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Direct internal recycling fractions approaching unity
Direct internal recycling (DIR) refers to the process of recovering pure hydrogen isotopes (D/T) from helium and other impurities in the fusion plasma exhaust and directing them back to the fuel injection system. Increasing the exhaust fraction purified through DIR significantly reduces the size and cost of the tritium plant and provides additional benefits including reduced requirements for both the tritium startup inventory and tritium breeding ratio. Metal foil pumps (MFPs) are the dominant technology for this separation, relying on the concept of superpermeation. We recently demonstrated that PdCu foils operated at low temperature provide both exceptional flux and resilience to helium absorption as the DIR fraction is increased. Herein we design and demonstrate continuous and semi-batch DIR processes using PdCu MFPs. Under continuous processing, stable performance was observed for DIR fractions up to 92 %. In addition, we demonstrate a semi-batch process capable of extending the DIR fraction to unity (> 99.8 %). Under the experimental conditions described within a PdCu MFP area of ~22 m 2 would be sufficient to process the fusion exhaust with 92 % DIR fraction at expected flowrates of 100 Pa·m 3 ·s -1 for a future fusion power plant.
The impact of argon addition on hydrogen superpermeation through palladium alloy metal foil pumps during direct internal recycling
Metal foil pumps (MFPs) are a leading technology for the direct internal recycling (DIR) of hydrogen isotopes from the plasma exhaust of fusion devices. MFPs rely on the concept of superpermeation, where plasma-generated atomic hydrogen absorbs into the metal foil, rapidly diffuses, and desorbs downstream. To date, studies of superpermeation have predominantly employed pure hydrogen or in some cases trace levels of impurities. In practice the plasma exhaust may contain significant levels of plasma enhancement gases such as argon, an inert gas with metastable states that can enhance the plasma. In this work, we systematically study the impact of Ar addition on the performance of PdCu and PdAg MFPs at low temperature. Performance was strongly dependent on the DIR fraction. At negligible DIR levels Ar addition did not significantly improve the flux over dilution effects. However, under appreciable DIR operation the flux was enhanced up to 90 % relative to pure H 2 , with the optimal concentration range being 5–10 % Ar exiting the system. Beyond 15 % addition plasma enhancement benefits were offset by dilution. Performance correlated with the atomic H emission, and benefits were more pronounced for PdAg than PdCu. Operation at significant DIR levels dramatically alters the flow dynamics resulting in concentration gradients near the MFP, creating plasma conditions that promote H 2 dissociation.
Preliminary Testing of a Continuous Cryopump for Primary Fusion Device Pumping and Direct Internal Recycling
Here, the concept of directly recirculating fusion machine exhaust gas, bypassing the tritium plant, to make fuel pellets was proposed in the 1990s and later termed direct internal recycling (DIR). In the DIR concept, the residual fusion fuel in the machine exhaust stream is separated from impurities locally and diverted directly to the fueling systems, bypassing isotopic separation and other processing equipment, and therefore significantly reducing the required size of the fuel processing plant, reducing plant inventory, and thus increasing the economic viability of fusion as an energy source. One concept for DIR consists of a series of cryogenic pumps to separate the impurities from the machine exhaust gas using different triple point temperatures and saturation curves of exhaust constituents. In this concept, the plasma exhaust is initially passed through an impurity trap operating at ~25–30 K to desublimate impurities such as hydrocarbons, argon, oxygen, and nitrogen. The resulting process stream will consist of DT fuel and helium. The process stream is then pumped by a continuous cryopump known as a “snail pump.” This pump is a steady-state continuous cryopump that desublimates all remaining exhaust gas constituents while allowing helium, a byproduct of the fusion reaction, to pass through. The helium is pumped to the tritium plant for processing while the desublimated material is continuously scraped off, heated up, and transported to the fueling system. This article will present the cryogenic DIR concept and outline the design and operation of the snail pump, along with results from preliminary testing. Tests to assess pumping and separation efficiency found that at D2 flows below 50.7 Pa ⋅ m3/s with 1% helium, the pump is capable of pumping and separating the gas with a resulting DIR fraction of >99%, with no helium entrained in the primary fuel exhaust stream. The main limitation is due to the thermal performance of the cryogenic circuits of the pump, which will be addressed in future testing.
The impact of helium on plasma-driven hydrogen permeation and implications for direct internal recycling in the fusion fuel cycle
Abstract Metal foil pumps (MFPs) are the leading technology for direct internal recycling (DIR) of hydrogen isotopes from the plasma exhaust in future fusion plants. MFPs rely on the concept of superpermeation, where superthermal H atoms directly absorb into the metal foil, rapidly diffuse, and desorb downstream. To date, studies of superpermeation have predominantly employed either pure hydrogen or in some cases trace levels of impurities. The plasma exhaust is expected to contain just ∼1% helium, but in DIR the source gas would be enriched in helium as hydrogen isotopes are extracted. In this work, we explore the impact of helium on hydrogen superpermeation at low temperature (75 °C–200 °C) using Pd-based foils. To first order, the flux scaled linearly with the hydrogen mole fraction. Stable permeation was observed until the helium fraction reached ∼80%, where the flux began to decline slowly with time. In addition, short term (1–5 min) exposure to pure helium plasma significantly attenuated subsequent hydrogen plasma permeation, and the degree was more dramatic at elevated temperature. This attenuation was correlated with He retention in the foils, which was detected by time-of-flight secondary ion mass spectrometry at low levels (<0.1 at. %) and limited to the near surface (<10 nm). Similar trends were observed among all alloys (Pd, PdAg, PdCu), and the foils were restored to full performance with an Ar + sputter clean. The potential for helium plasma exposure to impact MFP performance under these conditions has not been previously reported, and these findings have significant implications to the design and implementation of practical DIR systems.
Search for CP violation and measurement of branching fractions and decay asymmetry parameters for Λ$^+_c$ → Λ h + and Λ$^+_c$ → Σ 0 h + ( h = K, π )
Here, we report a study of Λ$^+_c$ → Λ h + and Λ$^+_c$ → Σ 0 h + ( h = K, π ) decays based on a data sample of 980 fb -1 collected with the Belle detector at the KEKB energy-asymmetric e + e - collider. The first results of direct CP asymmetry in two-body singly Cabibbo-suppressed (SCS) decays of charmed baryons are measured, A$^{dir}_{CP}(Λ^+_{c} → ΛK^{+})$ = +0.021 ± 0.026 ± 0.001 and A$^{dir}_{CP}(Λ^+_{c} → Σ^{0}K^{+})$ = +0.025 ± 0.054 ± 0.004. We also make the most precise measurement of the decay asymmetry parameters (α) for the four modes of interest and search for CP violation via the α-induced CP asymmetry (A$^α_{CP}$ ). We measure A$^α_{CP}$ ($Λ^+_{c} → ΛK^{+}$) = -0.023 ± 0.086 ± 0.071 and A$^α_{CP}$ ($Λ^+_{c} → Σ^{0}K^{+}$) = +0.08 ± 0.35 ± 0.14, which are the first A$^α_{CP}$ results for SCS decays of charmed baryons. We search for Λ -hyperon CP violation in $Λ^+_{c}$ → (Λ, Σ 0 )π + and find A$^α_{CP}$(Λ → pπ - ) = +0.013 ± 0.007 ± 0.011 . This is the first time that hyperon CP violation has been measured via Cabibbo-favored charm decays. No evidence of baryon CP violation is found. We also obtain the most precise branching fractions for two SCS Λ$^+_c$ decays, $\mathscr{B}$($Λ^+_{c} → ΛK^{+}$) = (6.57 ± 0.17 ± 0.11 ± 0.35) x 10 -4 and $\mathscr{B}$($Λ^+_{c} → Σ^{0}K^{+}$) = (3.58 ± 0.19 ±0.06 ±0.19) x 10 -4 . The first uncertainties are statistical and the second systematic, while the third uncertainties come from the uncertainties on the world average branching fractions of $Λ^+_{c}$ → (Λ, Σ 0 )π + .
Sticking Coefficients of Fusion Reactor Impurities from Molecular Dynamics Simulations for the Design of Cryopumps
A cryopump can be utilized as an impurity removal component of a direct internal recirculation (DIR) system for the fusion fuel cycle. The DIR facilitates a low fuel inventory by continuously pumping unburnt fuel while removing impurities from the fusion exhaust stream. A cryopump can target multiple impurity species by maintaining a temperature lower than the gas triple-point temperature that promotes desublimation. The desublimation/condensation of gases in cryopumps can be characterized by the sticking coefficient, which is defined as the probability for a gas particle to stick to a (cryo-)surface upon collision. The sticking coefficient is one of the important design/operation parameters for cryopumps, and it depends on a variety of surface and gas properties. Here, in this study, molecular dynamics simulations were utilized to estimate the sticking coefficients of typical fusion gas impurity species N 2 , CO 2 , and CH 4 over a Cu surface for a range of gas temperatures and surface coverages. The molecular dynamics study showed that the sticking coefficients for gases decrease with an increase in gas temperature. The presence of a single full monolayer of condensate on the metallic surface showed an adverse effect on the sticking of gases; however the sticking improved with two full monolayers of condensate on the surface. The sticking of gases over the mixed condensate on a surface was more favorable than the condensate of the same species for N 2 and CH 4 , with an exception for CO 2 , which showed a decrease in sticking over the mixed condensate.
Modeling and analysis of the tritium fuel cycle for ARC- and STEP-class D-T fusion power plants
Abstract The limited tritium resources available for the first fusion power plants (FPPs) make fuel self-sufficiency and tritium inventory minimization leading issues in FPP design. This work builds on the model proposed by Abdou et al (2020 Nucl. Fusion 61 013001), which analyzed the fuel cycle (FC) of a DEMOnstration nuclear FPP-class FPP with a time-dependent system-level model. Here, we use a modified version of their model to analyze the FC of an Affordable, Robust, Compact (ARC)-class tokamak and two versions of a Spherical Tokamak for Energy Production (STEP)-class tokamak. The ARC-class tokamak breeds tritium in a 2LiF + BeF 2 liquid immersion blanket, while the STEP-class tokamak breeds tritium utilizing either a liquid-lithium blanket design or an encapsulated breeding blanket. A time-dependent system-level model is developed in Matlab Simulink ® to simulate the evolution of tritium flows and tritium inventories in the FC. The main goals of this work are to assess tritium self-sufficiency of the ARC- and STEP-class designs and to determine quantitative design requirements that can be used to analyze the adequacy of a proposed FC system. These design requirements are aimed at achieving a low tritium inventory doubling time ( t d ) and a low start-up inventory ( I s t a r t u p ) while keeping the required tritium breeding ratio (TBR r ) as low as possible. We also consider how improvements in FC technology and POs affect TBR r and I s t a r t u p . The model results show that TBR r for ARC- and STEP-class FPPs should be achievable if the tritium burn efficiency (TBE) reaches 0.5%–1% (TBR r < 1.2). This assumes significant, but attainable, improvements over current abilities. However, the model results indicate that an FPP must achieve ambitious performance targets, including FPP availability > 70%, tritium processing time < 4 h, and the implementation of direct internal recycling (DIR). If future research yields major improvements to achievable TBE, it may be possible to achieve tritium self-sufficiency while operating at lower availability and without implementing DIR.
Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)
This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwater influenceData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2024streamdata, author = {Jared Willard and Fabio Ciulla and Helen Weierbach and Vipin Kumar and Charuleka Varadharajan}, title = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models"}, year = {2024}, doi = {10.15485/2448016}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016}}MLA: Willard, Jared, et al. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models". 2024. ESS-DIVE Repository, doi:10.15485/2448016.
Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).
This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.
Approach to Startup Inventory for Viable Commercial Power Plant
Summary • FPP realization within the next 10-15 years will require dedicated efforts to improve DIR, burn fraction, fueling efficiency, and/or processing times. • Modest improvements from either the fuel cycle side or plasma physics side should be possible with considered allocation of R&D funding. • Current gaps in particular include blanket extraction at scale, improved efficiency in isotope separation and detritiation, and maximizing DIR efficiency. • Total costs for FPP construction and commissioning depend heavily on site regulation, so decreases in required SI and OI can lead to large decreases in capital outlay. • Some subsystems in the fuel cycle are required from a environmental management perspective but are both energetically expensive and time consuming, particularly water detritiation. • Low inventories but high capital and operational costs mean a centralized water detritiation plant could greatly improve likelihood of deployment of multiple FPPs on the same time scale.
Semi-inclusive direct photon + jet and 𝜋 0 + jet correlations measured in 𝑝 + 𝑝 and central Au + Au collisions at $\sqrt{s_{NN}}$ = 200GeV
The STAR experiment at RHIC reports new measurements of jet quenching based on the semi- inclusive distribution of charged-particle jets recoiling from direct photon (γ dir ) and neutral pion (π 0 ) triggers in pp and central Au + Au collisions at $\sqrt{s_{NN}}$ = 200 GeV, for triggers in the range 9 < $E$$^{trig}_{T}$ < 20 GeV. The datasets have integrated luminosities of 3.9nb −1 for Au + Au and 23pb −1 for 𝑝𝑝 collisions. Jets are reconstructed using the anti-𝑘 𝑇 algorithm with resolution parameters 𝑅 = 0.2 and 0.5. The large uncorrelated jet background in central Au + Au collisions is corrected using a mixed-event approach, which enables precise charged-particle jet measurements at low transverse momentum 𝑝$^{ch}_{𝑇,jet}$ and large 𝑅. Recoil-jet distributions are reported in the range 𝑝$^{ch}_{𝑇,jet}$ < 25 GeV/𝑐. Comparison of the distributions measured in 𝑝𝑝 and Au + Au collisions reveals strong medium-induced jet yield suppression for 𝑅 = 0.2 with markedly less suppression for 𝑅 = 0.5. Comparison is also made to theoretical models incorporating jet quenching. Furthermore, these data provide new insight into the mechanisms underlying jet quenching and the angular dependence of medium-induced jet-energy transport and provide new constraints on modeling such effects.
Measurement of In-Medium Jet Modification Using Direct Photon + Jet and 𝜋 0 + Jet Correlations in 𝑝 + 𝑝 and Central Au + Au Collisions at $\sqrt{s_{NN}}$ = 200 GeV
The STAR Collaboration presents measurements of the semi-inclusive distribution of charged-particle jets recoiling from energetic direct-photon (𝛾 dir ) and neutral-pion (𝜋 0 ) triggers in 𝑝 + 𝑝 and central Au + Au collisions at $\sqrt{s_{NN}}$ =2 00 GeV over a broad kinematic range, for jet resolution parameters 𝑅 = 0.2 and 0.5. Medium-induced jet yield suppression is observed to be larger for 𝑅 = 0.2 than for 0.5, reflecting the angular range of jet energy redistribution due to quenching. The predictions of model calculations incorporating jet quenching are not fully consistent with the observations. Furthermore, these results provide new insight into the physical origins of jet quenching.
Computational Study of a Cryopump Design for Fusion Exhaust Gas Purification Using Direct Simulation Monte Carlo
Cryopump-based direct internal recycling (DIR) of fusion fuel is an attractive prospect because it provides pumping of helium ash from a reactor along with separation of helium and other impurities from the fuel. Previous studies have demonstrated a continuously regenerating cryopump, referred to as the Snail pump, that separates helium ash from fusion fuel for reactor-relevant flow rates. Here, in this study, a conceptual cryopump was designed as a proof-of-principle study to target other impurities, besides helium ash, from the fusion exhaust that would complement the Snail pump. The impurity-removal cryopump consists of four sets of chevron fins, two sets operating at 80 K and the other two at 30 K. A computational study using direct simulation Monte Carlo (DSMC) was performed with a gas mixture of 96.5% D2, 2.0% He, and 0.5% each of CO2, N2, and CH4. In this configuration, 80 K chevron fin sets are capable of capturing CO2 and 30 K sets capable of capturing CO2, N2, and CH4. The computational study showed that the cryopump is capable of reducing the impurity content by more than two orders of magnitude from the flow.
Electricity Baseline 2022 Background Data and Log File
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.
Electricity Baseline 2021 Background Data and Log File
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.
Electricity Baseline 2020 Background Data and Log File
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.
Testing of Engineered Membranes in Fusion-Relevant Metal Foil Pumps [Poster]
Mines investigated the impact of intentional (e.g. Ar) and unintentional (e.g. C,O) impurities on the performance of Pd based metal foil pumps (MFPs) for direct internal recycling (DIR).