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At least 1,729 records · Page 96

Synthesis and Structure–Activity Characterization of a Single-Site MoO 2 Catalytic Center Anchored on Reduced Graphene Oxide

Molecularly derived single-site heterogeneous catalysts can bridge the understanding and performance gaps between conventional homogeneous and heterogeneous catalysis, guiding the rational design of next-generation catalysts. While impressive advances have been made with well-defined oxide supports, the structural complexity of other supports and the nature of the grafted surface species present an intriguing challenge. In this study, single-site Mo(=O) 2 species grafted onto reduced graphene oxide (rGO/MoO 2 ) are characterized by XPS, DRIFTS, powder XRD, N 2 physisorption, NH 3 -TPD, aqueous contact angle, active site poisoning assay, Mo EXAFS, model compound single-crystal XRD, DFT, and catalytic performance. NH 3 -TPD reveals that the anchored MoO 2 moiety is not strongly acidic, while Mo 3d 5/2 XPS assigns the oxidation state as Mo(VI), and XRD shows little rGO periodicity change on MoO 2 grafting. Contact angle analysis shows that MoO 2 grafting consumes rGO surface polar groups, yielding a more hydrophobic surface. The rGO/MoO 2 DRIFTS assigns features at 959 and 927 cm –1 to the symmetric and antisymmetric Mo=O stretching modes, respectively, of an isolated cis-(O=Mo=O) moiety, in agreement with DFT computation. Moreover, the Mo EXAFS rGO/MoO 2 structural data are consistent with isolated (C–O) 2– Mo(=O) 2 species having two Mo=O bonds and two Mo–O bonds at distances of 1.69(3) and 1.90(3) Å, respectively. rGO/MoO 2 is also more active than the previously reported AC/MoO 2 catalyst, with reductive carbonyl coupling TOFs approaching 1.81 × 10 3 h –1 . rGO/MoO 2 is environmentally robust and multiply recyclable with 69 ± 2% of the Mo sites catalytically significant. Altogether, rGO/MoO 2 is a structurally well-defined and versatile single-site Mo(VI) dioxo heterogeneous catalytic system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗

Burner liner thermal/structural load modelling

The objective of this program is to develop a thermal data transfer computer program module for the burner liner thermal structural load modeling program. This will be accomplished by (1) reviewing existing methodologies for thermal data transfer and selecting three heat transfer codes for application in this program; (2) evaluating the selected codes to establish criteria for developing a computer program module to transfer thermal data from the heat transfer codes to selected stress analysis codes; (3) developing the automated thermal load transfer module; and (4) verifying and documenting the module. In aircraft turbine engine hot section components, cyclic thermal stresses are the most important damage mechanism. Consequently, accurate and reliable prediction of thermal loads is essential to improving durability. To achieve this goal, a considerable effort over the past 20 years has been devoted to the acquisition of engine temperature test data, as well as the development of accurate, reliable, and efficient computer codes for the prediction of steady state and transient temperatures and for the calculation of elastic and inelastic cyclic stresses and strains in hot section components. There is a need for continued development of these codes, because the availability of more accurate analysis techniques for complex configurations has enabled engine designers to use more sophisticated designs to achieve higher cycle efficiency and reduce weight.

Maffeo, R. J.↗

Understanding Skill in EVA Mass Handling: An Integrated Methodology for Evaluating Space Suit Mobility and Stability - Volume 4

The empirical investigation of extravehicular activity (EVA) mass handling conducted on NASA's Precision Air-Bearing Floor led to a Phase I SBIR from JSC. The purpose of the SBIR was to design an innovative system for evaluating space suit mobility and stability in conditions that simulate EVA on the surface of the Moon or Mars. The approach we used to satisfy the Phase I objectives was based on a structured methodology for the development of human-systems technology. Accordingly the project was broken down into a number of tasks and subtasks. In sequence, the major tasks were: 1) Identify missions and tasks that will involve EVA and resulting mobility requirements in the near and long term; 2) Assess possible methods for evaluating mobility of space suits during field-based EVA tests; 3) Identify requirements for behavioral evaluation by interacting with NASA stakeholders;.4) Identify necessary and sufficient technology for implementation of a mobility evaluation system; and 5) Prioritize and select technology solutions. The work conducted in these tasks is described in this final volume of the series on EVA mass handling. While prior volumes in the series focus on novel data-analytic techniques, this volume addresses technology that is necessary for minimally intrusive data collection and near-real-time data analysis and display.

McDonald, P. Vernon↗

Characterization of Microbe Resistant Coatings for Use in the ISS Water System

The ISS is a highly controlled environment; however, there are still microbes on it that astronauts bring from their own microbiome. Once within the ISS water treatment system, these microbes not only pose a health risk for astronauts, but they can form biofilms, causing material degradation and system failure. Due to their protective structure, biofilms are notoriously resistant to disinfectants and antibiotics. One proposed solution is applying polymer coatings to the metal parts of the water system to prevent biofilms from forming. Two types of polyampholyte polymers with previously demonstrated nonfouling properties were sent to the ISS in 2021 to test their viability for preventing microbial adhesion in space. However, the only microbial assays performed were microscopy. To enhance this ongoing research project, I developed procedures for culturing, applying, and quantifying Ralstonia pickettii on these polymers. R. pickettii was chosen because it is one of the most prevalent microbes found in the ISS water treatment system. Optical density data was recorded to obtain a growth curve demonstrating a doubling time between 2.83 and 4.88 hours in 30°C, not shaken conditions. Optical density was also correlated to colony forming units in a plating experiment. Another result was design (and pilot testing) of a surface colonization experiment. These results provided vital knowledge about R. pickettii, which will be used for a 2023 payload of antimicrobial polymer coatings on stainless steel, and improved methods for biofilm analysis upon its return to evaluate the efficacy of the coatings.

microbiology↗

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗

Residual strength of thin panels with cracks

The previous design philosophies involving safe life, fail-safe and damage tolerance concepts become inadequate for assuring the safety of aging aircraft structures. For example, the failure mechanism for the Aloha Airline accident involved the coalescence of undetected small cracks at the rivet holes causing a section of the fuselage to peel open during flight. Therefore, the fuselage structure should be designed to have sufficient residual strength under worst case crack configurations and in-flight load conditions. Residual strength is interpreted as the maximum load carrying capacity prior to unstable crack growth. Internal pressure and bending moment constitute the two major components of the external loads on the fuselage section during flight. Although the stiffeners in the form of stringers, frames and tear straps sustain part of the external loads, the significant portion of the load is taken up by the skin. In the presence of a large crack in the skin, the crack lips bulge out with considerable yielding; thus, the geometric and material nonlinearities must be included in the analysis for predicting residual strength. Also, these nonlinearities do not permit the decoupling of in-plane and out-of-plane bending deformations. The failure criterion combining the concepts of absorbed specific energy and strain energy density addresses the aforementioned concerns. The critical absorbed specific energy (local toughness) for the material is determined from the global specimen response and deformation geometry based on the uniaxial tensile test data and detailed finite element modeling of the specimen response. The use of the local toughness and stress-strain response at the continuum level eliminates the size effect. With this critical parameter and stress-strain response, the finite element analysis of the component by using STAGS along with the application of this failure criterion provides the stable crack growth calculations for residual strength predictions.

Madenci, Erdogan↗

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗

Effect of Random Thermal Spikes on Stirling Convertor Heater Head Reliability

Onboard radioisotope power systems being developed to support future NASA exploration missions require reliable design lifetimes of up to 14 yr and beyond. The structurally critical heater head of the high-efficiency developmental Stirling power convertor has undergone extensive computational analysis of operating temperatures (up to 650 C), stresses, and creep resistance of the thin-walled Inconel 718 bill of material. Additionally, assessment of the effect of uncertainties in the creep behavior of the thin-walled heater head, the variation in the manufactured thickness, variation in control temperature, and variation in pressure on the durability and reliability were performed. However, it is possible for the heater head to experience rare incidences of random temperature spikes (excursions) of short duration. These incidences could occur randomly with random magnitude and duration during the desired mission life. These rare incidences could affect the creep strain rate and therefore the life. The paper accounts for these uncertainties and includes the effect of such rare incidences, random in nature, on the reliability. The sensitivities of variables affecting the reliability are quantified and guidelines developed to improve the reliability are outlined. Furthermore, the quantified reliability is being verified with test data from the accelerated benchmark tests being conducted at the NASA Glenn Research Center.

Shah, Ashwin R.↗

Effect of Random Thermal Spikes on Stirling Convertor Heater Head Reliability

Onboard radioisotope power systems being developed to support future NASA exploration missions require reliable design lifetimes of up to 14 yr and beyond. The structurally critical heater head of the high-efficiency developmental Stirling power converter has undergone extensive computational analysis of operating temperatures (up to 650 C), stresses, and creep resistance of the thin-walled Inconel 718 bill of material. Additionally assessment of the effect of uncertainties in the creep behavior of the thin-walled heater head, the variation in the manufactured thickness, variation in control temperature, and variation in pressure on the durability and reliability were performed. However, it is possible for the heater head to experience rare incidences of random temperature spikes (excursions) of short duration. These incidences could occur randomly with random magnitude and duration during the desired mission life. These rare incidences could affect the creep strain rate and therefore the life. The paper accounts for these uncertainties and includes the effect of such rare incidences, random in nature, on the reliability. The sensitivities of variables affecting the reliability are quantified and guidelines developed to improve the reliability are outlined. Furthermore, the quantified reliability is being verified with test data from the accelerated benchmark tests being conducted at the NASA Glenn Research Center.

Shah, Ashwin R.↗

Down-Selection of Four Common Habitat Variants

The Common Habitat is a large habitat developed as an alternative architecture study, not part of the current NASA baseline, that uses the SLS core stage liquid oxygen tank as its primary structure. It has a gravity-independent internal architecture, such that identical units can be used on the lunar surface, Mars surface, and in microgravity. In developing the habitat, two key architectural questions emerged. Should the internal layout use a vertical or horizontal orientation of the tank? Should the crew size be four or eight? This led to the design of four variants: a four-crew horizontal, four-crew vertical, eight-crew horizontal, and eight-crew vertical. The four-crew variants use a shortened version of the tank while the eight-crew variants use the entire tank. The primary consideration applied for down-selection is the crew experience living and working in the habitat, inclusive of crew productivity, well-being, and survivability. Based on this consideration, a series of seven assessments were performed to compare the variants. This analysis was performed as an unfunded, volunteer activity leveraging civil servants across multiple field centers, most with expertise working in various Artemis teams. Additionally, the evaluation was limited to the use of CAD models, images, and spreadsheet data, with no resources available for mockups or Virtual Reality. A logistics analysis developed a standard logistics module and then estimated how much stowage could be carried onboard each Common Habitat and how many logistics modules are required by each variant for a given mission duration. It also considered the amounts of water to be stored in each variant. A functional analysis identified and compared the living and working functions across the habitats, ranking them relative to each other. A crew time assessment first estimated the total crew time, building a weekly crew timeline for both four and eight-person crews. It then allocated time to activities linked to living and working functions, comparing how much time was available for each function in each variant. A science productivity assessment developed a relative metric using crew time, science stowage, and assumed rates of experiment consumables use to analytically compare the four variants. It also comparatively ranked the habitats with respect to a number of subjective parameters and a workstation acceptability rating. A maintenance capacity assessment identified and compared eleven generic maintenance capabilities across the variants and also ranked them for their predicted ability to complete twelve fabrication, maintenance, and repair scenarios. A contingency responsiveness analysis examined twelve serious in-flight contingencies. For each scenario, the number of crew needed to respond were predicted and acceptability of various aspects of contingency response were evaluated, comparing the variants against each other. Finally, in a habitability assessment, 120 habitability characteristics reflecting 13 major categories were evaluated for each habitat. These results were compared to identify the most acceptable habitat in each category. Ultimately, the data favored the horizontal orientation over the vertical and an eight-person crew over four. Implications of selecting this variant are discussed, including specific architectural challenges that result from the use of the full tank.

Habitability↗

Computing Edge States without Hard Truncation

We present a numerical method which accurately computes the discrete spectrum and associated bound states of semi-infinite Hamiltonians which model electronic “edge” states localized at boundaries of one- and two-dimensional crystalline materials. The problem is nontrivial since arbitrarily large finite “hard” (Dirichlet) truncations of the Hamiltonian in the infinite bulk direction tend to produce spurious bound states partially supported at the truncation. Our method, which overcomes this difficulty, is to compute the Green's function of the semi-infinite Hamiltonian by imposing an appropriate boundary condition in the bulk direction; then, the spectral data is recovered via Riesz projection. We demonstrate our method's effectiveness by studies of edge states at a graphene zig-zag edge in the presence of defects modeled both by a discrete tight-binding model and a continuum PDE model under finite difference discretization. Our method may also be used to study states localized at domain wall-type edges in one- and two-dimensional materials where the edge Hamiltonian is infinite in both directions; we demonstrate this for the case of a tight-binding model of distinct honeycomb structures joined along a zig-zag edge. Here, we expect our method to be useful for designing novel devices based on precise wave-guiding by edge states.

97 MATHEMATICS AND COMPUTING↗

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO 2 (NO 3 ) 2 (DEHiBA) 2 , agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Effects of Propellant Slosh Dynamics on the Solar Dynamics Observatory

The Solar Dynamics Observatory (SDO) mission, which is part of the Living With a Star program, was successfully launched and deployed from its Atlas V launch vehicle on February 11, 2010. SDO is an Explorer-class mission now operating in a geosynchronous orbit (GEO). The basic mission is to observe the Sun for a very high percentage of the 5-year mission (10-year goal) with long stretches of uninterrupted observations and with constant, high-data-rate transmission to a dedicated ground station located in White Sands, New Mexico. A significant portion of SDO's launch mass was propellant, contained in two large tanks. To ensure performance with this level of propellant, a slosh analysis was performed. This paper provides an overview of the SDO slosh analysis, the on-orbit experience, and the lessons learned. SDO is a three-axis controlled, single fault tolerant spacecraft. The attitude sensor complement includes sixteen coarse Sun sensors, a digital Sun sensor, three two-axis inertial reference units, two star trackers, and four guide telescopes. Attitude actuation is performed either using four reaction wheels or eight thrusters, depending on the control mode, along with single main engine which nominally provides velocity-change thrust. The attitude control software has five nominal control modes: three wheel-based modes and two thruster-based modes. A wheel-based Safehold running in the Attitude Control Electronics (ACE) box improves the robustness of the system as a whole. All six modes are designed on the same basic proportional-integral-derivative attitude error structure, with more robust modes setting their integral gains to zero. To achieve and maintain a geosynchronous orbit for a 2974-kilogram spacecraft in a cost effective manner, the SDO team designed a high-efficiency propulsive system. This bi-propellant design includes a 100-pound-force main engine and eight 5-pound-force attitude control thrusters. The main engine provides high specific impulse for the maneuvers to attain GEO, while the smaller Attitude Control System (ACS) thrusters manage the disturbance torques of the larger main engine and provide the capability for much smaller orbit adjustment burns. SDO's large solar profile produces a large solar torque disturbance and momentum buildup. This buildup drives the frequency of momentum unloads via ACS thrusters. SDO requires 1409 kilograms (which is approximately half the launch mass) of propellant to achieve and maintain the GEO orbit while performing the momentum unloads for 10 years.

Mason, Paul↗

Thermal/Fluid Analysis of a Composite Heat Exchanger for Use on the RLV Rocket Engine

As part of efforts to design a regeneratively cooled composite nozzle ramp for use on the reusable vehicle (RLV) rocket engine, an C-SiC composites heat exchanger concept was proposed for thermal performance evaluation. To test the feasibility of the concept, sample heat exchanger panels were made to fit the Glenn Research Center's cell 22 for testing. Operation of the heat exchanger was demonstrated in a combustion environment with high heat fluxes similar to the RLV Aerospike Ramp. Test measurements were reviewed and found to be valuable for the on going fluid and thermal analysis of the actual RLV composite ramp. Since the cooling fluid for the heat exchanger is water while the RLV Ramp cooling fluid is LH2, fluid and thermal models were constructed to correlate to the specific test set-up. The knowledge gained from this work will be helpful for analyzing the thermal response of the actual RLV Composite Ramp. The coolant thermal properties for the models are taken from test data. The heat exchanger's cooling performance was analyzed using the Generalized Fluid System Simulation Program (GFSSP). Temperatures of the heat exchanger's structure were predicted in finite element models using Patran and Sinda. Results from the analytical models and the tests show that RSC's heat exchanger satisfied the combustion environments in a series of 16 tests.

Nguyen, Dalton↗

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗

Surface flaw reliability analysis of ceramic components with the SCARE finite element postprocessor program

The SCARE (Structural Ceramics Analysis and Reliability Evaluation) computer program on statistical fast fracture reliability analysis with quadratic elements for volume distributed imperfections is enhanced to include the use of linear finite elements and the capability of designing against concurrent surface flaw induced ceramic component failure. The SCARE code is presently coupled as a postprocessor to the MSC/NASTRAN general purpose, finite element analysis program. The improved version now includes the Weibull and Batdorf statistical failure theories for both surface and volume flaw based reliability analysis. The program uses the two-parameter Weibull fracture strength cumulative failure probability distribution model with the principle of independent action for poly-axial stress states, and Batdorf's shear-sensitive as well as shear-insensitive statistical theories. The shear-sensitive surface crack configurations include the Griffith crack and Griffith notch geometries, using the total critical coplanar strain energy release rate criterion to predict mixed-mode fracture. Weibull material parameters based on both surface and volume flaw induced fracture can also be calculated from modulus of rupture bar tests, using the least squares method with known specimen geometry and grouped fracture data. The statistical fast fracture theories for surface flaw induced failure, along with selected input and output formats and options, are summarized. An example problem to demonstrate various features of the program is included.

Gyekenyesi, John P.↗

Surface flaw reliability analysis of ceramic components with the SCARE finite element postprocessor program

The SCARE (Structural Ceramics Analysis and Reliability Evaluation) computer program on statistical fast fracture reliability analysis with quadratic elements for volume distributed imperfections is enhanced to include the use of linear finite elements and the capability of designing against concurrent surface flaw induced ceramic component failure. The SCARE code is presently coupled as a postprocessor to the MSC/NASTRAN general purpose, finite element analysis program. The improved version now includes the Weibull and Batdorf statistical failure theories for both surface and volume flaw based reliability analysis. The program uses the two-parameter Weibull fracture strength cumulative failure probability distribution model with the principle of independent action for poly-axial stress states, and Batdorf's shear-sensitive as well as shear-insensitive statistical theories. The shear-sensitive surface crack configurations include the Griffith crack and Griffith notch geometries, using the total critical coplanar strain energy release rate criterion to predict mixed-mode fracture. Weibull material parameters based on both surface and volume flaw induced fracture can also be calculated from modulus of rupture bar tests, using the least squares method with known specimen geometry and grouped fracture data. The statistical fast fracture theories for surface flaw induced failure, along with selected input and output formats and options, are summarized. An example problem to demonstrate various features of the program is included.

Gyekenyesi, J. P.↗