Coupled radiation-thermal Degradation of Polymeric Systems in Nuclear Plants: Data-driven Tools to Predict Damage Accumulation & Early Failures
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14 SOLAR ENERGY↗
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Supporting Nuclear Energy Research with MLOps
Artificial intelligence (AI) and machine learning (ML) have been shown to be increasingly helpful tools in a growing number of use-cases relevant to scientific research, despite significant software-related obstacles. There exist large technical costs to setting up, using, and maintaining AI/ML models in production. This often prevents researchers from utilizing these models in their work. The growing field of machine learning operations (MLOps) aims to automate much of the AI/ML life cycle while increasing access to these models. This paper presents our initial work in creating a nuclear energy MLOps platform for use by researchers at Idaho National Laboratory (INL) and aims to reduce the barriers of using AI/ML in scientific research. We hope to promote the integration of the latest AI/ML technologies into researchers' workflows and create more opportunity for scientific innovation. In this paper we discuss how our MLOps efforts aim to increase usage and the impact of AI/ML models created by engineers. We also present several use-cases that are currently integrated. Finally, we evaluate the maturity of our project as well as our plans for future functionality.
97 MATHEMATICS AND COMPUTING↗
Assessment and validation of NEAMS tools for high-fidelity multiphysics transient modeling of microreactors: Application of NEAMS codes to perform multiphysics modeling analyses of micro-reactor concepts
The NEAMS Multiphysics Applications team aims at providing assessment of code useability and functionality for microreactor design and analyses, together with demonstration of their capabilities to properly capture the steady-state and time-dependent behavior of different microreactor concepts. In FY-24, significant progress was achieved in improving multi-physics models of several microreactors systems: HP-MR, GC-MR and KRUSTY. These efforts focused on solving more complex multiphysics problems enabled by enhanced tools capability, verifying and validating results obtained, providing feedback to developers for suggested improvements, and sharing these models to facilitate user training. A series of new multiphysics transients were completed on the HP-MR (using Griffin/BISON/Sockeye) with core startup transient, control drum inadvertent rotation accident, and hydrogen leakage from hydride moderator (also including SWIFT). On the GC-MR, a new full-core model was developed and analyzed through a series of new multiphysics (Griffin/BISON/SAM) transients to simulate moderator leakage (also including SWIFT), flow blockage and coolant depressurization. Additional and updated TRISO failure analyses were completed on the HP-MR unit-cell and GC-MR assembly models leveraging improved TRISO modeling capabilities. The amount of SiC failure following accidental transients at end-of-life was null. However, GC-MR assembly TRISO analysis highlighted Pd penetration rate can be problematic and may require design changes on the studied microreactor concept. The neutronics discrepancies observed on the KRUSTY model in previous years were resolved using hybrid set of Monte Carlo/Deterministic cross-sections. The multiphysics (Griffin neutronics / BISON thermal-mechanics) 15₵ insertion transient simulation displayed good agreement when comparing with experimental data. Initial modeling of the 30 ₵ reactivity insertion also displays promising results. Such close agreement provides important validation data that can be leveraged by the NEAMS program and by microreactor vendors to support licensing of their technology. Finally, important experience was gathered with the NEAMS tools leading to several user feedback shared with tools developers, especially with regards to MOOSE mesh generator and Griffin. This project led to many publications demonstrating modeling capabilities, and to three models shared on the Virtual Test Bed.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Using Penelope to assess the correctness of NASA Ada software: A demonstration of formal methods as a counterpart to testing
Life-critical applications warrant a higher level of software reliability than has yet been achieved. Since it is not certain that traditional methods alone can provide the required ultra reliability, new methods should be examined as supplements or replacements. This paper describes a mathematical counterpart to the traditional process of empirical testing. ORA's Penelope verification system is demonstrated as a tool for evaluating the correctness of Ada software. Grady Booch's Ada calendar utility package, obtained through NASA, was specified in the Larch/Ada language. Formal verification in the Penelope environment established that many of the package's subprograms met their specifications. In other subprograms, failed attempts at verification revealed several errors that had escaped detection by testing.
Eichenlaub, Carl T.↗
Plants, plant pathogens, and microgravity--a deadly trio
Plants grown in spaceflight conditions are more susceptible to colonization by plant pathogens. The underlying causes for this enhanced susceptibility are not known. Possibly the formation of structural barriers and the activation of plant defense response components are impaired in spaceflight conditions. Either condition would result from altered gene expression of the plant. Because of the tools available, past studies focused on a few physiological responses or biochemical pathways. With recent advances in genomics research, new tools, including microarray technologies, are available to examine the global impact of growth in the spacecraft on the plant's gene expression profile. In ground-based studies, we have developed cDNA subtraction libraries of rice that are enriched for genes induced during pathogen infection and the defense response. Arrays of these genes are being used to dissect plant defense response pathways in a model system involving wild-type rice plants and lesion mimic mutants. The lesion mimic mutants are ideal experimental tools because they erratically develop defense response-like lesions in the absence of pathogens. The gene expression profiles from these ground-based studies will provide the molecular basis for understanding the biochemical and physiological impacts of spaceflight on plant growth, development and disease defense responses. This, in turn, will allow the development of strategies to manage plant disease for life in the space environment.
NASA Discipline Plant Biology↗
Space Shuttle Software Development and Certification
Man-rated software, "software which is in control of systems and environments upon which human life is critically dependent," must be highly reliable. The Space Shuttle Primary Avionics Software System is an excellent example of such a software system. Lessons learn from more than 20 years of effort have identified basic elements that must be present to achieve this high degree of reliability. The elements include rigorous application of appropriate software development processes, use of trusted tools to support those processes, quantitative process management, and defect elimination and prevention. This presentation highlights methods used within the Space Shuttle project and raises questions that must be addressed to provide similar success in a cost effective manner on future long-term projects where key application development tools are COTS rather than internally developed custom application development tools
Orr, James K.↗
Getting a Cohesive Answer from a Common Start: Scalable Multidisciplinary Analysis through Transformation of a Systems Model
One of the challenges of systems engineering is in working multidisciplinary problems in a cohesive manner. When planning analysis of these problems, system engineers must trade between time and cost for analysis quality and quantity. The quality often correlates with greater run time in multidisciplinary models and the quantity is associated with the number of alternatives that can be analyzed. The trade-off is due to the resource intensive process of creating a cohesive multidisciplinary systems model and analysis. Furthermore, reuse or extension of the models used in one stage of a product life cycle for another is a major challenge. Recent developments have enabled a much less resource-intensive and more rigorous approach than hand-written translation scripts between multi-disciplinary models and their analyses. The key is to work from a core systems model defined in a MOF-based language such as SysML and in leveraging the emerging tool ecosystem, such as Query/View/Transformation (QVT), from the OMG community. SysML was designed to model multidisciplinary systems. The QVT standard was designed to transform SysML models into other models, including those leveraged by engineering analyses. The Europa Habitability Mission (EHM) team has begun to exploit these capabilities. In one case, a Matlab/Simulink model is generated on the fly from a system description for power analysis written in SysML. In a more general case, symbolic analysis (supported by Wolfram Mathematica) is coordinated by data objects transformed from the systems model, enabling extremely flexible and powerful design exploration and analytical investigations of expected system performance.
Operational QVT↗
Using Thermal Desktop to Determine Equivalent Solar Hours on Spacecraft Surfaces
Equivalent solar hours (ESH) are the duration of exposure of a surface to solar radiation, particularly ultraviolet (UV) radiation. Since UV light causes degradation to optical properties of thermal control surfaces, the determination of ESH is important to calculate end of life (EOL) optical properties for spacecraft external surfaces. A higher ESH value generally correlates to greater UV degradation experienced by the surface, resulting in an increase in solar absorptivity. This paper details a novel approach for calculating ESH using Thermal Desktop. Since the ESH value is useful for thermal analysts, this technique is beneficial to quickly perform the analysis internally with tools readily available without relying on external support and funding. The example presented in this paper shows how this method was developed and used for NASA's lunar Gateway spacecraft. This method of analysis is used to calculate ESH for the transit, assembly in orbit, and operational phases for Gateway, utilizing each of the program’s planned configurations throughout its 15-year lifetime. ESH values were calculated using the Monte-Carlo ray tracing capability of Thermal Desktop using both direct solar heating and indirect solar heating due to reflections from other surfaces. The output of this analysis includes an ESH gradient map across the spacecraft. Post-processing of these results enabled identification of the average and maximum ESH on critical surfaces such as radiators. The final ESH values for each surface are then used for determination of EOL optical properties.
Thermal Control↗
Structure-guided design of pure orthosteric inhibitors of αIIbβ3 that prevent thrombosis but preserve hemostasis
A prevailing dogma is that inhibition of vascular thrombosis by antagonizing platelet integrin αIIbβ3 cannot be achieved without compromising hemostasis, thus causing serious bleeding and increased morbidity and mortality. It is speculated that these adverse outcomes result from drug-induced activating conformational changes in αIIbβ3 but direct proof is lacking. Here, we report the structure-guided design of peptide Hr10 and a modified form of the partial agonist drug tirofiban that act as “pure” antagonists of αIIbβ3, i.e., they no longer induce the conformational changes in αIIbβ3. Both agents inhibit human platelet aggregation but preserve clot retraction. Hr10 and modified tirofiban are as effective as partial agonist drugs in inhibiting vascular thrombosis in humanized mice, but neither causes serious bleeding, establishing a causal link between partial agonism and impaired hemostasis. Pure orthosteric inhibitors of αIIbβ3 may thus provide safer alternatives for human therapy, and valuable tools to probe structure–activity relationships in integrins.
60 APPLIED LIFE SCIENCES↗
A native IgE in complex with profilin provides insights into allergen recognition and cross-reactivity
Allergies have become a rising health problem, where plentiful substances can trigger IgE-mediated allergies in humans. While profilins are considered minor allergens, these ubiquitous proteins are primary molecules involved in cross-reactivity and pollen-food allergy syndrome. Here we report the first crystal structures of murine Fab/IgE, with its chains naturally paired, in complex with the allergen profilin from Hevea brasiliensis (Hev b 8). The crystallographic models revealed that the IgE’s six complementarity-determining regions (CDRs) interact with the allergen, comprising a rigid paratope-epitope surface of 926 Å 2 , which includes an extensive network of interactions. Interestingly, we also observed previously unreported flexibility at Fab/IgE’s elbow angle, which did not influence the shape of the paratope. The Fab/IgE exhibits a high affinity for Hev b 8, even when using 1 M NaCl in BLI experiments. Finally, based on the encouraging cross-reactivity assays using two mutants of the maize profilin (Zea m 12), this antibody could be a promising tool in IgE engineering for diagnosis and research applications.
59 BASIC BIOLOGICAL SCIENCES↗
Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study
Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.
60 APPLIED LIFE SCIENCES↗
Advanced Information Processing System (AIPS)-based fault tolerant avionics architecture for launch vehicles
An avionics architecture for the advanced launch system (ALS) that uses validated hardware and software building blocks developed under the advanced information processing system program is presented. The AIPS for ALS architecture defined is preliminary, and reliability requirements can be met by the AIPS hardware and software building blocks that are built using the state-of-the-art technology available in the 1992-93 time frame. The level of detail in the architecture definition reflects the level of detail available in the ALS requirements. As the avionics requirements are refined, the architecture can also be refined and defined in greater detail with the help of analysis and simulation tools. A useful methodology is demonstrated for investigating the impact of the avionics suite to the recurring cost of the ALS. It is shown that allowing the vehicle to launch with selected detected failures can potentially reduce the recurring launch costs. A comparative analysis shows that validated fault-tolerant avionics built out of Class B parts can result in lower life-cycle-cost in comparison to simplex avionics built out of Class S parts or other redundant architectures.
Lala, Jaynarayan H.↗
Formal methods for life-critical software
The use of computer software in life-critical applications, such as for civil air transports, demands the use of rigorous formal mathematical verification procedures. This paper demonstrates how to apply formal methods to the development and verification of software by leading the reader step-by-step through requirements analysis, design, implementation, and verification of an electronic phone book application. The current maturity and limitations of formal methods tools and techniques are then discussed, and a number of examples of the successful use of formal methods by industry are cited.
Butler, Ricky W.↗
Validation of Framework Code Approach to a Life Prediction System for Fiber Reinforced Composites
The grant was conducted by the MMC Life Prediction Cooperative, an industry/government collaborative team, Ohio Aerospace Institute (OAI) acted as the prime contractor on behalf of the Cooperative for this grant effort. See Figure I for the organization and responsibilities of team members. The technical effort was conducted during the period August 7, 1995 to June 30, 1996 in cooperation with Erwin Zaretsky, the LERC Program Monitor. Phil Gravett of Pratt & Whitney was the principal technical investigator. Table I documents all meeting-related coordination memos during this period. The effort under this grant was closely coordinated with an existing USAF sponsored program focused on putting into practice a life prediction system for turbine engine components made of metal matrix composites (MMC). The overall architecture of the NMC life prediction system was defined in the USAF sponsored program (prior to this grant). The efforts of this grant were focussed on implementing and tailoring of the life prediction system, the framework code within it and the damage modules within it to meet the specific requirements of the Cooperative. T'he tailoring of the life prediction system provides the basis for pervasive and continued use of this capability by the industry/government cooperative. The outputs of this grant are: 1. Definition of the framework code to analysis modules interfaces, 2. Definition of the interface between the materials database and the finite element model, and 3. Definition of the integration of the framework code into an FEM design tool.
Gravett, Phillip↗
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↗
Formal Methods for Life-Critical Software
The use of computer software in life-critical applications, such as for civil air transports, demands the use of rigorous formal mathematical verification procedures. This paper demonstrates how to apply formal methods to the development and verification of software by leading the reader step-by-step through requirements analysis, design, implementation, and verification of an electronic phone book application. The current maturity and limitations of formal methods tools and techniques are then discussed, and a number of examples of the successful use of formal methods by industry are cited.
Butler, Ricky W.↗
Atmospheric Chemistry Modeling Using a Regression Forest Model
Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.
Keller, Christoph A.↗
Open Science for Plants in Space: Improvements in NASA's Open Science Data Repository
Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, elevated CO2, and many other abiotic stressors. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. Current OSDR standards include the ISA (Investigation-Study-Assay) experiment model, assay metadata configurations, and standardized terminology and ontologies. In 2024 OSDR will include a new suite of features for improved FAIR compliance including downloadable plant metadata templates, data submission tools and overall improved AI-readiness of plant datasets. AI/ML methods can be helpful tools to overcome the inherent challenges of space biology research (small sample size, sparse and heterogeneous data etc.). However these methods are built on an assumption of normalized and well-curated data. OSDR’s new curation tools will improve users ability to leverage ML and AI methods to model space biology data and better understand the complex effects of spaceflight on living systems across hierarchical biological levels. We look forward to sharing our advances with the spaceflight community.
FAIR↗