NESSUS/NASTRAN Interface

The NESSUS and NASTRAN computer codes were successfully integrated. The enhanced NESSUS code will use NASTRAN for the structural Analysis and NESSUS for the probabilistic analysis. Any quantities in the NASTRAN bulk data input can be random variables. Any NASTRAN result that is written to the output2 file can be returned to NESSUS as the finite element result. The interfacing between NESSUS and NASTRAN is handled automatically by NESSUS. NESSUS and NASTRAN can be run on different machines using the remote host option

NESSUS/NASTRAN Interface

The NESSUS probabilistic analysis computer program has been developed with a built-in finite element analysis program NESSUS/FEM. However, the NESSUS/FEM program is specialized for engine structures and may not contain sufficient features for other applications. In addition, users often become well acquainted with a particular finite element code and want to use that code for probabilistic structural analysis. For these reasons, this work was undertaken to develop an interface between NESSUS and NASTRAN such that NASTRAN can be used for the finite element analysis and NESSUS can be used for the probabilistic analysis. In addition, NESSUS was restructured such that other finite element codes could be more easily coupled with NESSUS. NESSUS has been enhanced such that NESSUS will modify the NASTRAN input deck for a given set of random variables, run NASTRAN and read the NASTRAN result. The coordination between the two codes is handled automatically. The work described here was implemented within NESSUS 6.2 which was delivered to NASA in September 1995. The code runs on Unix machines: Cray, HP, Sun, SGI and IBM. The new capabilities have been implemented such that a user familiar with NESSUS using NESSUS/FEM and NASTRAN can immediately use NESSUS with NASTRAN. In other words, the interface with NASTRAN has been implemented in an analogous manner to the interface with NESSUS/FEM. Only finite element specific input has been changed. This manual is written as an addendum to the existing NESSUS 6.2 manuals. We assume users have access to NESSUS manuals and are familiar with the operation of NESSUS including probabilistic finite element analysis. Update pages to the NESSUS PFEM manual are contained in Appendix E. The finite element features of the code and the probalistic analysis capabilities are summarized

Triboelectric Sensor – Application and Characterization

This work describes advantages and possibilities of use triboelectric sensors. Sensor is possible to use for estimation applied force and movement (displacement). It opens door to wide spectrum of use, finally the sensor could be used for fitness applications especially for estimations of any applied force

Model Verification and Validation Concepts for a Probabilistic Fracture Assessment Model to Predict Cracking of Knife Edge Seals in the Space Shuttle Main Engine High Pressure Oxidizer

Physics-based models are routinely used to predict the performance of engineered systems to make decisions such as when to retire system components, how to extend the life of an aging system, or if a new design will be safe or available. Model verification and validation (V&V) is a process to establish credibility in model predictions. Ideally, carefully controlled validation experiments will be designed and performed to validate models or submodels. In reality, time and cost constraints limit experiments and even model development. This paper describes elements of model V&V during the development and application of a probabilistic fracture assessment model to predict cracking in space shuttle main engine high-pressure oxidizer turbopump knife-edge seals. The objective of this effort was to assess the probability of initiating and growing a crack to a specified failure length in specific flight units for different usage and inspection scenarios. The probabilistic fracture assessment model developed in this investigation combined a series of submodels describing the usage, temperature history, flutter tendencies, tooth stresses and numbers of cycles, fatigue cracking, nondestructive inspection, and finally the probability of failure. The analysis accounted for unit-to-unit variations in temperature, flutter limit state, flutter stress magnitude, and fatigue life properties. The investigation focused on the calculation of relative risk rather than absolute risk between the usage scenarios. Verification predictions were first performed for three units with known usage and cracking histories to establish credibility in the model predictions. Then, numerous predictions were performed for an assortment of operating units that had flown recently or that were projected for future flights. Calculations were performed using two NASA-developed software tools: NESSUS(Registered Trademark) for the probabilistic analysis, and NASGRO(Registered Trademark) for the fracture mechanics analysis. The goal of these predictions was to provide additional information to guide decisions on the potential of reusing existing and installed units prior to the new design certification

Relevance of Personal Interaction Factors between Customers and Sales Representatives in the Automotive Business

The purpose of this paper is to determine the personal factors in sales that are most relevant to the formation of positive consumer impressions and resulting sales effectiveness. The research enables scholars and practitioners to better understand the personal sales process and develop more efficient sales approaches. The study utilized an observational design methodology. Personal selling scenarios were filmed and shown to respondents who completed a consumer impression and sales effectiveness survey. Video content was also coded for statistical analysis. The resulting data was analysed via a multiple-regression model. A conceptual personal sales model was developed, and the model suggests that salesperson authenticity impacts consumer impressions and resulting sales effectiveness. Authenticity is shown to be a function of a given salesperson’s affective, behavioural, and cognitive profile. The study focused on automotive retail sales scenarios in the United States. However, results may be generalizable to similar markets. Practical guidelines and a scholarly research foundation are provided, research gaps identified, and proposals for future research suggested

Final design report of a personnel launch system and a family of heavy lift launch vehicles

The objective was to design both a Personnel Launch System (PLS) and a family of Heavy Lift Launch Vehicles (FHLLVs) that provide low cost and efficient operation in missions not suited for the Shuttle. The PLS vehicle is designed primarily for space station crew rotation and emergency crew return. The final design of the PLS vehicle and its interior is given. The mission of the FHLLVs is to place large, massive payloads into Earth orbit with payload flexibility being considered foremost in the design. The final design of three launch vehicles was found to yield a payload capacity range from 20 to 200 mt. These designs include the use of multistaged, high thrust liquid engines mounted on the core stages of the rocket

Probabilistic Modeling of Space Shuttle Debris Impact

On Feb 1, 2003, the Shuttle Columbia was lost during its return to Earth. As a result of the conclusion that debris impact caused the damage to the left wing of the Columbia Space Shuttle Vehicle (SSV) during ascent, the Columbia Accident Investigation Board recommended that an assessment be performed of the debris environment experienced by the SSV during ascent. A flight rationale based on probabilistic assessment is used for the SSV return-to-flight. The assessment entails identifying all potential debris sources, their probable geometric and aerodynamic characteristics, and their potential for impacting and damaging critical Shuttle components. A probabilistic analysis tool, based on the SwRI-developed NESSUS probabilistic analysis software, predicts the probability of impact and damage to the space shuttle wing leading edge and thermal protection system components. Among other parameters, the likelihood of unacceptable damage depends on the time of release (Mach number of the orbiter) and the divot mass as well as the impact velocity and impact angle. A typical result is visualized in the figures below. Probability of impact and damage, as well as the sensitivities thereof with respect to the distribution assumptions, can be computed and visualized at each point on the orbiter or summarized per wing panel or tile zone

Stability of gametocyte-specific Pfs25-mRNA in dried blood spots on filter paper subjected to different storage conditions

Background: Real-time quantitative nucleic acid sequence-based amplification (QT-NASBA) is a sensitive method for detection of sub-microscopic gametocytaemia by measuring gametocyte-specific mRNA. Performing analysis on fresh whole blood samples is often not feasible in remote and resource-poor areas. Convenient methods for sample storage and transport are urgently needed. Methods: Real-time QT-NASBA was performed on whole blood spiked with a dilution series of purified in-vitro cultivated gametocytes. The blood was either freshly processed or spotted on filter papers. Gametocyte detection sensitivity for QT-NASBA was determined and controlled by microscopy. Dried blood spot (DBS) samples were subjected to five different storage conditions and the loss of sensitivity over time was investigated. A formula to approximate the loss of Pfs25-mRNA due to different storage conditions and time was developed. Results: Pfs25-mRNA was measured in time to positivity (TTP) and correlated well with the microscopic counts and the theoretical concentrations of the dilution series. TTP results constantly indicated higher amounts of RNA in filter paper samples extracted after 24 hours than in immediately extracted fresh blood. Among investigated storage conditions freezing at -20 degrees C performed best with 98.7% of the Pfs25-mRNA still detectable at day 28 compared to fresh blood samples. After 92 days, the RNA detection rate was only slightly decreased to 92.9%. Samples stored at 37 degrees C showed most decay with only 64.5% of Pfs25-mRNA detectable after one month. The calculated theoretical detection limit for 24 h-old DBS filter paper samples was 0.0095 (95% CI: 0.0025 to 0.0380) per mu l. Conclusions: The results suggest that the application of DBS filter papers for quantification of Plasmodium falciparum gametocytes with real-time QT-NASBA is practical and recommendable. This method proved sensitive enough for detection of sub-microscopic densities even after prolonged storage. Decay rates can be predicted for different storage conditions as well as durations

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment

Probabilistic Structural Analysis Program

NASA/NESSUS 6.2c is a general-purpose, probabilistic analysis program that computes probability of failure and probabilistic sensitivity measures of engineered systems. Because NASA/NESSUS uses highly computationally efficient and accurate analysis techniques, probabilistic solutions can be obtained even for extremely large and complex models. Once the probabilistic response is quantified, the results can be used to support risk-informed decisions regarding reliability for safety-critical and one-of-a-kind systems, as well as for maintaining a level of quality while reducing manufacturing costs for larger-quantity products. NASA/NESSUS has been successfully applied to a diverse range of problems in aerospace, gas turbine engines, biomechanics, pipelines, defense, weaponry, and infrastructure. This program combines state-of-the-art probabilistic algorithms with general-purpose structural analysis and lifting methods to compute the probabilistic response and reliability of engineered structures. Uncertainties in load, material properties, geometry, boundary conditions, and initial conditions can be simulated. The structural analysis methods include non-linear finite-element methods, heat-transfer analysis, polymer/ceramic matrix composite analysis, monolithic (conventional metallic) materials life-prediction methodologies, boundary element methods, and user-written subroutines. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. NASA/NESSUS 6.2c is structured in a modular format with 15 elements