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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Novel Magnesium Pretreatment Coatings Offering Excellent Corrosion Protection

There is a growing interest in using magnesium alloys for automotive, industrial, and aerospace applications due to their low density, high strength-to-weight ratio, good machinability, and low cost. These alloys have the potential to lighten cars and aircraft, reducing fuel consumption and emissions. A major drawback to the use of magnesium alloys is their highly reactive nature, which can result in corrosion; as a result, most existing pretreatment systems for these alloys require the use of chromium, which is increasingly coming under regulatory pressures, as a corrosion inhibitor. Through a project funded by the Department of Energy and administered by the U.S. Automotive Materials Partnership (USAMP), PPG developed chromium-free metal pretreatment solutions for ZEK-100 and AZ21 magnesium alloys. In a major technical advance, several chromium-free cleaning and pretreatment processes were developed that offer excellent corrosion protection when compared to a typical automotive pretreatment systems. To expand the use cases for these systems beyond single-alloy substrates to broader multi-metal assemblies, these pretreatment solutions were also optimized for use in multi-metal applications: compatibility with magnesium and other metals was demonstrated without the need for additional process steps.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Facet-dependent growth and dissolution of hematite resulting from autocatalytic interactions with Fe(II) and oxalic acid

The ability to simultaneously monitor the flux of iron atoms within the solution and solid phases can provide considerable insight into mechanisms of iron oxide mineral transformations. The autocatalytic interaction between hematite and Fe(II)-oxalate has long been of interest for its environmental and industrial relevance. In this study we take advantage of iron isotopic labelling and mass-sensitive imaging at the single particle scale to determine how changes in solution composition correlate with the morphologic evolution of faceted, micrometer-sized hematite platelets. Net dissolution is confirmed through analyses of aqueous iron chemistry, as well as by quantitative atomic force microscopy. Isotopic mapping techniques show that Fe(II) readily adsorbs to (001) and (012) surfaces in the absence of oxalate, but when oxalate is present selective dissolution of the (001) surface prevails and 57Fe deposition via recrystallization is not observed. Comparison between particle microtopographies following reaction with Fe(II), oxalate, and Fe(II)-oxalate show substantially different behavior, consistent with distinct mechanisms of interaction with hematite surfaces. The extensive characterization conducted on the coupled solution/solid dynamics in this system provides new insight for distinguishing crystal growth, dissolution, and recrystallization processes.

Taylor, Sandra D. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2023

An Intercomparison of Large‐Eddy Simulations of a Convection Cloud Chamber Using Haze‐Capable Bin and Lagrangian Cloud Microphysics Schemes

Abstract Recent in situ observations show that haze particles exist in a convection cloud chamber. The microphysics schemes previously used for large‐eddy simulations of the cloud chamber could not fully resolve haze particles and the associated processes, including their activation and deactivation. Specifically, cloud droplet activation was modeled based on Twomey‐type parameterizations, wherein cloud droplets were formed when a critical supersaturation for the available cloud condensation nuclei (CCN) was exceeded and haze particles were not explicitly resolved. Here, we develop and adapt haze‐capable bin and Lagrangian microphysics schemes to properly resolve the activation and deactivation processes. Results are compared with the Twomey‐type CCN‐based bin microphysics scheme in which haze particles are not fully resolved. We find that results from the haze‐capable bin microphysics scheme agree well with those from the Lagrangian microphysics scheme. However, both schemes significantly differ from those from a CCN‐based bin microphysics scheme unless CCN recycling is considered. Haze particles from the recycling of deactivated cloud droplets can strongly enhance cloud droplet number concentration due to a positive feedback in haze‐cloud interactions in the cloud chamber. Haze particle size distributions are more realistic when considering solute and curvature effects that enable representing the complete physics of the activation process. Our study suggests that haze particles and their interactions with cloud droplets may have a strong impact on cloud properties when supersaturation fluctuations are comparable to mean supersaturation, as is the case in the cloud chamber and likely is the case in the atmosphere, especially in polluted conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Technology Advancement and Insertion into Operating Nuclear Facilities

Key challenges in the NNSA modernization effort are centered around the selection of new processing / production technologies to deploy in existing, operating, and oftentimes, aging nuclear facilities. New uranium processing technologies may offer significant improvements in worker and environmental safety, material control and accountability, and production efficiencies, but adaptation of these technologies into existing facilities presents unique (and usually, difficult) engineering solutions for each new technology. This presentation discusses approaches utilized at Y-12 to mature and demonstrate new processing and production technologies for deployment into nuclear facilities. The technology advancement lifecycle (from concept to deployment) of uranium electrorefining technologies will be reviewed, as a prototypical example, to describe steps and approaches utilized at Y-12 (and NNSA Sites) to (i) mature technologies and manufacturing strategies, (ii) establish testbed capabilities for full-scale process demonstrations, and (iii) reduce risks associated with operational disruptions when commissioning new uranium processing technologies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Technology Advancement and Insertion into Operating Nuclear Facilities

Key challenges in the NNSA modernization effort are centered around the selection of new processing / production technologies to deploy in existing, operating, and oftentimes, aging nuclear facilities. New uranium processing technologies may offer significant improvements in worker and environmental safety, material control and accountability, and production efficiencies, but adaptation of these technologies into existing facilities presents unique (and usually, difficult) engineering solutions for each new technology. This presentation discusses approaches utilized at Y-12 to mature and demonstrate new processing and production technologies for deployment into aging nuclear facilities. The technology advancement lifecycle (from concept to deployment) of uranium electrorefining technologies will be reviewed, as a prototypical example, to describe steps and approaches utilized at Y-12 (and NNSA Sites) to (i) mature technologies and manufacturing strategies, (ii) establish testbed capabilities for full-scale process demonstrations, and (iii) reduce risks associated with operational disruptions when commissioning new uranium processing technologies.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Important Factors for the Reliable and Reproducible Preparation of Non-aqueous Electrolyte Solutions for Lithium Batteries

Electrolyte solutions play a crucial role in rechargeable lithium batteries. Driven by an ever-increasing demand for better batteries, more and more stringent performance requirements are being asked of electrolytes. This demand to achieve electrolytes beyond the state-of-the-art has driven an increasing number of publications over the past decade. Nevertheless, a detailed description of electrolyte preparation is frequently missing when reporting new electrolytes for batteries, creating a barrier for others to reproduce important findings. In addition, false or misleading information in a paper may result from unintentional inaccuracies during the electrolyte preparation process. This article highlights important factors for the reliable and reproducible preparation of non-aqueous electrolyte solutions for lithium batteries, with the aim of encouraging researchers to follow more rigorous reporting standards.

Cao, Xia↗
DOE OSTI2026

A Python Tool for Aqueous Plutonium Nitrate Density Law Input Preprocessing in MCNP6

Here, this work develops a predictive density tool in Python, named Plutonium Nitrate Solutions (PuNS), to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer method and an empirical method were implemented into the PuNS tool to generate atom densities for use in MCNP6 material cards. These material cards are directly prepared into an MCNP6 input text file and are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, and plutonium isotope weight percentages. The PuNS tool is validated and verified against the International Criticality Safety Benchmark Evaluation Project Handbook experiments and is observed to predict densities within a root mean square error of 0.89% for the Pitzer method and 1.82% for the empirical method. These errors in density lead to up to 1569 pcm difference in MCNP6 calculated k eff for the Pitzer method and up to a 1751 pcm difference for the empirical method when compared to experimental benchmarks. Simultaneous work is also being performed at Los Alamos National Laboratory and the University of New Mexico to create a similar tool for plutonium chloride solutions, named Plutonium Chloride Solution, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also facilitate an improved understanding of solution systems and a potential relaxation in the conservatism of current aqueous plutonium processing criticality safety limits.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Feasibility of using nuclear microreactor process heat for bioconversion and agricultural processes

Introduction There is a global goal to reduce greenhouse gas emissions by 43% by 2023. Nuclear microreactors, a subset of small modular reactors, offer a potential solution due to their compact size, transportability, and carbon-neutral power generation capabilities. Methods This study explores the feasibility of using heat from nuclear microreactors for bioconversion and agricultural processes, including transforming biomass into energy carriers and products such as syngas, bio-oil, and pasteurized milk. Operating requirements for gasification, pyrolysis, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, ethanol production, anaerobic digestion, and pasteurization were obtained through a literature review. A Brayton cycle model based on the eVinci TM microreactor was developed to assess the feasibility of powering these processes using nuclear microreactor heat. Results and Discussion Exergetic efficiency values for high-temperature processes ranged from 72% to 100%, whereas lower-temperature processes ranged from 2% to 53%. These efficiencies depend on the available source temperature for each microreactor design. There were trade-offs between producing net power and using process heat, particularly for high-temperature processes. Three heat exchanger locations were considered: before the turbine (600 ℃ ), between the turbine and regenerator (370 ℃ ), and after the regenerator (192 ℃ ). High-temperature processes like gasification require temperatures too high for feasibility. Middle temperature processes are better suited to a heat exchanger between the turbine and regenerator, while also operable before the turbine. Lower-temperature processes like pasteurization and anaerobic digestion can use waste heat after the regenerator and do not impact power production. These findings are valuable for optimizing nuclear microreactor heat use and aligning with global climate initiatives.

09 BIOMASS FUELS↗
DOE OSTI2024

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids—including the density response, surface tension and size-dependent cavitation free energies—for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

First-Principles Modeling of the Repassivation of Corrosion Resistant Alloys: Part I. O and Cl Adsorption Energy

To understand the mechanism of repassivation, a key process that controls the performance of corrosion resistant alloys (CRAs), it is useful to obtain some fundamental parameters that describe the atomistic processes. Oxygen and chlorine adsorption energies were systematically calculated with density functional theory for Ni-22Cr alloys with seven different solute elements: Co, Cu, Fe, Mn, Mo, Ru and W. All elements other than Cu enhance both O and Cl adsorption to alloy surfaces. Cr has the strongest effect among these elements. Surface configurations that give stronger O adsorption also tend to give stronger Cl adsorption. While this makes it difficult to differentiate the effect of these solute elements on the oxidation/repassivation tendency of alloys, these fundamental properties provide the foundation for higher scale models that could be used to study the oxidation process during corrosion and potentially inform the design of CRAs. Bader charge analysis reveals a strong relationship between the adatom adsorption energy and the Bader charges of both O and Cl adatom. In conclusion, the work function of alloy surfaces is increased by O while decreased by Cl, suggesting that Cl adsorption can have an intrinsic negative impact on corrosion resistance by decreasing the barrier to electron transfer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spinel-Structured High Entropy Oxides: Low Temperature Synthesis, Characterization, and Potential Applications

High entropy oxides (HEOs) have recently attracted increasing attention due to their remarkable properties and relatively low cost. Herein we report a simple, highly scalable, and low temperature method for synthesizing spinel (FeNiCoCuZn) 3 O 4 (HEO). We heated an aqueous solution containing divalent cations in high alkali environments to temperatures of 25 - 95 °C for 24 h under atmospheric pressure. The HEO, synthesized at 95 °C for 24 h in 1 M KOH, was paramagnetic at room temperature, with a magnetic mass susceptibility of χ = (7.5 ± 0.2) × 10 –7 m 3 ·kg –1 . It demonstrated stable electrochemical lithium storage performance, with a gravimetric capacity of ∼300 mAh·g –1 at 100 mA·g –1 . It was also active in the electrocatalytic oxygen evolution reaction with an overpotential of 460 mV in alkaline media. The band gap energies were in the range of 2.4 eV. Our advancement in the synthesis and processing of transition metal-based HEOs will undoubtedly render them a viable solution for next generation materials for energy production and storage.

diffraction↗
DOE OSTI2021

Direct intercalation of MoS 2 and WS 2 thin films by vacuum filtration

In the development of next-generation electronics and energy devices, intercalation compounds of transition metal dichalcogenides (TMDCs) are gaining attention for their unique properties that result from synergistic interactions between guest species and host materials. Nowadays, intercalation compounds of MoS 2 and WS 2 are commonly prepared by a two-step process: (1) exfoliation to form single-layer and/or few-layer nanosheets and (2) restacking the nanosheets with the guest species by vigorously mixing the exfoliated suspension with the solution of guest species. While a wide variety of intercalation compounds have been synthesized using this approach, the intercalation process is often timeconsuming, and the product slurry limits material quality and impedes characterization and applications. Herein, we report a versatile method for preparing intercalated TMDCs in a thin-film morphology. Using this approach, we successfully prepared a range of existing intercalation compounds of MoS 2 and WS 2 (e.g., ferrocene and amine intercalated MoS 2 and WS 2 ). Additionally, by leveraging the versatility of this intercalation method, we report we intercalated phenazine and benzoquinone into MoS 2 and WS 2 for the first time.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recent Cryogenic Carbon Capture™ Field Test Results

Sustainable Energy Solutions (SES) has been developing Cryogenic Carbon Capture™ (CCC) since 2008. In that time, two processes have been developed, the External Cooling Loop and Compressed Flue Gas CCC processes (CCC-ECL and CCC-CFG, respectively). The CCC-ECL process cools the flue gas with an external refrigerant loop. This process currently captures up to 1 tonne of CO2 per day (TPD). SES has tested CCC-ECL on real flue gas slip streams from subbituminous coal, bituminous coal, biomass, natural gas, shredded tires, and municipal waste fuels at field sites that include utility power stations, heating plants, cement kilns, and pilot-scale research reactors. The CO2 concentrations from these tests ranged from 5 to 22% on a dry basis. CO2 capture ranged from 95-99+% during these tests. Several other condensable species were also captured including NO2, SO2 and PMxx at 95+%. NO was also captured at a modest rate. The CCC-CFG process has been scaled up to a 0.25 ton per day system. This system has been tested on real flue gas streams including subbituminous coal, bituminous coal, and natural gas at field sites that include utility power stations, heating plants, and pilot-scale research reactors. CO2 concentrations for these tests ranged from 5 to 15% on a dry basis. CO2 capture ranged from 95-99+% during these tests. Several other condensable species were also captured including NO2, SO2, and PMxx at 95+%. NO was also captured at 90+%. Hg capture was also verified and the resulting effluent from CCC-CFG was below a 1ppt concentration. This paper will focus on discussion of the capabilities of CCC generally, the results of CCC-ECL field testing, and future steps surrounding the development of this technology. Test results that will be presented have been collected during 9 months of testing at a commercial power plant under funding from the US Department of Energy (DOE) and the host utility. Testing of one of the systems at a commercial cement plant in the United States will also be discussed. During this testing, the system captured CO2 from the cement plant and stored the CO2 in pressurized tanks. These tanks were provided to a partner company that later used the CO2 in a CO2 utilization demonstration. The CO2 was utilized to cure concrete manufactured using cement from the same plant where the CO2 was captured. This integrated capture and utilization demonstration was the first time that the cement industry has shown in the field that it can sequester its CO2 emissions in its main product stream. This represents a potential game changing solution for industrial CO2 emissions. Operational data and host-site feedback indicate that the CCC process is ideally suited for deployment into a variety of commercial environments. A few areas of de-risking remain to make sure the technology can meet very strict industrial reliability standards. These areas of de-risking are identified and discussed. The product CO2 is shown to meet specification for many uses including industrial and merchant applications. The technology is nearing readiness for deployment at commercial scale and several initial target markets have been identified.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

System-Level Integration of Modular Language Models for Real-Time Risk Assessment in Third-Party Risk Management Systems

Large enterprises typically rely on dedicated teams to govern and implement security measures throughout their supply chains, ensuring compliance with enterprise security procedures. There is a significant reliance on Third-Party Risk Management (TPRM) platforms, which often require complete, highly structured information from potential vendors. The review and compliance assurance processes are time- and labor intensive, often requiring several rounds of review between the supply chain security risk management teams, business users, and potential vendors, leading to delays in the supply chain processing and consumer experience. Significant challenges in the risk management paradigm include handling unstructured data in various formats and providing real-time feedback to users to reduce the required review time. This paper presents a novel solution to these challenges. A modular multi-step system architecture is proposed using advances in language processing, specifically for unstructured responses and provides real-time feedback (i.e., 3 seconds) so that users can improve their responses before the TPSRM team review. This novel system architecture will increase information accuracy and significantly reduce time and labor during the review process.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Observations and Lessons Learned From Installing Residential Roofing-Integrated Photovoltaics

Building-sited solar photovoltaics (PV) could play a key role in decarbonizing the building sector either through racked and mounted PV or through Building-integrated PV (BIPV). BIPV is installed into the building envelope itself, with solar cells and/or modules forming the outer layer of a building structure, thus transforming a single-purpose structure into one that serves the dual purposes of the building envelope and electricity. BIPV can be applied to building roofs, facades, awnings, pergolas, windows, skylights, balustrades, and other external surfaces. Given BIPV products vary widely, the focus of this research is residential roofing integrated PV (RIPV), where solar is incorporated into or otherwise replaces the roofing material. Previous research suggests that residential RIPV could reduce customer acquisition, labor, supply chain, and equipment costs. These products have yet to realize these cost savings and deployment remains significantly less than conventional rooftop PV as a relative share of the addressable market in the US. One potential barrier to broader residential roofing integrated PV deployment may be higher costs relative to conventional rooftop PV, primarily because the design and installation of these products is still evolving. Here, we explore residential RIPV cost-reduction opportunities by analyzing installation processes. Our study documents residential RIPV installations at 2 reroofing sites and the equivalent of 9 new construction sites in California through a methodology known as time and motion study. We also conducted interviews with subject-matter experts to identify barriers and solutions to maximize these products' market penetration. Our time and motion study breaks the RIPV installation process into four steps: 1) staging, unloading, and roof preparation; 2) fire resistant underlayment(s) (synthetic material laid between roof shingles and roof deck); 3) flashings and PV installation; and 4) wiring and monitoring. We measure the time required for each step in terms of worker-hours, representing an hour of labor from a single worker. We further normalize process time by dividing worker-hours by kilowatt (kW) of system capacity. The most time-intensive step was flashings and PV installation, taking around 2.4 worker-hours per kW on average and accounting for around 60% of the process time for an average installation. The total installation process took on average about 6.4 and 3.5 worker-hours per kW at the reroofing sites and new construction sites, respectively. For comparison, a previous time and motion study documented a time of 6.9 worker-hours per kW for conventional rooftop PV. The shorter RIPV installation times are consistent with previous studies suggesting that RIPV could be installed faster than conventional rooftop PV. The time and motion results and feedback from interviewees provide insights into potential residential RIPV cost reduction opportunities. Several interviewees suggested that these products would be more efficient if PV installation was more fully integrated into the roofing/construction industries, which currently use separate supply chains and skillsets. Further integration could reduce supply chain delays and labor force redundancies. Future research could explore specific ways to integrate these industries to help realize the cost savings potential of RIPV.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimizing the Fabrication of Electrospun Nanofibrous Membrane Using Fractional Factorial Design

Electrospinning, a simple and easily tunable technique that utilizes the force balance between the electrostatic force and the solution surface tension to fabricate membranes out of 1D nanostructured fibers, has garnered much interest especially from the nanotechnology community. Depending on the morphology of the fibers, electrospun membrane has found its way in a wide range of applications, such as oil-water separation, desalination, tissue engineering, and batteries. During an electrospinning process, many factors can affect the morphology of the products, such as viscosity of the solution, voltage supply, discharge distance, feed rate of the solution, and electrospinning time. In this work, we investigated the impacts of the most important parameters on the morphology of electrospun fibrous membrane based on the fractional factorial design. We evaluated the electrospun fibrous membrane morphology using scanning electron microscopy (SEM) and capillary flow porometry. Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) was chosen for the current study due to its interesting and versatile functionalities including superior hydrophobicity, high free volume, and piezoelectricity. This work provides valuable insight for the fabrication of electrospun membranes, especially for tailoring membrane properties in accordance with any targeted applications.

Zhao, Yajing↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗