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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Model-driven prediction for accelerator magnet diagnostics to improve operation reliability

Reliability is one of the most critical metrics for accelerator operation, especially in user facilities. To reduce costly facility downtime and provide an operational environment where system performance can be reliably predicted in support of scientific studies, we are developing a model-driven approach for prediction and anomaly detection. Here, in this study, we present the application of a model-driven method that employs a linear regression model to predict the future temperature, in real time, of accelerator magnets at the NSLS-II light source. This approach enables proactive identification of magnet-heating issues, facilitating magnet flushing prior to the occurrence of permanent damage without interrupting machine operation. The implementation of this method in the NSLS-II control room is described and the analysis of the online results is presented. The results demonstrate the model’s effectiveness in providing early alerts to engineers and improving the reliability of accelerator operations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reliable operation of Cr 2 O 3 :Mg/ $β$-Ga 2 O 3 p–n heterojunction diodes at 600 °C

Beta gallium oxide (β-Ga 2 O 3 )-based semiconductor heterojunctions have recently demonstrated improved performance at high voltages and elevated temperatures and are, thus, promising for applications in power electronic devices and harsh environment sensors. However, the long-term reliability of these ultra-wideband gap (UWBG) semiconductor devices remains barely addressed and may be strongly influenced by chemical reactions at the p–n heterojunction interface. Here, we experimentally demonstrate operation and evaluate the reliability of Cr 2 O 3 :Mg/β-Ga 2 O 3 p–n heterojunction diodes during extended operation at 600 °C, as well as after 30 repeated cycles between 25 and 550 °C. The calculated pO 2 -temperature phase stability diagram of the Ga-Cr-O material system predicts that Ga 2 O 3 and Cr 2 O 3 should remain thermodynamically stable in contact with each other over a wide range of oxygen pressures and operating temperatures. The fabricated Cr 2 O 3 :Mg/β-Ga 2 O 3 p–n heterojunction diodes show room-temperature on/off ratios >104 at ±5 V and a breakdown voltage (V Br ) of -390 V. The leakage current increases with increasing temperature up to 600 °C, which is attributed to Poole–Frenkel emission with a trap barrier height of 0.19 eV. Over the course of a 140-h thermal soak at 600 °C, both the device turn-on voltage and on-state resistance increase from 1.08 V and 5.34 mΩ cm 2 to 1.59 V and 7.1 mΩ cm 2 , respectively. This increase is attributed to the accumulation of Mg and MgO at the Cr 2 O 3 /Ga 2 O 3 interface as observed from the time-of-flight secondary ion mass spectrometry analysis. These findings inform future design strategies of UWBG semiconductor devices for harsh environment operation and underscore the need for further reliability assessments for β-Ga 2 O 3 -based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluating the Impact of Managed EV Charging for Reliable Operation of Bulk Power Systems with High Non-Dispatchable Generation

The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.

14 - SOLAR ENERGY↗
DOE OSTI2022

Plasma Ignition and Combustion Stabilization Technology to Improve Flexible Operation, Reliability and Economics of an Existing Coal-Fired Boiler

GE Steam Power, Inc. (GE) proposed to improve reliability, flexibility, and economics of an existing coal-fired power plant by applying a new advanced technology developed by GE, a plasma-assisted pulverized fuel firing system. The objective of this program is to demonstrate the achievement of lower load by improved flame stabilization and therefore lower operating costs in a full-scale field installation at coal-fired electric utility. GE’s Plasma Ignition and Combustion Stabilization System is designed to operate continuously to support low load operation. With the plasma on, the flame will be attached and stable, removing the firing system as a limitation to low load operation. In addition, GE’s exclusively from ABENZ company licensed AC based technology has a 90+% system efficiency compared to all other systems at which are DC and operate with ~75% efficiency. Plant operating costs are lowered by eliminating use of expensive support fuel as well as the ability to operate at lower loads. The utilities’ ability to better match the demand curve will result in significant savings. Maintenance is lower for an AC system than a DC system as it operates at lower current. This eliminates the need for a demineralized cooling water system and provides longer electrode life which translates into both material and labor savings. It is the objective of GE to not only demonstrate the additional low load achievable with a plasma system after best achievable tuning, sensor and software approach has been exhausted, but also the increased stability of the flame at all loads with plasma assistance as well as cost savings at all low loads using plasma instead of oil. Upon successful completion of this project, GE will have sufficient field experience to rapidly deploy the Plasma Technology. The project objectives were achieved through the implementation of plasma ignitor technology at PacifiCorp Hunter Station Unit 3. A plasma ignitor system was retrofitted on ten wall-fired burners, Mill 3-4 combustion system. The Plasma Ignitors installed at Hunter proved that this GE technology is a direct and complete replacement for the original oil ignitors. The Hunter Unit 3 burner management system allows the plasma system to be used in all applications that originally required oil to be burned. This includes any time the Mill 3-4 is started or stopped for any reason including boiler starts, load changes, and low load support.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Powering Up Secure and Reliable Operations at the Port of Honolulu's Kapalama Container Terminal

The National Laboratory of the Rockies (NLR) is providing resilience planning support to the Pasha Group, the operator of Kapalama Container Terminal (KCT) at the Port of Honolulu, by exploring how distributed clean energy resources could help the terminal sustain a 36-hour outage and continue emergency operations. The Department of Energy's Energy Transitions Initiative (ETI) aims to advance self-reliant island and remote communities by promoting resilient, affordable, and sustainable clean energy resources.

25 ENERGY STORAGE↗
DOE OSTI2025

Development of Transformative Preparation Methods to Push up High Q&G Performance of FRIB Spare HWR Cryomodule Cavities

The FRIB accelerator project construction, a top priority of US nuclear science, was completed in January 2022, and is now moving to user operation. The stable and reliable operation of the accelerating cryomodules is essential in achieving/fulfilling DOE and user expectations. So far, FRIB cryomodules meet all FRIB specifications for cavity performance. However, during the lifetime of machine operation, degradation of cryomodule performance is possible, as reported in similar operating facilities (CEBAF, SNS). If cryomodule degradation is observed at FRIB, the under-performing cryomodule will require replacement/maintenance. In effort to manage operational reliability, FRIB plans to construct a 0.53 half-wave cryomodule to serve as an active spare. In a parallel effort, FRIB will also work toward increasing operational Q and gradient of spare cryomodule cavities to gain an overall performance margin to support future operational reliability. The current FRIB cavity designs have a potential to operate at gradients higher than 8 MV/m, but are currently limited by field emission (FE) and/or high field Q slope (HFQS); known issue in buffered chemical polished (BCP) treated cavities. The proposal looks to develop transformative surface preparation treatments to improve the operational gradient of spare cryomodules higher than 10 MV/m while maintaining high Q. Thus, increasing operational margin by 30 - 50%. With the goal to improve operational reliability set, the proposal will investigate multiple objectives as possible paths forward to achieve an overall increase in cavity performance and gain a better understanding of SRF limiting mechanisms. The proposal will study the application of different chemical surface treatments to 0.53 half-wave cavities, with the addition of low temperature bakes (LTB), and measure their effects on accelerating performance. Proposed chemical treatments to be explored in this proposal include conventional EP acid mixtures, as well as innovated EP and BCP acid mixtures designed to simplify processing paths in migrating FE and HFQS. The proposed transformative treatment wet N-doping also has the potential to replicate recent advancements in SRF technology relating to nitrogen doping and high Q operation without the requirement for an ultra-high vacuum annealing furnace; currently being developed at FNAL and JLAB. In parallel, high Q performance relating to flux trapping will be investigated with the installation of a second layer of magnetic shielding in the vertical test Dewar. The research objectives presented in the proposal, and their corresponding effects on cavity performance, will provide essential knowledge and future guidance to the SRF community and provide possible paths for future SRF based projects and applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Real-time avoidance of the L-mode and H-mode density limit via machine-learned stability metrics

Reliable operation of burning plasma tokamaks will require robust control strategies to avoid macroscopic instability limits such as the L-mode and H-mode density limits (LDL, HDL). In this work, we explore closed-loop avoidance of these phenomena at DIII-D using machine-learned risk metrics. Feedback control is implemented via the ‘DL Supervisor’ scheme, which regulates the chosen risk metric by reducing the density target or increasing NBI heating in real-time. Using the LDL 25 risk metric, the LDL is reproducibly suppressed. We also introduce an HDL risk metric in this study, HDL 25 , which reduces the False Positive Rate by 2x compared to the Greenwald fraction. Applying this scaling to a plasma current ramp-down, we successfully avoid an HDL-driven H/L back-transition. These experiments constitute the first demonstration of real-time DL avoidance using machine-learned risk metrics. These instability metrics outline a path to safer high-density operation, more reliable ramp-down scenarios, and improved off-normal control for next-step devices such as ITER and SPARC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2024

Solar and Storage Integration in the Southeastern United States: Economics, Reliability, and Operations

Solar energy has the potential to be a core energy resource for the southeastern United States. To better understand the implications of higher levels of solar PV (27%-43% of total generation capacity) and electricity storage (13%-49% of peak load) would affect electricity system reliability, costs, and operations in the U.S. Southeast, this study sought to address two main questions. First, how would higher levels of solar PV and electricity storage impact the costs, reliability, and operations of electricity systems in the Southeast in 2035? Second, at different levels of solar PV and electricity storage, what are the benefits of operational coordination among utilities in the Southeast, through more efficient regional dispatch and sharing operating reserves? To answer these questions, the study used detailed capacity expansion and dispatch modeling to develop and examine 15 scenarios with different levels of solar PV, electricity storage, and operational coordination, focusing on the year 2035. The study also evaluates the benefits of operational coordination among utilities through more efficient regional dispatch and reserve sharing, at different levels of solar and storage. The study focuses on five balancing regions that cover Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, and parts of Mississippi and Missouri.

14 SOLAR ENERGY↗
DOE OSTI2024

Solar and Storage Integration in the Southeastern United States: Economics, Reliability, and Operations

Solar energy has the potential to be a core energy resource for the southeastern United States. To better understand the implications of higher levels of solar PV (27%-43% of total generation capacity) and electricity storage (13%-49% of peak load) would affect electricity system reliability, costs, and operations in the U.S. Southeast, this study sought to address two main questions. First, how would higher levels of solar PV and electricity storage impact the costs, reliability, and operations of electricity systems in the Southeast in 2035? Second, at different levels of solar PV and electricity storage, what are the benefits of operational coordination among utilities in the Southeast, through more efficient regional dispatch and sharing operating reserves? To answer these questions, the study used detailed capacity expansion and dispatch modeling to develop and examine 15 scenarios with different levels of solar PV, electricity storage, and operational coordination, focusing on the year 2035. The study also evaluates the benefits of operational coordination among utilities through more efficient regional dispatch and reserve sharing, at different levels of solar and storage. The study focuses on five balancing regions that cover Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, and parts of Mississippi and Missouri.

14 SOLAR ENERGY↗
DOE OSTI2024

Research Reactors Division Infrastructure Investment Plan for the High Flux Isotope Reactor

The High Flux Isotope Reactor (HFIR) is a unique national asset. Operational for nearly 60 years, continued investment into the aging infrastructure is necessary to ensure operation for another 6 decades. Additionally, growing missions require HFIR as well as important upgrades. Consequently, carefully integrated planning is required to ensure that infrastructure investments are timely executed to ensure long-term, reliable operation of HFIR. Concerns about challenges to the operational reliability of HFIR resulted in a recommendation from the 2023 Operations Review by the US Department of Energy (DOE) Office of Basic Energy Sciences that a HFIR management strategy be developed to address the infrastructure needs. This report defines the investment needs, which are evolving as new upgrade efforts are better defined. HFIR is part of the three-source strategy within the Neutron Sciences Directorate (NScD) and contributes to the five strategic science areas outlined in the NScD 10 Year Strategic Science Plan: quantum materials, soft matter, materials and engineering, chemistry, and biosciences. Fundamental to this strategy are three core values: operational excellence, responsible stewardship, and servant leadership. These values guide our mission of safe and reliable operation of the reactor and require a strong and just nuclear safety culture, a solemn respect for responsible care of the facility, good workforce development, robust procedures and processes, an effective communication strategy, world-class asset management, a determined customer focus, and a commitment to protecting the environment, the safety and health of the public and our people, and the quality of work performed within our facility. These principles are all essential to operate HFIR at a world-class level. The Research Reactors Division (RRD) will lead a new era of neutron science and isotope production at HFIR through responsible and purposeful leadership and unwavering support of the science community. The approach outlined in this plan highlights the direction leadership is taking to ensure that HFIR is ready to support the science challenges and national needs of the future and that the United States maintains world leadership in neutron sciences. The plan is in alignment with the DOE’s desire to continue operating HFIR and with the NScD strategic science goals for the future. HFIR is an aging facility with numerous infrastructure challenges and needs. It has an aging workforce in relation to the general population of Oak Ridge National Laboratory (ORNL), with many expected retirements over the next 5–10 years. With an increase in work scope caused by changing national priorities and science goals, several critical hires have been identified. To manage HFIR’s infrastructure needs, a prioritized list of equipment upgrades has been identified along with an analysis of future staffing requirements. A desire to operate HFIR at eight cycles per year will necessarily require some significant changes to procedures and processes currently in place as well as targeted staffing additions. Many of the equipment upgrades identified in this plan will significantly increase the reliability of the plant, thus contributing to the effort to reach the goal of safely operating eight cycles per year. A plan to attain eight-cycle operation is being prepared in parallel with the activities identified in this plan, although the actions identified to satisfy both plans will overlap. This plan identifies new infrastructure needs—for both plant equipment and staffing—thus necessitating formulation of future budget requests to fund the increased work scope and improvement activities. Some activities are currently being scheduled with the expectation that funding will be received. Any delays to funding or reductions of funding from the identified cost estimations will directly and negatively affect the plan’s implementation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Using Artificial Intelligence to Improve Reliability and Operational Efficiency of Small-Scale Hydroelectric Distributed Generation

Reliability and resilience are critical concerns for distributed generation (DG) at the rural electric level. The integration of renewable energy sources, such as small-scale hydroelectric distributed generators (hydro DGs), introduces operational challenges, particularly regarding aging infrastructure and grid stability. Artificial Intelligence (AI)-driven Machine Learning (ML) models and applications of Large Language Models (LLMs) offer promising solutions for optimizing DG operations and enhancing resilience. This paper explores AI-based models for improving efficiency, fault resolution, and outage mitigation in small-scale hydro DGs. Furthermore, it highlights the development of a centralized, AI-powered information portal for rural electric cooperatives and municipalities. The research evaluates hydro DG plant models and discusses the applicability of AI-powered question-answering tools for real-time operations, focusing on statistical data, load flow, voltage regulation, and generation power. The findings demonstrate AI’s potential to transform DG management to ensure greater stability and resilience in rural electric grids.

Bhattacharyya, Arjun [ORNL] (ORCID:000900060976046↗
DOE OSTI2023

Chapter 32 - Power Grid Resilience

The new energy paradigm is altering the current power grid trend from synchronous generator reliant system toward power-electronics-based distributed energy resources (DERs). The resilient operation of modern power grid is highly dependent upon cyber and physical reliable operation of DERs. Power grid resilience broadly refers to the ability of the grid to be robust to eventualities and singularities that may disrupt the continuity of reliable power flow. This could involve resilience related to the physical system but more recently, the focus on cyber resilience has been evolving rapidly especially given the burgeoning growth of distributed generation and power-electronics-based DERs under smart grid and given that such threats to reliable operation do not have to evolve localized to where the physical asset is. Commonly, in power grids dominated by DERS, control and energy management are structured at a multitime scale multilayer fashion: (i) primary control layer at microseconds time scale, (ii) secondary control layer at millisecond time scale, and (iii) tertiary control layer at seconds to minutes time scale. The cyber-related issues that may affect the power grid resilience can be introduced in through primary, secondary, and tertiary control layers. The failure of each of these control layers may impact the reliable and resilient operation of the overall network and cause widespread failures which leads to unintended blackouts. As such, this chapter focuses on cyber-related resilience issues in primary control layer, secondary control layer, tertiary control layer, wide area/utility control, and anomaly detection and resilient communication.

anomaly detection↗
DOE OSTI2025

Hybrid HEFA-HDCJ Process for the Production of Jet Fuel Blendstocks

The hydrotreatment of bio-oil derived from the pyrolysis and biocrude from hydrothermal liquefaction of lignocellulosic materials to produce hydrocarbons faces significant technological challenges, mainly due to the high reactivity and poor thermal stability of bio-oil, resulting in the formation of large quantities of coke. This problem has been addressed by existing PNNL patents with a two-step hydrotreatment technology in which the bio-oil is first stabilized with a noble hydrogenation metal (often Pt or Ru). Then, in the second step, the bio-oil is deoxygenated with a Ni-Mo or Co-Mo sulfide catalyst. The main problem with this approach is that the Pt/Ru catalysts deactivate easily in the presence of S or other impurities, which are commonly present in pyrolysis oils. In this project, we explored technological solutions to mitigate coke formation, avoiding the use of Pt/Ru catalysts. Our strategy is based on three actions: (1) Bio-oil stabilization in the presence of alcohols. In this project, we studied the stabilization with butanol. (2) the use of a cosolvent to solubilize the bio-oil. Because coke formation reactions are second-order reactions a reduction in the concentration of reactive bio-oil molecules. In this case, we used yellow greases as a co-solvent. (3) Separation of bio-oil reactive fractions. In this project, we studied the removal of water-soluble fractions. Our batch co-hydrotreatment studies confirmed that the addition of butanol and methanol and the blend with lipids effectively contributed to mitigating coke formation (reducing coke yield to about 1 wt.% %). The removal of sugars did not have a noticeable effect on the overall coke yield, suggesting that coke precursors are present in all bio-oil fractions. Our analytical work suggests that they may be concentrated in the water-insoluble/CH 2 Cl 2 insoluble fractions of pyrolysis oils. Although the technological strategies tested resulted in significant coke reductions, the levels achieved were not sufficiently low to ensure a reliable operation in continuous, fixed-bed trickle-bed reactors. Long runs of more than 100 hours (maximum: 255 h) of co-processing time on stream were achieved in a continuous 40 mL reactor. When the same test was conducted in a larger 400 mL reactor, pressure drop increases associated with coke formation were observed. This increase in coke formation could be due to larger temperature gradients in the bed. Hydrodeoxygenation tests in moving bed reactors and using more active hydrogenation catalysts (for example Ni) could lead to more reliable operations. Unfortunately, our team did not have access to such experimental setups. The technoeconomic analysis suggests that although alcohol use is an effective means to reduce coke formation, the use of alcohol increases production cost. Thus, its use needs to be minimized. A delicate balance needs to be found between the use of technological solutions that allow the reliable operation of the system (stabilization with Ni catalysts, use of small quantities of solvents, processing in moving bed reactors) with a tolerable level of coke formation for the hydrodeoxygenation reactor used and that result in minimum production costs.

09 BIOMASS FUELS↗
DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2022

Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant

We report as wind and solar technologies improve and their costs decrease, the share of power produced by these sources will increase. As the market penetration increases, these power sources will need to provide grid services, such as dispatchability, in addition to providing energy. One way to reduce variability, provide higher quality power to the grid, and address local grid stability issues is through colocating wind and solar power plants. In addition to operating reliably during normal operating conditions, in scenarios with high penetrations of renewable generation, it is important that these hybrid plants can withstand production disruptions and continue to supply power despite prolonged resource reduction, extreme weather events, or other disruptions. In this paper, we present a methodology to optimize a wind-solar-battery hybrid power plant down to the component level that is resilient against production disruptions and that can continually produce some minimum required power. We introduce the models and assumptions we used to simulate a hybrid power plant as well as the design variable parameterization and specific methods we used to optimize the plant. We demonstrate the performance of our method by comparing a plant optimized for different objectives, generation outage durations, minimum power requirements, and power purchase agreements. Although the plant design is sensitive to model parameters and various other assumptions, our results demonstrate some of the optimal designs that occur in different scenarios and what one should expect when designing a hybrid wind-solar-storage power plant.

14 SOLAR ENERGY↗
DOE OSTI2021

A Compact Gas Liquid Separator for the Spallation Neutron Source Mercury Process Loop

Upgrades at the spallation neutron source (SNS) accelerator at Oak Ridge National Laboratory are underway to double its proton beam power from 1.4 to 2.8 MW. About 2 MW will go to the current first station while the rest will go to the future Second Target Station. The increase of beam power to the first target station is especially challenging for its mercury target. When the short proton beam hits the target, strong pressure waves are generated, causing cavitation erosion and challenging stresses for the target's weld regions. SNS has successfully operated reliably at 1.4 MW by mitigating the pressure wave with the injection of small Helium bubbles into the mercury. To operate reliably at 2 MW, more gas will be injected into mercury to mitigate the pressure wave further. However, the mercury process loop was not originally designed for gas injection, and the accumulation of gas in the pipes is a concern. Due to space constraints, a custom gas liquid separator (GLS) was designed to fit a 90-deg horizontal elbow space in the SNS mercury loop. Simulations and experiments were performed, and a successful design was developed that has the desired efficiency while keeping the pressure losses acceptable.

42 ENGINEERING↗