradient domain favored by CW operation. The choice of doping for the LCLS-II project has given the community a wealth of statistics and experience on the challenge of transferring the doping technology to industry. Overall, industry-produced nitrogen-doped cavities have shown excellent performance, however some technical issues have arisen. This talk focuses on lessons learned from the production of over 300 nitrogen-doped cavities for LCLS-II and how issues were mitigated to further improve performance. Finally, I will discuss pushing the boundaries of nitrogen-doping further by exploring different doping regimes in order to maintain excellent Q0 performance, while reaching higher quench fields.

Gonnella, D.↗