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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Understanding Flow Characteristics in Metal Additive Manufacturing

In fluid flow, the frictional effects from interior surfaces of flow components create pressure loss across the surface. Understanding this inherent pressure loss due to friction and other phenomena is important when designing a flow system. For conventional manufacturing, friction loss has been empirically studied and is well known in the engineering community. However, newer manufacturing techniques, such as laser powder bed fusion (LPBF), an additive manufacturing technique, need to be understood. The inherent flow conditions resulting from the additive processes were studied. Test samples were printed with the flow path oriented in the vertical direction with different channel diameters to characterize the inherent fundamental flow properties of the process. Moody diagrams were replicated for a greater understanding of possibilities and shortfalls when implementing LPBF into design applications. Experimental flow testing revealed that greater effective sand grain roughness resulted from this process when compared to conventional machining methods. This roughness did not correlate well to the average roughness measured with a laser scanning microscope but was consistent with the average particle size of the powder metal feedstock.

42 ENGINEERING↗
DOE OSTI2023

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. Here, the presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

42 ENGINEERING↗
DOE OSTI2024

Assessments of advanced reactor heat supply to high temperature industrial unit operations: Heat Engines and Heat Pumps

In this report, the feasibility of employing Advanced Nuclear Reactors to supply the necessary high-quality heat to Industrial Processes is investigated. As a first step, the most relevant industrial applications that could benefit from this coupling were identified. Four industries including petroleum refining (distillation, catalyst regeneration, hydro-steam cracking), chemical polymers, steel manufacturing (metals annealing/soaking) and cement manufacturing (calcination of limestone and dolomite industries) were considered. An overview of the heat duties, i.e., the temperatures and thermal powers required by representative plants of the different processes, was provided. Secondly, the thermodynamic conditions of the steam produced by representative designs of Advanced Reactor concepts (Liquid Metal Fast Breeder Reactors, Molten Salt Reactors, and High Temperature Gas-cooled Reactors) were summarized. The comparison between the requirements of the industrial processes and the capabilities of the candidate nuclear power plants showed that only a few processes could benefit from the coupling. Although Advanced Reactors operate at higher temperatures than Light Water Reactors, the thermodynamic conditions of the generated steam flow rates are generally not suitable for the selected applications. At the same time, data indicate that most of the processes can be suitably supplied if the temperature of steam is elevated to 900 °C. One possible solution to boost the quality of the steam from the nuclear island involves the use of a heat pump. Based on a technology similar to that found in refrigerators and air conditioners, heat pumps extract heat from a source, elevate its temperature and transfer it to where it is needed. A simple numerical example illustrating the viability of this method is described. Specifically, the energy conversion cycle of a conventional Pressurized Water Reactor was extended to incorporate a compressor to raise the heating value of the steam produced by the reactor. Two reference configurations, i.e., one where the industrial process coupled to the nuclear power plant exhausts saturated liquid and the other where it exhausts saturated steam, were considered. The thermal efficiencies achievable by increasing the heating value of the reactor steam in this way are significantly greater than by direct electric heating.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A Bayesian Approach to the Eagar–Tsai Model for Melt Pool Geometry Prediction with Implications in Additive Manufacturing of Metals

Here, this paper focuses on improving the melt pool geometry predictions and quantifying uncertainties using an adapted version of the Eagar–Tsai (E–T) model that incorporates temperature-dependent properties of the material as well as powder conditions. Additionally, Bayesian inference is employed to predict distributions for the E–T model input parameters of laser absorptivity and powder bed porosity by incorporating experimental results into the analysis. Monte Carlo uncertainty propagation is then used with these parameter distributions to estimate the melt pool depth and associated uncertainty. Our results for the 316L stainless steel suggest that both the absorptivity and powder bed porosity are strongly influenced by the laser power. In contrast, the scanning speed has only a marginal effect on both the absorptivity and powder bed porosity. We constructed a printability map using the Bayesian E–T model based on power-dependent input parameter values to demonstrate the merit of the approach. The Bayesian approach improved the accuracy in predicting the keyhole regions in the laser power-scan speed parameter space for the 316L stainless steel. Although applied to a specific adaptation of the E–T model, the method put forth can be extended to quantify uncertainties in other numerical models as well as in the estimation of unknown parameters.

316L Stainless Steel (SS).↗
DOE OSTI2020

Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. Finally, the powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A quality-agnostic combinatoric cost estimation model for large-format directed energy deposition metal additive manufacturing

Directed energy deposition (DED) additive manufacturing (AM) processes are amenable to synergistic combination into multi-process AM systems due to similar requirements for automation and energy sources. This work analyzes the economic performance of such DED AM systems from a quality-agnostic combinatoric standpoint with a model that calculates lowest-cost system combinations based on part geometry and process performance metrics. Common DED AM systems research focuses on a single process and does not consider the process, system, and application in the context of all possible system combinations (e.g., the combined set of process selection(s), motion system(s), and process hardware), leading to limited applicability of the resulting DED AM systems to cost-sensitive components such as those found in energy generation applications. The model developed herein incorporates the capital, material, and energy costs associated with DED AM system combinations into a predictive tool for estimating part and system cost, the output of which is intended to guide deployment of finite research and development resources towards DED AM system combinations with the lowest costs and greatest likelihood of economic impact. The DED AM systems identified by this framework may enable domestic production of the large conventionally cast and forged components necessary for energy generation.

Shanafield, Alexandra [ORNL]↗
DOE OSTI2021

Statistical analysis of spatial distribution of pores in metal additive manufacturing

We report mechanical properties of additively manufactured parts are sensitive to the presence of pores form during the manufacturing process. The impact of pores on the mechanical performance has been investigated extensively with respect to different parameters, such as pore volume fraction, shape, and size. However, statistical investigations focusing on the relationships between the spatial distribution of pores and process parameters; and consequently, the performance of the manufactured parts are scattered and limited. Also, these sparse investigations usually suffer from an ambiguous definition of terminologies. For instance, the required criteria to consider a point pattern as complete spatial randomness (CSR) are generally not clarified. Moreover, no numerical formalism is yet developed to show how much the observed spatial results are statistically significant. To address these shortcomings, the statistical definition of CSR in a point pattern and the procedure to quantitatively determine the deviation of a pattern from CSR were discussed. The explained statistical approach was used to investigate the effect of scanning speed parameter on the spatial distribution of gas pores in laser powder bed fusion manufactured stainless steel parts. Furthermore, and to highlight the impact of the spatial distribution of pores on mechanical properties, fatigue performances of parts with clustered and randomly distributed pores were simulated by finite element analysis. It is shown that by reducing the scanning speed, the spatial distribution of gas pores deviated more from CSR, and correspondingly fatigue performance deteriorated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Novel processing route for design and manufacturing of metal toughened nanoceramics: Al–Al 2 O 3 nanocermets

Monolithic alumina is a highly brittle ceramic with a very low fracture toughness, making it an unreliable material for mechanical and structural applications. Ceramic-metal composites (cermets) are the subject of intensive interest to obtain materials which can address the intrinsic brittleness of ceramics without compromising their high strength and light weight. In the present investigation, aluminium-alumina (Al–Al 2 O 3 ) nanocermets were developed. The nanocermets possessed the toughness of Al phase and the strength of reinforced nano Al 2 O 3 particles. Here, the nanocermets green bodies were prepared such that upon sintering each adjacent Al 2 O 3 grain was conformally coated with a continuous Al film. Mechanical milling via a combination of cryo-milling and ball-milling was used to prepare Al–Al 2 O 3 nano-powder mixture. Spark plasma sintering was used to consolidate the green nanocermets. Results show that specimens prepared from 35 h milled powders exhibited superior mechanical properties as compared to other nanocermets, with an improvement in microhardness, elastic modulus, compressive strength, and flexural strength by 88%, 78%, 69% and 40% with respect to sintered pure Al. The indentation toughness of 35 h milled nanocermet showed an increment of 12.5% with respect to sintered pure Al 2 O 3 . Characterization techniques such as XRD, SEM, HR-TEM, X-ray tomography were employed to investigate the phase evolution and structural morphology, while FEM simulations were performed for understanding the crack propagation mechanisms in nanocermets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗