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Results for “optimization for fusion”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Magnetized liner inertial fusion platform development to assess performance scaling with drive parameters

Magnetized liner inertial fusion (MagLIF) experiments have demonstrated fusion-relevant ion temperatures up to 3.1 keV and thermonuclear production of up to 1.1 × 1013 deuterium–deuterium neutrons. This performance was enabled through platform development that provided increases in applied magnetic field, coupled preheat energy, and drive current. Advanced coil designs with internal reinforcement enabled an increase from 10 to 20 T. An improved laser pulse shape, beam smoothing, and thinner laser entrance foils increased preheat energy coupling from less than 1 to 2.3 kJ. A redesign of the final transmission line and load region increased peak load current from 16 to 20 MA. The wider range of input parameters was leveraged to study target performance trends with preheat energy, applied magnetic field, and peak load current. Ion temperature and neutron yield generally followed trends in two-dimensional clean Lasnex calculations. Stagnation performance improved with peak load current when other input parameters were also increased such that convergence was maintained. This dataset suggests that reducing convergence to less than 30 would improve predictability of target performance. Lasnex was used to identify a simulation-optimized scaling path, which suggests 10+ kJ of fusion yield is possible on the Z facility with achievable input parameters. This path also indicates >10 MJ could be generated through volume burn on a future facility with a path to high yield (>200 MJ) using cryogenic dense fuel layers. The newly developed MagLIF platform enables exploration of both this simulation optimized scaling path and a recently developed similarity-scaling path.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Including the vacuum energy in stellarator coil design

Being three-dimensional, stellarators have the advantage that plasma currents are not essential for creating rotational-transform; however, the external current-carrying coils in stellarators can have strong geometrical shaping, which can complicate the construction. Reducing the inter-coil electromagnetic forces acting on strongly shaped 3D coils and the stress on the support structure while preserving the favorable properties of the magnetic field is a design challenge. In this work, we recognize that the inter-coil forces are the gradient of the vacuum magnetic energy. We introduce an objective functional built on the usual quadratic flux on a prescribed target surface together with a weighed penalty on the vacuum energy. The Euler–Lagrange equation for stationary states is derived, and numerical illustrations are computed using a modern stellarator optimization framework. A study of the effect of the energy functional on the inter-coil forces is conducted and the energy is shown to be a promising quantity in producing coils with low forces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Additive Manufacturing of Cryogenic Austenitic Steel JK2LB via Wire-Fed Directed Energy Deposition (DED) for Fusion Energy Applications

This study explores the feasibility of fabricating cryogenic austenitic steel JK2LB via both laser-based directed energy deposition (laser-DED) and arc-based directed energy deposition (arc-DED) additive manufacturing processes for potential application in fusion reactors. JK2LB, a low-nickel, high-manganese stainless steel developed for ITER, offers excellent cryogenic toughness, radiation resistance, and decay-to-clearance characteristics. Although JK2LB was originally designed to endure cyclic stresses at cryogenic temperatures in tokamaks, its low-temperature mechanical integrity and radiation tolerance also make it a promising candidate for structural components, such as the coil case/support structure in nonplanar high-temperature superconducting magnet assemblies in stellarators. Directed energy deposition (DED) additive manufacturing was selected for this study due to its capability to fabricate large structures with complex geometries. Here, to address the long lead time and high cost associated with acquiring conventional JK2LB solid wire, JK2LB powder-cored wire was developed as the feedstock material. Testing blocks were then fabricated using both wire-fed laser-DED and arc-DED processes. Microstructural and compositional analyses revealed that both DED approaches yield fully austenitic phase and columnar grain structures. Mechanical testing at room temperature revealed that both DED routes achieved yield strength and elongation comparable to those of conventionally processed JK2LB via vacuum melting, electroslag remelting, extrusion, and drawing, though ultimate tensile strength was reduced due to Mn loss and large columnar grains. As a study mainly focusing on the additive manufacturing process, this work demonstrates the potential of additive manufacturing for fusion energy applications and provides a basis for optimization and future cryogenic mechanical evaluation.

Cryogenic steel↗
DOE OSTI2024

Strength stability at high temperatures for additively manufactured alumina forming austenitic alloy

Several fast-spectrum nuclear reactors designed to generate high power (~450 MWe) rely on forced convection of media such as supercritical CO 2 , sodium, or liquid lead to cool the nuclear core, operating at temperatures up to 600 °C. Cost-effective, high-strength Fe-based alumina forming austenitic (AFA) alloys are a promising candidate for the fabrication of critical nuclear components. This study investigated laser powder bed fusion (LPBF) processing of an AFA alloy composition optimized for improved creep resistance. Electron microscopy revealed an elongated grain structure along the build direction with a fine sub-grain cellular structure decorated with (Cr,Fe,Nb) 23 C 6 carbide precipitates at the intercellular boundaries. Finally, at temperatures of 20–900 °C, the LPBF alloy's superior tensile properties compared to its arc-melted counterpart and other advanced steels (e.g., SS316) were attributed to the distribution of nano-sized carbide precipitates, whereas the high ductility was attributed to the LPBF alloy's elongated grain structure.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct microstability optimization of stellarator devices

Turbulent transport is regarded as one of the key issues in magnetic confinement nuclear fusion, both for tokamaks and stellarators. Here, in this work, we show that a significant decrease in a microstability-based proxy, as opposed to a geometric one, for the turbulent heat flux, namely the quasilinear heat flux, can be obtained in an efficient manner by coupling stellarator optimization with linear gyrokinetic simulations. This is accomplished by computing the quasilinear heat flux at each step of the optimization process, as well as the deviation from quasisymmetry, and minimizing their sum, leading to a balance between neoclassical and the turbulent transport proxy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Optimizing the Smoothness and Thickness Uniformity of Thin-Film Parylene-N Vapor-Deposited Coatings for Inertial Confinement Fusion Experiments

Polymer coatings with submicrometer smoothness and constant thickness are a required component in a variety of inertial confinement fusion experiments. Smoothness is important for minimizing Rayleigh-Taylor-driven hydrodynamic instabilities, and uniform thickness is important for uniform shock propagation and shell convergence, both of which are critical phenomena that affect the experiment. The preferred polymer coating method is to vapor deposit the parylene-N polymer because it provides nominally smooth conformal coatings. As the coating thickness exceeds 5 µm, however, dome-shaped nodular growth defects develop and the thickness will vary by up to 17% over a distance of 3 cm. This study presents a deterministic method for achieving uniform film thicknesses with ±2% variability over 3 cm and a predictive method to control the thickness to within 5% of the desired value. A coating smoothness of ∼50 nm rms, measured over 40 000 µm 2 , was achieved by adding additional surfaces near the substrates. This additional area improved the thickness uniformity, an effect that is attributed to the low sticking coefficient of the parylene monomer.

chemical vapor deposition (CVD)↗
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Advanced Materials for Plasma-Exposed Robust Electrodes

The AMPERE project developed a new class of electrode materials that dramatically improve fusion device performance and longevity. By using Volumetrically Complex Materials (VCMs)—advanced porous metal foams optimized via plasma-material interaction science—the project achieved up to 85% reduction in sputtering erosion under fusion-relevant plasma conditions, far surpassing the goal of 40% reduction. This means these novel electrodes produce far fewer impurities and debris in the plasma, addressing a key challenge in fusion reactors by allowing greater plasma efficiency and power output due to the reduction of power losses due to unwanted interactions with wall-borne impurities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
DOE OSTI2024

Autonomous hybrid optimization of a SiO 2 plasma etching mechanism

Computational modeling of plasma etching processes at the feature scale relevant to the fabrication of nanometer semiconductor devices is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface, reaction probabilities, yields, rate coefficients, and threshold energies that characterize these processes. The increasing complexity of the structures being fabricated, new materials, and novel gas mixtures increase the complexity of the reaction mechanism used in feature scale models and increase the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity, or inadequate models. In this paper, we discuss a method to automate the selection of fundamental data in a reduced reaction mechanism for feature scale plasma etching of SiO 2 using a fluorocarbon gas mixture by matching predictions of etch profiles to experimental data using a gradient descent (GD)/Nelder–Mead (NM) method hybrid optimization scheme. These methods produce a reaction mechanism that replicates the experimental training data as well as experimental data using related but different etch processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗
DOE OSTI2024

Corrosion Testing Of Additively Manufactured Stainless Steel 316H In Molten Salt Environments

The development of a new ASTM standard for evaluating the corrosion resistance of additive manufactured (AM) stainless steel (SS) 316H in chloride molten salts is critical for the use of these materials in extreme environments such as molten salt reactors (MSRs). This report pertains to a work package of the Advaned Materials and Manufacturing Technologies (AMMT) developing a systematic methodology to link changes in AM fabrication parameters, namely surface finishing, porosity, microstructure, and chemical heterogeneity, to corrosion performance in NaCl-MgCl2 salt, a proposed secondary coolant for MSRs. During Fiscal Year 2024, Idaho National Laboratory investigators in the AMMT program, utilized SS316H bars, fabricated with laser bed powder fusion at Los Alamos National Laboratory, to establish and optimize the workflow for evaluating these process-to-performance relationships. Standard practices for specimen preparation were established using these specimens, with particular focus on descaling and sectioning methods that align with ASTM guidelines. A comprehensive experimental design for static corrosion testing was developed, with pre- and post-exposure analysis utilizing optical microscopy and scanning electron microscopy. So far standard descaling techniques were optimized, one static corrosion test was conducted on the LANL AM SS316H specimens, and pre- and post-corrosion practices were established. In addition, the work package yielded a review paper on corrosion testing gaps for AM materials in nuclear applications and submitted a proposal for a rapid-turnaround experiment to the Nuclear Science User Facilities Program to investigate the combined effects of proton irradiation and corrosion on AM SS316H. This work package establishes a foundation for evaluating processing-to-performance relationships for AM SS316H in harsh conditions, contributing to the safe and efficient design of components for next-generation nuclear reactors. The creation of a standardized methodology and the generation of relevant publications and future research pathways represent significant strides towards integrating AM materials into critical applications where corrosion resistance is paramount.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effects of Target Protium Content on SteadyState Isotope Rebalancing and Protium Removal Distillation Column Operation

• SRNL Fusion Fuel Cycle Research • SRNL Fuel Cycle Tritium Inventory Optimization Approaches • RHINO/Aspen • CODFISH • Direct Internal Recycling • IFE Fuel Cycle Overview • Direct Internal Recycling Applied to IFE • CODFISH Isotope Rebalancing and Protium Removal Column Optimization • RHINO Fuel Cycle Analysis • Conclusions and Future Work

Somers, Alex [Savannah River National Laboratory (↗
DOE OSTI2026

Auxiliary heating and current drive physics for the ST-E1 fusion power plant

This work describes the physics basis for the proposed auxiliary heating and current drive system on the ST-E1 fusion power plant. The ST-E1 flattop plasma considered here is fully non-inductive with a bootstrap fraction of 0.9 and the remaining current driven by EC waves. Using the recently published physics-based optimization method for EC launchers (Lopez et al 2025 Plasma Phys. Control. Fusion 67 055012), we show that the target flattop ECCD can be achieved with a net efficiency of 52 kA MW −1 using fundamental O-mode (O1) with frequency range 160–200 GHz launched from the low-field side top half of the vacuum vessel (LFS top-launch). From considering two candidate rampup scenarios, we conclude that LFS top-launch O1 ECCD can be equally effective during the early stages of plasma operation, although poloidal steering might be needed. X-mode waves injected from the LFS midplane are also shown to be effective for rampup even when T e < 1 keV. We also present modeling results for the pre-conceptual design of an ICRH system proposed for ST-E1. Using TORIC, we find that an ICRH system aiming for 42–48 MHz and toroidal mode number n φ ~ 10 robustly achieves dominant ion damping via Helium-3 minority heating transitioning to second-harmonic Tritium heating. We then show that such waves can be efficiently generated by a 5-strap traveling-wave antenna (TWA) using the Petra-M code. The TWA has a 40–45 MHz passband within which ~60% of the power entering the TWA is coupled to the plasma with the remaining ~40% of the power being transmitted through the TWA and possibly recirculated; the power reflected back into the transmission lines is negligible. This passband structure persists even when the evanescent distance is increased by a factor of two, or when the magnetic-field angle is increased by 30°, demonstrating inherent load resilience that will be crucial for effective ICRH on ST-E1.

electron cyclotron↗
DOE OSTI2024

Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models

The viability of the tokamak as a potential fusion reactor depends on the ability to keep the plasma in a stable regime while achieving temperatures, densities, and confinement times that are as high as possible. Tokamak scenario development attempts to find plasma regimes that achieve all of these conditions and are accessible with a given set of hardware constraints. This requires the ability to control plasma properties such as the normalized beta, the internal inductance, safety factor, rotation, etc. One property that has received less attention than some of the others, but is no less critical to achieving high performance, is the electron temperature (T e ) profile. In this work, Linear Quadratic Integral (LQI) control is used to develop a controller for the electron temperature profile in DIII-D. The controller is based on a linearized model derived from the transport equation that describes the evolution of the electron temperature, and includes contributions from the neural network surrogate models NubeamNet and MMMnet. Furthermore, the controller is tested in simulation using COTSIM, and is proven capable of tracking a target T e profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗