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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Metal Alloy and RHEA Additive Manufacturing for Nuclear Energy and Aerospace Applications

An open-literature search was conducted to consider the current status of the additive manufacturing (AM) industry with respect to metal alloys and refractory high entropy alloys (RHEAs). Key areas of interest include methodologies and applications that are suitable for the nuclear and aerospace industries, as well as other industrial applications. We investigated various promising 3D metal technologies, with emphasis on cost, operation, throughput, maintenance, and output volume size. In addition, technical issues and the current status of the metal printing market are summarized. The project scope also included the manufacturing of open-literature RHEA test coupons at Sandia's laser engineered net shape (LENS) machine.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection

In this project, the emission of electrons during laser powder bed fusion additive manufacturing was identified and investigated to resolve the underlying dynamics driven by laser-material interactions. Laser powder bed fusion involves the selective laser melting of metal powder particles layer by layer which fuse together forming a larger 3D structure. Laser-based additive manufacturing approaches such as laser powder bed fusion hold the potential to revolutionize manufacturing of complex metal components in the aerospace, medical, and automotive industries. However, the difficulty in producing defect-free components by metal 3D printing is a major hurdle for widespread commercial adoption. This project resolved important dynamics in laser powder bed fusion including the identification of plasma formation mechanisms, transitions between laser-induced melting modes and stochastic precursors to defect formation. These findings were realized through implementation of electronic sensing diagnostics into an existing testbed system and harnessing capabilities developed for the time dependent analysis of laser powder bed fusion datasets. Importantly, the results advance our understanding of laser-material interactions and reveal that detection of thermionic emission can also resolve information critical to optimization of the laser powder bed fusion fabrication process. The findings are critical for advancing our fundamental understanding of the laser powder bed fusion process and can help assure the requisite fidelity of fabricated components.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Discrete Element Method Analysis for Metal Powders Used in Additive Manufacturing, and DEM Simulation Tutorial Using LIGGGHTS-PUBLIC [PowerPoint and paper]

Discrete Element Method (DEM) is a method of analysis to evaluate the dynamic interactions between granular particles. This method has been used in the pharmaceutical industry to improve the powder compaction process for tablet manufacturing. There are also applications in agriculture, food industry, and manufacturing. Direct energy deposition is an additive manufacturing technique which uses metallic powders fed through a nozzle, melted using a directed laser, and transformed into a solid object layer by layer. One way of feeding metal particles into the system involves the use of a vibrating hopper. Given a specified amplitude and frequency input, the hopper will enable the powder to travel up a path, and inject through the system with assistance from a stream of gas. The mechanical properties of a printed object can vary, depending on the characteristics of the powder flow and the particles’ as-received properties. Improved understanding of dynamic interactions of flowing powders could enable additive manufacturing components with 2D or 3D variations in mechanical properties, e.g., density. This work uses DEM simulation software to investigate the effects of particle cohesion, friction, and density on the quality of the flow by performing an angle of repose simulation, which is often used as a metric to evaluate the flowability of powders.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of Thermal Tomography Imaging System for In-Service Nondestructive Evaluation of Additively Manufactured Components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. We investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin. MIT research reactor (MITR) has been identified as a representative environment to perform investigation of feasibility of in-service NDE during shutdown. Preliminary study of radiation level in the MITR viewport, with geometric parameters potentially compatible with compact TT system, has been conducted.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Bezier Curve Informed Melt Pool Geometry to Model Additive Manufacturing Microstructures Using SPPARKS

Additive manufacturing is a transformative technology with the potential to manufacture designs which traditional subtractive machining methods cannot. Additive manufacturing offers fast builds at near final desired geometry; however, material properties and variability from part to part remain a challenge for certification and qualification of metallic components. AM induced metallic microstructures are spatially heterogeneous and highly process dependent. Engineering properties such as strength and toughness are significantly affected by microstructure morphologies resulting from the manufacturing process Linking process parameters to microstructures and ultimately to the dynamic response of AM materials is critical to certifying and qualifying AM built parts and components and improving the performance of AM materials. The AM fabrication process is characterized by building parts layer by layer using a selective laser melt process guided by a computer. A laser selectively scans and melts metal according to a designated geometry. As the laser scans, metal melts, fuses, and solidifies forming the final geometry in a layerwise fashion. As the laser heat source moves away, the metal cools and solidifies forming metallic microstructures. This work describes a microstructure modeling application implemented in the SPPARKS kinetic Monte Carlo computational framework for simulating the resulting microstructures. The application uses Bzier curves and surfaces to model the melt pool surface and spatial temperature profile induced by moving the laser heat source; it simulates the melting and fusing of metal at the laser hot spot and microstructure formation and evolution when the laser moves away. The geometry of the melt pool is quite flexible and we explore effects of variances in model parameters on simulated microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of Monitoring Techniques for Laser Powder Bed Additive Manufacturing of Metal Structures (Progress Report)

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear grade components to be assembled into a fully functional microreactor core. Compared to traditional manufacturing technologies, additive manufacturing technologies allow (1) observation of the manufacturing process at a much higher resolution in real time using in situ monitoring technologies to capture the sensor signature that scientifically describes each event occurring over time and space, and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on laser powder bed fusion in-situ process monitoring and associated data analytics results. Examples are provided to illustrate the progress. Elements of the Digital Thread and data management are discussed in the main document, and an extensive supplemental material section is provided detailing the Digital Platform, as well as its implementation and components. In conclusion the path forward for the next fiscal year is discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Additively Manufactured Intensified Device for Enhanced Carbon Capture

Additively manufactured packed bed devices for the enhancement of carbon capture were successfully developed, built, characterized, and tested in this project. The objective was to demonstrate that in situ cooling of chemical absorption could have beneficial effects on the absorption rate of CO 2 . This was achieved in two stages, divided between FY 2018 and FY 2019. First, the feasibility of printing conventional packing elements was investigated, and the core hydrodynamic metrics were experimentally validated. The data obtained in those studies informed the operating conditions set for the next stage, which was to validate the efficacy of in situ cooling provided by an additively manufactured packed bed element, referred to as the intensified device. The intensified device is designed to enable heat exchange between an internally flowing coolant and the gas-liquid system flowing on the external corrugated surface of the packing. Polymer and metal were both considered as the base materials for the device, and comparative studies on hydrodynamic properties were performed. These considerations, as well as thermal properties and leak tests led to aluminum being chosen as the base material for experimental validation of capture enhancement. The experimental validation was first conducted under non-reactive conditions, and after establishing heat-exchange capabilities, was subsequently conducted under reactive conditions. These studies demonstrated that in situ cooling can benefit absorption given the appropriate operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

TCR Input to NUREG-1537 Process for Advanced Nuclear Technologies Derived from Additive Manufacturing

There has been a renewed interest by several advanced reactor developers to use NUREG-1537 “Guidelines for Preparing and Reviewing Applications for the Licensing of Non-Power Reactors” as a basis for their safety analysis report content and organization. Recently, SHINE Medical Technologies, LLC (SHINE), which is a non-power, Aqueous Homogenous Reactor design radioisotope production facility, received a construction permit based around their NUREG-1537 safety evaluation report (ADAMS No. ML16229A140). Advanced reactor developers are interested in using NUREG-1537 as a basis for their safety analysis report content and organization because of its successful application towards research reactors, graded approach, and simplicity in structure and requirements. However, NUREG-1537 is still largely geared toward light water reactors (LWRs) and many improvements could be made or supported through guidance documents for advanced reactors. For nuclear power to play a role in the future zero-carbon energy portfolio, a supportive regulatory structure is needed to lower regulatory uncertainty and barriers to deployment. At the time of this report, no such document or pathway exists for advanced nuclear technologies, including those derived from nontraditional technology such as advanced manufacturing technology (AMT) and, specifically, additive manufacturing. This report will explore and provide recommendations as to how advanced nuclear technologies derived from additive manufacturing technologies could employ the use of an ISG, other guidance document, or revisions to NUREG-1537 to lower the regulatory uncertainty and barriers for adoption.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A Comparative Study of Direct and Indirect Additive Manufacturing Approaches for the Production of a Wind Energy Component

Additive manufacturing (AM) was developed in the 1980s to create three-dimensional prototypes through layer-wise approaches to fabrication. Since then, these approaches have seen improvements in both materials and processing technologies. To date, there are now 7 types of additive manufacturing processes and hundreds of materials, which can be directly printed – going directly from digital design to fabricated components. In this project, Oak Ridge National Laboratory (ORNL), Vestas Wind Systems, and The National Renewable Energy Laboratory (NREL) collaborated to evaluate the effectiveness of state-of-the-art large-scale AM processes in the production of a structural component for use in a wind turbine nacelle, through both direct and indirect manufacturing approaches. Here, experienced AM design engineers detail techniques for AM design, including topology optimization (TO), support minimization, reverse engineering, and techniques for mitigating poor interlaminar performance. Fabrication of the components is presented, including printing parameters and postprocessing, and followed with full-scale component testing by a 3rd party testing laboratory. To evaluate the potential of the developed approaches, a complete techno-economic analysis is provided which evaluates the cost of these techniques given current and near to long-term projections of AM system capabilities.

17 WIND ENERGY↗
DOE OSTI2020

Development of Repair Techniques for Cast Iron Engine Blocks by Additive Manufacturing

ORNL partnered with Cummins Inc to demonstrate the feasibility of using additive manufacturing techniques to deposit large volume of new material on cast iron in the context of remanufacturing. Remanufacturing at Cummins Inc is carried out to repair any field damage and, in some cases, provide additional value by adding new features. Large volume deposition on difficult to weld materials is a challenging problem due to involved metallurgy. Phase-1 of this work evaluated the feasibility of using laser directed energy deposition technique to deposit new material layer by layer on cast iron engine blocks. During phase-1 deposits were made using Inconel-718, Nickel, Nr-Cr-B braze filler. Leveraging the knowledge gained during Phase-1, Phase-2 focused on developing a low- cost crack resistant ferrous alloy to repair cast iron via volume deposition techniques without preheating. Three different alloy chemistries were identified, and trials were performed on cast iron substrates to identify the optimal chemistry that provided adequate resistance to cracking in both, the deposits as well as the cast iron substrate.

42 ENGINEERING↗
DOE OSTI2021

Rapid Quality Assessment in Additive Manufacturing: New Procedure Accelerates Build Quality Assessment. White paper

Accelerated growth of the additive manufacturing (AM) industry in recent years is accompanied by a rising need for methods to quickly assess quality at-scale. Current practices for quality inspection include nondestructive test methods and destructive testing of witness coupons, which are artifacts built alongside the actual part. However, these methods can be costly and time-consuming. Recognizing this need, the Additive Manufacturing Center of Excellence (AM CoE) initiated a project led by its partner, Auburn University, to develop rapid testing procedure using asbuilt samples tested in torsion to quantitatively assess build quality. The presented work developed a rapid testing procedure using as-built samples tested in torsion to quantify small variances for assessing build quality.

42 ENGINEERING↗
DOE OSTI2021

Surface Roughness Effects on Heat Transfer in Additively Manufactured sCO 2 Cycle Heat Exchangers

An experimental study was performed to consider the impact of surface roughness on heat transfer and pressure drop to supercritical carbon dioxide (sCO 2 ) in additively manufactured channels. All tests were performed in the Heat Exchange and Experimental Testing (HEET) rig at the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) in Morgantown, West Virginia. Four test articles were considered. The first was a hydrodynamically smooth, drawn tube. The other three were additively manufactured channels with square and rectangular cross sections, which were characterized by sand-grain roughness to hydraulic diameter ratios spanning 0.0029 to 0.0073. Friction factors were determined by measuring the tube mass flow rate and pressure drop. The tube side heat transfer coefficient was measured using the Wilson plot technique. It was found that the friction factor results were 8% greater than the Colebrook correlation. Greater deviation was observed between the heat transfer results and the correlations. The Gnielinski correlation overpredicted the experimental points by nominally 30% and the Norris correlation overpredicting the experimental points by nominally 25%. A thermal performance factor was formed from the friction factor augmentation and Nusselt number augmentation results. These results indicated a 10% improvement in heat duty for a heat exchanger constructed utilizing a tube with ϵ/D_h =0.0073 relative to a heat exchanger utilizing a smooth, conventional tube.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Adhesion of Titanium Coatings on Additively Manufactured Stainless Steel [Slides]

Additive manufacturing can improve fuel cell development by increasing design development speed, enabling complex designs through more flexible fabrication, and reducing wasted excess material. The goal of the project is to develop a process for strongly adhering a coating of titanium to additively manufactured stainless steel.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling the Nonlinear Rheology of Polymer Additive Manufacturing

This report summarizes molecular and continuum simulation studies focused on developing physics - based predictive models for the evolution of polymer molecular order during the nonlinear processing flows of additive manufacturing. Our molecular simulations of polymer elongation flows identified novel mechanisms of fluid dissipation for various polymer architectures that might be harnessed to enhance material processability. In order to predict the complex thermal and flow history of polymer realistic additive manufacturing processes, we have developed and deployed a high - performance mesh - free hydrodynamics module in Sandia's LAMMPS software. This module called RHEO – short for Reproducing Hydrodynamics and Elastic Objects – hybridizes an updated - Lagrange reproducing - kernel method for complex fluids with a bonded particle method (BPM) to capture solidification and solid objects in multiphase flows. In combination, our two methods allow rapid, multiscale characterization of the hydrodynamics and molecular evolution of polymers in realistic processing geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of the Impact of Additional Manganese from the Recycle Collection Tank (RCT) Glycolate Destruction Process on Glass Properties

The Defense Waste Processing Facility (DWPF) is planning to implement glycolic acid as a reductant within the waste processing flowsheet. An assessment of the glycolic acid flowsheet has revealed the potential for thermolytic production of hydrogen in the Concentration, Storage and Transfer Facilities (CSTF) from glycolate entrained in the DWPF recycle stream. To mitigate this potential scenario, a glycolate destruction process utilizing sodium permanganate (NaMnO 4 ) is being developed for use in the DWPF Recycle Collection Tank (RCT). The RCT is fed by the Slurry Mix Evaporator Condensate Tank (SMECT) and the Off-Gas Condensate Tank (OGCT). The SMECT could receive glycolate via a foamover from the Sludge Receipt and Adjustment Tank (SRAT) or the Slurry Mix Evaporator (SME), and the OGCT could receive glycolate via carryover of sludge particles in the purge from the melter during surge conditions. The use of NaMnO 4 additions in the RCT will result in additional manganese (Mn) in the waste stream and needs to be evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗