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Radiation Beamline Testbeds for the Simulation of Planetary and Spacecraft Environments for Human and Robotic Mission Risk Assessment

The Center for Radiation Engineering and Science for Space Exploration (CRESSE) at Prairie View A&M University, Prairie View, Texas, USA, is establishing an integrated, multi-disciplinary research program on the scientific and engineering challenges faced by NASA and the international space community caused by space radiation. CRESSE focuses on space radiation research directly applicable to astronaut health and safety during future long term, deep space missions, including Martian, lunar, and other planetary body missions beyond low earth orbit. The research approach will consist of experimental and theoretical radiation modeling studies utilizing particle accelerator facilities including: 1. NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory; 2. Proton Synchrotron at Loma Linda University Medical Center; and 3. Los Alamos Neutron Science Center (LANSCE) at Los Alamos National Laboratory. Specifically, CRESSE investigators are designing, developing, and building experimental test beds that simulate the lunar and Martian radiation environments for experiments focused on risk assessment for astronauts and instrumentation. The testbeds have been designated the Bioastronautics Experimental Research Testbeds for Environmental Radiation Nostrum Investigations and Education (BERT and ERNIE). The designs of BERT and ERNIE will allow for a high degree of flexibility and adaptability to modify experimental configurations to simulate planetary surface environments, planetary habitats, and spacecraft interiors. In the nominal configuration, BERT and ERIE will consist of a set of experimental zones that will simulate the planetary atmosphere (Solid CO2 in the case of the Martian surface.), the planetary surface, and sub-surface regions. These experimental zones can be used for dosimetry, shielding, biological, and electronic effects radiation studies in support of space exploration missions. BERT and ERNIE are designed to be compatible with the experimental areas associated with the above facilities. CRESSE has broad expertise in space radiation in the areas of space radiation environment modeling, Monte-Carlo radiation transport modeling, space radiation instrumentation and dosimetry, radiation effects on electronics, and multi-functional composite shielding materials. The BERT and ERNIE testbeds will be utilized in individual and collaborative research incorporating this expertise. The research goal is to maximize the technical readiness level (TRL) of radiation instrumentation for human and robotic missions, optimizing the return value of CRESSE for NASA exploration and international co-operative missions. Outcomes and knowledge from research utilizing BERT and ERNIE will be applied to a variety of scientific and engineering disciplines vital for safe and reliable execution of future space exploration missions, which can be negatively impacted by the space radiation environment. The testbeds will be central to a variety of university educational activities and educational goals of NASA. Specifically, BERT and ERNIE will enhance educational opportunities in science, technology, engineering and mathematics (STEM) disciplines for engineering and science students at PVAMU, a historically black college/university. Preliminary data on prototype testbed configurations, including simulated lunar regolith (JSC-1A stimulant based on Apollo 11 samples), regolith/polyethylene composites, and dry ice, will be presented to demonstrate the usefulness of BERT and ERNIE in radiation beam line experiments.

Wilkins, Richard↗

WBS 4.2.1.31 Integrated Life Cycle Sustainability Analysis

The main objective of this project (following the 2017 BETO Program Peer-Review) is to provide BETO with a coherent methodology and modeling framework based on life cycle (LCA) and economic input-output analysis, tested and vetted with stakeholders, to better understand the effects of an expanding U.S. bioeconomy at specific levels including economic dimensions such as jobs, gross domestic product (value added), and environmental criteria such as air pollutant emissions, water and land use, ecotoxicity, and others. This supports a 2019 milestone in the BETO MYP and simultaneously addresses a gap previously identified in the BETO program and highlighted, e.g., by BETO Program Peer Reviewers in 2015 and 2017. To fill in this gap, NREL started collaborating with developers of the national level U.S. Environmentally-Extended Input-Output (USEEIO) model in FY18. This peer-reviewed, open source model is based on publicly available data, and developed and maintained by U.S. EPA where it has been the basis for EPA's Sustainable Material Management analyses and program. Also, USEEIO was initially designed for use with EPA's Tool for the Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI). It aligns well with similar efforts including the Federal LCA Commons and the Sustainable Acquisition Program at the Department of Defense (DoD). In FY18, NREL convened stakeholders from federal agencies, universities, and private sector entities to identify and form a common understanding of (1) models describing the U.S. economy and how to build off of them, (2) a bioeconomy definition for the next modeling steps, (3) the (sub)sectors that can/should be used in the framework, and (4) opportunities for collaboration and data sharing. NREL then created a first order model by disaggregating, i.e., separating the economic activities associated with and attributed to a selected product of the bioeconomy: corn ethanol. Corn ethanol is well studied and has a significant amount of public, validated economic industry information. This is important as disaggregation is a critical step in creating a representative and accurate framework. Corn ethanol served as a validation test case for the method and framework. By the end of FY18, the team was able to generate several scenarios for corn ethanol on national and selected regional levels providing results on the absolute and relative (Megajoule) effects in several environmental and economic impact categories. A second objective of the project is to inform BETO's strategic decision making, specifically its sustainability research portfolio, by engaging in, evaluating, and synthesizing selected global multi-year activities from the perspectives of a variety of complex multilateral and multi-stakeholder processes that pertain to bioenergy. This helps to (1) identify gaps, barriers, and critical areas for future BETO bioenergy and sustainability assessments; (2) identify opportunities and challenges to a sustainable U.S. bioeconomy; and (3) contribute to and disseminate BETO information to and from major global bioenergy and sustainability efforts. This project's work with multilateral organizations ensures that U.S. science-based viewpoints are considered and helps to balance the influence of the large number of countries with the same centralized philosophy. This engagement builds a more solid foundation for sustainable biomass and bioenergy systems in the U.S. and in the world. Working with multi-lateral, international platforms including IEA Bioenergy, Global Bio-Energy Partnership (GBEP), and their partner organizations also allows engagement with important stakeholders including policy makers, industry, NGOs, and academia.

09 BIOMASS FUELS↗

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system that allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.

97 MATHEMATICS AND COMPUTING↗

Strategic Environmental Assessment practices in European small islands: Insights from Azores and Orkney islands

The literature concerning Strategic Environmental Assessment (SEA) often refers to the importance of context-specific approaches. However, there is a lack of systematised and consistent studies that enhance tailor-made SEA practices and procedures. Small islands are bounded units of study which may help explore SEA theory and practice in special territories. Small islands present particular features and unique values, such as, small size and population, geographic isolation, limited resources and vulnerable ecosystems. Hence, the main goal of this research was to profile SEA practices and procedures in European small islands and provide a background for future research aiming to improve context-specific SEA applications. To achieve this goal, an exploratory case study was developed using Azores (Portugal) and Orkney (Scotland) archipelagos. An analysis of the corresponding mainland was also carried out to contextualise both case studies. The data collection was achieved through a qualitative content analysis of 43 Environmental Reports. The research found that there is not an SEA context-specific approach used within these European small islands, including guidelines, assessment topics, assessment techniques, follow-up and stakeholders engagement. The debate concerning specific approaches to small islands must be re-focused on the enhancement of SEA capacity-building amongst different stakeholders (including decision-makers), on the development and implementation of collaborative approaches, and on the exchange of knowledge and experiences between small islands networks. - Highlights: • Reviewed the differences between the Portuguese and Scottish SEA system • Showed a low integration of SEA specific features in reports of European small islands • Provides background for future SEA research for small islands approaches.

54 ENVIRONMENTAL SCIENCES↗

Comparison of Optical Modelling Tools for Sunshape and Surface Slope Error

The sunshape and mirror surface slope error are two main factors that influence the characteristics of incoming and reflected solar beams in a solar concentrator. Different optical modelling tools, however, address these two factors with different methods and simplifications. In this study, simulation methods are reviewed in five optical modelling tools, namely SolTrace, Tonatiuh, Tracer, Heliosim and SolarPILOT. A series of simple test cases are simulated in each tool to compare and verify the implementation of sunshape and slope error models separately. This strategy has successfully identified discrepancies among the tools and contributed to a better understanding of their reasons. It has also uncovered few glitches in some of those tools and contributed to improve them. It is our hope that this study will help to resolve common misconceptions, to build confidence amongst CSP researches on the accuracy of tools with which they have less experience, and to ensure better agreement in optical modelling work in future.

14 SOLAR ENERGY↗

Improved Offshore Wind Resource Assessment in Global Climate Stabilization Scenarios

This paper introduces a technique for digesting geospatial wind-speed data into areally defined -- country-level, in this case -- wind resource supply curves. We combined gridded wind-vector data for ocean areas with bathymetry maps, country exclusive economic zones, wind turbine power curves, and other datasets and relevant parameters to build supply curves that estimate a country's offshorewind resource defined by resource quality, depth, and distance-from-shore. We include a single set of supply curves -- for a particular assumption set -- and study some implications of including it in a global energy model. We also discuss the importance of downscaling gridded wind vector data to capturing the full resource potential, especially over land areas with complex terrain. This paperincludes motivation and background for a statistical downscaling methodology to account for terrain effects with a low computational burden. Finally, we use this forum to sketch a framework for building synthetic electric networks to estimate transmission accessibility of renewable resource sites in remote areas.

17 WIND ENERGY↗

The Aviation System Analysis Capability Noise Impact Model

To meet its objective of assisting the US aviation industry with the technological challenges of the future, NASA must identify research areas that have the greatest potential for improving the operation of the air transportation system. To accomplish this, NASA is building an Aviation System Analysis Capability (ASAC). The Noise Impact Model (NIM) has been developed as part of the ASAC. Its primary purpose is to enable users to examine the impact that quieter aircraft technologies and/or operation might have on community noise impact and air carrier operating efficiency at any of 16 large and medium size US airports. The analyst chooses an airport and case year for study, selects a runway use configuration and set of flight tracks for the scenario, and has the option of reducing the noise of the aircraft that operate at the airport by 3, 6, and 10 decibels, NIM computes the resultant noise impact and estimates any airline operational improvements. Community noise impact is characterized in three ways: the size of the noise contour footprint, the number of people living within the contours, and the number of homes located in the same contours. Distance and time savings are calculated by comparing the noise abatement flight path length to a less circuitous alternated routing. For a more efficient runway use configuration, the increase in capacity and reduction in delay are shown.

Ege, Russell A.↗

A Preliminary Study of Building a Transmission Overlay for Regional US Power Grid

Many European countries have taken steps toward a Supergrid in order to transmit large amount of intermittent and remote renewable energy over long distance to load centers. In the US, as the expected increase in renewable generation and electricity demand, similar problem arises. A potential solution is to upgrade the transmission system at a higher voltage by constructing a new overlay grid. This paper will first address basic requirements for such an overlay grid. Potential transmission technologies will also be discussed. A multi-terminal VSC HVDC model is developed in DSATools to implement the overlay grid and a test case on a regional NPCC system will be simulated. Another test system of entire US power grid, with three different interconnections tied together using back-to-back HVDC, is also introduced in this paper. Building an overlay system on top of this test case is ongoing, and will be discussed in future work.

Lei, Yin↗

Quantifying Capital Cost Reduction Pathways for Advanced Nuclear Reactors

The framework developed in this study is provided both as an excel sheet (https://inl.gov/content/uploads/2023/11/Nuclear-Reactor-Cost-Reduction-Pathway-Spreadsheet-Tool.xlsx) and a Python (Jupyter) Notebook (link: https://github.com/accert-dev/ACCERT/tree/main/Cost%20Reduction). Capital cost considerations are one of the primary inhibitors to the large-scale deployment of nuclear power plants. While it is widely accepted that first units will likely be expensive and relatively uncompetitive, it is reasonable to expect that subsequent units, built in relative quick succession, will be cheaper as they benefit from the so-called “learning effects”. However, the large degree of uncertainty associated with this parameter renders it challenging for first movers to invest in the first few expensive units. To resolve this impasse, the U.S. Department of Energy’s Advanced Nuclear Liftoff study advocated for the formation of large, committed order books of plants of the same technology to spread the costs across several units and kickstart the nuclear supply chain. The study also advocated best practices for avoiding overruns and keeping reactors on budget. This report builds on these key recommendations by attempting to quantify specific pathways toward cost reduction for nuclear energy. A capital cost estimation framework was built to untangle the effect of learning into a subset of key cost drivers, referred to as “levers”. Collectively, the choice of these levers is intended to reflect the decision-making of high-level stakeholders like plant owners and the government. In addition to the size of the firm orderbook, these levers included (a) cost drivers that are most often attributed to cost overruns such as architect/engineering (A/E) proficiency, construction proficiency, procurement service proficiency, design completion prior to the start of construction, and design maturity, and (b) cost reduction strategies such as modular construction, cross-site standardization, safety classification of the reactor building, and of the balance of plant. Two advanced reactor designs were leveraged as use cases and bottom-up cost estimates made with assumptions consistent with a well-executed first-of-a-kind project (WE-FOAK, i.e., almost no overruns) were used as baselines for the models. Cost correlations were surveyed from the literature to determine the impact of important variables on projected timelines and costs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Development, verification, and validation of comprehensive acoustic fluid-structure interaction capabilities in an open-source computational platform

The acoustic fluid-structure interaction (FSI) formulation is a practical numerical approach for the seismic analysis of fluid-filled tanks. However, there are no verification and validation studies reported in the literature that demonstrate the ability of an acoustic FSI numerical model to predict responses important to structural and mechanical design for intense translational and rotational earthquake inputs. Herein, an acoustic FSI formulation is implemented in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE), and is formally verified and validated using analytical solutions and code-to-code verification, and experimental data, respectively. The analytical solutions are for small amplitude, unidirectional seismic inputs. The code-to-code verification utilizes a previously verified and validated Arbitrary Lagrangian-Eulerian (ALE) numerical model in the commercial finite element code LS-DYNA. The validation studies utilize a comprehensive data set assembled from results of 3D earthquake-simulator tests of a fluid-filled vessel. The acoustic numerical model in MOOSE is verified and validated for hydrodynamic pressures and support reactions except for cases that involve significant convective response. For small amplitude inputs, numerically predicted wave heights match those of the analytical solutions. The numerical model is not verified and validated for wave height calculations under intense 3D seismic inputs. The run times for the acoustic FSI simulations in MOOSE are an order of magnitude, or more, shorter than for the corresponding ALE simulations in LS-DYNA. The utility of the MOOSE acoustic FSI implementation is demonstrated by seismic analysis of a building equipped with a fluid-filled, advanced nuclear reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Introduction to the design rules for Metal Big Area Additive Manufacturing

Wire feed metal additive manufacturing offers advantages, such as large build volumes and high build rates, over powder bed and blown powder techniques, but it has its own disadvantages, i.e., lower feature resolution and bead morphology control issues. A new wire feed metal additive manufacturing process called Metal Big Area Additive Manufacturing (mBAAM) uses a Gas Metal Arc Weld system on an articulated robot arm to increase build volume and deposition rate in comparison to powder bed techniques. The high deposition rate implies a low-resolution process; therefore, parts designed for mBAAM must incorporate the use of machining to achieve certain features. Here, this paper presents an introduction to how design rules, such as overhang constraint, large weld bead thickness, and support structure, for mBAAM interact in the context of an excavator arm case study, which was designed using topology optimization.

42 ENGINEERING↗

Scalable modular dynamic molten salt reactor system model with decay heat

Nodal dynamic models allow rapid reduced-order simulations of complex systems. This paper presents a publicly available system dynamic model of a molten salt reactor system with a topology of the Molten Salt Reactor Experiment (MSRE), in MATLAB/Simulink, building upon a model previously published and validated with MSRE data. It adds scaling of nominal power, dynamic representation of decay heat generation, a generic decay heat removal system, improved documentation, modularity, and test cases. One hour of system transient runs in less than 10 min on a laptop. This model is intended for first order engineering calculations, developing insights into system transient behavior, equipment sizing, parameter sensitivity studies. The model contains an easy to use toolkit which can be customized and expanded to describe any molten salt reactor system. Finally, scenarios involving off-normal transients are presented. Relation between shutdown reactivity insertions, decay heat removal, core recriticality, and safe shutdown conditions are discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Energy efficiency and thermal resilience analysis for row houses using representative building energy models

Row houses, a prevalent medium-density urban archetype, were popularized during industrial era to accommodate the working class in the U.S. cities. Today, many of these structures are deteriorating, exacerbating the energy cost for residents with limited economic resources. Moreover, many households that lack Air Conditioning (AC) are vulnerable to extreme summer heat, an issue expected to worsen in the future. Energy-efficient retrofits can help mitigate these challenges. However, current literature provides limited insight into how existing building energy codes impact the energy efficiency and thermal resilience of row houses in current and future weather scenarios. This study provides a detailed method to address this gap by employing calibrated representative energy models of row houses as a baseline, and evaluating the annual energy savings and summertime thermal safety improvements from code compliant retrofits under current and future typical weather conditions. Using Baltimore City as a case study, results show that under current weather, retrofitting row houses yields significant energy savings along with notable thermal resilience improvements. The end unit row house realizes higher energy savings, whereas the interior unit experiences greater thermal resilience improvement after retrofitting. In future, retrofitted row houses can still save energy, but residents will face numerous hours of fatal indoor temperatures without AC in summer. Additionally, findings reveal stark zone-wise variations in thermal safety, and elevated humidity risks in certain zones post-retrofit, emphasizing the need for zonal retrofit strategies and better natural ventilation strategies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Application of automated measurement and verification to utility energy efficiency program data

Trustworthy savings calculations are critical to convincing regulators of both the cost-effectiveness of energy efficiency program investments and their ability to defer supply-side capital investments. Today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of energy efficiency programs. They also require time-consuming data acquisition. A spectrum of savings calculation approaches is used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data. The increasing availability of “smart” meters and devices that report near-real time data, combined with new analytical approaches to quantify savings, offers the potential to conduct M&V more quickly and at lower cost, with comparable or improved accuracy. Commercial energy management and information systems (EMIS) technologies are beginning to offer these ‘M&V 2.0’ capabilities, and program administrators want to understand how they might assist programs in quickly and accurately measuring energy savings. This paper presents the results of recent testing of the ability to use automation to streamline the M&V process. In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. For the data sets studied we evaluate the fraction of buildings that are well suited to automated baseline characterization, the uncertainty in gross savings that is due to M&V 2.0 tools’ model error, and indications of labor time savings, and how the automated savings results compare to prior, traditionally determined savings results. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14. In addition the findings suggest that M&V 2.0 methods may also offer time-savings relative to traditional approaches. Lastly, we discuss the implications of these findings relative to the potential evolution of M&V, and pilots currently being launched to test how M&V automation can be integrated into ratepayer-funded programs and professional implementation and evaluation practice.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modular Integrated System for Carbon-Neutral Methanol Synthesis Using Direct Air Capture and Carbon-Free Hydrogen Production

This study investigates the development and economic analysis of a modular integrated system for carbon-neutral methanol synthesis, leveraging direct air capture (DAC) and solid oxide electrolysis cells (SOEC) for carbon dioxide and hydrogen production, respectively. The proposed system integrates a novel building-based DAC process, functionalized solid sorbents, and low-energy SOEC technology, aiming to minimize operational and capital costs. A comparison between the base case system (1,000 t methanol/year) and a scaled-up model (14,758 t methanol/year) reveals significant improvements in efficiency and economic feasibility. The scaled-up system achieves a levelized cost of methanol (LCOM) of $740/t, a 7.5% reduction compared to that of conventional DAC-based systems, while utilizing existing building HVAC infrastructure for air handling. Detailed sensitivity analyses were conducted, evaluating the effects of plant capacity and air flow rate on the LCOM, demonstrating the scalability of the building-based DAC system. The cradle-to-gate life cycle analysis shows that the proposed process using renewable-sourced electricity achieves a 38% reduction in greenhouse gas (GHG) emission compared to reported values of green methanol production technologies that use a conventional DAC and a conventional methanol synthesis catalyst. When fossil-sourced electricity is used in the proposed process, it leads to about a 37.5% reduction in GHG emission in comparison to reported values for conventional methanol production technologies using steam methane reforming technology and fossil-sourced electricity.

alcohols↗

Fast inference of deep neural networks in FPGAs for particle physics

Here, recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗