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The objective of this presentation is to discuss the PRA process and the reliability engineering discipline, their differences and similarities, and how they are used as complimentary analyses to support design and flight decisions.
The U.S. nuclear industry is facing a challenge in maintaining required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects of light-water reactor nuclear power plant operations. Safety can become more economical by using a risk-informed ecosystem, such as the one being developed in the Risk-Informed Systems Analysis Pathway under the U.S. Department of Energy Light Water Reactor Sustainability Program. The Light Water Reactor Sustainability Program promotes a wide range of research and development activities to maximize both the safety and economic efficiency of nuclear power plants through improved scientific understanding, especially given that many plants are now considering second license renewals. The Risk-Informed Systems Analysis Pathway has two main goals: Deploy methodologies and technologies that better represent safety margins and cost and safety factors; Develop advanced applications that enable cost-effective plant operations. The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. This report summarizes genetic-algorithm-based multi-objective fuel reload optimization activities, specifically: Developing the non-dominated sorting genetic algorithm II optimizer in the Risk Analysis and Virtual ENviroment (RAVEN); Demonstrating and validating the developed non-dominated sorting genetic algorithm II optimizer using benchmark optimization problems.
Under the NASA Aeronautics Research Mission Directorate (ARMD) Convergent Aeronautical Solutions (CAS) project, NASA Glenn Research Center has been leading Multifunctional Structures for High Energy Lightweight Load-bearing Storage (M-SHELLS) research efforts. The technology of integrating load-carrying structures with electrical energy storage capacity has the potential to reduce the overall weight of future electric aircraft. The proposed project goals were to develop M-SHELLS in the form of honeycomb coupons and subcomponents, integrate them into the structure, and conduct low-risk flight tests onboard a remotely piloted small aircraft. Experimental M-SHELLS energy-storing coupons were fabricated and tested in the laboratory for their electrical and mechanical properties. In this paper, finite element model development and structural analyses of two small test aircraft candidates are presented. The finite element analysis of the initial two-spar wing is described for strain, deflection, and weight estimation. After a test aircraft Tempest was acquired, a load- deflection test of the wing was conducted. A finite element model of the Tempest was then developed based on the test aircraft dimensions and construction detail. The component weight analysis from the finite element model and test measurements were correlated. Structural analysis results with multifunctional energy storage panels in the fuselage of the test vehicle are presented. Although the flight test was cancelled because of programmatic reasons and time constraints, the structural analysis results indicate that the mid-fuselage floor composite panel could provide structural integrity with minimal weight penalty while supplying electrical energy. To explore potential future applications of the multifunctional structure, analyses of the NASA X-57 Maxwell electric aircraft and a NASA N+3 Technology Conventional Configuration (N3CC) fuselage are presented. Secondary aluminum structure in the fuselage sub-floor and cargo area were partially replaced with reinforced five-layer composite panels with M-SHELLS honeycomb core. The N3CC fuselage weight reduction associated with each design without risking structural integrity are described. The structural analysis and weight estimation with the application of composite M-SHELLS panels to the N3CC fuselage indicate a 3.2% reduction in the fuselage structural weight, prior to accounting for the additional weight of core material required to complete the energy storage functionality.
Under the NASA Aeronautics Research Mission Directorate (ARMD) Convergent Aeronautical Solutions (CAS) project, NASA Glenn Research Center has been leading Multifunctional Structures for High Energy Lightweight Load-bearing Storage (M-SHELLS) research efforts. The technology of integrating load-carrying structures with electrical energy storage capacity has the potential to reduce the overall weight of future electric aircraft. The proposed project goals were to develop M-SHELLS in the form of honeycomb coupons and subcomponents, integrate them into the structure, and conduct low-risk flight-tests onboard a remotely piloted small aircraft. Experimental M-SHELLS energy-storing coupons were fabricated and tested in the laboratory for their electrical and mechanical properties. In this report, finite element model development and structural analyses of two small test aircraft candidates are presented. The finite element analysis of the initial two-spar wing is described for strain, deflection, and weight estimation. After a test aircraft Tempest was acquired, a load-deflection test of the wing was conducted. A finite element model of the Tempest was then developed based on the test aircraft dimensions and construction detail. The component weight analyses from the finite element model and test measurements were correlated. Structural analysis results with multifunctional energy storage panels in the fuselage of the test vehicle are presented. Although the flight test was cancelled because of programmatic reasons and time constraints, the structural analysis results indicate that the mid-fuselage floor composite panel could provide structural integrity with minimal weight penalty while supplying electrical energy. To explore potential future applications of the multifunctional structure, analyses of the NASA X-57 Maxwell electric aircraft and a NASA N+3 Technology Conventional Configuration (N3CC) fuselage are presented. Secondary aluminum structures in the fuselage sub-floor and cargo area were partially replaced with reinforced five-layer composite panels with M-SHELLS honeycomb core. The N3CC fuselage weight reduction associated with each design without risking structural integrity are described. The structural analysis and weight estimation with the application of composite M-SHELLS panels to the N3CC fuselage indicate a 3.2% reduction in the fuselage structural weight, prior to accounting for the additional weight of core material required to complete the energy storage functionality.
Both the conventional and reliability analyses for determining safe fatigue life are predicted on a population having a specified (usually log normal) distribution of life to collapse under a fatigue test load. Under a random service load spectrum, random occurrences of load larger than the fatigue test load may confront and cause collapse of structures which are weakened, though not yet to the fatigue test load. These collapses are included in reliability but excluded in conventional analysis. The theory of risk determination by each method is given, and several reasonably typical examples have been worked out, in which it transpires that if one excludes collapse through exceedance of the uncracked strength, the reliability and conventional analyses gave virtually identical probabilities of failure or survival.
NASA’s Orbital Debris Program Office (ODPO) and Hypervelocity Impact Technology (HVIT) team have coordinated to better understand the risks to upper stages and spacecraft from non-spherical orbital debris. It is well understood that fragmentation (collision or explosion) events in orbit produce fragments of various materials, sizes, and shapes. To further characterize these parameters, the ODPO is developing the next-generation Orbital Debris Engineering Model (ORDEM) version 4.0 to include orbital debris shape distributions. Ground-based assets, such as radar and optical sensors, can provide size estimates and some insight into material based on radar return or optical filter photometry/spectroscopy, respectively. Characterizing an object’s shape requires more laboratory analyses to infer how shape affects these measurements. More importantly, in addition to size and material/density, the shape of fragments in orbit will alter the ballistic limit equations used in orbital debris risk assessments with NASA’s Bumper Code. The ODPO plans to release ORDEM 4.0 in the coming years. Performing ground-based laboratory impact tests on high-fidelity spacecraft mockups provides the means to directly measure size, mass, material/density, and shape of fragments, all key parameters needed to characterize real-world break up events. The DebriSat test, the results of which are provided, showcases the details of this type of experiment. The goal of this collaborative research between the ODPO and the HVIT team is to include a shape parameter in the environmental and breakup models used to assess risk for various space structures. This paper examines ground-based laboratory impact tests and the associated fragment shape categories. Provided these defined shapes, the approach is simplified by assuming a right circular cylinder (RCC) approximation with varying length-to-diameter ratios. Highlights of impact tests conducted by the HVIT team using non-spherical projectiles based on the RCC approximation are presented. Hydrocode simulations have also been performed to expand on the complexity of variations with non-spherical projectiles. Lastly, ray-tracing simulations of various RCCs of known material are provided to support the ongoing research on optical reflectance distributions with known shapes and to highlight how this may modify the current optical size estimation model. The status and plan forward are outlined for NASA's orbital debris shape effect investigation using a multidisciplinary approach by the ODPO and the HVIT team.
NASA’s Orbital Debris Program Office (ODPO) and Hypervelocity Impact Technology (HVIT) team have coordinated to better understand the risks to upper stages and spacecraft from non-spherical orbital debris. It is well understood that fragmentation (collision or explosion) events in orbit produce fragments of various materials, sizes, and shapes. To further characterize these parameters, the ODPO is developing the next-generation Orbital Debris Engineering Model (ORDEM) version 4.0 to include orbital debris shape distributions. Ground-based assets, such as radar and optical sensors, can provide size estimates and some insight into material based on radar return or optical filter photometry/spectroscopy, respectively. Characterizing an object’s shape requires more laboratory analyses to infer how shape affects these measurements. More importantly, in addition to size and material/density, the shape of fragments in orbit will alter the ballistic limit equations used in orbital debris risk assessments with NASA’s Bumper Code. The ODPO plans to release ORDEM 4.0 in the coming years. Performing ground-based laboratory impact tests on high-fidelity spacecraft mockups provides the means to directly measure size, mass, material/density, and shape of fragments, all key parameters needed to characterize real-world break up events. The DebriSat test, the results of which are provided, showcases the details of this type of experiment. The goal of this collaborative research between the ODPO and the HVIT team is to include a shape parameter in the environmental and breakup models used to assess risk for various space structures. This paper examines ground-based laboratory impact tests and the associated fragment shape categories. Provided these defined shapes, the approach is simplified by assuming a right circular cylinder (RCC) approximation with varying length-to-diameter ratios. Highlights of impact tests conducted by the HVIT team using non-spherical projectiles based on the RCC approximation are presented. Hydrocode simulations have also been performed to expand on the complexity of variations with non-spherical projectiles. Lastly, ray-tracing simulations of various RCCs of known material are provided to support the ongoing research on optical reflectance distributions with known shapes and to highlight how this may modify the current optical size estimation model. The status and plan forward are outlined for NASA's orbital debris shape effect investigation using a multidisciplinary approach by the ODPO and the HVIT team.
Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.
One of the key components of the Advanced Launch System (ALS) is a large liquid rocket, booster engine. To keep the overall vehicle size and cost down, this engine will probably use liquid oxygen (LOX) and a heavy hydrocarbon, such as RP-1, as propellants and operate at relatively high chamber pressures to increase overall performance. A technology program (Heavy Hydrocarbon Main Injector Technology) is being studied. The main objective of this effort is to develop a logic plan and supporting experimental data base to reduce the risk of developing a large scale (approximately 750,000 lb thrust), high performance main injector system. The overall approach and program plan, from initial analyses to large scale, two dimensional combustor design and test, and the current status of the program are discussed. Progress includes performance and stability analyses, cold flow tests of injector model, design and fabrication of subscale injectors and calorimeter combustors for performance, heat transfer, and dynamic stability tests, and preparation of hot fire test plans. Related, current, high pressure, LOX/RP-1 injector technology efforts are also briefly discussed.
The missions of the National Aeronautics and Space Administration (NASA) routinely produce unique requirements and challenges for development and application of Computational Fluid Dynamics (CFD) methods. NASA presently embodies four distinct Mission Directorates: Aeronautics Research, Exploration Systems, Science, and Space Operations. These missions generate requirements for systems that operate in a wide variety of environments. They range from the high-speed flight of aerodynamically optimized vehicles operating in the earth’s atmosphere to spacecraft designed for missions that don’t favor aerodynamic optimization, some operating in the atmosphere of planets and planetary moons such as Mars and Venus or Saturn’s moon Titan. Systems supporting these vehicles, such as rocket and jet propulsion, reaction control systems, fluid and thermal transfer systems, etc. can also generate their own unique set of flow phenomena that challenge today’s CFD methodology. Through the NASA Engineering and Safety Center (NESC), NASA annually conducts state-of-the-discipline assessments in fifteen distinct engineering disciplines. These assessments are performed by the NASA Technical Fellows that lead Technical Discipline Teams (TDT) of recognized experts in these fifteen areas. In the Aerosciences discipline, three topics have been identified as the top challenges for the discipline: aero-plume interaction prediction, unsteady separated flows, and aerothermodynamic prediction. These challenge areas are defined by the Agency’s high-risk projects and problems on which the NESC is requested to perform independent testing, analysis, and assessments. When viewed as a whole, these tests, analyses, and assessments provide a clear view of the recurring technical challenges facing Agency engineers and researchers and can be used to guide future research and technology development. The present state-of-the-art in the application of CFD at NASA is the use of Reynolds-Averaged Navier- Stokes (RANS) solvers, primarily executed in a steady-state mode of operation. In isolated cases, Unsteady RANS (URANS) solvers have been employed when steady RANS solutions produce poorly converging or oscillating results or in cases, such as aeroelastic analysis, which require unsteady aerodynamic simulation. For most traditional external and internal aerodynamic flows, structured overset grids or unstructured grids are employed to minimize geometric modeling and grid generation times. Grid adaptation, primarily as a series of coarse-grain intermediate processing steps is also seeing use on particularly complex flow problems and configurations. In the case of aerothermodynamic flows, engineers have been forced to continue to employ structured grid techniques as the present unstructured grid methodology has proven inadequate in the prediction of surface heating. In the area of aero-plume interaction modeling, two-gas, frozen chemistry simulation is generally the state-of-the- art, with some production solvers capable of predicting flows with only a single gas component. Prediction of flows falling into the afore-mentioned top Aerosciences technical challenges have severely stressed the present state-of-the-art in CFD prediction and for some problems, such as unsteady separated flows and aero-plume interaction cases, engineers have begun employing Large Eddy Simulation (LES) and Hybrid RANS/LES techniques. In some isolated aero-propulsion interaction cases, chemically reacting flow simulations have been applied. These methods are highly evolutionary and engineers have little experience in their application, so they cannot be heavily relied upon in today’s application environment. Therefore, this leads one to muse over which numerical technologies will be included in the CFD tools that will be employed 30 years in the future. This presentation will describe specific technical problems that have stressed NASA’s traditional CFD methods to their breaking point and will link these issues to the Agency’s top Aerosciences technical challenges. The discussion will then shift to the characteristics of future CFD solvers that will be required to attack these challenges and how these characteristics differ from the present state-of-the- art. High-order methods certainly appear to have a place in the development of future CFD tools and some of the physical characteristics of our most challenging problems suggest that high-order methods are the only way to effectively solve them. But there are some relatively severe implementation issues that face these methods, particularly in the area of general applicability and robust operation as an engineering tool. Desired characteristics of next-generation CFD solvers will be discussed and the author’s view of which emerging numerical technologies might be employed to address these attributes will also be presented
In 2007, a JPL Rapid Mission Architecture (RMA) analysis team identified and evaluated a broad set of mission architecture options for a suite of scientific exploration objectives targeting the Saturnian moon Enceladus. Primary science objectives were largely focused on examination of the driving mechanisms and extent of interactions by the plumes of Enceladus recently discovered by Cassini. Investigation of the architectural trade space spanned a wide range of options, from high-energy flybys of Enceladus as a re-instrumented expansion on the Cassini mission, to more complex, multi-element combinations of Enceladus orbiters carrying multiple variants of in-situ deployable systems. Trajectory design emerged as a critical element of the mission concepts, enabling challenging missions on Atlas V and Delta IV-Heavy class launch vehicles. Various Enceladus Flagship-class mission concepts identified were analyzed and compared against several first-order figures of merit, including mass, cost, risk, mission timeline, and associated science value with respect to accomplishment of the full set of science objectives. Results are presented for these comparative analyses and the characterization of the explored trade space.
The potential to accelerate the processing of low- and high-level tank waste by applying real-time monitoring (RTM) of chemical and physical properties has prompted research into the suitability of multiple analytical methods for that purpose. The broad variety of waste stream properties and the large number of analytes of interest (as evidenced by Waste Acceptance Criteria (WAC) and Process Control Limit (PCL) lists) lead to an overwhelming set of possible analytical scenarios. This report describes the use of Best Basis Inventory (BBI) data to find the most relevant analytical targets for the specific case of monitoring the blending of High Level Waste from multiple tanks prior to introduction into a vitrification facility. Campaigns for blending this waste to minimize the risk of exceeding WACs and PCLs have been proposed. However, the predicted compositions of the blended materials do not incorporate any uncertainties that may be associated with the representativeness of the waste layer samples or the laboratory analyses that generated the BBI data. Also, they do not include any uncertainty associated with the precision of collecting highly specific fractions of the layers during a blending campaign or any inhomogeneities that may exist in those layers. Monte Carlo methods are used to apply uncertainties to the compositions of the individual layers specified in the campaign recipes. The resulting variations in the compositions of the blended materials allow estimation of the risks of exceeding WACs and PCLs for each campaign. A critical subset of WACs/PCLs – NOx, NaK, AlFeZr, and S – are especially at risk of being exceeded in multiple campaigns. These analytes should be the focus of instrument development. We also have extracted the expected solid/supernate distribution for these analytes, which establishes important performance criteria for individual analytical methods. The BBI data also permits an understanding of the different chemical forms in which the analytes appear. Thus, the need to establish instrumental sensitivity to these forms can be gainfully addressed. Although concentrating on one specific application – the blending of tank waste - this approach should be generalizable for the analysis of other possible RTM applications for waste processing.
A variety of static electron radiation belt models, space weather prediction tools, and energetic electron datasets are used by spacecraft designers and operations support personnel as internal charging code inputs to evaluate electrostatic discharge risks in space systems due to exposure to relativistic electron environments. Evaluating the environment inputs is often accomplished by comparing whether the data set or forecast tool reliability predicts measured electron flux (or fluence over a given period) for some chosen period. While this technique is useful as a model metric, it does not provide the information necessary to evaluate whether short term deviances of the predicted flux is important in the charging evaluations. In this paper, we use a 1-D internal charging model to compute electric fields generated in insulating materials as a function of time when exposed to relativistic electrons in the Earth's magnetosphere. The resulting fields are assumed to represent the "true" electric fields and are compared with electric field values computed from relativistic electron environments derived from a variety of space environment and forecast tools. Deviances in predicted fields compared to the "true" fields which depend on insulator charging time constants will be evaluated as a potential metric for determining the importance of predicted and measured relativistic electron flux deviations over a range of time scales.
The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.
NASA’s risk classification system dates back to an era when every new NASA space mission was a one-of-a-kind build, and the only way to obtain reliability was as a by-product through a combination of reliability analyses, extensive and stringent quality requirements, and extensive testing. Originally, there were very limited commercial capabilities to develop systems to work reliably in space, so NASA considered its own homegrown approach the only recipe for success. This approach involved very detailed and prescriptive piece-part controls and no reliance on (and to some extent a rejection of) any type of commercial practices. Often risk was considered to be the lowest when NASA had the maximum amount of control and prescription, and the highest when commercial practices were largely employed, and these principles drove risk classification in the agency. Over time, however, commercial capabilities grew, and many products became standardized and commercialized, while the agency maintained its tried-and-true approach, paying little attention to the evolution of the commercial sector. This presentation introduces GSFC’s new approach at risk classification, which includes classification of components and full spacecraft, as well as of constellations of spacecraft.
NASA's risk classification system dates back to an era when every new NASA space mission was a one-of-a-kind build, and the only way to obtain reliability was as a by-product through a combination of reliability analyses, extensive and stringent quality requirements, and extensive testing. Originally, there were very limited commercial capabilities to develop systems to work reliably in space, so NASA considered its own homegrown approach the only recipe for success. This approach involved very detailed and prescriptive piece-part controls and no reliance on (and to some extent a rejection of) any type of commercial practices. Often risk was considered to be the lowest when NASA had the maximum amount of control and prescription, and the highest when commercial practices were largely employed, and these principles drove risk classification in the agency. Over time, however, commercial capabilities grew, and many products became standardized and commercialized, while the agency maintained its tried-and-true approach, paying little attention to the evolution of the commercial sector. In fact, the commercial sector was developing systems that have direct, proven reliability, established over time, while NASA still maintained the approach to ignore the reality of the commercialized aspects of standard products, label them as high risk, and attempt to change them to align with the agency's piece-part control practices. A table of mission classification vs lifetime for missions launched after 2000 indicates no correlation between lifetime and classification, with the few exceptions involving missions that have very limited objectives and no valid purpose to continue after they were met. This paper steps through some of the key historical elements in risk classification and NASA's overall approach to assurance, and presents some elements being brought forward to modernize the approach and take advantage of the growing capability in the commercial sector.
NASA’s risk classification system dates back to an era when every new NASA space mission was a one-of-a-kind build, and the only way to obtain reliability was as a by-product through a combination of reliability analyses, extensive and stringent quality requirements, and extensive testing. Originally, there were very limited commercial capabilities to develop systems to work reliably in space, so NASA considered its own homegrown approach the only recipe for success. This approach involved very detailed and prescriptive piece-part controls and no reliance on (and to some extent a rejection of) any type of commercial practices. Often risk was considered to be the lowest when NASA had the maximum amount of control and prescription, and the highest when commercial practices were largely employed, and these principles drove risk classification in the agency. Over time, however, commercial capabilities grew, and many products became standardized and commercialized, while the agency maintained its tried-and-true approach, paying little attention to the evolution of the commercial sector. This presentation introduces GSFC’s new approach at risk classification, which includes classification of components and full spacecraft, as well as of constellations of spacecraft.
Previous research has shown that crewmembers returning on the Space Shuttle have an increased incidence of herniated nucleus pulposus after spaceflight. This increased risk is thought to be related to disc volume expansion due to unloading and prolonged exposure to microgravity. Although there is an increased risk of disc herniation in Space Shuttle astronauts, it is unknown if dynamic landing loads further contribute to the risk of herniation. To determine if dynamic loads increase the risk of incidence, data from crewmembers (excluding cosmonauts) returning on the Soyuz spacecraft will be compared to Space Shuttle astronauts. These data will be obtained from the Lifetime Surveillance of Astronaut Health (LSAH) Project at NASA. Severity and incidence after spaceflight will be mined from the data, and statistical analyses will be used to determine if Soyuz crewmembers have a higher incidence of disc herniation than Space Shuttle crewmembers. The results are expected to show no difference between Space Shuttle and Soyuz crewmembers, indicating that higher dynamic loads on landing and long-duration spaceflight do not significantly increase the risk of disc herniation. If no difference is shown between the two crewmember populations, then disc volume expansion due to microgravity does not significantly increase the risk of injury due to dynamic loads for deconditioned crewmembers. Any risk associated with deconditioning would be primarily due to bone structure changes and resulting bone strength changes. This study is an important first step in determining whether the spinal disc plays a role in injury due to dynamic loads.