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All sources NASA NTRS DOE OSTI

DOE OSTI2014

Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case

Dynamic Probabilistic Risk Assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). While system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by: 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the Risk Informed Safety Margin Characterization (RISMC) Boiling Water Reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A Standardized Analysis Process Using Digital Image Correlation to Calculate In Situ Cladding Strain from Modified Burst Tests for Fuel Performance Code Validation

Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.

Reactivity-initiated accident↗

NASA NTRS2002

Integrating Theory and Practice: Applying the Quality Improvement Paradigm to Product Line Engineering

My assertion is that not only are product lines a relevant research topic, but that the tools used by empirical software engineering researchers can address observed practical problems. Our experience at NASA has been there are often externally proposed solutions available, but that we have had difficulties applying them in our particular context. We have also focused on return on investment issues when evaluating product lines, and while these are important, one can not attain objective data on success or failure until several applications from a product family have been deployed. The use of the Quality Improvement Paradigm (QIP) can address these issues: (1) Planning an adoption path from an organization's current state to a product line approach; (2) Constructing a development process to fit the organization's adoption path; (3) Evaluation of product line development processes as the project is being developed. The QIP consists of the following six steps: (1) Characterize the project and its environment; (2) Set quantifiable goals for successful project performance; (3) Choose the appropriate process models, supporting methods, and tools for the project; (4) Execute the process, analyze interim results, and provide real-time feedback for corrective action; (5) Analyze the results of completed projects and recommend improvements; and (6) Package the lessons learned as updated and refined process models. A figure shows the QIP in detail. The iterative nature of the QIP supports an incremental development approach to product lines, and the project learning and feedback provide the necessary early evaluations.

Stark, Michael↗

NASA NTRS2015

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

Robotics↗

NASA NTRS2019

A Three-Dimensional Thermodynamically Based Function for the Progressive Failure of Unidirectional Composites

A thermodynamically-based work potential theory for modeling progressive damage for laminated, unidirectional composites assuming plane stress (2D Schaperystheory) is extended to three dimensioanl (3D). An internal state variable, S, is defined to account for the dissipated energy due to damage evolution in the form of microstructure changes in the matrix. With the stationary of the total work potential with respect to the internal state variable, a thermodynamically-consistent set of evolution equationsis derived. The internal state variable is related to the transverse and shear modulithrough microdamage functions. In the first part of this work, coupon specimensare prepared to conduct experiments to characterize the relations between the internalstate variable and the transverse modulus as well as shear modulus. The informationis subsequent used for the prediction of three point bending test. In the second partof this work, objectivity is studied. Three separate methods utilizing different definitions of a reduced internal state variable or of the order of the polynomials are used to represent the matrix micro-damage functions are employed. The three methods are implemented in a user defined subroutine within a commercial finite element method software package. Results from numerical simulations of a center-notched composites panel are compared. The agreement in the maximum stress predictions among the three methods indicates that objectivity, with respect to the functional form of themicrodamage functions, is satisfied.

carbon fiber reinforced polymer composites↗

NASA NTRS2007

Automated Target Planning for FUSE Using the SOVA Algorithm

The SOVA algorithm was originally developed under the Resilient Systems and Operations Project of the Engineering for Complex Systems Program from NASA s Aerospace Technology Enterprise as a conceptual framework to support real-time autonomous system mission and contingency management. The algorithm and its software implementation were formulated for generic application to autonomous flight vehicle systems, and its efficacy was demonstrated by simulation within the problem domain of Unmanned Aerial Vehicle autonomous flight management. The approach itself is based upon the precept that autonomous decision making for a very complex system can be made tractable by distillation of the system state to a manageable set of strategic objectives (e.g. maintain power margin, maintain mission timeline, and et cetera), which if attended to, will result in a favorable outcome. From any given starting point, the attainability of the end-states resulting from a set of candidate decisions is assessed by propagating a system model forward in time while qualitatively mapping simulated states into margins on strategic objectives using fuzzy inference systems. The expected return value of each candidate decision is evaluated as the product of the assigned value of the end-state with the assessed attainability of the end-state. The candidate decision yielding the highest expected return value is selected for implementation; thus, the approach provides a software framework for intelligent autonomous risk management. The name adopted for the technique incorporates its essential elements: Strategic Objective Valuation and Attainability (SOVA). Maximum value of the approach is realized for systems where human intervention is unavailable in the timeframe within which critical control decisions must be made. The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite, launched in 1999, has been collecting science data for eight years.[1] At its beginning of life, FUSE had six gyros in two IRUs and four reaction wheels. Over time through various failures, the satellite has been left with one reaction wheel on the vehicle skew axis and two gyros. To remain operational, a control scheme has been implemented using the magnetic torque rods and the remaining momentum wheel.[2] As a consequence, there are attitude regions where there is insufficient torque authority to overcome environmental disturbances (e.g. gravity gradient torques). The situation is further complicated by the fact that these attitude regions shift inertially with time as the spacecraft moves through earth s magnetic field during the course of its orbit. Under these conditions, the burden of planning targets and target-to-target slew maneuvers has increased significantly since the beginning of the mission.[3] Individual targets must be selected so that the magnetic field remains roughly aligned with the skew wheel axis to provide enough control authority to the other two orthogonal axes. If the field moves too far away from the skew axis, the lack of control authority allows environmental torques to pull the satellite away from the target and can potentially cause it to tumble. Slew maneuver planning must factor the stability of targets at the beginning and end, and the torque authority at all points along the slew. Due to the time varying magnetic field geometry relative to any two inertial targets, small modifications in slew maneuver timing can make large differences in the achievability of a maneuver.

Heatwole, Scott↗

DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗

DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1996

Conversion-Integration of MSFC Nonlinear Signal Diagnostic Analysis Algorithms for Realtime Execution of MSFC's MPP Prototype System

NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.

Jong, Jen-Yi↗

DOE OSTI2023

Investigation of the Use of Dynamic Probabilistic Risk Assessment Methodologies for Identifying Digital I&C System Common Cause Failures

Digital Instrumentation and Control (I&C) systems have a key role in nuclear power plants in the upgrade of aging analog systems. Digital systems improve plant safety and reliability through features such as increased hardware reliability and stability and improved failure detection capability. There is no consensus on which of the current probabilistic risk assessment methods are most suitable for use in the reliability analysis of digital I&C systems. While the traditional event-tree/fault-tree (ET/FT) approach is still used for their reliability modeling, there are concerns regarding this approach in properly accounting for dynamic interactions among system components since potentially significant dependencies among failure events may not be identified and/or their likelihood may not be properly quantified. Dynamic methodologies are expected to provide a much more accurate representation of probabilistic evolution of the I&C systems in time due to their capability to more properly account for complex interactions than the static approach. The applicability of dynamic PRA methodologies for digital I&C system is investigated using the criteria presented in the NUREG/CR-6901, and the comparisons made in NUREG/CR-6901 are updated in light of the latest studies. The Dynamic Event Tree (DET) approach has been identified as one of the top dynamic methods when evaluated against the requirements for the reliability modeling of digital I&C systems. The DET method is a strong candidate for integration into existing PRA studies, as it bears many similarities to the traditional ET approach. In this study, the DET approach has been applied to the Plant Protection System of the APR1400 design, and the results are compared to results from its available traditional ET/FT analysis. Possible approaches to evaluate and quantify the effects of common cause failures on system safety using dynamic methods are also examined.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1995

An approach to verification and validation of a reliable multicasting protocol: Extended Abstract

This paper describes the process of implementing a complex communications protocol that provides reliable delivery of data in multicast-capable, packet-switching telecommunication networks. The protocol, called the Reliable Multicasting Protocol (RMP), was developed incrementally using a combination of formal and informal techniques in an attempt to ensure the correctness of its implementation. Our development process involved three concurrent activities: (1) the initial construction and incremental enhancement of a formal state model of the protocol machine; (2) the initial coding and incremental enhancement of the implementation; and (3) model-based testing of iterative implementations of the protocol. These activities were carried out by two separate teams: a design team and a V&V team. The design team built the first version of RMP with limited functionality to handle only nominal requirements of data delivery. This initial version did not handle off-nominal cases such as network partitions or site failures. Meanwhile, the V&V team concurrently developed a formal model of the requirements using a variant of SCR-based state tables. Based on these requirements tables, the V&V team developed test cases to exercise the implementation. In a series of iterative steps, the design team added new functionality to the implementation while the V&V team kept the state model in fidelity with the implementation. This was done by generating test cases based on suspected errant or off-nominal behaviors predicted by the current model. If the execution of a test in the model and implementation agreed, then the test either found a potential problem or verified a required behavior. However, if the execution of a test was different in the model and implementation, then the differences helped identify inconsistencies between the model and implementation. In either case, the dialogue between both teams drove the co-evolution of the model and implementation. We have found that this interactive, iterative approach to development allows software designers to focus on delivery of nominal functionality while the V&V team can focus on analysis of off nominal cases. Testing serves as the vehicle for keeping the model and implementation in fidelity with each other. This paper describes (1) our experiences in developing our process model; and (2) three example problems found during the development of RMP. Although RMP has provided our research effort with a rich set of test cases, it also has practical applications within NASA. For example, RMP is being considered for use in the NASA EOSDIS project due to its significant performance benefits in applications that need to replicate large amounts of data to many network sites.

Callahan, John R.↗

NASA NTRS2006

An Efficient Approach for the Reliability Analysis of Phased-Mission Systems with Dependent Failures

We consider the reliability analysis of phased-mission systems with common-cause failures in this paper. Phased-mission systems (PMS) are systems supporting missions characterized by multiple, consecutive, and nonoverlapping phases of operation. System components may be subject to different stresses as well as different reliability requirements throughout the course of the mission. As a result, component behavior and relationships may need to be modeled differently from phase to phase when performing a system-level reliability analysis. This consideration poses unique challenges to existing analysis methods. The challenges increase when common-cause failures (CCF) are incorporated in the model. CCF are multiple dependent component failures within a system that are a direct result of a shared root cause, such as sabotage, flood, earthquake, power outage, or human errors. It has been shown by many reliability studies that CCF tend to increase a system's joint failure probabilities and thus contribute significantly to the overall unreliability of systems subject to CCF.We propose a separable phase-modular approach to the reliability analysis of phased-mission systems with dependent common-cause failures as one way to meet the above challenges in an efficient and elegant manner. Our methodology is twofold: first, we separate the effects of CCF from the PMS analysis using the total probability theorem and the common-cause event space developed based on the elementary common-causes; next, we apply an efficient phase-modular approach to analyze the reliability of the PMS. The phase-modular approach employs both combinatorial binary decision diagram and Markov-chain solution methods as appropriate. We provide an example of a reliability analysis of a PMS with both static and dynamic phases as well as CCF as an illustration of our proposed approach. The example is based on information extracted from a Mars orbiter project. The reliability model for this orbiter considers the various phases of Launch, Cruise, Mars Orbit Insertion, and Orbit. Some of the CCF for the orbiter in this mission include environmental effects, such as micrometeoroids, human operator errors, and software errors.

reliability analysis↗

NASA NTRS2006

STRANAL-PMC Version 2.0

Version 2.0 of the Strain Rate Dependent Analysis of Polymer Matrix Composites (STRANAL-PMC) software has been released. A prior version was reported in Analyzing Loads and Strains in Polymer- Matrix Composites (LEW-17227), NASA Tech Briefs, Vol. 26, No. 11 (November 2002), page 36. To recapitulate: Modified versions of constitutive equations of viscoplasticity of metals are used to represent deformation of a polymeric matrix. The equations are applied in a micromechanical approach, proceeding upward from slices of unit cells, through the ply level, to the laminate level. The constitutive equations are integrated in time by a Runge- Kutta technique. To predict the ultimate strength of each composite ply, failure criteria are implemented within the micromechanics. The inputs to STRANAL-PMC are the laminate geometry, properties of the fiber and matrix materials, and applied stress or strain versus time. The outputs are time-dependent stresses and strains at the slice, ply, and laminate levels. The improvements in version 2.0 include more rigorous representation of hydrostatic- stress effects in the matrix, refinement and extension of ply failure models, and capabilities to analyze transverse shear stresses. Version 2.0 can be implemented as a material-model code within transient dynamic finite-element codes.

Goldberg, Robert↗

DOE OSTI2016

SU-F-P-07: Applying Failure Modes and Effects Analysis to Treatment Planning System QA

Purpose: A small-scale implementation of Failure Modes and Effects Analysis (FMEA) for treatment planning system QA by utilizing methodology of AAPM TG-100 report. Methods: FMEA requires numerical values for severity (S), occurrence (O) and detectability (D) of each mode of failure. The product of these three values gives a risk priority number (RPN). We have implemented FMEA for the treatment planning system (TPS) QA for two clinics which use Pinnacle and Eclipse TPS. Quantitative monthly QA data dating back to 4 years for Pinnacle and 1 year for Eclipse have been used to determine values for severity (deviations from predetermined doses at points or volumes), and occurrence of such deviations. The TPS QA protocol includes a phantom containing solid water and lung- and bone-equivalent heterogeneities. Photon and electron plans have been evaluated in both systems. The dose values at multiple distinct points of interest (POI) within the solid water, lung, and bone-equivalent slabs, as well as mean doses to several volumes of interest (VOI), have been re-calculated monthly using the available algorithms. Results: The computed doses vary slightly month-over-month. There have been more significant deviations following software upgrades, especially if the upgrade involved re-modeling of the beams. TG-100 guidance and the data presented here suggest an occurrence (O) of 2 depending on the frequency of re-commissioning the beams, severity (S) of 3, and detectability (D) of 2, giving an RPN of 12. Conclusion: Computerized treatment planning systems could pose a risk due to dosimetric errors and suboptimal treatment plans. The FMEA analysis presented here suggests that TPS QA should immediately follow software upgrades, but does not need to be performed every month.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Comprehensive uncertainty quantification (UQ) for full engineering models by solving probability density function (PDF) equation

This report details a new method for propagating parameter uncertainty (forward uncertainty quantification) in partial differential equations (PDE) based computational mechanics applications. The method provides full-field quantities of interest by solving for the joint probability density function (PDF) equations which are implied by the PDEs with uncertain parameters. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known apriori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the driving (dynamics) term of the PDEs based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the dynamics term of the governing PDEs. The efficacy of the method is demonstrated by applying it to two target problems: solid mechanics explicit dynamics with uncertain material model parameters, and reacting hypersonic fluid mechanics with uncertain chemical kinetic rate parameters. A minimally invasive implementation of the method for representative codes SPARC (reacting hypersonics) and NimbleSM (finite- element solid mechanics) and associated software details are described. For solid mechanics demonstration problems the method shows order of magnitudes improvement in accuracy over traditional stochastic collocation. For the reacting hypersonics problem, the method is implemented as a streamline integration and results show very good accuracy for the approximate sample solutions of re-entry flow past the Apollo capsule geometry at Mach 30.

42 ENGINEERING↗

DOE OSTI2022

Quantitative assessment of particle dispersion in polymeric composites and its effect on mechanical properties

In this work, an automated image analysis tool is developed to establish quantitative correlations between the particle/cluster size distribution and the mechanical properties of particle reinforced polymer composites (PRPC).This automated image analysis tool is developed within python programming software to process and analyze the microstructural images of the polymerbased composite materials. The spent coffee bean powder (SCBP) reinforced poly-propylene carbonate (PPC) polymer composite with differing wt.% of the filler is selected for the analysis. Detailed statistical analysis of the microstructural images reveals that ‘clustering of clusters’ is also presented in addition to the most commonly reported ‘clustering of particles’, and the distribution of particle/clusters is bimodal. Based on these findings, an effective volume fraction for the filler material is proposed to mainly capture the agglomeration effect. With this effective volume fraction, the standard rule-of-mixture model correctly captures the experimentally measured tensile strength and modulus as a function of filler wt.%. Further, the applicability of this effective volume fraction for other theoretical models is also analyzed. The detailed statistical analysis of the microstructure and the proposed effective volume fraction helps to develop a deeper quantitative understanding of the PRPC than the conventional qualitative correlation of microstructural features with the properties and failure processes.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Distribution of Inclusion-Initiated Fatigue Cracking in Powder Metallurgy Udimet 720 Characterized

In the absence of extrinsic surface damage, the fatigue life of metals is often dictated by the distribution of intrinsic defects. In powder metallurgy (PM) alloys, relatively large defects occur rarely enough that a typical characterization with a limited number of small volume fatigue test specimens will not adequately sample inclusion-initiated damage. Counterintuitively, inclusion-initiated failure has a greater impact on the distribution in PM alloy fatigue lives because they tend to have fewer defects than their cast and wrought counterparts. Although the relative paucity of defects in PM alloys leads to higher mean fatigue lives, the distribution in observed lives tends to be broader. In order to study this important failure initiation mechanism without expending an inordinate number of specimens, a study was undertaken at the NASA Glenn Research Center where known populations of artificial inclusions (seeds) were introduced to production powder. Fatigue specimens were machined from forgings produced from the seeded powder. Considerable effort has been expended in characterizing the crack growth rate from inclusion-initiated cracks in seeded PM alloys. A rotating and translating positioning system, with associated software, was devised to map the surface inclusions in low-cycle fatigue (LCF) test bars and to monitor the crack growth from these inclusions. The preceding graph illustrates the measured extension in fatigue cracks from inclusions on a seeded LCF test bar subjected to cyclic loading at a strain range of 0.8 percent and a strain ratio (max/min) of zero. Notice that the observed inclusions fall into three categories: some do not propagate at all (arrest), some propagate with a decreasing crack growth rate, and a few propagate at increasing rates that can be modeled by fracture mechanics. The following graph shows the measured inclusion-initiated crack growth rates from 10 interrupted LCF tests plotted against stress intensities calculated for semi-elliptical cracks with the observed surface lengths. The expected scatter in the crack growth rates for stress intensity ranges near threshold is observed. These data will be used to help determine the distribution in growth rates of cracks emanating from inclusions as well as the proportion of cracks that arrest under various loading conditions.

Bonacuse, Peter J.↗

NASA NTRS2001

LabHEART: an interactive computer model of rabbit ventricular myocyte ion channels and Ca transport

An interactive computer program, LabHEART, was developed to simulate the action potential (AP), ionic currents, and Ca handling mechanisms in a rabbit ventricular myocyte. User-oriented, its design allows switching between voltage and current clamp and easy on-line manipulation of key parameters to change the original formulation. The model reproduces normal rabbit ventricular myocyte currents, Ca transients, and APs. We also changed parameters to simulate data from heart failure (HF) myocytes, including reduced transient outward (I(to)) and inward rectifying K currents (I(K1)), enhanced Na/Ca exchange expression, and reduced sarcoplasmic reticulum Ca-ATPase function, but unaltered Ca current density. These changes caused reduced Ca transient amplitude and increased AP duration (especially at lower frequency) as observed experimentally. The model shows that the increased Na/Ca exchange current (I(NaCa)) in HF lowers the intracellular [Ca] threshold for a triggered AP from 800 to 540 nM. Similarly, the decrease in I(K1) reduces the threshold to 600 nM. Changes in I(to) have no effect. Combining enhanced Na/Ca exchange with reduced I(K1) (as in HF) lowers the threshold to trigger an AP to 380 nM. These changes reproduce experimental results in HF, where the contributions of different factors are not readily distinguishable. We conclude that the triggered APs that contribute to nonreentrant ventricular tachycardia in HF are due approximately equally (and nearly additively) to alterations in I(NaCa) and I(K1). A free copy of this software can be obtained at http://www.meddean.luc.edu/lumen/DeptWebs/physio/bers.html.

NASA Discipline Cardiopulmonary↗