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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ultrasonic Characterization of Ethylene Vinyl Acetate (EVA) Crosslinking for Quality Assurance and Lamination Process Control (US-Xlink)

Module makers strive to cut lamination processing times to enhance production throughput and reduce costs. If overdone, this might lead to poor EVA quality due to incomplete EVA crosslinking and a large concentration of potentially harmful reactants. Those deficiencies are frequently missed during production quality testing as initially their impact on power output is small. Additionally, the crosslinking agent is frequently inhomogeneously dispersed across the EVA foils such that local destructive characterization procedures such as differential scanning calorimetry (DSC), Soxhlet extraction, swelling methods, or mechanical tests provide only a limited amount of information. However, these defects and inhomogeneities can become considerably more important during field operation, dramatically reducing long-term power yield, and increasing LCOE. Examples of typical long-term module degradation modes relating to poor lamination process conditions include cell breakage, corrosion of the metallization, delamination, and local quality deficiencies. To provide reliable material characterization in a manufacturing setting, we devised a non-destructive technology that uses ultrasound to evaluate the quality of interface adhesion and the degree of crosslinking. As a calibration reference, DSC measurements were employed. Although the potential of ultrasonic approaches for this purpose has already been noted, those previous methods were generally limited to local qualitative measurements. Furthermore, variations in EVA thickness and temperature had a substantial impact on them. The solution we propose solves these limitations by employing ultrasonic absorption rather than relying solely on sound velocity data.

14 SOLAR ENERGY↗
DOE OSTI2023

Reinforcement learning for real-time process control in high-temperature superconductor manufacturing

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. In conclusion, based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Support Vector Machines for Classification of Direct Energy Deposition Standoff Distance for Improved Process Control

A critical factor in the implementation of direct energy deposition is the ability to maintain the standoff distance between the nozzle and the build surface, as this influences powder capture efficiency and overall part quality. Due to process-related variations, layer height may vary, causing unintended variation in standoff distance and poor build quality. While prior work has utilized contact probing to qualify standoff distance during processing, in situ methods for qualification of standoff distance are of major interest. The present work seeks to understand efficacy of image-based methods for classifying standoff distance variation in real-time using support vector machines (SVMs). It was hypothesized that the size of the melt pool and the amount of spatter will have significant correlations with deviations in the standoff distance; thus, SVMs were used on a dataset that is comprised of morphological features of melt pool size and image entropy. The SVM model was used to classify melt pool images into categories according to standoff distance variation from nominal. K-folds cross validation was used to find the optimal hyperparameters for the SVM model. To understand the impact of the selected features on the classification performance and inference speed, multiple models were trained with differing numbers of included features. Results for classification score, inference time, and image preprocessing/feature extraction from these data are reported. The present results show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance.

Klesmith, Zoe↗
DOE OSTI2022

Controlling process instability for defect lean metal additive manufacturing

The process instabilities intrinsic to the localized laser-powder bed interaction cause the formation of various defects in laser powder bed fusion (LPBF) additive manufacturing process. Particularly, the stochastic formation of large spatters leads to unpredictable defects in the as-printed parts. Here we report the elimination of large spatters through controlling laser-powder bed interaction instabilities by using nanoparticles. The elimination of large spatters results in 3D printing of defect lean sample with good consistency and enhanced properties. We reveal that two mechanisms work synergistically to eliminate all types of large spatters: (1) nanoparticle-enabled control of molten pool fluctuation eliminates the liquid breakup induced large spatters; (2) nanoparticle-enabled control of the liquid droplet coalescence eliminates liquid droplet colliding induced large spatters. The nanoparticle-enabled simultaneous stabilization of molten pool fluctuation and prevention of liquid droplet coalescence discovered here provide a potential way to achieve defect lean metal additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Lamination of >21% Efficient Perovskite Solar Cells with Independent Process Control of Transport Layers and Interfaces

Transport layer and interface optimization is critical for improving the performance and stability of perovskite solar cells (PSCs) but is restricted by the conventional fabrication approach of sequential layer deposition. While the bottom transport layer is processed with minimum constraints, the narrow thermal and chemical stability window of the halide perovskite (HP) layer severely restricts the choice of top transport layer and its processing conditions. To overcome these limitations, we demonstrate lamination of HPs—where two transport layer-perovskite half-stacks are independently processed and diffusion-bonded at the HP-HP interface—as an alternative fabrication strategy that enables self-encapsulated solar cells. Power conversion efficiencies (PCE) of >21% are realized using cells that incorporate a novel transport layer combination along with dual-interface passivation via self-assembled monolayers, both of which are uniquely enabled by the lamination approach. This is the highest reported PCE for any laminated PSC encapsulated between glass substrates. We further show that this approach expands the processing window beyond traditional fabrication processes and is adaptable for different transport layer compositions. The laminated PSCs retained >75% of their initial PCE after 1000 h of 1-sun illumination at 40 °C in air using an all-inorganic transport layer configuration without additional encapsulation. Furthermore, a laminated 1 cm 2 device maintained a V oc of 1.16 V. Finally, the scalable lamination strategy in this study enables the implementation of new transport layers and interfacial engineering approaches for improving performance and stability.

14 SOLAR ENERGY↗
DOE OSTI2025

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Surface Processes Control the Fate of Reactive Oxidants Generated by Electrochemical Activation of Hydrogen Peroxide on Stainless-Steel Electrodes

Low-cost stainless-steel electrodes can activate hydrogen peroxide (H 2 O 2 ) by converting it into a hydroxyl radical ( • OH) and other reactive oxidants. At an applied potential of +0.020 V, the stainless-steel electrode produced • OH with a yield that was over an order of magnitude higher than that reported for other systems that employ iron oxides as catalysts under circumneutral pH conditions. Decreasing the applied potential at pH 8 and 9 enhanced the rate of H 2 O 2 loss by shifting the process to a reaction mechanism that resulted in the formation of an Fe(IV) species. Significant metal leaching was only observed under acidic pH conditions (i.e., at pH <6), with the release of dissolved Fe and Cr occurring as the thickness of the passivation layer decreased. Despite the relatively high yield of • OH production under circumneutral pH conditions, most of the oxidants were scavenged by the electrode surface when contaminant concentrations comparable to those expected in drinking water sources were tested. The stainless-steel electrode efficiently removed trace organic contaminants from an authentic surface water sample without contaminating the water with Fe and Cr. With further development, stainless-steel electrodes could provide a cost-effective alternative to other H 2 O 2 activation processes, such as those by ultraviolet light.

42 ENGINEERING↗
DOE OSTI2021

Coupled Biotic-Abiotic Processes Control Biogeochemical Cycling of Dissolved Organic Matter in the Columbia River Hyporheic Zone

A critical component of assessing the impacts of climate change on watershed ecosystems involves understanding the role that dissolved organic matter (DOM) plays in driving whole ecosystem metabolism. The hyporheic zone—a biogeochemical control point where ground water and river water mix—is characterized by high DOM turnover and microbial activity and is responsible for a large fraction of lotic respiration. Yet, the dynamic nature of this ecotone provides a challenging but important environment to parse out different DOM influences on watershed function and net carbon and nutrient fluxes. We used high-resolution Fourier-transform ion cyclotron resonance mass spectrometry to provide a detailed molecular characterization of DOM and its transformation pathways in the Columbia river watershed. Samples were collected from ground water (adjacent unconfined aquifer underlying the Hanford 300 Area), Columbia river water, and its hyporheic zone. The hyporheic zone was sampled at five locations to capture spatial heterogeneity within the hyporheic zone. Our results revealed that abiotic transformation pathways (e.g., carboxylation), potentially driven by abiotic factors such as sunlight, in both the ground water and river water are likely influencing DOM availability to the hyporheic zone, which could then be coupled with biotic processes for enhanced microbial activity. The ground water profile revealed high rates of N and S transformations via abiotic reactions. The river profile showed enhanced abiotic photodegradation of lignin-like molecules that subsequently entered the hyporheic zone as low molecular weight, more degraded compounds. While the compounds in river water were in part bio-unavailable, some were further shown to increase rates of microbial respiration. Together, river water and ground water enhance microbial activity within the hyporheic zone, regardless of river stage, as shown by elevated putative amino-acid transformations and the abundance of amino-sugar and protein-like compounds. This enhanced microbial activity is further dependent on the composition of ground water and river water inputs. Our results further suggest that abiotic controls on DOM should be incorporated into predictive modeling for understanding watershed dynamics, especially as climate variability and land use could affect light exposure and changes to ground water essential elements, both shown to impact the Columbia river hyporheic zone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Additive manufacturing of a metastable high entropy alloy: Metastability engineered microstructural control via process variable driven elemental segregation

Compositional paradigm shift in high entropy alloys (HEAs) provided new opportunities for microstructural engineering, whereas process control during laser powder bed fusion (LPBF) additive manufacturing (AM) enable fine microstructure tailoring. Metastability engineering in transformation induced plasticity (TRIP) HEAs by addition of minor alloying elements is an attractive strategy for fine microstructural tuning. This study explored in detail the microstructural evolution during LPBF AM of a metastable Fe 40 Mn 20 Co 20 Cr 15 Si 5 (at.%) dual phase HEA (CS-HEA). LPBF processing window for CS-HEA was established based on quantitative analysis and experiments. Based on melt pool overlap lack of fusion pores were observed at lower energy densities (J) of $\textit{J}$ ≤ 31.25 J/mm 3 and key-hole formation by melt pool destabilization in case of $\textit{J}$ ≥ 75 J/mm 3 . The microstructure of CS-HEA consists of metastable FCC-γ and HCP-ε phases; LPBF process parameters governed the final phase fraction in the alloy which has been correlated to metastability alteration of the high temperature γ phase. Final microstructural engineering was devised by LPBF process control which enabled cooling rate manipulation to guide Mn and Si segregation at the cell boundaries, thereby controlling the matrix metastability and final phase fraction. Additionally, high resolution transmission electron microscopy (TEM) revealed disparity in stacking fault morphology in CS-HEA with LPBF process variable alterations associated with local variations in chemical composition and stacking fault energy. Further, the phase evolution with process parameters also affected the nanomechanical behavior of the alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Developing a peptide concatemer (PepCon) as a process control for LC-MS based proteomics

Quantitative concatenated peptide (QconCAT) technology is currently used for the absolute quantification of proteins of interest in a biological sample. It relies on artificially created proteins that are concatenations of different, isotopically labeled peptides. Because these isotopically labeled peptides are at a 1:1 ratio and correspond to naturally occurring peptides in the biological sample, each peptide can be used as a standard for the absolute quantitation of all proteins of interest at once. Although the QconCAT technology has utility for quantitation of known peptides in a mixture, it is not helpful for scientists who need a proteomics standard (1) that can be spiked into a protein mixture at an extremely low level, (2) that can be co-purified during sample fractionation, and (3) that is optimized for ESI used in mass spectrometry. Thus, there exists a need for an ideal standard protein that is large enough to behave as a protein but consists of multiple, concatenated copies of the same peptide, which, upon digestion, amplifies (e.g., >10-fold) into a detectable peptide species. Here, this technical report describes the development of “PepCon”, a peptide concatemer that fulfills this unmet need.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Processes controlling helium bubble dynamics at varying temperatures in simulated radioactive materials

Alpha-radiation damage is known to degrade materials exposed to harsh radiation environments via swelling and embrittlement by nano-scale helium bubbles. How these bubbles grow under varying temperature is essential to understanding and predicting material failure and optimization. Using in situ X-ray scattering, we probed ≈1012 bubbles before, during and after annealing at different temperatures in He-implanted aluminum to reveal a critical growth activation temperature that depends strongly on the helium implantation conditions. An analysis of the data aided by our molecular dynamics simulations identifies multiple temperature-dependent bubble growth mechanisms above and below the critical growth activation temperature. Moreover, the irreversible bubble growth that occurs after annealing for ≈4 h was evaluated using dislocation dynamics and found to negligibly affect the yield strength but may affect subsequent bubble growth at temperature. These in situ results provide valuable insight into bubble growth never observed before and reveal, for the first time, bubble shrinkage after cooling.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Geophysical Retrievals in an Artificial Intelligence (AI) Framework for Illuminating Processes Controlling Water Cycle

Focal Area(s): This white paper responds to Focal Area #3: Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: This white paper addresses the water-cycle and data-model integration grand challenge. It leverages data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, and Next-Generation Ecosystem Experiment (NGEE), and Science Focus Area (SFA). The white paper focuses on controlling cloud, precipitation, and radiative properties as observed and simulated by the Earth System Models (ESM). The described framework can be readily applied to any other ensemble of instruments, including satellites and other ground-based networks.

54 ENVIRONMENTAL SCIENCES↗