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DOE OSTI2025

Ion-Pair Proton Exchange Membrane Fuel Cells for Heavy-duty Transportation

The purpose of this project is to build a fuel cell prototype that demonstrates high temperature proton exchange membrane fuel cell technology (HT PEMFC) commercial readiness. It advances from a technology baseline established in a prior CRADA in which these same Parties collaborated during the years 2020-2024. These efforts are aligned with the intentions of the DOE Hydrogen Fuel Cell Technology Office (HFTO) L’Innovator Pilot Program. Participant observes that its US-based competitive advantage may be improved by further optimizing Laboratory inventions. This CRADA is intended to further improve National Laboratory compositions and to develop commercial HT PEMFC membrane electrode assemblies (MEAs) that perform over a range of operating temperatures and relative humidity, that generate power from both low grade and pure hydrogen, and that are resistant to air impurities. Participant’s goal is to advance the commercial competitiveness of National Laboratory technologies in fuel cell products for heavy-duty truck integrators, for stationary power generation, marine applications, and for emerging interest from commercial aviation. The Parties will collaborate on producing a HT PEMFC MEA that integrates LANL ion pair technology and Brookhaven catalyst technology. The Laboratories plan to research and develop compositions for testing and evaluation. The participant will manufacture multiple MEAs based on the Laboratory compositions and will enable selected customers to validate performance characteristics. The Laboratories will convey to Participant the methods of making the National Laboratory compositions such that Participant may reproduce and then scale Laboratory methods into commercial production. Throughout the project, the Laboratories and Participant will iteratively test and optimize the compositions of materials of the MEAs, intending to optimize characteristics such as power density and lifecycle durability. Participant’s intended customers include fuel cells for large trucks, aviation, power back up, marine, and portable power.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Design, Optimization, and Validation of GaN-Based DAB Converter for Active Cell Balancing in BTMS Applications: Preprint

This paper focuses on the design of a bidirectional dual active bridge (DAB) DC/DC converter that utilizes Gallium Nitride (GaN) switches as active components. In the existing literature, MOSFET-based DAB for active cell balancing is available, but GaN-based DAB converter for active cell balancing is still new. The proposed modular isolated GaN-based DAB converter is designed as an individual module of active cell balancing for behind-the-meter storage (BTMS) applications, targeting high-power charging stations. Modular isolated converters are connected to each cell (low voltage bus), and each cell is connected in series to build up a battery module. According to the reference current command of supervisory control, each DAB converter can transfer power back and forth through the high voltage (HV) bus to balance the State of Charge (SoC) between the cells. Each module DAB converter is designed at a 50W power rating. Switch power and transformer losses are analyzed for different switching frequencies, showing the optimum switching frequency for minimum losses. Furthermore, the procedure to select the required gate driver and the PCB layout optimization are discussed. Finally, the DAB performance analysis of GaNbased DAB and Si-based DAB is provided for a battery module operating with a LiFeMnPO4 prismatic cell with 3.2V 20Ah rated values.

active cell balancing↗

DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗

DOE OSTI2022

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Geothermal Direct-Use Applications for the District Energy System in Bucharest, Romania

The city of Bucharest, Romania, hosts the second-largest district energy system (DES) in the world. Geothermal resources can be considered as a supplementary heat source to support the demand for domestic hot water and space heating in the winter and shoulder seasons. The National Laboratory of the Rockies (NLR) has conducted a study that considers geothermal energy to serve a fraction of the existing district heating network operated by Electrocentrale Bucure?ti (ELCEN), the utility operating the DES. Lower Cretaceous and Jurassic limestones make up the main geothermal aquifer underlying Bucharest, which hosts temperatures suitable for district heating (up to 90 degrees C to the north of the city). Anomalous geothermal gradients have been observed to the north of the city, where a pumped well has produced 82 degrees C brine at the wellhead to feed the Therme Bucharest Spa. An anomalous gradient has also been reported to the southeast of the city (35 degrees C/km). NLR modeled the building heating loads of a small portion of the DES (a block of nine prototypical buildings) in its Urban Renewable Building and Neighborhood Optimization (URBANopt ) platform. To simulate meeting a baseload benchmark of 20 MWth deliverable to a small portion of the DES, the NLR team used GEOPHIRES to model production scenarios for (1) hydrothermal systems coupled with heat pumps targeting the main geothermal aquifer in the north, (2) enhanced geothermal systems targeting hot dry rock in the southeast, and (3) huff-and-puff systems targeting a gradient of 25 degrees C/km. Finally, NLR conducted a high-level sensitivity study around the techno-economics of these systems. The outcomes of this work indicate that hot dry rock geothermal resources that can deliver at least 90 degrees C hot water to the Geothermal District Energy System (GeoDES) offer a possible solution for supplemental geothermal heat delivered to the existing DES.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

On-line and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges that include compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time consuming. Here, in this study, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensors cell parameters for targeted gas phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other hazardous processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Co-simulation Framework for Community-scale Building-grid Integration [SWR-21-75]

Distributed energy resources (DERs), including rooftop solar, energy storage, and flexible loads, are gaining popularity as costs decline and as building owners and utilities realize their benefits. DERs can improve distribution system efficiency, help prevent the need for expensive grid upgrades, and increase the resilience of local communities. However, they can also cause difficulties in grid operations and can require controls to achieve their benefits. To address this challenge, NREL researchers have developed a community-scale solution that assesses the impacts of DERs and their control strategies on a distribution system. The framework has been shown to reduce solar photovoltaic (PV) curtailment to 0%, mitigate the adverse impact of solar variability on the distribution voltage, and provide up to 5-day critical load support during emergency events. Utilizing 5 different modules representing the feeder, buildings, home energy management systems, an aggregator, and a utility controller, NREL expects this simulation technology to play a critical role in the continued integration of DERs. According to the Energy Information Administration (EIA), solar curtailments accounted for 94% of the total energy curtailed in the California Independent System Operator (CAISO) in 2020. By enabling Independent System Operators (ISOs) and utility operators to bring solar curtailments to 0%, the electrical grid can become less dependent on fossil-fueled power generation sources. NREL's co-simulation framework contains five major components: Distribution Feeder Model: describes the distribution feeder topology using OpenDSS, including the locations of all DERs. Residential Building Model: simulates a large number of buildings at a high resolution using OCHRETM. The model is equipped to control equipment based on signals from an external module. The model includes major household appliances such as HVAC and a water heater, non-dispatchable load models, a distributed PV system, and a home battery system. Home Energy Management System: optimizes the controls for the devices in a home using foreseeTM. The control can adjust based on the user preferences including cost, comfort, and convenience. In hierarchical control scenarios, where the houses follow signals from an aggregator, the home energy management system provides a flexibility band with a range of power and follows the dispatch signals received from aggregator. Community-Level Aggregator: solves for optimal energy dispatch based on the flexibility bands received from each home and the grid service signal received from the utility controller. Utility-Level Controller: provides grid signals for voltage control using Distributed Energy Resources (DERs), such as solar systems, in the community.

Balamurugan, Sivasathya Pradha↗

DOE OSTI2025

JGI-Trichoderma v1.0

There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction

Stecca Steindorff, Andrei [Lawrence Berkeley Natio↗

DOE OSTI2023

Design, Optimization, and Validation of GaN-Based DAB Converter for Active Cell Balancing in BTMS Applications

This paper focuses on the design of a bidirectional dual active bridge (DAB) DC/DC converter that utilizes Gallium Nitride (GaN) switches as active components. In the existing literature, MOSFET-based DAB for active cell balancing is available, but GaN-based DAB converter for active cell balancing is still new. The proposed modular isolated GaN-based DAB converter is designed as an individual module of active cell balancing for behind-the-meter storage (BTMS) applications, targeting high-power charging stations. Modular isolated converters are connected to each cell (low voltage bus), and each cell is connected in series to build up a battery module. According to the reference current command of supervisory control, each DAB converter can transfer power back and forth through the high voltage (HV) bus to balance the State of Charge (SoC) between the cells. Each module DAB converter is designed at a 50 W power rating. Switch power and transformer losses are analyzed for different switching frequencies, showing the optimum switching frequency for minimum losses. Furthermore, the procedure to select the required gate driver and the PCB layout optimization are discussed. Finally, the DAB performance analysis of GaN-based DAB and Si-based DAB is provided for a battery module operating with a LiFeMnPO4 prismatic cell with 3.2V 20Ah rated values.

active cell balancing↗

DOE OSTI2024

Gains in operational flexibility, safety margins, and cost efficiencies via integrated Plant Reload Optimization platform

The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Task 2.3: Continuous Counter-Current Chromatography

This task in the Separations Consortium focuses on the development of counter-current chromatography (CCC), which is an advanced liquid-liquid chromatography method. Today, CCC is primarily practiced in batch mode operation, but continuous operation will be required for at-scale deployment in a biorefinery setting. In this task, we are developing a continuous CCC process in collaboration with a company that builds CCC units. This work will be demonstrated on the biorefining challenge of lignin valorization. Specifically, we demonstrate use of continuous CCC for both monomer-monomer and monomer-oligomer separations in multiple lignin streams of relevance to the biorefining industry. Breakthroughs in this area would be useful for BETO goals in both sustainable aviation fuel and biochemicals production, including directly contributing to BETO's 2030 lignin valorization goal. Our approach includes developing new computational modeling approaches to optimize both batch and continuous CCC processes, experimental work to determine optimal solvent systems for multiple lignin streams sourced from BETO-funded projects and industrial collaborators, and techno-economic analysis and life cycle assessment to identify the most impactful areas to ultimately enable this approach in the biorefinery. This task overall will enable high-resolution, multi-component, continuous separations at scale, demonstrated on a grand challenge biorefining problem.

BIOMASS FUELS↗

DOE OSTI2024

Energy characteristics of multi-chiller load distribution algorithms in a large office building

Here, this study evaluates the energy efficiency of multi-chiller systems in large office buildings, focusing on their optimization across various climate zones as defined by ASHRAE. Using EnergyPlus for simulations, the research examines five different load distribution algorithms in multi-chiller systems that range from one to ten chillers, aiming to understand their effectiveness in 15 distinct climate zones. The primary objectives of the study include identifying the energy efficiency of multi-chiller systems in each climate zone, determining the appropriate number of chillers for each zone, and evaluating the performance of the load distribution algorithms. Based on the U.S. Department of Energy’s commercial building model, the results suggest that multi-chiller systems can significantly reduce cooling energy consumption in various climates. Among the algorithms evaluated, the Sequential Uniform Part Load Ratio (SUPLR) algorithm demonstrates notable efficiency, especially in the 4A climate zone (Baltimore), where it achieves substantial energy savings. Applying the SUPLR algorithm in a multi-chiller setup with four chillers in this zone leads to an estimated 24.5 % reduction in energy usage, equivalent to 183 MW annually. The research indicates that a range of 3 to 5 chillers is typically optimal for most climate zones. In-depth analysis in the 4A climate zone highlights the importance of minimizing operation hours at low Part Load Ratios (PLR) to ensure that chillers operate at a high Coefficient of Performance (COP). This strategy underscores the potential of well-designed multi-chiller systems to reduce cooling energy demand, particularly in climates with transitional seasons. This study provides an overview of the energy-saving potential of multi-chiller systems, applicable across a variety of climatic scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Parametric Testing and Evaluation of a Novel Biphasic Absorption Process for Post-Combustion Carbon Capture

A novel biphasic CO2 absorption process (BiCAP) is being developed at the University of Illinois. BiCAP is enabled by using a new class of biphasic solvents developed based on multi-property criteria for postcombustion carbon capture. The process was previously demonstrated at the lab-scale, including in 10 kWe absorption and desorption column systems. In the current study, the process has been scaled up by building and testing a 40 kWe integrated capture skid at a power plant. Parametric tests for two biphasic solvents were conducted with the synthetic flue gas made of bottle CO2 gas and air to validate the performance and operational flexibility of the unit with respect to varying process or operating conditions. Operation of the phase separator, which is unique equipment to BiCAP, was verified to be stable and efficient. Two liquid phases could be separated via static settlement based on a density difference and >90% of the CO2 absorbed was concentrated in the separated CO2-rich phase. Results of parametric tests showed that the minimum heat duty appeared at the optimal stripping pressure. A higher stripping temperature was more favourable to improve overall energy performance. Introducing a secondary cold solvent feed stream to the top of the stripper via bypassing the cross heat exchanger helped reduce the heat loss carried with water vapour. Test results also revealed that the heat duty requirement was comparable when the CO2 concentration in flue gas decreased from ~11 to 4 vol.% as the phase separation decoupled the absorption and desorption steps in BiCAP. Under typical steady-state conditions, both biphasic solvents lowered the process heat duty by >40% compared with the reference 30 wt.% monoethanolamine solvent.

Absorption↗

DOE OSTI2025

High-Throughput Microfluidic Electroporation (HTME): A Scalable, 384-Well Platform for Multiplexed Cell Engineering

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME's core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform's ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME's potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck.

Gaillard, William R↗

DOE OSTI2026

Data-Driven Modeling of High-Resolution Residential Load Profiles Using Low-Resolution Smart Meter Measurements

Accurate and high-resolution residential load profiles are essential for power system modeling, demand response planning, and effective grid operation. As the energy sector moves towards a more actively managed distribution system, the ability to understand residential energy consumption at a minute-by-minute scale becomes increasingly critical. High-resolution load profiles provide key insights into demand patterns and user behavior, enabling grid operators to design more effective energy solutions; however, residential load measurements in the field are typically recorded at low resolutions, such as 15-60 minutes, which makes it hard to study the characteristics of different residential customers. This paper addresses these challenges by introducing a data-driven approach to generate realistic, high-resolution residential load profiles based on lowre-solution measurements and weather information. The proposed method retains the key features of the actual residential load measurements while offering appliance-level energy consumption details for each residential building. The results demonstrate the effectiveness of the proposed load profile generator, proving its capability to support utilities in optimizing residential energy management and ensuring a more reliable and resilient grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Smart Labs Final Report Summer 2021

The Smart Labs Project at Los Alamos National Laboratory (LANL) is an initiative derived from The University of California, Irvine and is part of the Department of Energy’s (DOE) Better Buildings Challenge. These carbon abatement strategies aim to reduce energy consumption of laboratories while also maintaining health and safety requirements. Smart Labs designs incorporate seven key principles which are: digital control systems, demand-based ventilation, low power-density demand-based lighting, exhaust fan discharge velocity optimization, pressure drop optimization, fume hood flow optimization, and commissioning with automated cross-platform fault detection. As the ALDCP Smart Labs team for the summer of 2021, the scope of the project is to determine the energy savings within building 03-1698 (Material Science Laboratory - MSL). Over the past couple of years, the Sustainability Group has been adding Smart Labs upgrades into the MSL building and the summer team would like to understand the impact made for the overall energy consumption/demand and safety for the building, determine the overall return on investment (ROI), and recommend more Smart Labs upgrades that can be added to the MSL building. The goal is to enable the UI FOD (Utilities and Infrastructure Facility Operation Division) to promote more Smart Labs projects in the future and further the reputation LANL and DOE facilities have of being leading examples of developers of high performing buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Core performance predictions in projected SPARC first-campaign plasmas with nonlinear CGYRO

This work characterizes the core transport physics of SPARC early-campaign plasmas using the PORTALS-CGYRO framework. Empirical modeling of SPARC plasmas with L-mode confinement indicates an ample window of breakeven (Q > 1) without the need of H-mode operation. Extensive modeling of multi-channel (electron energy, ion energy, and electron particle) flux-matched conditions with the nonlinear CGYRO code for turbulent transport coupled to the macroscopic plasma evolution using PORTALS reveals that the maximum fusion performance to be attained will be highly dependent on the near-edge pressure. Stiff core transport conditions are found, particularly when fusion gain approaches unity, and predicted density peaking is found to be in line with empirical databases of particle source-free H-modes. Impurity optimization is identified as a potential avenue to increase fusion performance while enabling core-edge integration. Extensive validation of the quasilinear TGLF model builds confidence in reduced-model predictions. The implications of projecting L-mode performance to high-performance and burning-plasma devices is discussed, together with the importance of predicting edge conditions.

Rodriguez-Fernandez, P. (ORCID:0000000273611131)↗