Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “reliability methodologies”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 1,243 records · Page 69

Accurate Prediction of pKb in Amines: Validation of the CAM-B3LYP/6-311+G(d,p)/SMD Model

Amines play several key roles in chemistry and biology and are involved in numerous industrial processes, often with significant economic impacts. Recently, amines are also garnering interest as catalysts for polymer synthesis and for CO 2 fixation, incentivizing the need to rapidly design and screen new amino compounds. Hence, developing reliable methods to predict their physicochemical properties, e.g., the base dissociation constant (pKb), is pivotal. Here, a density functional theory (DFT)-based approach was employed to compute the pKb of substituted amines, exploring the impact of several key parameters, including (i) the number of explicit water molecules at the reaction center, (ii) the van der Waals (vdW) surface, and (iii) solvent polarizability. In previous work, it was determined that including two explicit water molecules at the reaction center resulted in highly accurate pKb estimates for primary amines. Here, we find that including a third water molecule at the reaction center is essential for accurate pKb for secondary and tertiary amines. The revised methodology was then applied to a wider selection of amines, obtaining a minimum average error (MAE) < 0.4. In conclusion, this result represents an extension of our “easy-to-use method,” a simple and direct DFT approach exploiting CAM-B3LYP/SMD/6-311G+(d,p) to compute pKb without post facto modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluating the limitations of Bayesian metabolic control analysis

AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

Shin, Janis (ORCID:0000000216572455)↗

Chapter 4: "Waste"-to-Energy for Decarbonization - Transforming Nut Shells Into Carbon-Negative Electricity

This chapter presents a study demonstrating waste pistachio nut shells as a renewable feedstock for climate-friendly electricity generation via industrial gasification technology. The study includes biomass feedstock characterization (i.e., pistachio waste critical material attributes), process variability (i.e., bulk material handling), and overall operational reliability and conversion performance through extended testing. Additionally, techno-economic analysis (TEA) and life cycle assessment (LCA) were performed to assess the economic feasibility and environmental impact of the technology to transform agricultural waste to biopower. For processing pistachio waste material, among critical material attributes, fines content in the biomass (<1/4") had the largest potential to reduce the operating time of the gasifiers due to plugging. Pelletizing fines and co-feeding them with the mixed pistachio waste increased the average feed density, feed rate, and biochar production. Compared to pine wood chips, mixed pistachio waste yielded higher biochar quantity but slightly reduced quality. In general, a systematic Quality by Design methodology is the preferred approach for designing preprocessing and material conveyance systems, where a downstream technology (end user) for the produced intermediate is specified at the outset. TEA results show that the biochar production rate and selling price had an overwhelming impact on the modeled Minimum Electricity Selling Price (MESP), which ranged from 35.5 to 39.9 cents/kWh for the cases studied (16 h/day operational basis). Moreover, LCA results show that the valorization of pistachio shells for biopower generation is a "carbon negative" process that can help decarbonize the U.S. electricity grid. The specific carbon intensity was -0.29 to -0.71 kg CO2e/kWh, compared to 0.45 kg CO2e/kWh for the average U.S. electricity mix. Biochar production from pistachio waste as a potential means for carbon sequestration was a significant driver for the LCA. The highly stable biochar permanently sequesters a considerable fraction of biochar carbon in the ground, more than enough to offset the life cycle emissions, and can be a complementary climate change mitigation strategy.

bio-char↗

Trend analysis for large solid rocket motors - A program level approach

This paper defines a program-level trend analysis effort that can be applied to large solid rocket motors. The effort is especially applicable to large segmented rocket motors such as the Space Transportation System's solid rocket boosters. Five types of trend analysis are discussed for different aspects of a rocket motor program, (performance, reliability, problem, supportability, and programmatic). Ideas are offered for implementing and performing a program-level trend analysis effort by giving suggestions for selecting computer capabilities, choosing parameters, selecting statistical processes, routine screening for trends, analyzing significant trends (for example, looking for related trends and determining if adverse trends are resolved), and reporting results of trend analysis. The use of program-level trending allows for the ability to easily transfer data between organizations and easily use the data to correlate trends from the various organizations to find causal relationships. Efficiency is enhanced from upper level management to the shop floor at vendor locations by using the same trend analysis software and methodology at all organizations.

Babbitt, Norman E., III↗

Product Lifecycle Management and the Quest for Sustainable Space Exploration Solutions

Product Lifecycle Management (PLM) is an outcome of lean thinking to eliminate waste and increase productivity. PLM is inextricably tied to the systems engineering business philosophy, coupled with a methodology by which personnel, processes and practices, and information technology combine to form an architecture platform for product design, development, manufacturing, operations, and decommissioning. In this model, which is being implemented by the Marshall Space Flight Center (MSFC) Engineering Directorate, total lifecycle costs are important variables for critical decision-making. With the ultimate goal to deliver quality products that meet or exceed requirements on time and within budget, PLM is a powerful concept to shape everything from engineering trade studies and testing goals, to integrated vehicle operations and retirement scenarios. This briefing will demonstrate how the MSFC Engineering Directorate is implementing PLM as part of an overall strategy to deliver safe, reliable, and affordable space exploration solutions and how that strategy aligns with the Agency and Center systems engineering policies and processes. Sustainable space exploration solutions demand that all lifecycle phases be optimized, and engineering the next generation space transportation system requires a paradigm shift such that digital tools and knowledge management, which are central elements of PLM, are used consistently to maximum effect. Adopting PLM, which has been used by the aerospace and automotive industry for many years, for spacecraft applications provides a foundation for strong, disciplined systems engineering and accountable return on investment. PLM enables better solutions using fewer resources by making lifecycle considerations in an integrative decision-making process.

Caruso, Pamela W.↗

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Cyber Attack Sequences Generation for Electric Power Grid

Security assessment of cyber-physical energy systems (CPESs) such as the electric power grid is a critical operation to maintain availability, reliability, and quality of service in the presence of persistent threats from malicious cyber actors. Existing security assessment approaches such as penetration testing and red teaming rely on subject matter expert experience and forensic cyber analysis of historical events to perform realistic, threat-informed assessments of CPES defense. CPESs have a large attack surface because of the heterogeneity and complexity of underlying topology, devices, measurements, and vulnerabilities. The aforementioned approaches lead to partial coverage of the attack surface with a large set of unknown but possible exploits. There is a need to automate the CPES attack surface discovery and contextualize it for relevant, highly probable, real-world attack scenarios. We propose a methodology and framework to facilitate the discovery of the CPES attack surface. We present a multilayer attack graph with ranked attack sequences to describe CPES failure scenarios. We present a work-in-progress framework that lists key components to automate the attack modeling and sequence generation. We demonstrate the published National Electric Sector Cybersecurity Organization Resource CPES failure scenario to highlight the trustworthiness of generated attack sequences.

Dutta, Ashutosh↗

Satellite Radiation Products for Ocean Biology and Biogeochemistry: Needs, State-Of-The-Art, Gaps, Development Priorities, and Opportunities

Knowing the spatial and temporal distribution of the underwater light field, i.e., the spectral and angular structure of the radiant intensity at any point in the water column, is essential to understanding the biogeochemical processes that control the composition and evolution of aquatic ecosystems and their impact on climate and reaction to climate change. At present, only a few properties are reliably retrieved from space, either directly or via water-leaving radiance. Existing satellite products are limited to planar photosynthetically available radiation (PAR) and ultraviolet (UV) irradiance above the surface and diffuse attenuation coefficient. Examples of operational products are provided, and their advantages and drawbacks are examined. The usefulness and convenience of these products notwithstanding, there is a need, as expressed by the user community, for other products, i.e., sub-surface planar and scalar fluxes, average cosine, spectral fluxes (UV to visible), diurnal fluxes, absorbed fraction of PAR by live algae (APAR), surface albedo, vertical attenuation, and heating rate, and for associating uncertainties to any product on a pixel-by-pixel basis. Methodologies to obtain the new products are qualitatively discussed in view of most recent scientific knowledge and current and future satellite missions, and specific algorithms are presented for some new products, namely sub-surface fluxes and average cosine. A strategy and roadmap (short, medium, and long term) for usage and development priorities is provided, taking into account needs and readiness level. Combining observations from satellites overpassing at different times and geostationary satellites should be pursued to improve the quality of daily-integrated radiation fields, and products should be generated without gaps to provide boundary conditions for general circulation and biogeochemical models. Examples of new products, i.e., daily scalar PAR below the surface, daily average cosine for PAR, and sub-surface spectral scalar fluxes are presented. A procedure to estimate algorithm uncertainties in the total uncertainty budget for above-surface daily PAR, based on radiative simulations for expected situations, is described. In the future, space-borne lidars with ocean profiling capability offer the best hope for improving our knowledge of sub-surface fields. To maximize temporal coverage, space agencies should consider placing ocean-color instruments in L1 orbit, where the sunlit part of the Earth can be frequently observed.

Frouin, Robert↗

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the framework provides a means to address relevant technical issues by: (1) defining a risk-informed analysis process for DI&C upgrade, that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies on nuclear power plant (NPPs).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

A Quantitative Failure Analysis on Capacity Fade in Rechargeable Lithium Metal Cells

Rechargeable lithium battery (RLB) technology is transforming portable devices, vehicle electrification, and grid modernization. To make RLB durable, reliable and safe, conducting failure mode and effect analysis (FMEA) to identify failure mechanism under the operating conditions is very desirable. However, this ability is often overlooked or even lacking. The failure analysis (FA) is often conducted by laboratory testing and postmortem analysis, and the knowledge typically empirical. Here we present a quantitative approach for FMEA that can reveal how failure modes and effects reduce the capacity of a RLB. This approach is based on the state of the battery for FMEA, contrary to the conventional approach based on operating or testing conditions. The key aspect of this FMEA method is to convert the experimental results to a state-of-charge (SOC)-based analytic methodology. Such a conversion can separate the thermodynamic and kinetic attributes of capacity fade based on compositional correspondence in the electrode, so the loss and the decreased utilization of the active materials can be determined respectively.

25 ENERGY STORAGE↗

Security by Design Economics Analysis for Advanced Reactors and Small Modular Reactors Project Interim Report for FY2021

Advanced Reactor and Small Modular Reactor (AR/SMR) designs have the potential to provide clean, reliable baseload energy. Ensuring the capability to deploy these reactors in an economically viable fashion is of interest to industry. A large portion of the expected operating costs of AR/SMRs involves the security of the plant. Security by Design (SeBD) is the practice of including features in the design and construction of the site, with the intent to decrease the operating costs related to security. Quantifying the increase or decrease in the overall lifetime cost to the plant as a result of SeBD is of paramount importance in understanding the disadvantages and benefits of such activities. The National Nuclear Security Administration’s (NNSA) Office of International Nuclear Security (INS) is funding the development of a methodology whereby the capital expenses and operating expenses, as well as the physical security effectiveness, of SeBD can be quantified for AR/SMRs. This report is an interim report on the progress of the work performed by Sandia National Laboratories (SNL), Idaho National Laboratory, and Oak Ridge National Laboratory (ORNL). It is the second annual report on this work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

A perspective on future directions in aerospace propulsion system simulation

The design and development of aircraft engines is a lengthy and costly process using today's methodology. This is due, in large measure, to the fact that present methods rely heavily on experimental testing to verify the operability, performance, and structural integrity of components and systems. The potential exists for achieving significant speedups in the propulsion development process through increased use of computational techniques for simulation, analysis, and optimization. This paper outlines the concept and technology requirements for a Numerical Propulsion Simulation System (NPSS) that would provide capabilities to do interactive, multidisciplinary simulations of complete propulsion systems. By combining high performance computing hardware and software with state-of-the-art propulsion system models, the NPSS will permit the rapid calculation, assessment, and optimization of subcomponent, component, and system performance, durability, reliability and weight-before committing to building hardware.

Miller, Brent A.↗

Characterization of Polyimide Foams for Ultra-Lightweight Space Structures

Ultra-lightweight materials have played a significant role in nearly every area of human activity ranging from magnetic tapes and artificial organs to atmospheric balloons and space inflatables. The application range of ultra-lightweight materials in past decades has expanded dramatically due to their unsurpassed efficiency in terms of low weight and high compliance properties. A new generation of ultra-lightweight materials involving advanced polymeric materials, such as TEEK (TM) polyimide foams, is beginning to emerge to produce novel performance from ultra-lightweight systems for space applications. As a result, they require that special conditions be fulfilled to ensure adequate structural performance, shape retention, and thermal stability. It is therefore important and essential to develop methodologies for predicting the complex properties of ultra-lightweight foams. To support NASA programs such as the Reusable Launch Vehicle (RLV), Clark Atlanta University, along with SORDAL, Inc., has initiated projects for commercial process development of polyimide foams for the proposed cryogenic tank integrated structure (see figure 1). Fabrication and characterization of high temperature, advanced aerospace-grade polyimide foams and filled foam sandwich composites for specified lifetimes in NASA space applications, as well as quantifying the lifetime of components, are immensely attractive goals. In order to improve the development, durability, safety, and life cycle performance of ultra-lightweight polymeric foams, test methods for the properties are constant concerns in terms of timeliness, reliability, and cost. A major challenge is to identify the mechanisms of failures (i.e., core failure, interfacial debonding, and crack development) that are reflected in the measured properties. The long-term goal of the this research is to develop the tools and capabilities necessary to successfully engineer ultra-lightweight polymeric foams. The desire is to reduce density at the material and structural levels, while at the same time maintaining or increasing mechanical and other properties.

Meador, Michael↗

Demonstration of Linear Covariance Analysis Techniques to Evaluate Entry Descent and Landing Guidance Algorithms, Vehicle Configurations, Analysis Techniques, and Trajectory Profiles

Linear covariance analysis techniques have been previously developed to analyze closed-loop entry, descent, and landing (EDL) scenarios and the initial validation efforts are under-way confirming the generated GN&C system performance results. Given both the theoretical foundation and previous conceptual demonstration, this work begins to flex the potential of linear covariance analysis for atmospheric flight and highlight its versatility and reliability by evaluating multiple entry guidance algorithms, vehicle configurations, trajectory profiles, environment conditions, and analysis techniques for a variety of trade studies. To demonstrate the benefit linear covariance analysis can provide in producing rapid yet accurate performance data, two entry profiles are adopted including the NASA Mars Science Laboratory (MSL) and Exploration Flight Test-1 (EFT-1) while utilizing two different guidance algorithms, the Apollo Final Phase (AFP) and the Fully Numeric Predictor-Corrector Entry Guidance (FNPEG) with different navigation sensor suites in a 6 degree-of-freedom (6-DOF) simulation environment. Results are shown using both linear covariance and Monte Carlo analysis techniques to high-light the consistency between the two methodologies and continue the validation maturation of linear covariance analysis for entry, descent, and landing.

EDL↗

Gateway Power Quality Lessons Learned

Power Quality is a physical description of the electrical characteristics that allow the system to function properly without significant loss of performance or life. This physical description includes steady state voltage limits, transient voltage limits in normal/abnormal conditions, ripple voltage, stability, fault conditions, and more. All which are vital for improving reliability, ensuring stable operation, defining proper fault recovery, and ensuring a ‘plug and play’ approach to design and integration. Typically, a specification for Power Quality is created based on expected system performance or an existing standard, such as the International Space Power System Interoperability Standards (ISPSIS). One example of such a specification is the Gateway Power Quality Specification. It defines the requirements and characteristics of the 120 Volt direct current electrical power system for the Gateway Electrical Power System (EPS) and the Gateway Electrical Power Consuming Equipment (EPCE). This specification also maintains a separate requirement verification section that defines test methods for requirement verification. The test methods include analysis, test, inspection, and demonstration. These test methodologies and requirements are used to ensure that the loads operate when connected to the specified power quality and performance as defined by this specification. The challenge with developing a specification is that desired system characteristics are not always fully matured before the specification is needed and many performance requirements may be application specific. This drives the need to utilize lessons learned through extensive analysis and testing as well as historical knowledge to finalize requirements. Some example requirements where this is important are Small and Large Signal Stability, Ripple Voltage, Inrush/Surge Currents, and Fault Containment. Lessons learned are also important in the testing, analysis, and verification to ensure consistent and accurate results to verify performance. This presentation will cover the lessons learned for power quality relative to ripple, inrush/surge, fault containment, testing/verification, and more.

Power Quality↗

Safety-Related Instrumentation and Control Pilot Upgrade (Initial Scoping Phase Implementation Report)

This research report (1) describes the process followed and products developed during the SR I&C Pilot Project Initial Scoping Phase, and (2) captures lessons learned. Exelon Generation and LWRS collaborated to develop a Digital Transformation Strategy as part of a larger Advanced Concept of Operations. The proposed SR I&C Pilot Upgrade provides a foundation stone for this Digital Transformation that will improve plant safety, reliability, and operational performance while lowering plant Total Cost of Ownership (TCO). Initial Scoping Phase activities for this Pilot Project have been performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process (SDP), NISP-EN-04, Standard Digital Engineering Process (SDEP), and Electric Power Research Institute (EPRI) Report 3002011816, Digital Engineering Guide (DEG). Completing Initial Scoping Phase Engineering and Operations, Licensing, and Project Management Activities was necessary to sufficiently bound the scope, schedule, and estimated cost of the Project to enable utility management to authorize moving into the Conceptual Design Phase. A significant finding of the Business Case Analysis (BCA) methodology developed and applied as part of this effort was that the growth rate of material costs for sustaining the operation of obsolete SR I&C equipment is accelerating. This directly contributed to the Project Economic Analysis created to justify continuing the Project. Project Initial Scoping Phase lessons learned have also been captured to assist the larger industry in understanding the Digital Transformation Strategy and SR I&C Pilot Project Initial Scoping Phase efforts. This is in keeping with the public/private partnership that has been established between the Department of Energy (DOE) and Exelon for this effort with engagement from the NRC. By addressing first-of-a-kind (FOAK) risks and capturing lessons learned, the SR I&C Pilot Upgrade Project addresses technical, regulatory, and business risks to enable subsequent implementers of similar upgrade.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Development of an Information Fusion System for Engine Diagnostics and Health Management

Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Volponi, Allan J.↗

Development of a Robust and Efficient Parallel Solver for Unsteady Turbomachinery Flows

The traditional design and analysis practice for advanced propulsion systems relies heavily on expensive full-scale prototype development and testing. Over the past decade, use of high-fidelity analysis and design tools such as CFD early in the product development cycle has been identified as one way to alleviate testing costs and to develop these devices better, faster and cheaper. In the design of advanced propulsion systems, CFD plays a major role in defining the required performance over the entire flight regime, as well as in testing the sensitivity of the design to the different modes of operation. Increased emphasis is being placed on developing and applying CFD models to simulate the flow field environments and performance of advanced propulsion systems. This necessitates the development of next generation computational tools which can be used effectively and reliably in a design environment. The turbomachinery simulation capability presented here is being developed in a computational tool called Loci-STREAM [1]. It integrates proven numerical methods for generalized grids and state-of-the-art physical models in a novel rule-based programming framework called Loci [2] which allows: (a) seamless integration of multidisciplinary physics in a unified manner, and (b) automatic handling of massively parallel computing. The objective is to be able to routinely simulate problems involving complex geometries requiring large unstructured grids and complex multidisciplinary physics. An immediate application of interest is simulation of unsteady flows in rocket turbopumps, particularly in cryogenic liquid rocket engines. The key components of the overall methodology presented in this paper are the following: (a) high fidelity unsteady simulation capability based on Detached Eddy Simulation (DES) in conjunction with second-order temporal discretization, (b) compliance with Geometric Conservation Law (GCL) in order to maintain conservative property on moving meshes for second-order time-stepping scheme, (c) a novel cloud-of-points interpolation method (based on a fast parallel kd-tree search algorithm) for interfaces between turbomachinery components in relative motion which is demonstrated to be highly scalable, and (d) demonstrated accuracy and parallel scalability on large grids (approx 250 million cells) in full turbomachinery geometries.

West, Jeff↗