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At least 865 records · Page 48

Probing the in situ volumes of Arabidopsis leaf plastids using three‐dimensional confocal and scanning electron microscopy

SUMMARY Leaf plastids harbor a plethora of biochemical reactions including photosynthesis, one of the most important metabolic pathways on Earth. Scientists are eager to unveil the physiological processes within the organelle but also their interconnection with the rest of the plant cell. An increasingly important feature of this venture is to use experimental data in the design of metabolic models. A remaining obstacle has been the limited in situ volume information of plastids and other cell organelles. To fill this gap for chloroplasts, we established three microscopy protocols delivering in situ volumes based on: (i) chlorophyll fluorescence emerging from the thylakoid membrane, (ii) a CFP marker embedded in the envelope, and (iii) calculations from serial block‐face scanning electron microscopy (SBFSEM). The obtained data were corroborated by comparing wild‐type data with two mutant lines affected in the plastid division machinery known to produce small and large mesophyll chloroplasts, respectively. Furthermore, we also determined the volume of the much smaller guard cell plastids. Interestingly, their volume is not governed by the same components of the division machinery which defines mesophyll plastid size. Based on our three approaches, the average volume of a mature Col‐0 wild‐type mesophyll chloroplasts is 93 μm 3 . Wild‐type guard cell plastids are approximately 18 μm 3 . Lastly, our comparative analysis shows that the chlorophyll fluorescence analysis can accurately determine chloroplast volumes, providing an important tool to research groups without access to transgenic marker lines expressing genetically encoded fluorescence proteins or costly SBFSEM equipment.

Knoblauch, Jan↗

A Facility and Architecture for Autonomy Research

Autonomy is a key enabling factor in the advancement of the remote robotic exploration. There is currently a large gap between autonomy software at the research level and software that is ready for insertion into near-term space missions. The Mission Simulation Facility (MST) will bridge this gap by providing a simulation framework and suite of simulation tools to support research in autonomy for remote exploration. This system will allow developers of autonomy software to test their models in a high-fidelity simulation and evaluate their system's performance against a set of integrated, standardized simulations. The Mission Simulation ToolKit (MST) uses a distributed architecture with a communication layer that is built on top of the standardized High Level Architecture (HLA). This architecture enables the use of existing high fidelity models, allows mixing simulation components from various computing platforms and enforces the use of a standardized high-level interface among components. The components needed to achieve a realistic simulation can be grouped into four categories: environment generation (terrain, environmental features), robotic platform behavior (robot dynamics), instrument models (camera/spectrometer/etc.), and data analysis. The MST will provide basic components in these areas but allows users to plug-in easily any refined model by means of a communication protocol. Finally, a description file defines the robot and environment parameters for easy configuration and ensures that all the simulation models share the same information.

Pisanich, Greg↗

Factors Influencing Building Demand Flexibility

The U.S. Department of Energy’s National Roadmap for Grid-interactive Efficient Buildings (GEB) acknowledged that building demand flexibility (DF) is both an important strategy to decarbonizing the buildings sector and an important resource for meeting the changing needs of the electrical grid such as improving grid reliability. However, understanding the complexity and uncertainties in real building field performance of DF strategies is a large gap hindering stakeholders on both grid and buildings side to make investments on deploying such strategies. The research work in this report intended to advance understanding of the variability and influential factors in building demand flexibility. Adding such knowledge based on lab testing results and measured performance data from real buildings is an important contribution. The report uses standardized metrics and methods to quantify DF performance from field-measured DF datasets of two significant building groups of big-box retail and medium office buildings to present the challenge of building DF variability in multiple dimensions. The report presents findings related to how several key factors influence building demand flexibility from implementing a common, cost-effective DF control strategy (i.e., adjusting zone temperatures). The findings are supported by full-scale lab testing, field data analysis and simulation research. The authors also provided application-oriented recommendations to stakeholders such as building aggregators, utility program design professionals, sophisticated building portfolio owners, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DER Cybersecurity Standards: Assessment and Gap Analysis

The purpose of this report is to share the comprehensive gap analysis of existing cybersecurity standards applicable to Distributed Energy Resources (DERs) within the electric power sector. This analysis aims to identify critical deficiencies in current standards, assess their alignment with industry needs, and provide actionable recommendations for enhancing cybersecurity measures. The scope encompasses various DER technologies, including solar, wind, energy storage, and hydrogen fuel cells, and emphasizes the significance of establishing robust cybersecurity frameworks and standards to safeguard these increasingly integrated systems. The report provides valuable insights for stakeholders in the DER ecosystem, including manufacturers, utilities, and regulators. It underscores the importance of continued development and refinement of cybersecurity standards to keep up with the technical advances in DERs and associated cybersecurity challenges. The analysis evaluated IEC, IEEE, ISA, ISO, and UL standards relevant to DER cybersecurity. Standards were assessed on their coverage of key requirements including data availability, integrity, confidentiality, access control, authentication, encryption, and system hardening. For each standard, the analysis assessed its alignment with current industry practices, regulatory compliance, effectiveness in addressing known risks, coverage of emerging risks, and how it promotes interoperability. The evaluation also considered potential integration challenges and barriers to adoption.

97 MATHEMATICS AND COMPUTING↗

Progress in Modeling Global Atmospheric CO2 Fluxes and Transport: Results from Simulations with Diurnal Fluxes

Progress in better determining CO2 sources and sinks will almost certainly rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. Use of advanced data requires improved modeling and analysis capability. Under NASA Carbon Cycle Science support we seek to develop and integrate improved formulations for 1) atmospheric transport, 2) terrestrial uptake and release, 3) biomass and 4) fossil fuel burning, and 5) observational data analysis including inverse calculations. The transport modeling is based on meteorological data assimilation analysis from the Goddard Modeling and Assimilation Office. Use of assimilated met data enables model comparison to CO2 and other observations across a wide range of scales of variability. In this presentation we focus on the short end of the temporal variability spectrum: hourly to synoptic to seasonal. Using CO2 fluxes at varying temporal resolution from the SIB 2 and CASA biosphere models, we examine the model's ability to simulate CO2 variability in comparison to observations at different times, locations, and altitudes. We find that the model can resolve much of the variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The influence of key process representations is inferred. The high degree of fidelity in these simulations leads us to anticipate incorporation of realtime, highly resolved observations into a multiscale carbon cycle analysis system that will begin to bridge the gap between top-down and bottom-up flux estimation, which is a primary focus of NACP.

Collatz, G. James↗

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗

Lost and Found Opportunities Around the Chlorine Worth Study

Los Alamos National Laboratory performed a series of critical experiments in 2021 to examine the worth of chlorine in plutonium-fueled systems. This series of experiments has been dubbed the “Chlorine Worth Study,” and the evaluation of the experiments was presented to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Technical Review Group in April, 2023. The primary purpose of these experiments was to enable validation of aqueous solutions crediting neutron absorption in 35 Cl. An external, independent view of the events leading up to the design and execution of these experiments indicates a missed opportunity to leverage sensitivity/uncertainty (S/U) analysis to assert validation without the experiments by taking an additional margin for the lack of direct validation of chlorine. On the other hand, the execution of these experiments also presents a rare opportunity to examine the efficacy of the S/U approach and extract useful information about the evaluated chlorine covariance data. TSUNAMI-1D models of representative application solutions were created and used to generate sensitivity data. Varying plutonium and chlorine concentrations were considered to examine the impact of these differences on the chlorine sensitivities and uncertainties. The data-induced uncertainty in k eff resulting from chlorine was calculated directly from uncertainty information calculated in the TSUNAMI-1D sequence. In all cases, this uncertainty was less than 0.1 %Δk. This result could potentially be used to justify a reactivity margin to account for the validation gap related to chlorine in the validation set. On the other hand, given that the experiments were performed, the community should endeavor to extract as much value from them and their results as possible. The results can be used to examine the actual bias associated with chlorine in these systems once the evaluations have been released. These data can be compared with the data-induced uncertainty margin discussed above to test the sufficiency of the validation gap penalty. This result will provide an indication of the performance of the chlorine covariance data specifically and the S/U validation approach generally. More advanced S/U techniques may also be employed to determine reactivity sensitivities associated with the chlorine in the experiments, potentially generating a more robust test of the chlorine covariance data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

GEWEX Water and Energy Budget Study

Closing the global water and energy budgets has been an elusive Global Energy and Water-cycle Experiment (GEWEX) goal. It has been difficult to gather many of the needed global water and energy variables and processes, although, because of GEWEX, we now have globally gridded observational estimates for precipitation and radiation and many other relevant variables such as clouds and aerosols. Still, constrained models are required to fill in many of the process and variable gaps. At least there are now several atmospheric reanalyses ranging from the early National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and NCEP/Department of Energy (DOE) reanalyses to the more recent ERA40 and JRA-25 reanalyses. Atmospheric constraints include requirements that the models state variables remain close to in situ observations or observed satellite radiances. This is usually done by making short-term forecasts from an analyzed initial state; these short-term forecasts provide the next guess, which is corrected by comparison to available observations. While this analysis procedure is likely to result in useful global descriptions of atmospheric temperature, wind and humidity, there is no guarantee that relevant hydroclimate processes like precipitation, which we can observe and evaluate, and evaporation over land, which we cannot, have similar verisimilitude. Alternatively, the Global Land Data Assimilation System (GLDAS), drives uncoupled land surface models with precipitation, surface solar radiation, and surface meteorology (from bias-corrected reanalyses during the study period) to simulate terrestrial states and surface fluxes. Further constraints are made when a tuned water balance model is used to characterize the global runoff observational estimates. We use this disparate mix of observational estimates, reanalyses, GLDAS and calibrated water balance simulations to try to characterize and close global and terrestrial atmospheric and surface water and energy budgets to within 10-20% for long term (1986-1995), large-scale global to regional annual means.

Roads, J.↗

2FHL- The Second Catalog of Hard Fermi-LAT Sources

We present a catalog of sources detected above 50 GeV by the Fermi-Large Area Telescope (LAT) in 80 months of data. The newly delivered Pass8 event-level analysis allows the detection and characterization of sources in the 50 GeV-2 TeV energy range. In this energy band, Fermi-LAT has detected 360 sources, which constitute the second catalog of hard Fermi-LAT sources (2FHL). The improved angular resolution enables the precise localization of point sources (1.7 radius at 68% C.L.) and the detection and characterization of spatially extended sources. We find that 86% of the sources can be associated with counterparts at other wavelengths, of which the majority (75%) are active galactic nuclei and the rest (11%) are Galactic sources. Only 25% of the 2FHLsources have been previously detected by Cherenkov telescopes, implying that the 2FHL provides a reservoir of candidates to be followed up at very high energies. This work closes the energy gap between the observations performed at GeV energies by Fermi-LAT on orbit and the observations performed at higher energies byCherenkov telescopes from the ground.

catalogs – gamma-rays: general↗

Affordability Engineering: Bridging the Gap Between Design and Cost

Affordability is a commonly used term that takes on numerous meanings depending on the context used. Within conceptual design of complex systems, the term generally implies comparisons between expected costs and expected resources. This characterization is largely correct, but does not convey the many nuances and considerations that are frequently misunderstood and underappreciated. In the most fundamental sense, affordability and cost directly relate to engineering and programmatic decisions made throughout development programs. Systems engineering texts point out that there is a temporal aspect to this relationship, for decisions made earlier in a program dictate design implications much more so than those made during latter phases. This paper explores affordability engineering and its many sub-disciplines by discussing how it can be considered an additional engineering discipline to be balanced throughout the systems engineering and systems analysis processes. Example methods of multidisciplinary design analysis with affordability as a key driver will be discussed, as will example methods of data visualization, probabilistic analysis, and other ways of relating design decisions to affordability results.

Reeves, J. D.↗

Damage progression and failure of SiC/SiC composite tubes under hard-contact radial expansion

The response of silicon carbide (SiC) fiber-reinforced SiC matrix (SiC/SiC) composite cladding to mechanical interaction with fissile fuel is a knowledge gap that must be overcome to design and assess SiC-based cladding systems for advanced nuclear applications. This study developed the relevant mechanical testing capability and identified the failure behavior and the critical microstructural features and processing defects. Sections of SiC composite tube were subjected to a modified expansion-due-to-compression (EDC) test in an X-ray computed tomography microscope: a polyurethane plug pressed surrogate Al 2 O 3 into the inner walls of the SiC/SiC composite tubes to achieve hard contact. A pure EDC test with just a polyurethane plug was also performed as a reference. Through the use of displacement fields, digital volume correlation revealed inhomogeneous deformation fields in the tubes, even for pure EDC, which was related to the inherent defects in the structure. Deep learning–aided segmentation and systematic data analysis revealed that the presence of inhomogeneous deformation applied by the hard contact was exaggerated by the presence of inner surface imperfections left behind from the matrix densification process. In conclusion, the findings provide insights into the applications, highlighting the necessity for improvements in inner surface roughness and the incorporation of localized contacts in pellet–cladding mechanical interaction computational models.

Composites↗

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗

Gas-Particle Interaction Model Development in Plume Surface Interaction Erosion and Cratering

The development of a predictive simulation capability for extra-terrestrial Plume Surface Interaction (PSI) environments is undertaken by the Fluid Dynamics Branch at NASA/MSFC under the Game Changing Development (GCD) Program funded by NASA’s Space Technology Mission Directorate (STMD). Predictive simulation capability for propulsive Martian and Lunar landing PSI requires accurate modeling of the complex high-speed plume impingement and resulting gas/particle-cloud and ejecta stream formation. Physics modeling gap analysis during formulation of the PSI project scope identified two particular phenomena of first order importance in gas-particle interactions that lacked existing modeling formulations. The first arises from the lack of models capturing the unsteady drag forces imparted on particles by the rapidly expanding supersonic compressible flow. The second addresses the fluctuating forces and dispersions imparted on both the particle and the fluid resulting from the interference and wake turbulence generated by close proximity particle clouds, dubbed the Particle Turbulent Kinetic Energy(PTKE). Their first order significance has been identified in experiments, but simulation models currently do not exist for either effect. The development of models and the eventual implementation into the Eulerian Gas-Granular Flow Solver (GGFS) simulation tools was constructed as one of four major tasks of the PSI project. In this process, data on particle kinetics and gas-particle interactions are collected from carefully designed experiments of particles embedded in jets. The effects observed in the experiments are then replicated in high-fidelity particle-resolved CFD simulations to inform the formulation of improvements to particle phase drag models for implementations in the more efficient Eulerian-Lagrangian CFD simulations. The resulting models are ultimately ported to the Eulerian-Eulerian models applied for most efficient simulations in PSI production application tools. This paper describes the activities and accomplishments of the past year in the gas-particle interaction modeling task of the PSI project.

Jeff West↗

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Abstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Experimentally Validated Computational Modeling of Creep and Creep-Cracking for Nuclear Concrete Structures

In a Nuclear Power Plant, one of the most important components is the concrete nuclear reactor cavity, which serves both a structural and protective function as the biological radiation shield. Given that creep has been identified as a major knowledge gap in the assessment of nuclear structures (NUREG/CR-7153), this work helps to further the understanding of creep behavior of massive concrete containment structures for decades to enable safe and long-term operation of these facilities. This project has developed a robust, experimentally validated model to predict creep in nuclear concrete structures for up 60 years using short-term creep data thereby enabling a longer service life of critical facilities and early detection of structural failure. The work presented in this report is a pairing of computational and experimental methods. For the first time, the time temperature superposition (TTS) principle was successfully used to generate a uniaxial creep compliance master curve to predict mortar creep response for up to 22,500 days (nearly 60 years) at a reference temperature of 20°C. These data were used as input into finite element analysis (FEA) codes that use highly realistic random, 3D concrete microstructures from reconstructed coarse limestone aggregates. Finite element analysis performed provides the ability to quickly upscale mortar viscoelastic behavior to long-term concrete creep/relaxation data. A master creep compliance curve, constructed from the TTS principle, spanning 27 years, was used to validate two and a half decades of simulated concrete creep. Concurrently, three different simulated wall specimens were designed to mimic the behavior of post-tensioned concrete nuclear containment facility vessel walls over time as a result of concrete creep. The specimens were designed with different thicknesses, transverse and longitudinal reinforcement ratios, and level of post-tensioning stress. Each specimen contained various instrumentation to measure internal concrete temperature, concrete strain, and post-tensioning strain hourly for over 3 years. The concrete creep model developed in this project, based on the FEA concrete simulations, was applied to simulate the structural-scale experiments of prestressed concrete walls conducted in this project using the Grizzly code. These models can represent the effects of reinforcing and prestressing. Although there are some discrepancies with the experimental data, the model can predict the overall trends of the creep response in these experiments. One of these experimental models was also applied to an extended time to demonstrate how the findings from this study can be used to predict the behavior of actual structures of interest that have been in service for extended periods of time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Fully Coupled Aeroelastic Stability Analysis of Adaptive Shape Memory Alloy Structural Technologies for Airframe Noise Reduction

The objective of this work is the development of computational models and analysis of the coupled fluid-structure response of a slat gap filler (SGF) noise treatment applied to the leading-edge-slat component of a high-lift system typical of modern transport aircraft. The representative airframe chosen for this work is NASA’s High-lift Common Research Model (CRM-HL) in a baseline high-lift configuration. Superelastic shape memory alloys (SMAs) have been identified as enabling materials for these structural treatments. Since the technology elements rely upon having a highly reconfigurable structure, designs must be assessed for static aeroelastic deflection as well as dynamic aeroelastic stability using coupled computational fluid dynamics (CFD) and nonlinear computational structural dynamics (NL CSD) tools. The technical approach consists of solving for the flow field around the entire vehicle using a global CFD model, followed by extraction of relevant local subdomain data for CFD and NL-CSD co-simulations. The SGF design is assessed using both 2D and 3D co-simulations to predict quasi-static aeroelastic deformations and to assess dynamic aeroelastic stability.

Fluid structure interaction↗