Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “POROSITY”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effects of spatial energy distribution-induced porosity on mechanical properties of laser powder bed fusion 316L stainless steel

Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The impact of infill percentage and layer height in small-scale material extrusion on porosity and tensile properties

We report material extrusion additive manufacturing is prone to introducing porosity within the structure due to the layer-by-layer construction using elliptical beads of material. This open porosity ultimately plays a role in determining the mechanical properties of printed parts. The shape, size, and amount of porosity within a printed part is influenced by a variety of factors, including nozzle diameter, infill percentage, layer height, raster orientation, and print speed. While several studies have investigated these and other parameters’ effects on mechanical performance and porosity, better understanding the interconnected relationships is crucial in balancing the various input parameters to achieve maximum strength. This work initially examined the influence of key print parameters (infill percentage and layer height) on the internal porosity of a printed Acrylonitrile Butadiene Styrene (ABS) part. Then, the print parameters and internal porosity were statistically correlated to final mechanical properties. Porosity was further classified as either open or closed to differentiate between connected voids in the mesostructure from isolated voids within the material itself. Mechanical performance increased with an increasing density and infill percentage, displaying a 224 % increase in elastic modulus and a 150 % increase in ultimate tensile strength. The contribution of layer height was found to be conditional upon the infill percentage.

3D Printing↗
DOE OSTI2025

Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis

An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.

Additive manufacturing↗
DOE OSTI2018

Generation and healing of porosity in high purity copper by high-pressure torsion

Highlights: • Direct characterization of severe plastic deformation induced and healed porosities. • HPT process of disk with pre-fabricated holes reveals healing of porosity. • Ultrafine grains coexisting with shear bands and pores exhibit high stability. • Excess vacancies and pores cause drag effect on the grain boundary motion. • Accelerated vacancy diffusion leads to the generation and growth of pores. - Abstract: Generation and healing of pores in metals under loading or during processing have long been important research topics, and their mechanisms are still debatable. In recent years, unexpected percolating networks of pores acting as ultra-fast diffusion paths were found in ultrafine-grained metals processed by severe plastic deformation. Herein, we conducted a systematic study of the mechanisms of generation and healing of pores in ultrahigh-purity copper (99.9995 wt%) during high-pressure torsion (HPT). In addition to a solid Cu disk, the disk with holes that were drilled before processing, resulting in nominal initial porosity of 4.48%, was processed by HPT. After HPT, the percolating porosities of 0.07 ± 0.01% and 0.04 ± 0.01% were found in the solid disk, and in the disk with pre-fabricated holes, respectively. Thus, higher initial porosity resulted in lower percolating porosity in the HPT-processed disk. Systemic characterization using scanning electron microscopy revealed that in both cases shear bands and ultra-fine grains are located in the regions with induced and healed pores. We discussed the mechanisms of porosity evolution in both cases. In the end, to verify the possibility of pores nucleation and growth by vacancy agglomeration, vacancy diffusivity during HPT processing was estimated. This work sheds a new light on the mechanisms of pores nucleation and evolution during severe plastic deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning

Development and preservation of carbonate porosity and permeability are critical to characterizing reservoirs. However, secondary porosity, such as vugs and fractures, are difficult to identify and require collection of expensive wireline tools or core sampling. Wireline logs and cores have traditionally been used to identify the presence of secondary porosity but fail to quantify the contribution to total reservoir porosity. Additionally, advanced wireline logs and core are not readily available for most wells. Dual energy CT scans were collected on whole core from the A-1 Carbonate and Brown Niagaran formations drawn from six wells in northern Michigan. 3D analysis techniques were applied to identify and isolate secondary porosity features. A series of machine learning and data analytics techniques were applied to the dataset to predict secondary porosity features on basic wireline logs. The predictive model successfully predicted secondary porosity with high confidence.

02 PETROLEUM↗
DOE OSTI2014

Porosity of additive manufacturing parts for process monitoring

Some metal additive manufacturing processes can produce parts with internal porosity, either intentionally (with careful selection of the process parameters) or unintentionally (if the process is not well-controlled.) Material porosity is undesirable for aerospace parts - since porosity could lead to premature failure - and desirable for some biomedical implants, since surface-breaking pores allow for better integration with biological tissue. Changes in a part's porosity during an additive manufacturing build may also be an indication of an undesired change in the process. We are developing an ultrasonic sensor for detecting changes in porosity in metal parts during fabrication on a metal powder bed fusion system, for use as a process monitor. This paper will describe our work to develop an ultrasonic-based sensor for monitoring part porosity during an additive build, including background theory, the development and detailed characterization of reference additive porosity samples, and a potential design for in-situ implementation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Detection of Part Porosity in Additive Manufacturing Using Machining

Monitoring of part porosity in additive manufacturing is critical for part qualification and process parameter development. This article describes the monitoring of porosity in additive manufacturing using machining. Grade 91 steel samples with different levels of porosity were additively manufactured using a laser blown-powder process. The samples were machined, and the tool position and cutting power were monitored using the machine controller and an external power transducer, respectively. Results show that the power transducer is sensitive to machining porosities. A novel approach using the standard deviation of cutting power was developed to determine the location of porosities along the length of the machining pass. Test cuts were completed at different machining process parameters. Results showed that the probability of detection was one for pore volume greater than 0.3 mm 3 at different machining parameters. In conclusion, although the detection of porosity using machining is a destructive method for evaluation, it offers a low-cost and fast approach for monitoring porosity in additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2023

Mitigation of spall fracture by evolving porosity

Ductile materials subject to impact loading conditions can undergo spall fracture when an incoming compressive stress wave reflects off interfaces and free surfaces as a tensile stress wave. Experimental observations suggest that in ductile materials, spall fracture is driven by the evolution of porosity. However, the presence of initial porosity in ductile materials also introduces plastic compressibility, which can attenuate the incoming compressive stress wave and, as a result, reduce the amplitude of the reflected tensile stress wave. This, in turn, can mitigate or delay spall fracture. In this work, we report on finite deformation finite element calculations that analyze the response of porous ductile materials subjected to impact loading conditions. Two sets of calculations are carried out, in the first set the material contains initial porosity values ranging from 0% to 5% while in the second set the material also undergoes stress-controlled porosity nucleation. Both sets of calculations are carried out for a wide range of imposed impact velocities. Here, our results show that porosity in ductile materials can, under certain circumstances, mitigate spall fracture by attenuating stress waves. Results correlating the effects of impact velocity, initial porosity, and porosity nucleation on spall fracture are presented and the underlying mechanisms are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Quantification of Organic Porosity and Water Accessibility in Marcellus Shale Using Neutron Scattering

Pores within organic matter (OM) are a significant contributor to the total pore system in gas shales. These pores contribute most of the storage capacity in gas shales. Here we present a novel approach to characterize the OM pore structure (including the porosity, specific surface area, pore size distribution, and water accessibility) in Marcellus shale. By using ultrasmall and small-angle neutron scattering, and by exploiting the contrast matching of the shale matrix with suitable mixtures of deuterated and protonated water, both total and water-accessible porosity were measured on centimeter-sized samples from two boreholes from the nanometer to micrometer scale with good statistical coverage. Samples were also measured after combustion at 450 °C. Analysis of scattering data from these procedures allowed quantification of OM porosity and water accessibility. OM hosts 24–47% of the total porosity for both organic-rich and -poor samples. This porosity occupies as much as 29% of the OM volume. In contrast to the current paradigm in the literature that OM porosity is organophilic and therefore not likely to contain water, our results demonstrate that OM pores with widths >20 nm exhibit the characteristics of water accessibility. In conclusion, our approach reveals the complex structure and wetting behavior of the OM porosity at scales that are hard to interrogate using other techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Geochemistry & Geophysics↗
DOE OSTI2020

Micro- and nano-porosity of the active Alpine Fault zone, New Zealand

Porosity reduction in rocks from a fault core can cause elevated pore fluid pressures and consequently influence the recurrence time of earthquakes. We investigated the porosity distribution in the New Zealand's Alpine Fault core in samples recovered during the first phase of the Deep Fault Drilling Project (DFDP-1B) by using two-dimensional nanoscale and three-dimensional microscale imaging. Synchrotron X-ray microtomography-derived analyses of open pore spaces show total microscale porosities in the range of 0.1 %–0.24 %. These pores have mainly non-spherical, elongated, flat shapes and show subtle bipolar orientation. Scanning and transmission electron microscopy reveal the samples' microstructural organization, where nanoscale pores ornament grain boundaries of the gouge material, especially clay minerals. Our data imply that (i) the porosity of the fault core is very small and not connected; (ii) the distribution of clay minerals controls the shape and orientation of the associated pores; (iii) porosity was reduced due to pressure solution processes; and (iv) mineral precipitation in fluid-filled pores can affect the mechanical behavior of the Alpine Fault by decreasing the already critically low total porosity of the fault core, causing elevated pore fluid pressures and/or introducing weak mineral phases, and thus lowering the overall fault frictional strength. We conclude that the current state of very low porosity in the Alpine Fault core is likely to play a key role in the initiation of the next fault rupture.

58 GEOSCIENCES↗
NASA NTRS1996

Porosity and Permeability of Chondritic Materials

We have investigated the porosity of a large number of chondritic interplanetary dust particles and meteorites by three techniques: standard liquid/gas flow techniques, a new, non-invasive ultrasonic technique, and image processing of backscattered images . The latter technique is obviously best suited to sub-kg sized samples. We have also measured the gas and liquid permeabilities of some chondrites by two techniques: standard liquid/gas flow techniques, and a new, non-destructive pressure release technique. We find that chondritic IDP's have a somewhat bimodal porosity distribution. Peaks are present at 0 and 4% porosity; a tail then extends to 53%. These values suggest IDP bulk densities of 1.1 to 3.3 g/cc. Type 1-3 chondrite matrix porosities range up to 30%, with a peak at 2%. The bulk porosities for type 1-3 chondrites have the same approximate range as exhibited by matrix, indicating that other components of the bulk meteorites (including chondrules and aggregates) have the same average porosity as matrix. These results reveal that the porosity of primitive materials at scales ranging from nanogram to kilogram are similar, implying similar accretion dynamics operated through 12 orders of size magnitude. Permeabilities of the investigated chondrites vary by several orders of magnitude, and there appears to be no simple dependence of permeability with degree of aqueous alteration, or chondrite type.

Zolensky, Michael E.↗
NASA NTRS1999

Interaction of Porosity with an Advancing Solid/Liquid Interface: a Real-Time Investigation

Problems associated with formation of porosity during solidification continue to have a daily impact on the metal forming industry. Several past investigations have dealt with the nucleation and growth aspects of porosity. However, investigations related to the interaction of porosity with that of a solidification front has been limited mostly to organic analogues. In this paper we report on real time experimental observations of such interactions in metal alloys. Using a state of the art X-Ray Transmission Microscope (XTM) we have been able to observe and record the dynamics of the interaction. This includes distortion of the solid/liquid interface near a poro.sity, solute segr,egation patterns surrounding a porosity and the change in shape of the porosity during interaction with an advancing solid/liquid interface. Results will be presented for different Al alloys and growth conditions. The experimental data will be compared to theory using a recently developed 2D numerical model. The model employs a finite difference approach where the solid/liquid interface is defined through the points at which the interface intersects the grid lines. The transport variables are calculated at these points and the motion of the solidification front is determined by the magnitude of the transport variables. The model accounts for the interplay of the thermal and solutal field and the influence of capilarity to predict the shape of the solid/liquid interface with time in the vicinity of porosity. One can further calculate the perturbation of the solutal field by the presence of porosity in the melt.

Sen, S.↗
NASA NTRS2001

Friction Factor Characterization for High-Porosity Random Fiber Regenerators

NASA Glenn Research Center, the Department of Energy (DOE), and Stirling Technology Company (STC) of Kennewick, Washington are developing a Stirling convertor for a high-efficiency Stirling Radioisotope Power System to provide electric power for NASA Space Science Missions. STC is developing the 55-We Technology Demonstration Convertor (TDC) under contract to DOE. Steady-flow tests were completed to determine the friction factor for the high-porosity regenerators that are used in the TDC. STC fabricated a flow test fixture and three random fiber regenerator test samples, one each at approximately 80, 88, and 96 percent porosities. The flow tests were then completed by the NASA Glenn Flow Calibration Laboratory, and the data reduced to Reynolds number and friction factor. The results showed that the 80 and 88 percent porosity samples had similar characteristics while the 96 percent porosity sample had significantly higher friction factors for given Reynolds numbers compared to the samples with lower porosities. Comparisons were also made between the test data and existing correlations. STC used this data to derive a modified regenerator friction factor correlation for use in the Stirling design code GLIMPS for porosities greater than 88 percent. Using this new correlation, the final optimized regenerator design porosity was reduced from 96 to 90 percent.

Thieme, Lanny G.↗
DOE OSTI2019

CHARACTERIZING THE INFLUENCE OF PRINT PARAMETERS ON POROSITY AND RESULTING DENSITY

Extrusion deposition additive manufacturing produces parts with inherent porosity, which typically manifests as easily accessible voids between beads. This open porosity can also be accompanied by voids within the beads themselves, and both types can impact a part’s desired performance. Porosity is influenced by a variety of factors, including infill percentage, layer height, nozzle diameter, print speed, and raster orientation. While their influence on mechanical properties and porosity have been studied previously, there has been minimal work connecting print parameters to porosity and subsequently to mechanical performance. This study investigates the relationships between print parameters, volumetric porosity, and mechanical performance. In addition, this study measures both open and closed porosity through use of a helium pycnometer rather than image analysis of a cross-section. Thus, this study will identify correlations between the volumetric density of parts and the resulting mechanical performance as a function of print parameters.

Brackett, James↗
DOE OSTI2023

Variable-porosity panel systems and associated methods

Variable-porosity panel systems and associated methods. A variable-porosity panel system includes a panel assembly with an exterior layer defining a plurality of exterior layer pores and a sliding layer adjacent to the exterior layer and defining a plurality of sliding layer pores. The variable-porosity panel system additionally includes a shape memory alloy (SMA) actuator configured to translate the sliding layer relative to the exterior layer to modulate a porosity of the panel assembly. The SMA actuator includes an SMA element configured to exert an actuation force on the sliding layer and at least partially received within an SMA element receiver of the sliding layer. The SMA element extends out of the sliding layer only at a sliding layer first end. A method of operating the variable-porosity panel system includes assembling the variable-porosity panel system and/or transitioning the panel assembly of the variable-porosity panel system among the plurality of panel configurations.

Calkins, Frederick T.↗
DOE OSTI2019

Characterization of Roughness, Porosity and Thermal Resistances of Continuous Casting Mold Slag Layers Devitrified and Crystallized in Laboratory

This paper presents an experimental investigation about the variation, with temperature, of surface roughness, porosity, degree of crystallization and microstructure, occurring in solid layers of a commercial mold powder slag used for casting medium carbon steel thin slabs. Characterization is done with samples devitrified and crystallized at 698 K, 823 K, 898 K and 973 K (425 °C, 550 °C, 625 °C and 700 °C) for 10,800 seconds, in the furnace of a high-temperature confocal laser scanning microscope. As well as, with slag disks devitrified non-isothermally in a heat flow apparatus based on the application of Fourier's law in steady state conditions, where the high temperature surface is maintained at selected values, between 927 K and 1241 K (654 °C and 968 °C). Surface roughness and porosity determinations are performed by image analysis of confocal laser scanning and scanning electron micrographs, respectively. For the time considered and temperatures ≥ 823 K (550 °C) the slag crystallizes completely, and variations of roughness and porosity with temperature are associated with changes in size and packing of the primary crystalline-phase grains. Crystallization induced porosity is significant and causes slag layer expansion. Hence crystallization does not lead to gap formation with a neighboring surface (e.g., mold wall) by shrinkage. Instead, surface separation arises from development of roughness, and measurements with the heat flow apparatus confirm that interfacial thermal resistance between a slag layer and a metallic wall can be estimated from the sum of the means of maximum peak to valley height of their surfaces. Furthermore, the results establish that slag layers roughness and porosity increase with temperature and reveal effective thermal conductivities in the range of ~ 0.3 to ~ 0.5 W/m K. Low effective thermal conductivities must be a consequence of porous bands, whose porosity depends on temperature and reaches values as high as ~ 0.20 and ~ 0.50.

36 MATERIALS SCIENCE↗