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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Additive Manufacturing of ZrC for Nuclear Thermal Propulsion Applications

Looking into the future, we are seeing that nuclear is becoming a more and more viable fuel source for space applications due to it being one of the safest energy forms, low greenhouse gas emissions, and inexpensive maintenance. National Aeronautics and Space Administration (NASA) and a few others are looking at nuclear fuels in relation to space travel, specifically in the form of Nuclear Thermal Propulsion (NTP) [1]. NTP provides the answer for space travel to Mars where chemical rockets would not complete the job. A concept art of what these spacecrafts could look like is shown (Figure 1). Uranium Carbide fuel has been chosen to heat a liquid propellant for propulsion. The complex fuel geometries required by space reactors can be achieved by novel techniques fairly easily, cheaply, and safely. In this specific study, we are looking at using Zirconium Carbide (ZrC) to use as a surrogate for Uranium Carbide (UC) due to their similar behaviors under high temperatures. An ink made with ZrC will be made and will be extruded into unique, complex geometries. This is a great starting point to test additive manufacturing on these ceramics.

Additive Manufacturing↗
NASA NTRS2015

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification.

Liou, Frank↗
DOE OSTI2022

Additive Manufacturing Flaw Assessment Methodology

An evolution fatigue data and flaw tolerance of components produced using the Powder Bed Fusion (PBF) Additive Manufacturing (AM) process is documented in this report. Initial differences in fatigue data for AM components compared to smooth bar fatigue data indicated a need for a more detailed analysis of AM data available in technical literature. The investigation was initiated to support the development of a fatigue analysis methodology for AM components to support the of codification of AM technology for pressure equipment. The project was initiated to collect and analyze stainless steel 304L and 316L AM fatigue test data and corresponding process and quality information to develop S-N and E-N based fatigue data representation. Additional AM fatigue test data including Inconel Ti-6-4 and aluminum alloys were also considered for comparison purposes Metallic AM parts tend to contain various forms of defects distributed throughout the part. If an AM part is subjected to fatigue loading in service, a fatigue analysis needs to be performed during the design process to ensure an acceptable service life for the part. Post-process machining and polishing do not to improve fatigue resistance in any significant degree. The low cycle fatigue regime is of particular interest to this project in support of flaw acceptance criteria currently under development by ASME’s BPTCS/BNCS. Internal defects become exposed as external surface defects during machining for the machining of the AM part to final dimensions. This implies that as long as inherent AM defects are within a controlled limit in terms of both size and distribution characteristics, the corresponding fatigue test data in terms of either S-N (stress life) or E-N (strain life) can be investigated and characterized to establish fatigue properties of AM parts for design and fatigue evaluation purposes. The resulting S-N or E-N curves and their scatter bands can be used to derive fatigue design allowable stress values by capturing the effects of distributed discontinuities within an acceptable limit.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. Furthermore, the window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Repurposing the θ (Al 2 Cu) phase to simultaneously increase the strength and ductility of an additively manufactured Al–Cu alloy

The Al–Cu–Mn–Zr (ACMZ) cast family of alloys offers unique properties and value propositions for higher strength, higher temperature lightweight components of future vehicles. Earlier research has demonstrated trade-offs in the selection of the alloy chemistry in which an increase in Cu content from 6 up to 9 wt% improves hot tear resistance but lowers ductility. However, a recent study has demonstrated that higher-Cu (9Cu) ACMZ fabricated with laser powder bed fusion additive manufacturing (AM) results in an increase in both ductility and strength when compared to as-aged microstructure of cast 9Cu alloys. The mechanisms of differing mechanical performance of the cast and AM ACMZ alloys are elucidated in the current paper through the utilization of in situ high energy x-ray diffraction (HEXRD) tensile testing wherein lattice strains of different phases are calculated and correlated to their stresses. The ACMZ alloys consisted of theta (θ) and theta prime (θ') phases (both Al 2 Cu in nominal composition) within an aluminum matrix. The larger micron-size θ phase which decorated the grain boundaries in 9Cu ACMZ cast alloys recorded small lattice strains, while the submicron, homogeneously distributed θ phase in the 9Cu ACMZ AM alloy recorded considerably higher lattice strains. The maximum stress reached in the θ phase for the cast 9Cu alloy was found to be ~280 MPa, which was lower than the AM 9Cu alloy which registered a maximum stress of ~1.4 GPa. These measurements indicate that delayed fracture of the finer intermetallic phases simultaneously improves the ductility and strength of AM 9Cu alloy relative to the cast 9Cu alloy, which exhibits early fracture of the larger intermetallic particles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Controlling microstructure and B2 ordering kinetics in Fe–Al system through additive manufacturing

Multi-material fabrication between steel and aluminum is challenging because of the formation of several intermetallic phases. Embrittling B2 ordered intermetallics form in the steel rich side and are stable till about 60% of Al dilution in steel. In this work by using multi-length scale characterization coupled with integrated computational process and thermokinetic modeling, we show that the ordered B2 intermetallics in the steel rich side of the Fe–Al system forms via a nucleation and growth mechanism. The extent of B2 ordered intermetallics can be controlled by modifying the directed energy deposition-additive manufacturing (DED-AM) process parameters. Our findings lay the foundation for enabling fabrication of crack-free functionally graded compositions between the two alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.

additive manufacturing↗
NASA NTRSDate not supplied

High-Throughput Strategies that Encompass Experiments and Machine Learning to Predict the Mechanical Properties of Additive Manufactured Aerospace Alloys

Small Punch Test (SPT) uses a thin disk of material to predict mechanical properties. While SPT has existed for decades, it has been used largely as a qualitative evaluator of mechanical properties. Recent advances in computational modeling have enabled the extraction of uniaxial stress-strain response from the measured SPT load-displacement data. Due to small sample volumes and unidirectional testing, SPT is conducive to high-throughput automation and ideally suited to extract properties from high-cost materials. Aerospace alloys have been of recent interest to the Additive Manufacturing (AM) community due to AM’s unique ability to fabricate complex designs not possible, or extremely arduous, with conventional manufacturing. In this research, SPT, coupled with Materials Informatics and computational modeling, is used to develop relevant Process-Structure-Property relationships to decrease the cost and time of process optimization for AM aerospace alloys, namely Inconel 718, Inconel 625, and Niobium C103.

High-throughput Testing↗
DOE OSTI2020

High-throughput additive manufacturing and characterization of refractory high entropy alloys

Refractory High Entropy Alloys (RHEAs) and Refractory Complex Concentrated Alloys (RCCAs) are high-temperature structural alloys ideally suited for use in harsh environments. While these alloys have shown promising structural properties at high temperatures that exceed the practical limits of conventional alloys, such as Ni-based superalloys, exploration of the complex phase-space of these materials remains a significant challenge. We report on a high-throughput alloy processing and characterization methodology, leveraging laser-based metal additive manufacturing (AM) and mechanical testing techniques, to enable rapid exploration of RHEAs/RCCAs. We utilized in situ alloying and compositional grading, unique to AM processing, to rapidly-produce RHEAs/RCCAs using readily available and inexpensive commercial elemental powders. We demonstrate this approach with the MoNbTaW alloy system, as a model material known for having exceptionally high strength at elevated temperature when processed using conventional methods (e.g., casting). Microstructure analysis, chemical composition, and strain rate dependent hardness of AM-processed material are presented and discussed in the context of understanding the structure-properties relationships of RHEAs/RCCAs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Functional fiber-optic sensors embedded in stainless steel components using ultrasonic additive manufacturing for distributed temperature and strain measurements

The nuclear industry is progressing toward microreactors that can be factory assembled and deployed to remote regions for reliable, scalable power generation. However, the reduced power output requires enhanced monitoring capabilities to reduce staffing and eventually move toward autonomous control to improve the economics of microreactors. The ability to embed sensors such as fiber-optics, which can provide spatially distributed temperature and strain measurements, within microreactor components for real-time health monitoring would be particularly advantageous. Recent advances in ultrasonic additive manufacturing (UAM) have demonstrated the successful embedding of fiber-optics in soft materials such as Al and Cu by placing the sensors in machined cavities and ultrasonically welding over the top with thin foils. This article reports the first successful embedment of fiber-optic sensors and thermocouples within a common nuclear reactor material, SS304, via UAM. UAM parameters were first explored with simple plate geometries before moving to more complex geometries, such as pipes and other test articles for heat pipe–based microreactors. Select samples were sectioned for microscopy to evaluate the sensor/foil and foil/foil interfaces from samples fabricated by using 100% SS304 foils vs. foils plated with a Ni coating to improve UAM bonding. Furthermore, embedding metal-coated, low-bend loss fibers resulted in greatly reduced signal attenuation and adequate compressive residual strain in the embedded region to ensure successful strain coupling. Pipe specimen functional testing was performed by monitoring temperature and strain during transient and steady-state thermal testing.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance

The maturation of computational materials approaches for fatigue performance prediction in a qualification and certification process is stifled by the ability to validate complex, microstructure-based simulations. Such a validation strategy bears immediate challenges including generating accurate virtual microstructures, efficiently solving physics-based mechanical simulations over relevant spatial and temporal scales, and acquiring high-fidelity calibration and validation data at the appropriate length scale. This presentation will overview these common challenges and present a case study to demonstrate a computational materials workflow for additively manufactured metallic materials. In this study, process-specific defects are characterized using segmented X-Ray micro-computed tomography measurements and overlaid on virtual microstructures. Accelerated crystal plasticity-based fatigue simulations are performed to demonstrate cyclic evolution and localization of mechanical fields in the vicinity of defects in response to their precise spatial configuration. An example of how this computational materials workflow may support next-generation qualification is discussed.

computational materials↗
DOE OSTI2022

Novel Tube Design for Superheater Heat Exchanger Enabled Via Additive Manufacturing

Superheater tubes are critical boiler components that operate at relatively higher temperatures and pressure. Amongst the primary concerns for these tubes is the deposition of ash particles on the tube surface, leading to the reduced thickness of the tube due to material corrosion, consequently causing early creep failure of the component. In this research, a novel tube design has been proposed which resembles a teardrop or ogive shape to reduce the drag and concurrently improve the creep life of the superheater tubes. To administer the practicality of novel tubes, metal additive manufacturing (AM), for instance, laser-powder bed fusion (L-PBF), has been proposed. These unconventional designs were assessed and compared with the baseline circular tube design for mechanical design requirements (hoop stress and creep life) and the particle and flue gas flow characteristics around the differently shaped tubes. A thermomechanical finite element (FE) analysis was performed for hoop stress calculations. This study also emphasizes on effect of circumferential thermal variation on hoop stress distribution in tubes. Therefore, a detailed two-dimensional (2D) thermal simulation has been performed to report the circumferential thermal variation on the tube. A computational fluid dynamics (CFD) analysis coupled with particle tracing was performed for gas flow visualization and particle tracing around the proposed shapes and baseline circular-shaped tube design. The Schlieren optic setup was built and leveraged for qualitative validation of the proposed design. The complete design methodology established in the paper shows teardrop-shaped tubes better in terms of drag and creep life in contrast to the circular-shaped tube.

42 ENGINEERING↗
DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary optimization where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The new approach to shape optimization resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗
DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing: Preprint

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The free-form boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗
DOE OSTI2023

Design and additive manufacturing of optimized electrodes for energy storage applications

Supercapacitors exhibit fast charging/discharging ability and have attracted considerable attention within the automotive, aerospace, and telecommunication industries. Porous carbons, prized for their high electrical conductivity and high surface area, have been attractive candidates for supercapacitor electrodes. Moving to thick electrodes is one strategy to further increase energy density due to a higher volume fraction of active material. However, thick electrodes suffer from sluggish charged species transport, which is why thin electrodes are currently favored. In this work, we investigate the use of computational optimization and additive manufacturing to design and fabricate thick porous electrodes with improved performance. Electrode performance was maximized by designing their morphologies via topology optimization and printing by projection micro stereolithography (PμSL) using commercial resin (PR48). The PR48 resin was then pyrolyzed (PR48-P) to create the final conductive electrode. The optimized PR48-P electrodes exhibited 99% improvement in capacitance compared to control electrodes printed with cubic lattice morphologies. To further improve performance, we formulated a resin combining graphene oxide (GO) and trimethylolpropane triacrylate (TMPTA). Electrodes printed with 3 wt% GO in TMPTA exhibited improved capacitance retention after pyrolysis compared to the PR48-P electrodes. Finally, this work demonstrates the benefits of using topology optimization to design electrodes and material development to improve functional properties of 3D printable electrodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Preliminary Assessment of Additively Manufactured Cooling Channel Performance for Helium-Cooled Blanket Concepts

Sufficient cooling of the blanket first wall remains a critical challenge for the design and deployment of fusion power plants. Helium has been targeted as a potential blanket coolant due to its inertness and low neutron interactivity, among other advantages. However, the low thermal mass of helium creates a need for heat transfer enhancements in coolant channels to provide adequate cooling to the blanket’s first wall. Toward this end, a series of ribbed flow channels of various rib cross sections and configurations has been produced via additive manufacturing (AM) to study the efficacy of AM for first wall heat transfer enhancement and the optimization of heat transfer geometries. Helium cooling performance is studied in AM test articles at 4 MPa operating pressure, Reynolds numbers up to 197 000, and outer surface heat fluxes up to 42 kW/m 2 in the recently commissioned helium flow loop experiment (HFLE). Preliminary results of this study are presented herein. Heat transfer performance of nominally smooth (i.e., featureless) AM channels is quantified via measured Nusselt numbers and friction factors and compared to off-the-shelf smooth pipe experiments. Results are compared to existing correlations and used to discuss the effects of the AM processes on thermal-hydraulic performance. It is seen that the inherent roughness of the AM channels leads to an increase in both heat transfer coefficient and pressure drop when compared to the conventional pipe. In conclusion, recommendations are made for future studies based on these findings and additional considerations for the deployment of AM blanket cooling components.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Characterization of Additively Manufacture GRCop-42 and GRCop-84

GRCop-42 (Cu-4 at.% Cr-2 at.% Nb) is a copper-based alloy optimized for high temperature, high thermal conductivity applications such as regenerative cooled rocket engine liners. For this application, it is important to minimize contamination such as trace elements and ceramic inclusions. NASA initiated a series of ten builds by eight companies to assess the repeatability of GRCop-42 between vendors and machines. Utilizing inductively coupled plasma – atomic emission spectroscopy (ICP-AES) and energy dispersive spectroscopy (EDS) techniques allowed for identification of contaminants and quantification of their chemistry. In addition, additive manufacturing (AM) creates a unique microstructure that is often highly textured in the build (Z) direction. Electron backscattered diffraction (EBSD) was used to begin quantifying the degree of crystallographic texture in the copper matrix grains. It was also used to identify the crystallographic form of Cr2Nb, the main strengthening phase, present in the samples. Results showed the presence of ceramic inclusions, highly textured grains, and both the low and high temperature phases of Cr2Nb being present.

copper↗
DOE OSTI2021

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. Finally, in the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

36 MATERIALS SCIENCE↗