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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Compare low-gain 1D and 3D models toward a storage ring-based X-ray free-electron-laser oscillator

Fourth-generation diffraction-limited synchrotron light sources (DL-SLS) present a compelling platform for advanced light source applications due to their ultra-low emittance (around tens of picometers), sub-micrometer beam stability, and high repetition rates (greater than 1 MHz). Despite these advantages, fundamental limitations, such as long bunch durations (tens of picoseconds), large relative energy spread (~10⁻³), and low peak current (<300 A), have hindered their effectiveness in free-electron laser (FEL) applications. Using a low-gain three-dimensional (3D) model based on NSLS-II upgrade lattice parameters, we demonstrate that a 6% single-pass gain is achievable with an optimized transverse gradient undulator (TGU). Concurrently, recent advances in X-ray optics—specifically, a four-mirror bow-tie resonator utilizing diamond mirrors and beryllium compound refractive lenses (CRLs)—enable total round-trip losses below 4% and an output coupling efficiency near 1%, rendering a Storage Ring based X-ray FEL oscillator (XFELO) a realistic possibility. In the 3D framework, the TGU effectively mitigates the detrimental effects of large energy spread by spatially dispersing the beam, leading to enhanced gain relative to conventional one-dimensional (1D) models. We establish both analytical and numerical connections between the 1D and 3D descriptions, leveraging GENESIS simulations to map equivalent gain conditions. While 1D resonance is defined in the energy domain, the 3D TGU system shifts the resonance dependence to transverse gradient and dispersion parameters. Once optimized, this framework allows predictive modeling using 1D formulas, which can then be validated through full 3D simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Material and Design Optimization of Printed Melt Wire Arrays

Recent work conducted by the Advanced Sensors and Instrumentation (ASI) program at Idaho National Laboratory resulted in the establishment of in-house capabilities for fabricating and testing new advanced manufactured sensors for measuring irradiation temperatures inside a nuclear test reactor. Though current methods of real-time temperature monitoring (e.g., thermocouples) can still be used, the complexity of the feedthroughs and attachments needed for collecting real time measurements greatly increases the experiment-related costs. On the other hand, passive monitoring techniques can be used for collecting post irradiation temperature measurements by inferring reactor temperatures, based on the melting points of well-characterized materials (i.e., standard melt wires). However, challenges have arisen due to the limited space available for including instrumentation in experiments. To resolve this issue, the ASI program expanded its temperature detection capabilities to include advance manufactured melt wires for post-irradiation temperature measurements. These melt wires can determine reactor temperatures while also accommodating space limitations in irradiation experiments. To improve performance reliability and enhance melt wire readability following irradiation, FY-22 efforts have focused on optimizing the materials used in the encapsulation and printed melt wire array. This report details the design and fabrication tasks, along with the subsequent x ray computed tomography (XCT) evaluation process. The melt wire array consisted of indium with a melting point of 157°C, indium/silver (96/4 at%) with a melting point of 219°C, and tin with a melting point of 230°C. The encapsulation disc was made of vanadium due to its low activation properties and radiation resistance when deployed in nuclear reactors. Additionally, the melt wire design consisted of a ceramic sublayer (alumina disc) to further enhance the XCT post melting images of the printed melt wires. However, when sealing the vanadium container, all three melt wires melted, reflecting the temperature limitations that must be considered when employing metal containers in the sealing process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Photosynthetic terpene hydrocarbon production for fuels and chemicals

Summary Photosynthetic hydrocarbon production bypasses the traditional biomass hydrolysis process and represents the most direct conversion of sunlight energy into the next‐generation biofuels. As a major class of biologically derived hydrocarbons with diverse structures, terpenes are also valuable in producing a variety of fungible bioproducts in addition to the advanced ‘drop‐in’ biofuels. However, it is highly challenging to achieve the efficient redirection of photosynthetic carbon and reductant into terpene biosynthesis. In this review, we discuss four major scientific and technical barriers for photosynthetic terpene production and recent advances to address these constraints. Collectively, photosynthetic terpene production needs to be optimized in a systematic fashion, in which the photosynthesis improvement, the optimization of terpene biosynthesis pathway, the improvement of key enzymes and the enhancement of sink effect through terpene storage or secretion are all important. New advances in synthetic biology also offer a suite of potential tools to design and engineer photosynthetic terpene platforms. The systemic integration of these solutions may lead to ‘disruptive’ technologies to enable biofuels and bioproducts with high efficiency, yield and infrastructure compatibility.

Wang, Xin↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2021

Demonstration of Dimensional and Microstructural Postirradiation Examination Capabilities on MiniFuel Disk Specimens

This work demonstrates two new MiniFuel postirradiation examination (PIE) capabilities, laser profilometry and serial sectioning, for future use on alpha-U specimens for the upcoming Mini-99 irradiation campaign. Representative U–Mo disk specimens—3 mm in diameter and approximately 0.75 mm in thickness—were subjected to dimensional measurements using standard calipers, as well as a new Keyence CL-3000 series laser profilometry system designed to measure the surface of both sides of the disk simultaneously. The laser profilometry system captured thickness gradients across the specimens as large as 0.2 mm. Additionally, because of the limitations inherent to the use of calipers, caliper measurements consistently overestimated the initial volume of MiniFuel disk specimens compared to the laser profilometry method by up to 5%. However, the results show that optimal data collection and analysis procedures are crucial to avoiding significant artifacts during data collection that can lead to large errors (approximately 10%). Serial sectioning using a combination of ion milling and electron imaging was also demonstrated for the analysis of micron-sized control volumes. Nanometer- to micron-scale porosity was resolved on a representative U–Mo specimen before irradiation, even when the surface of the specimen appeared free of porosity. Future efforts to relate microstructural features (porosity, fission product distributions, and so on) to macroscale effects (dimensional changes, thermal conductivity degradation) require accurate characterization methods such as those demonstrated in this work. These advanced PIE capabilities will be applied to optimized alpha-U specimens for the upcoming Mini-99 neutron irradiation campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2021

Combustion Performance and Emissions Optimization through Integration of Miniaturized High Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2019

Optimal placement of data concentrators for expansion of the smart grid communications network

Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high‐performing, reliable, and resilient power system. Three network design formulations based on mixed‐integer linear and non‐linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much‐improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality‐of‐service in the event of an outage. Analysis of multi‐period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics

Advances in theoretical modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials emerging as a result of complex non-local interactions. Correspondingly, of interest is the inverse problem of finding the model parameter that will yield desired macroscopic responses, such as stress–strain curves, ferroelectric hysteresis loops, etc. Here, we suggest and implement Gaussian process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities. In this work, we discuss the specific adaptation of the acquisition function and sampling function to make the process efficient and balance the efficient exploration of parameter space for multiple possible minima and exploitation to densely sample the regions of interest where target behaviors are optimized. Additionally, this approach is illustrated via the hysteresis loop engineering in ferroelectric materials but can be adapted to other functionalities and generative models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Developing Near Optimal Control Sequences for Chiller Plants with Water-side Economizers: A Case Study in a Warm and Marine Climate

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the optimization of those controls is limited. It is possible to maximize energy savings by developing near-optimal control sequences, which are dependent on several factors such as the load profile. To address these gaps, we first identify an advanced control sequence and three key control parameters for chiller plants with WSE. Next, optimizations are performed to minimize energy consumption for seven combinations of control parameters. A chiller plant with WSE system in a warm and marine climate is studied and two load profiles are considered. The system and controls are modeled using the Modelica Buildings library. The results show optimizing the selected control parameters can reduce energy consumption by up to 11% depending on the load profile. Specifically, optimizing the cooling tower efficiency threshold in the condenser water reset control can significantly reduce energy savings for the variable load profile by efficiently shifting the load from the cooling tower to the chiller. This paper provides practical guidance for developing near-optimal control sequences for chiller plant with WSE systems considering impacts such as the load profile.

chiller plant↗
DOE OSTI2021

PHYSICS-BASED AUTOMATED REASONING FOR HEALTH MONITORING: SENSOR SET SELECTION

This paper addresses the problem of how to select a sensor set for equipment health monitoring that meets the needs of advanced O&M tasks that target cost reduction. They include maintenance optimization and asset management for the existing fleet and near-autonomous operation as currently envisioned for advanced reactors. The method uses physics-based automated reasoning to provide for a more “explainable” diagnosis. The algorithm is described along with its implementation on a computational cluster. Preliminary results for application to a use case in the current fleet are described.

diagnosis↗
DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Systems-Level Synthetic Biology for Advanced Biofuel Production

Cyanobacteria have been shown to be capable of producing a variety of advanced biofuels; however, product yields remain well below those necessary for large scale production. New genetic tools and high throughput metabolic engineering techniques are needed to optimize cyanobacterial metabolisms for enhanced biofuel production. Towards this goal, this project advances the development of a multiple promoter replacement technique for systems-level optimization of gene expression in a model cyanobacterial host: Synechococcus sp. PCC 7002. To realize this multiple-target approach, key capabilities were developed, including a high throughput detection method for advanced biofuels, enhanced transformation efficiency, and genetic tools for Synechococcus sp. PCC 7002. Moreover, several additional obstacles were identified for realization of this multiple promoter replacement technique. The techniques and tools developed in this project will help to enable future efforts in the advancement of cyanobacterial biofuels.

09 BIOMASS FUELS↗
DOE OSTI2026

Influence of fabrication on microstructure and heat affected zone width in weldments of nuclear reactor pressure vessel steel

Advanced manufacturing routes such as electron beam welding and powder metallurgy with hot isostatic pressing are increasingly used across energy and aerospace industries, where the reliable prediction of weld behavior and heat affected zone (HAZ) evolution is critical. This study examines how fabrication routes and post-weld heat treatments influence phase distribution, crystallite size, microstrain, and dislocation density in nuclear reactor pressure vessel steels using synchrotron X-ray diffraction (SXRD). Retained austenite occurs only in samples that did not undergo austenitization, whereas an austenitizing heat treatment fully eliminates retained austenite and produces a more uniform microstructure across the weldment in terms of phase fraction, dislocation density, and microstrain. The Rosenthal solution underestimates the HAZ width for powder metallurgy samples. A newly proposed modified Rosenthal solution, reducing density by accounting for porosity, matches the SXRD-measured HAZ width with a 0.65% error. Structure–property correlations reveal that dislocation density correlates strongly with nanohardness in homogenous microstructures, while in heterogenous weldments nanohardness is further influenced by the presence of dissimilar phase boundaries. These findings provide new insight into the thermal and microstructural response of powder metallurgy fabricated steels and offer a framework for optimizing welding procedures and heat treatments in advanced manufacturing applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Final Techno-Economic Analysis of 550 MWe Supercritical PC Power Plant CO 2 Capture with Linde-BASF Advanced PCC Technology

This topical report presents the techno-economic evaluation of a 550 MWe supercritical pulverized coal (PC) power plant utilizing Illinois No. 6 coal as fuel, integrated with 1) a previously presented (for a subcritical PC plant) Linde-BASF post-combustion CO 2 capture (PCC) plant incorporating BASF’s OASE® blue aqueous amine-based solvent (LB1) [Ref. 6] and 2) a new Linde-BASF PCC plant incorporating the same BASF OASE® blue solvent that features an advanced stripper interstage heater design (SIH) to optimize heat recovery in the PCC process. The process simulation and modeling for this report is performed using Aspen Plus V8.8. Technical information from the PCC plant is determined using BASF’s proprietary thermodynamic and process simulation models. The simulations developed and resulting cost estimates are first validated by reproducing the results of DOE/NETL Case 12 representing a 550 MWe supercritical PC-fired power plant with PCC incorporating a monoethanolamine (MEA) solvent as used in the DOE/NETL Case 12 reference [Ref. 2]. The results of the techno-economic assessment are shown comparing two specific options utilizing the BASF OASE® blue solvent technology (LB1 and SIH) to the DOE/NETL Case 12 reference. The results are shown comparing the energy demand for PCC, the incremental fuel requirement, and the net higher heating value (HHV) efficiency of the PC power plant integrated with the PCC plant. A comparison of the capital costs for each PCC plant configuration corresponding to a net 550 MWe power generation is also presented. Lastly, a cost of electricity (COE) and cost of CO 2 captured assessment is shown illustrating the substantial cost reductions achieved with the Linde-BASF PCC plant utilizing the advanced SIH configuration in combination with BASF’s OASE® blue solvent technology as compared to the DOE/NETL Case 12 reference. The key factors contributing to the reduction of COE and the cost of CO 2 captured, along with quantification of the magnitude of the reductions achieved by each of these factors, are also discussed. Additionally, a high-level techno-economic analysis of one more highly advanced Linde-BASF PCC configuration case (LB1-CREB) is also presented to demonstrate the significant impact of innovative PCC plant process design improvements on further reducing COE and cost of CO 2 captured for overall plant cost and performance comparison purposes. Overall, the net efficiency of the integrated 550 MWe supercritical PC power plant with CO 2 capture is increased from 28.4% with the DOE/NETL Case 12 reference to 30.9% with the Linde-BASF PCC plant previously presented utilizing the BASF OASE® blue solvent [Ref. 6], and is further increased to 31.4% using Linde-BASF PCC plant with BASF OASE® blue solvent and an advanced SIH configuration. The Linde-BASF PCC plant incorporating the BASF OASE® blue solvent also results in significantly lower overall capital costs, thereby reducing the COE and cost of CO 2 captured from $147.25/MWh and $56.49/MT CO 2 , respectively, for the reference DOE/NETL Case 12 plant, to $128.49/MWh and $41.85/MT CO 2 for process case LB1, respectively, and $126.65/MWh and $40.66/MT CO 2 for process case SIH, respectively. With additional innovative Linde-BASF PCC process configuration improvements, the COE and cost of CO2 captured can be further reduced to $125.51/MWh and $39.90/MT CO 2 for LB1-CREB. Most notably, the Linde-BASF process options presented here have already demonstrated the potential to lower the cost of CO2 captured below the DOE target of $40/MT CO 2 at the 550 MWe scale for second generation PCC technologies.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

Calibration of the E3SM Land Model Using Surrogate‐Based Global Optimization

Abstract Calibration of the Energy Exascale Earth System Model (E3SM), land model (ELMv0) is challenging because of its model complexity, strong model nonlinearity, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near‐optimal solution within an affordable time. The goal of this study is to calibrate some of the ELMv0 parameters to improve model projection of carbon fluxes. We propose a computationally efficient global optimization procedure using sparse‐grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the ELMv0, and then calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated a sufficiently large number of times in the optimization, which facilitates the global search. We calibrate eight parameters against five years of net ecosystem exchange, total leaf area index, and latent heat flux data from the U.S. Missouri Ozark flux tower. The calibrated model is then used for predicting the three variables in the following 4 years. The results indicate that an accurate surrogate model can be created for the ELMv0 with a relatively small number of SG points, i.e., a few ELMv0 simulations that can be fully parallel. And, the application of the optimized parameters leads to a better model performance and a higher predictive capability than the default parameter values in the ELMv0.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structural evolution and magnetic hardness of (Sm,Zr)(Fe,Co,Ti) 12 alloy particles via reduction-diffusion

The quest for achieving high coercivity in Sm(Fe,Co,Ti) 12 alloys, despite their inherent strong magnetocrystalline anisotropy, has posed significant challenges. Recently, (Sm,Zr)(Fe,Co,Ti) 12 monocrystalline particles have exhibited coercivity μ 0 H c > 1.2 T, showcasing promising prospects and significant potential for both manufacturing and research endeavors. This study delves into the structural evolution of (Sm,Zr)(Fe,Co,Ti) 12 (1:12) alloy particles made via the calciothermic reduction-diffusion synthesis process as influenced by the molar ratios of Ca atoms to O 2- ions (Ca/O), annealing time and annealing temperature. Critical insight that informs conditions to optimize the magnetic response is gained via systematic experimentation and advanced electron microscopy. Complex structural features, including core-shell morphologies and intricate multiphase compositions within individual particles, are unveiled. An optimal Ca/O ratio of 1.30 produces particles with a coercivity up to μ 0 H c = 1.63 T, while higher Ca/O ratios induce the formation of a Sm-rich TbCu 7 -type (1: 7 ) phase, which only partially transforms into the desired 1:12 phase during annealing. Persistent remnants of the 1:7 phase locally impact atomic structure, particle morphology, and coercivity. Furthermore, these findings underscore the complex interplay between synthesis parameters, resulting structures, and magnetic properties, informing the design and optimization of high-performance permanent magnets comprised of the (1:12) compound.

36 MATERIALS SCIENCE↗