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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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Crack Catcher AI – Enabling smart fracture mechanics approaches for damage control of thin silicon cells or wafers
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Next-Generation Modeling Approaches for Exhaust Control and Divertor Optimization
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Floating Photovoltaic Technical Potential: A Novel Geospatial Approach on Federally Controlled Reservoirs in the United States
Floating photovoltaic generation is a rapidly expanding sector of the solar energy industry, and understanding the quantity that can feasibly be installed is a crucial step to understand its role in future energy systems. This paper presents a novel spatially explicit methology of FPV potential for federally owned and managed reservoirs in the United States that uses site-specific attributes of reservoirs to estimate available area and potential generation capacity. The analysis finds that the proportion reservoir area that is found to be available for FPV development is similar to assumed values used in previous research on average, however there is a wide variability in this proportion on a site by site basis. Potential FPV generation capacity on these reservoirs is estimated to be in the range of 861 to 1,042 GWdc depending on input assumptions, likely representing a significant portion of future US solar generation needs. This work represents an advancement in methods used to estimate FPV potential that presents many natural extensions for further research.
Resilience Through Data-Driven, Intelligent Designed Control: A Formal Methods Approach
The PNNL and GTRI team developed a strategy to integrate temporal logic rule specification for detection of cyber-intrusion in the source code and control algorithms of CPS using advanced cyber-data. The GTRI team utilized its capabilities in rule synthesis and temporal logic specifications for software assurance and verification to detect and predict impact of cyber-intrusions and malware in the computational and control algorithms of cyber-physical systems. The team also developed a testing and verification approach that could be used to validate the suggested approach against a realistic use-case CPS showcasing improvements in system impact prediction performance. Temporal logic offers a compact expression of events in absolute and relative time and has a formalized translation to state machines. As such, temporal logic rules can feasibly be synthesized to any system as a rule engine, with the process being formally verified to be correct. The goal here is to utilize temporal logic rules to detect cyber-attacks and manipulations in the computational algorithms and provide real-time software assurance and verification guarantees.
Surface with wettability gradient — A novel approach for crystallization fouling control on the horizontal falling film flow
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A New Approach to Robot Motor Control
This poster details the motor control improvements to Fermilab's Remote Viewing Robot (RVR). It is a robot tasked with remotely investigating issues within the accelerator tunnels at Fermilab. Initially controlled by a single Raspberry Pi that housed all the robot s operations, the RVR will now use a Raspberry Pi Pico W for its motor control. This was achieved using Pulse Width Modulation (PWM) to allow for precise speed and torque control for the robot. This enhances the robot s reliability. By addressing the challenge of navigating high radiation environments, the upgrade helps assist the RVR s goal of decreasing the need for human intervention in accelerator tunnels.
A New Approach to Robot Motor Control
This essay details the motor control improvements to Fermilab's Remote Viewing Robot (RVR). It is a robot tasked with remotely investigating issues within the accelerator tunnels at Fermilab. Initially controlled by a single Raspberry Pi that housed all the robot s operations, the RVR will now use a Raspberry Pi Pico W for its motor control. This was achieved using Pulse Width Modulation (PWM) to allow for precise speed and torque control for the robot. This enhances the robot s reliability. By addressing the challenge of navigating high radiation environments, the upgrade helps assist the RVR s goal of decreasing the need for human intervention in accelerator tunnels.
Enhance Resistance Spot Welding Quality Control: A Machine Learning Approach
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Extending the operational boundaries of RMP-ELM suppression with optimized 3D field control
The use of 3D magnetic fields is one of the promising approaches to control edge localized modes (ELMs), and ITER has plans to utilize a flexible 3D coil set for ELM suppression using 3D fields. This study focuses on optimizing the 3D field spectrum to expand the operational window for n = 1 resonant magnetic perturbation (RMP) ELM suppression in KSTAR. The optimized n = 1 RMP effectively suppresses ELMs throughout the entire H-mode discharge, including the first ELM crash, while avoiding the onset of disruptive locked modes in low-density L-mode plasmas. The predicted suppression window aligns well with experimental data, highlighting the challenges and solutions of using n = 1 RMP at low densities. Moreover, the optimization successfully achieved n = 1 RMP ELM suppression for the first time in ITER-relevant q 95 and shaping conditions, including cases with q95 as low as 3.6, as well as other q 95 and shape configurations. This highlights the importance and utility of 3D coil optimization while emphasizing the potential of long-wavelength low-n RMP, which will be valuable for ex-vessel coils designed to avoid complications of nuclear degradation.
Spin-on deposition of amorphous zeolitic imidazolate framework films for lithography applications
Amorphous zeolitic imidazolate framework (aZIF) films have been recently introduced as resists for electron beam and extreme ultraviolet lithography. aZIFs are also being considered for separation applications, including thin film membranes. However, the reported methods for aZIF deposition are currently based on highly empirical trial-and-error approaches that hinder control of film composition, thickness and uniformity as well as scale-up and transferability to different coating geometries. This work presents a method for depositing aZIF films with controllable thickness using dilute precursors mixed immediately before encountering the substrate. Importantly, the method is amenable to quantitative analysis by computational fluid dynamics to extract intrinsic deposition rates and limiting reactant transport diffusivities, enabling predictive physics-based modeling of the deposition process. This allows the deposition method to be adapted for spin coating on silicon wafers to prepare high-quality aZIF films with consistently controlled thickness. Using this approach, high-resolution resist performance and wafer-scale use for beyond extreme-ultraviolet lithography of aZIF films is demonstrated.
A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings
Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.
Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays
This paper presents a multi-agent reinforcement learning (MARL) approach for controlling adjustable metallic reflector arrays to enhance wireless signal reception in non-line-of-sight (NLOS) scenarios. Unlike conventional reconfigurable intelligent surfaces (RIS) that require complex channel estimation, our system employs a centralized training with decentralized execution (CTDE) paradigm where individual agents corresponding to reflector segments autonomously optimize reflector element orientation in three-dimensional space using spatial intelligence based on user location information. Through extensive ray-tracing simulations with dynamic user mobility, the proposed multi-agent beam-focusing framework demonstrates substantial performance improvements over single-agent reinforcement learning baselines, while maintaining rapid adaptation to user movement within one simulation step. Comprehensive evaluation across varying user densities and reflector configurations validates system scalability and robustness. The results demonstrate the potential of learning-based approaches for adaptive wireless propagation control.
Active Light-Controlled Frontal Ring-Opening Metathesis Polymerization
Frontal ring-opening metathesis polymerization (FROMP) is a self-propagating, energy-efficient polymerization method used to fabricate polymeric materials. This dataset describes a photochemical approach for controlling the FROMP of dicyclopentadiene (DCPD). A photobase generator is used to inhibit polymerization under 365 nm light, while a photosensitizer combined with a co-initiator enables acceleration of the reaction under 470 nm light. Together, these components provide orthogonal, light-meditated control over front velocity.
Comparing approaches for introducing polycyclic aromatic hydrocarbons to Ge(001) to seed graphene nanoribbon synthesis by CH 4 chemical vapor deposition
Here, we evaluate two approaches for introducing polycyclic aromatic hydrocarbons (PAHs) to a graphene catalyst substrate as part of a two-step chemical vapor deposition (CVD) process for growing graphene nanoribbons (GNRs). In this process, PAHs first form graphene-like seeds on Ge, and then GNRs are subsequently evolved from these PAH-derived seeds via substrate-mediated anisotropic growth kinetics during the CVD of CH4. The first seeding approach sublimes controlled doses of PAH thin films into the CVD chamber, while the second delivers PAHs directly from the vapor phase at set concentrations. Using these two approaches, we measure the dependence of GNR density on PAH dose and compare the experimental results with predictions from a rate model of PAH diffusion, desorption, and clustering. We find that PAHs that more strongly adsorb to the catalyst surface (generally larger PAHs) desirably remain more individualized prior to GNR evolution whereas smaller, more weakly bound PAHs aggregate into larger clusters with various sizes on Ge that are undesirable for synthesizing more monodisperse GNRs. As a result, this work is important because it offers a framework that enables the rational selection and design of seed molecules, advancing anisotropic GNR CVD synthesis.
An adaptive, data-driven multiscale approach for dense granular flows
The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.
Simultaneous control of the electron temperature and safety factor profiles in DIII-D using model-based optimal control techniques
Future tokamak power plants will likely operate using a single, well-defined plasma scenario, either in steady state or for very long pulse lengths. In order to enhance the robustness of the scenario, feedback controllers for a variety of plasma properties will be necessary to counteract any disturbances and ensure safe operation. However, only a limited set of actuators will be available to control many different quantities. Because of this, it is necessary to develop controllers that are able to regulate multiple plasma properties using a limited set of actuators. To this end, a controller has been developed for the simultaneous regulation of both the electron temperature and safety factor profiles in DIII-D. This algorithm uses a linear quadratic integral control synthesis approach based on a linearized model of the dynamics of the two profiles. Two neural network surrogate models, NubeamNet and MMMnet, are included to improve the fidelity of the model. Furthermore, the controller has been tested in simulation using COTSIM, and has demonstrated the ability to simultaneously track changes in both the electron temperature and safety factor targets, including changes in both the magnitude and the shape of the profiles.