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DOE OSTI2020

Regulating off-centering distortion maximizes photoluminescence in halide perovskites

Abstract Metal halide perovskites possess unique atomic and electronic configurations that endow them with high defect tolerance and enable high-performance photovoltaics and optoelectronics. Perovskite light-emitting diodes have achieved an external quantum efficiency of over 20%. Despite tremendous progress, fundamental questions remain, such as how structural distortion affects the optical properties. Addressing their relationships is considerably challenging due to the scarcity of effective diagnostic tools during structural and property tuning as well as the limited tunability achievable by conventional methods. Here, using pressure and chemical methods to regulate the metal off-centering distortion, we demonstrate the giant tunability of photoluminescence (PL) in both the intensity (>20 times) and wavelength (>180 nm/GPa) in the highly distorted halide perovskites [CH3NH3GeI3, HC(NH2)2GeI3, and CsGeI3]. Using advanced in situ high-pressure probes and first-principles calculations, we quantitatively reveal a universal relationship whereby regulating the level of off-centering distortion towards 0.2 leads to the best PL performance in the halide perovskites. By applying this principle, intense PL can still be induced by substituting CH3NH3+ with Cs+ to control the distortion in (CH3NH3)1-xCsxGeI3, where the chemical substitution plays a similar role as external pressure. The compression of a fully substituted sample of CsGeI3 further tunes the distortion to the optimal value at 0.7 GPa, which maximizes the emission with a 10-fold enhancement. This work not only demonstrates a quantitative relationship between structural distortion and PL property of the halide perovskites but also illustrates the use of knowledge gained from high-pressure research to achieve the desired properties by ambient methods.

halide perovskites↗

DOE OSTI2021

Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications

Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Carbon use efficiency diagnostics in Nannochloropsis salina

We report that carbon dioxide (CO 2 ) can be a significant resource input affecting the cost of algae biomass production on an industrial scale. Improvements in biofuel productivity therefore require characterization of CO 2 use efficiency (CUE). RuBisCO saturation with CO 2 is an important factor influencing biomass productivity. During CO2 fixation by RuBisCO, fractionation of carbon isotopes occurs, with preferential fixation of 12 CO 2 , resulting in assimilation of the lighter isotope in algae. This photosynthetic discrimination (Δ DIC-algae ), approximated by the difference between the δ 13 C of external medium and that of algae, is a function of the proportion of CO 2 fixed relative to supply. Δ DIC-algae has been applied to the study of photosynthesis in algae over the past few decades and we have adopted the tool to characterize improvements in conditions optimized for biofuel productivity, such as controlled photobioreactors. We report the use of Δ DIC-algae as a tool for characterizing CO 2 dynamics and RuBisCO saturation in Nannochloropsis salina CCMP 1776, a benchmark strain in algal biofuels research. We use Δ DIC-algae to describe the conditions under which RuBisCO saturation can be achieved to maximize biomass productivity. Finally, our results suggest that determination of Δ DIC-algae can provide important feedback to support engineering and cultural improvements that can impact carbon use efficiency and biomass productivity.

09 BIOMASS FUELS↗

DOE OSTI2020

Design, Development and Testing Plan for Energy Efficiency Algorithms Related to Building-Integrated Cooling, Heating, and Power Systems

Building-integrated cooling, heating and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. It is estimated that the total CHP technical potential in the commercial building sector is approximately 76 GW, but only a small fraction of it is actually deployed. Advanced control and monitoring systems that realize the full potential of these CHP systems can transform this capacity into an asset for both the owner and the grid and also accelerate the adoption of these systems in the building sector. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. It has been demonstrated by several studies that as much as 30% of building energy use is wasted. Much of the waste results from the inability to anticipate load variations (design day vs. non-design day operations; diurnal variation of loads), identify improper operation, and efficiently operate, control and maintain building systems. Moreover, many buildings, even those with adequate control infrastructure, lack knowledgeable staff to operate and maintain energy systems. Operators of buildings with CHP systems will constantly need to make tradeoffs (economic dispatch) between onsite power generation and purchasing power, between onsite heat production from distributed generation versus heating/cooling from conventional systems, and different passive and active load management measures. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of a clean, efficient, reliable and affordable next-generation integrated energy systems. This report lists the components for which PM, real-time CxV and AFDD algorithms will be developed, how the algorithms will be tested, and metrics that will be used to validate the algorithms and easy of deployment. Deployment of these algorithms in the field will result in savings reduction in energy consumption of between 10% and 20%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1991

Technology for increased human productivity and safety on orbit

Technologies are addressed that can facilitate the efficient performance of station operations on the Space Station Freedom (SSF) and thereby optimize the utilization of SSF for scientific research. The dedication of SSF capabilities to scientific study and to the payload-user community is a key goal of the program. Robotics applications are discussed in terms of automating the processing of experiment materials on-orbit by transferring ampules to a furnace system or by handling plant-tissue cultures. Noncontact temperature measurement and medical support technology are considered important technologies for maximizing time for scientific purposes. Detailed examinations are conducted of other technologies including advanced data systems and furnace designs. The addition of the listed technologies can provide an environment in which scientific research is more efficient and accurate.

Ambrus, Judith↗

DOE OSTI2026

Seasonal performance evaluation of zero-superheat active refrigerant charge control for variable-speed heat pumps

Heat pumps account for a significant portion of electricity consumption in buildings, making their energy efficiency a critical area of research. Control optimization is recognized as an effective approach for enhancing the efficiency of building HVAC systems. Specifically, since the efficiency of heat pumps increases with reduced superheat and the optimal refrigerant charge level varies with operating conditions, zero-superheat active charge control is a promising strategy to maximize the system’s COP. However, due to its complexity, this control strategy remains in the research phase and has only been tested under limited operating conditions. This paper proposes a methodology to evaluate the seasonal performance improvement of zero-superheat active charge control across a full range of heating conditions in buildings. The methodology includes the development of an optimal controller that utilizes an accumulator to store excess refrigerant and manages the active charge level at zero superheat through subcooling control, as well as the creation of a detailed physics-based heat pump model. Targeted a 3-ton variable-speed heat pump with R410A refrigerant, we selected ten U.S. cities representing different climate regions for seasonal performance evaluation. Our simulations indicate that, depending on the climate conditions, the optimal control strategy could achieve energy savings ranging from 5.8 % to 8.0 % over the entire heating season. These results suggest that zero-superheat active charge control, combined with coordinated compressor and fan speeds, could substantially improve the energy efficiency of heat pump systems while maintaining the required heating capacity.

Guo, Fangzhou↗

DOE OSTI2024

Enabling Ultra-Compact, Lightweight, Efficient, and Reliable 6.6 kW On-Board Bi-Directional Electric Vehicle Charger with Advanced Topology and Control

The research explored new topologies, control methods, mechanical integration, and thermal management methods for electric vehicle (EV) on-board chargers. The team investigated capacitor-based power conversion, leveraging the high energy densities inherent to capacitive energy storage compared to inductive methods. The proposed topologies simultaneously enabled high power density and high efficiency of the design. The proposed architecture was demonstrated in a 6.6 kW bi-directional charger prototype. The research pursued several directions to improve system performance. Innovative topologies were studied for both the main power conversion stage as well as the single-phase twice-line-frequency energy buffer. To ensure robust and efficient operation, new control methods were developed to integrate these two subsystems. To achieve high power density in the full system solution, the mechanical structure of the charger is highly optimized to maximally fill the converter box volume. In parallel with the mechanical design effort, the converter was packaged with high-performance cooling methods which removed heat from key areas of power dissipation in the converter. The thermal management system was optimized to minimize its weight and volume, ultimately motivating the design of a custom additively manufactured cold-plate. The full system achieves a peak power of 7 kW with less than 0.3% total harmonic distortion (THD) and greater than 0.994 power factor in power factor correction (PFC) operation, corresponding to a total box-volume power density of 47.9 kW/L and gravimetric power density of 24.6 W/g. The system achieves a peak efficiency of 98.9%, with 97.9% efficiency at maximum power.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

FuseIM: Fusing Probabilistic Traversals for Influence Maximization on Exascale Systems

Probabilistic breadth-first traversals (BPTs) are used in many network science and graph machine learning applications. In this paper, we are motivated by the application of BPTs in stochastic diffusion-based graph problems such as influence maximization. These applications heavily rely on BPTs to implement a Monte-Carlo sampling step for their approximations. Given the large sampling complexity, stochasticity of the diffusion process, and the inherent irregularity in real-world graph topologies, efficiently parallelizing these BPTs remains significantly challenging. In this paper, we present a new algorithm to fuse massive number of concurrently executing BPTs with random starts on the input graph. Our algorithm is designed to fuse BPTs by combining separate traversals into a unified frontier on distributed multi-GPU systems. To show the general applicability of the fused BPT technique, we have incorporated it into two state-of-the-art influence maximization parallel implementations (gIM and Ripples). Our experiments on up to 4K nodes of the OLCF Frontier supercomputer (32,768 GPUs and 196K CPU cores) show strong scaling behavior, and that fused BPTs can improve the performance of these implementations up to 34x (for gIM) and ~360x (for Ripples).

Neff, Reece W.↗

DOE OSTI2021

AI-enabled Dynamic Finish Machining Optimization for Sustained Surface Integrity

While machining processes are typically leveraged to establish geometric features, many functional characteristics of advanced materials are directly determined by their machining-induced surface integrity (SI). Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters. We present a novel integrated approach based on model-informed artificial intelligence (AI), which optimizes ‘dynamic’ process parameters in real-time. Here, by maximizing the useful life of a cutting tool over which a required set of SI parameters can be maintained, our paradigm will enable significantly more efficient processing of next-generation materials and components.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling

The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗

DOE OSTI2025

Characterization of W production during ICRF operations: experiments and modeling

For successfully heating plasma with waves in the ion cyclotron range of frequencies (ICRFs), mitigating impurity production is just as crucial as maximizing power coupling, especially in high-Z environments. ICRF can effectively deposit energy on ions, modify turbulence-driven transport, and enhance fusion reaction efficiency, but only when its power coupling has minimal impact on impurity production. To do so, one must rely on a toroidal array of at least three active elements excited with appropriate phasing and power ratio to reduce the currents induced on the antenna frame below levels critical for physical sputtering. In contrast to classic two-strap antennas, which are optimized for dipole phasing with equal power on both straps, three-strap antennas in ASDEX Upgrade (AUG)—but also four-strap antennas in JET, Alcator C-Mod, SPARC and ITER—offer the possibility to act also on the power ratio between the central and outer straps. With optimal settings, impurity production can be reduced substantially, making the ICRF compatible with the high-Z wall. This paper explores the characteristics of the AUG three-strap antennas in terms of impurity production, as well as the key role of plasma composition in this process. Numerical simulations were performed using SSWICH and Petra-M (finite element codes) to quantify impurity production and compare with experimental results. Energies of ions falling on antenna limiters (measured with probes) are well predicted by both codes. These tools are then used to further describe the source of the impurity, namely the gross erosion of tungsten from an ICRF antenna, for different plasma mixtures. Results are also compared to spectroscopy data. Ultimately, we show that deleterious effects of the ICRF on plasma surface interactions will be weaker in plasmas containing larger fractions of highly ionized heavier low-Z impurity, which is typically relevant for experiments relying on impurity seeding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

A rapid embodied carbon assessment tool for priority materials

Embodied carbon limits within building materials are a driving factor in global trade, generating new research and analysis tools in industry. These product assessments, which require utilizing life-cycle assessment (LCA) across broad supply chains, can be expensive, time and data-intensive, and subject to significant variations. Existing methods and tools, such as specific environmental product declarations, typically do not capture these variations and dynamics in supply and manufacturing. Moreover, models and tools must enable stakeholders to assess customized supply chains and future scenarios. In this study, we present the Rapid Embodied Carbon Assessment and Target-setting for Emissions-intensive Materials (REDuCE) tool for building materials. We developed a tool that allows users to select production technologies, transportation mode and distances, concrete carbonation, fuel sources, and regional electricity mixes to supply customization for cement and concrete produced and consumed in California. We generated and integrated a material demand model using residential building stock projections. We provide the user with a wide range of mitigation alternatives through low-carbon production pathways, material use efficiency, transportation modes, and projected electricity grid mixes. In a case study application through four mitigation scenarios, we find emission savings up to 80% by maximizing user mitigation alternatives, primarily driven by reductions in material use intensities. This work represents a foundation for expanding LCA and embodied carbon tools to better enable stakeholders to rapidly and accurately assess customized supply chains while meeting trade requirements.

building materials↗

DOE OSTI2016

Disk Density Tuning of a Maximal Random Packing

We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Community size-spectra applied to recreational freshwater fisheries in Puerto Rican reservoirs

We developed a management framework based on community size-spectra (CSS) to establish targets to maximize desirable gamefish. We evaluated the influence of relative abundance of fish functional groups and watershed characteristics on the slope of the CSS or ecological efficiency. Both ecological efficiency and capacity were strongly influenced by relative abundance of fish functional groups and the degree of water level variation. Steeper slopes indicated overfished gamefish populations, whereas flatter slopes indicated systems dominated by undesirable invasive large-bodied low trophic position fish. We hypothesize that a desirable fishery would have a target range of CSS slopes that provides a deterministic community-based framework of predictable responses to environmental and food web changes that can be used to evaluate outcomes of management actions and a means to assess annual progress toward desirable conditions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Historical Trends in PM 2.5 -Related Premature Mortality during 1990$-$2010 across the Northern Hemisphere

Air quality across the northern hemisphere over the past two decades has witnessed dramatic changes, with continuous improvement in developed countries in North America and Europe, but a contrasting sharp deterioration in developing regions of Asia. Objective: This study investigates the historical trend in the long-term exposure to PM 2.5 and PM 2.5 -related premature mortality (PM 2.5 -mortality) and its response to changes in emission that occurred during 1990-2010 across the northern hemisphere. Implications for future trends in human exposure to air pollution in both developed and developing regions of the world are discussed. Methods: We employed the integrated exposure-response model developed by Health Effects Institute to estimate the PM 2.5 -mortality. The 1990-2010 annual-average PM 2.5 concentrations were obtained from the simulations using WRF-CMAQ model. Emission mitigation efficiencies of SO 2 , NO x , NH 3 and primary PM are estimated from the PM 2.5 -mortality responses to the emission variations. Results: Estimated PM 2.5 -mortalities in East Asia and South Asia increased by 21% and 85% respectively, from 866,000 and 578,000 in 1990, to 1,048,000 and 1,068,000 in 2010. PM2.5-mortalities in developed regions, i.e., Europe and high-income North America decreased substantially by 67% and 58% respectively. Conclusions: Over the past two decades, correlations between population and PM 2.5 have become weaker in Europe and North America due to air pollution controls but stronger in East Asia due to deteriorating air quality. Mitigation of primary PM appears to be the most efficient way for increasing health benefits, i.e., providing the largest mortality reduction per unit emissions. However, reductions in emissions of NH 3 are needed to maximize the effectiveness of NO x emission controls.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Dissolution Flowsheet for High Flux Isotope Reactor Fuel

As part of the Spent Nuclear Fuel (SNF) processing campaign, H-Canyon is planning to begin dissolving High Flux Isotope Reactor (HFIR) fuel in late FY17 or early FY18. Each HFIR fuel core contains inner and outer fuel elements which were fabricated from uranium oxide (U 3 O 8 ) dispersed in a continuous Al phase using traditional powder metallurgy techniques. Fuels fabricated in this manner, like other SNF’s processed in H-Canyon, dissolve by the same general mechanisms with similar gas generation rates and the production of H 2 . The HFIR fuel cores will be dissolved and the recovered U will be down-blended into low-enriched U. HFIR fuel was previously processed in H-Canyon using a unique insert in both the 6.1D and 6.4D dissolvers. Multiple cores will be charged to the same dissolver solution maximizing the concentration of dissolved Al. The objective of this study was to identify flowsheet conditions through literature review and laboratory experimentation to safely and efficiently dissolve the HFIR fuel in H-Canyon. Laboratory-scale experiments were performed to evaluate the dissolution of HFIR fuel using both Al 1100 and Al 6061 T6 alloy coupons. The Al 1100 alloy was considered a representative surrogate which provided an upper bound on the generation of flammable (i.e., H 2 ) gas during the dissolution process. The dissolution of the Al 6061 T6 alloy proceeded at a slower rate than the Al 1100 alloy, and was used to verify that the target Al concentration in solution could be achieved for the selected Hg concentration. Mass spectrometry and Raman spectroscopy were used to provide continuous monitoring of the concentration of H 2 and other permanent gases in the dissolution offgas, allowing the development of H 2 generation rate profiles. The H 2 generation rates were subsequently used to evaluate if a full HFIR core could be dissolved in an H-Canyon dissolver without exceeding 60% of the calculated lower flammability limit (LFL) for H 2 at a given Hg concentration. Complete dissolution of the Al 1100 and Al 6061 T6 alloys up to a final Al concentration of 2 M was obtained using a 7 M HNO 3 solution containing a 0.002 M Hg catalyst. However, following the dissolutions, solids were observed in the solution. The solids were amorphous, but likely originated from the Si present in the alloys. No crystalline materials, such as Al(NO 3 ) 3 were observed. During the course of the dissolution experiments, it was determined that delaying the addition of Hg once the HNO 3 solution reached the boiling point can reduce the total offgas and H 2 generation rates. The delay in starting the Hg addition is not necessary for HFIR fuel dissolution, but could be useful in other research reactor dissolution campaigns. The potential to generate flammable concentrations of H 2 in the offgas during a HFIR fuel dissolution was evaluated using the experimental data. The predicted H 2 concentration in the dissolver offgas stream was compared with 60% of the calculated H 2 LFL at 200 °C using several prototypical experiments. The calculations showed that a full HFIR core can be dissolved using nominally 0.002 M Hg to catalyze the dissolution. The margin between the predicted H 2 concentration and the calculated LFL was greater when the solution was allowed to boil for 45 min prior to initiating the Hg addition. When the Hg was increased to 0.004 M, the predicted H 2 concentration exceeded the calculated LFL early in the dissolution. The dissolution experiments also demonstrated that additional Hg (beyond the initial 0.002 M) could be added as the Al concentration increases. The ability to add more Hg during a HFIR fuel dissolution could be beneficial if slow dissolution rates are observed at high Al concentrations. Experimental data were used to demonstrate that the predicted H 2 concentration in a dissolver was below 60% of the calculated LFL at 200 °C when 0.004 M Hg was used to catalyze the dissolution if the Al concentration is conservatively greater than 0.5 M. Data also show that the Hg concentration during a HFIR fuel dissolution can be increased from 0.002 to 0.008 M at an Al concentration of 1.3 M.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Dissolution flowsheet for high flux isotope reactor fuel

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗