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NASA NTRS2018

NASA SPLICE Project: Development and Testing of Precision Landing GN&C Technologies

NASA's technology advancement needs for entry, descent and landing call for high-precision, high-rate sensors that can improve navigation accuracy and vehicle control performance. Higher landing accuracy is required for any future human lander missions, and likely, for most robotic missions 1,2. Sensors and algorithms that significantly reduce navigation errors and can image the local terrain will enable landing at locations of high scientific interest that would otherwise pose significant risk to the vehicle. The Safe and Precise Landing-Integrated Capabilities Evolution project, or SPLICE, is developing precision landing and hazard avoidance (PL&HA) technologies for NASA and for potential commercial space flight missions. SPLICE technologies include sensors, algorithms, advanced space flight computing capabilities, and simulation tools used to integrate and study guidance, navigation, and control (GN&C) system performance. SPLICE efforts include hardware-in-the-loop (HWIL) simulation testing, ground testing, and flight testing, including reuse of hardware from the CoOperative Blending of Autonomous Landing Technologies (COBALT) suborbital flight-test payload3,4. Two of the precise navigation sensors that are being developed and matured within SPLICE are LiDARs. Since 2006, NASA Langley has been developing a Navigation Doppler LiDAR (NDL) for precise velocity measurements, and SPLICE is building an NDL engineering test unit (ETU) that will be brought up to TRL 6 following environmental and high-speed1,2 testing. NASA Goddard is developing a Hazard Detection LiDAR (HD LiDAR) engineering development unit (EDU) for SPLICE that has relevance to future human and robotic lander missions. The HD LiDAR will be flight test and matured to TRL 5.

Restrepo, Carolina I.↗

NASA NTRS2013

Environmentally Friendly Rotorcraft Concepts: 2011-2014: US/France MoA Helicopter Aeromechanics - Task 5

Motivation: Greenhouse gas concentrations have increased well beyond their pre-industrial levels due to human activities; Aircraft emissions are becoming regulated in industrialized nations, so future rotorcraft will need to be designed for minimal environmental impact. Objectives: Determine suitable metrics to measure environmental impact of rotorcraft, focusing first on air pollution; Integrate performance metrics with rotorcraft design codes; Generate rotorcraft designs with minimal environmental impact. Outcomes: Obtain an understanding of how targeting reduced emissions affects the rotorcraft design process; Advance the capability of existing rotorcraft design tools to include environmental performance metrics. Tools: Evaluation and predesign - NDARC (NASA Design and Analysis of Rotorcraft) and CREATION (Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network); Flight simulation - CAMRAD II (Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics) and HOST (Helicopter Overall Simulation Tool).

Environmentally Friendly↗

NASA NTRS2024

Development of a High Frequency Class-A Amplifier for High Temperature Wireless Microsystems based on a Silicon Carbide Static Induction Transistor

Next generation gas turbine engine health monitoring for high performance jet engine-powered aircraft will incorporate integrated microelectronic sensors specifically designed to enhance aircraft functionality, engine efficiency, and safety. Temperature, air flow and pressure associated with the gas path as well as emissions from the engine correlate to the state of the combustor and other engine health conditions. Monitoring these parameters accurately requires positioning electronic sensors in the hot zone of the gas turbine engine, which exposes them to temperatures in excess of 400ºC. At these temperatures, signal amplification is needed at the transducer level to distinguish the desired measured signal from the significant electronic noise generated by the harsh environment. In this presentation, we report on the simulation, development, fabrication, and testing on a high temperature Class-A amplifier which utilizes a 4H-SiC Static Induction Transistor (SIT) as the active device and input/output matching and DC bias networks comprised of thin-film spiral inductors, metal-insulator-metal capacitors, and thick film chip resistors. A small signal model that emulates the operation of the 4H-SiC SIT from 25 to 400ºC, with an emphasis on operation at 400οC, was utilized. Measurements were performed from 25 to 400ºC to generate current-voltage curves, capacitive transistor characteristics and high frequency scattering parameters (S-parameters). The measured data was used to extrapolate the transconductance, gm, as a function of temperature for model development. Circuit simulation tools were used to generate S-parameters, which were compared to the measured values. At 400οC, a maximum difference between measured and simulated S-parameters for frequencies from 20 to 100 MHz were 3.84%, 0.68%, 10.61%, and 3.26% for S21, S12, S11, and S22, respectively. The average transit frequency, ft, was calculated from measured values to be 197.8 MHz, while the simulated value from the model was found to be 200 MHz. The Amplifier’s S-parameters were recorded at 20-100 MHz over a temperature range of 25-400ºC and show a gain of approximately 15.8 and 5.80 dBm at 25 and 400ºC, respectively. The input and output reflection coefficients at 50 MHz and 400ºC were -18.5 and -15.2 dB, respectively. The noise figure and phase noise were measured over the temperature range of 25-400ºC and recorded. The noise figure increased 21% at 50 MHz over the temperature range, while the 1 kHz offset of the phase noise remained below -110 dB. The stability factor, K, calculated with measured and simulated data demonstrates unconditional stability over the frequency range at 400ºC. Lastly, the 1-dB compression point was measured at 50 MHz and 400ºC with an approximated output of 9.5 dBm.

SiC↗

DOE OSTI2018

High Performance GPU-Based FEM Code for Simulating Welding and Additive Manufacturing

An in-house HPC finite element code with novel computational schemes provides fast and high-fidelity simulation capability for welding processes and allied manufacturing processes such as metal additive manufacturing. Benchmark comparison confirmed that, for the testing cases studied, the newly developed in-house code resulted in increases in computational speed in the ranges of 100x to 1000x depending on the size and complexity of the testing cases, when compared to today's commercial welding process simulation tools. For a reference problem in nuclear reactor repair welding, ORNL's in-house high fidelity code was able to complete the simulation in 3.2 days, which would take estimated 10 to 20 years using today's commercial code. Similar order of magnitudes of reduction in computational times for metal additive manufacturing process were also achieved.

Feng, Zhili↗

DOE OSTI2022

Enabling High-Energy-Density Physics Using X-Ray Free Electron Lasers

New High-Energy-Density (HED) Physics research facilities, such as x-ray free electron lasers (XFEL) are becoming available to the research community. These new tools can be used to address stockpile stewardship science questions in the HED regime with greater accuracy and in parameter ranges not previously accessible. Two important challenges that must be overcome before using the XFEL are developing simulation tools needed to design the experiment and developing large quantities of inexpensive targets from which the HED state is created. We developed two-dimensional simulations in a new geometry (planar) to determine basic plasma parameters that could be expected in a notional experiment. We developed a postprocessor that used the results of the simulations to predict the x-ray Thomson scattering (XRTS) signal that yield the plasma density and pressure at a specified location and time. Finally, we developed several possible target geometries that are inexpensive and easy to use. With these tools in hand, the risk in designing an experiment for an XFEL is significantly reduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Interactive Visualization of Multi-Group Cross Sections on High-Fidelity Spatial Meshes

Core simulation tools utilizing Monte Carlo transport methods represent the 'Holy Grail' for nuclear reactor analysis. However, deterministic transport methods pose a number of advantages to Monte Carlo from a computational perspective, which seems likely to make these methods more viable in the near to intermediate term. Deterministic methods almost universally discretize the energy domain in a few to hundreds of energy groups. One of the key challenges for deterministic transport-based methods to be viable is accurate, reactor agnostic multi-group cross section generation. This paper describes tools for data processing and visualization of multigroup cross sections generated on high-fidelity spatial meshes from the OpenMC Monte Carlo code. Next generation deterministic transport reactor core modeling tools will require multi-group cross section (MGXS) generation on high-fidelity spatial meshes. High-fidelity MGXS are necessary for the accurate simulation of neutron physics in nuclear reactors with a high degree of spectral heterogeneity - e.g., large localized neutron flux gradients throughout a reactor core. Although many local (e.g., pin cell) approximations have been developed to model the scalar flux to compute spectral-averaged MGXS, these techniques cannot account for complex intra-pin and intra-assembly spatial self-shielding effects. Monte Carlo presents the most accurate method for reactor agnostic multi-group cross section generation since it does not require the use of local approximations to the flux. Monte Carlo has been proposed as a highly desirable framework for reactor agnostic cross section generation for many years. Monte Carlo methods can effectively embed all of the relevant spectral effects into the MGXS through the random particle tracking and interaction sampling processes. Previous work has applied MCNP and Serpent to compute few-group constants for diffusion calculations, and OpenMC to compute multi-group cross sections for transport-based methods. The primary difficulty with using Monte Carlo for MGXS generation is the tremendous computational resources required to adequately converge multi-group cross sections in each spatial region and energy group. Recent development of the OpenMC Monte Carlo code for high-performance computing platforms has made multigroup cross section generation from Monte Carlo more tractable in the foreseeable future. The recent release of OpenMC v0.7.1 includes a new Python-based module for MGXS generation. The MGXS generation module leverages many of the algorithms and features for 'big data' analysis of large tally datasets on complex spatial meshes. This paper describes the data management workflow and visualization tools which have been developed to explore the plethora of MGXS data generated with OpenMC. It is the authors' hope that the development of the visualization tool described in this paper will ultimately enable the design of machine learning algorithms to accelerate the convergence rate of high-fidelity multi-group cross section generated with Monte Carlo. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Accelerating Hanford Site Cleanup through Operations Research Modeling - 20238

The Hanford Site cleanup effort will require the integration of dozens of unique facilities and processes, many of which will be first-of-a-kind in implementation and design. Each facility will be governed by its own set of operating logic, configured with a unique array of unit operations, and subject to a set of constraints that will affect its behavior. The collection of facilities have multiple points of interface, making the operations of any one facility potentially significant to the operations of other up- or downstream processes. It is therefore highly desirable to accurately predict these operations, as it allows for Site officials to identify and preempt bottlenecks and vulnerabilities before they unexpectedly inhibit the cleanup mission. With the quantity and complexity of the processes that will be on Site, building a pen-and-paper or even a spreadsheet-assisted model of the cleanup mission quickly becomes overwhelming in scope and inaccurate in execution. The Engineering organization for the Site's Tank Operations Contract (TOC) has therefore implemented the use of operations research (OR) modeling to simulate and predict future operations of Site facilities. These models are created using a discrete event simulation tool that allows for the development of detailed, versatile, and robust models. Not only can these models account for complex logical behaviors, but they can also simulate process details down to the level of vessel sizing, labor utilization, equipment reliability, and resource availability. To date, the TOC has developed OR models for several facilities on Site, including for single-shell tank (SST) farms, double-shell tank (DST) farms, the Effluent Treatment Facility (ETF), and the waste transfer system. These models have focused on identifying bottlenecks and operational constraints, and have been used to quantify the effects of implementing process changes. This latter point is particularly valuable, as it allows for several alternatives to be studied in a virtual setting before committing resources to making a change in the field. The decision to develop OR models has gained tremendous support from the Site's stakeholders and the U.S. Department of Energy (DOE) management, and has prompted the use of the tool to support additional internal and external initiatives. Recently, an initiative was proposed to use the models to help identify and provide quantitative backing for risks and opportunities for the TOC. This application of OR could not only help inform how the TOC manages its risks (e.g. quantities and types of spare parts), but could also help drive process improvements whose benefits might otherwise be hard to quantify. The models have also been used to drive the TOC's cloud computing, artificial intelligence (AI), and machine learning (ML) initiatives. These initiatives will not only improve the ability of the TOC to more rapidly respond to the needs of its customers, but it will also aid in the ability of the TOC to analyze and improve the processes it studies. Partnership with two external software development and consulting companies (Lanner and Ynformed) has furthered not only the application of AI and ML within the TOC, but has also spurred the development of new/improved software tools and platforms used by the companies. These partnerships have proven to be mutually beneficial and productive, and have set a precedent for the types of gains that can be made by exploring such options. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2000

Software Released by LEWICE 2.0 Ice Accretion Software Development Project

Computational icing simulation methods are making the transition from the realm of research to commonplace use in design and certification. As such, standards of software management, design, validation, and documentation must be adjusted to accommodate the increased expectations of the user community with respect to accuracy, reliability, capability, and usability. With this in mind, in collaboration with Glenn's Engineering Design and Analysis Division, the Icing Branch of the NASA Glenn Research Center at Lewis Field began a software improvement project focused on the two-dimensional ice accretion simulation tool LEWICE. This project is serving as an introduction to the concepts of software management and is intended to serve as a pilot project for future icing simulation code development. The LEWICE 2.0 Software Development Project consisted of two major elements: software management and software validation. The software management element consisted of identifying features of well-designed and well-managed software that are appropriate for an analytical prediction tool such as LEWICE and applying them to a revised version of the code. This element included tasks such as identification of software requirements, development and implementation of coding standards, and implementation of software revision control practices. With the application of these techniques, the LEWICE ice accretion code became a more stable and reliable software product. In addition, the lessons learned about software development and maintenance can be factored into future software projects at the outset. The software validation activity was an integral part of our effort to make LEWICE a more accurate and reliable analysis tool. Because of the efforts taken to extensively validate this software, LEWICE 2.0 is more robust than previous releases and can reproduce results accurately across several computing platforms. It also differs from previous versions in the extensive quantitative comparisons of its results with a data base of ice shapes that were generated in Glenn's Icing Research Tunnel. The results of the shape comparisons were analyzed to determine the range of meteorological conditions under which LEWICE 2.0 is within the repeatability found for ice shapes obtained from experiments. These comparisons show that the average variation of LEWICE 2.0 from experimental data is 7.2 percent, whereas the overall variability of the experimental data is 2.5 percent. The LEWICE 2.0 Software Development Project resulted in the release, at the recent Icing Branch LEWICE Workshop, of a more useful and robust analysis tool than was previously available to the icing community. The lessons learned from this project will be applied to the Icing Branch s future software development efforts.

Potapczuk, Mark G.↗

DOE OSTI2017

Reduced order methods for the improvement of control-oriented modelling of nuclear power plants - 185

In the development of the control system of a nuclear power plant, it is of primary importance to rely on simulation tools for its realization, testing and validation. These tools should provide an accurate description of the controlled response of the entire power plant in different scenarios. The classic control-oriented modelling based on 0D/1D approach is well suited for control purposes since it provides fast-running simulations and it is easy to linearize, but it may not be sufficient to deeply investigate the complexity of some systems, in particular when/where spatial phenomena have a remarkable impact on dynamics. Reduced Order Methods (ROMs) can offer the proper trade-off between computational cost and solution accuracy, combining the high-detail modelling (e.g., 3D modelling) usually adopted for design purposes with the requirements demanded for a control-oriented tool, firstly the computational efficiency. In this work, ROMs are used to improve a control-oriented plant simulator of a Lead-cooled Fast Reactor (LFR). In particular, the attention is focused on the reactor core neutronics, and on the thermal-hydraulics of the reactor pool as well. A test case regarding the design of a Kalman Filter is set up showing promising results for the application of ROM in the control context. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Development of Graphite Thermal and Mechanical Modeling Capabilities in Grizzly

Nuclear-grade graphites are used extensively in the core designs of multiple types of advanced nuclear reactors. In the reactor environment, graphite is exposed over long durations to extreme conditions, including high temperatures, radiation and potentially molten salt and oxygen. Exposure to these conditions can cause several degradation mechanisms in graphite, including nonuniform volumetric strains induced by irradiation and thermal expansion, which lead to stresses that can compromise the performance of graphite components. Evaluating component integrity, predicting component performance over the reactor lifetime, and developing design standards all require robust tools for predicting fracture initiation and propagation in graphite structural components in nuclear reactors. This report documents progress in an ongoing effort to develop modeling and simulation tools in the Grizzly code for predicting the performance of graphite exposed to reactor conditions. Recent developments include a set of thermal and mechanical models that now include the IG-110, NBG-18, and H-451 graphite grades. Improvements have also been made to a nonlinear damaged plasticity model applicable to predicting damage under tension and compression to quasibrittle materials, including graphite. In addition, enhancements have been made to the extended finite element method implementation targeted at simulating graphite fracture. These include new capabilities for crack nucleation in the interior of a solid body, improved treatment of crack nucleation on free surfaces, and more robust modeling approaches for crack growth approaching free surfaces or other cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Center for Extended Magnetohydrodynamics Modeling

This researcher participated in the DOE-funded Center for Extended Magnetohydrodynamics Modeling (CEMM), a multi-institutional collaboration led by the Princeton Plasma Physics Laboratory with Dr. Stephen Jardin as the overall Principal Investigator. This project developed advanced simulation tools to study the non-linear macroscopic dynamics of magnetically confined plasmas. The collaborative effort focused on the development of two large numerical simulation codes, M3D-C1 and NIMROD, and their application to a wide variety of problems. Dr. Ramos was responsible for theoretical aspects of the project, deriving consistent sets of model equations applicable to weakly collisional plasmas and devising test problems for verification of the numerical codes. This activity was funded for twelve years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Tagged Short-Range Correlations for Medium to Heavy Ions at an Electron Ion Collider

Understanding short-range correlations between pairs of nucleons in the nucleus is an important nuclear physics topic that can address longstanding mysteries in nuclear physics such as the behavior of dense nuclear systems and the modification of parton distributions. As a collider and with a large acceptance detector, the Jefferson Lab version of the Electron Ion Collider has unique capabilities that are complementary to previous short-range correlation studies, most notably the ability to more fully reconstruct the final state. Optimization of the collider’s detector to maximally exploit these capabilities called for important extensions of existing simulation tools. For this one year laboratory directed research and development project, we upgraded the benchmark electron-nucleon event generator for lepto-production simulation code Benchmark eA Generator for LEptoproduction (BeAGLE) to be able to handle tagged medium-to-heavy nucleons. This included updating the code to correctly handle Fermi motion of nucleons in the nucleus as well as the large momentum caused by short-range correlations. In addition, we also investigated the possibility of improved kinematic reconstruction methods for electron nucleon collisions and looked at pair-spectator tagging of short-range correlations with the Jefferson Lab electron ion collider design. The project progressed very much as planned.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Coupling of Spark Plasma Sintering with Advanced Modeling to Enable Process Scale-Up: Presentation to DOE-NE [Slides]

The research goal of this project is to develop at Idaho National Laboratory (INL) a first-of-its-kind Multiphysics Object-Oriented Simulation Environment (MOOSE)-based, multiscale, multiphysics spark plasma sintering (SPS) modeling and simulation code application, termed “Freya.” Freya will simulate the thermo-mechanical-electrical aspects of the SPS fabrication process and will be paired with lower length scale sub-models, such as phase-field, to predict the resulting microstructure. SPS is an advanced manufacturing process that can be used to solve a variety of material manufacturing challenges; however, this process is an extremely challenging problem for modeling and simulation. The SPS process is inherently multiphysics and multi-scale, with the macroscale electro-thermo-mechanical behavior linked intricately to the microstructure evolution of the part being sintered. Accurate modeling and simulation tools, specifically geared towards the SPS process, are needed to predict the influence of the multiple variables involved in the manufacturing process. Modeling and simulation accuracy is achieved and demonstrated through comparison to multiple validation experiments. The validation efforts for Freya include both separate effects and complete multiphysics SPS process experiments. One of the key benefits this Laboratory Directed Research & Development (LDRD) project offers stems from the emphasis placed on experimental validation of the Freya models, both on the individual length scales and of the final coupled multiscale multiphysics simulations. Experimental validation of Freya’s multiscale coupling capability provides the technical credibility necessary for potential future industry and research partners to accept the simulation predictions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Development of High-Fidelity and Efficient Modeling Capabilities for Enabling Co-Optimization of Fuels and Multi-Mode Engines

The objective of this closely coordinated research project is to develop improved physical sub-models and innovative numerical algorithms to enable accurate and efficient predictions of multi-mode combustion to support EERE’s Co-Optima program. To this end, a research pro-gram is proposed that seeks • to develop accurate submodels for predicting multi-mode combustion regimes, wall-heat transfer, non-equilibrium plasma ignition, and combustion-mode transition, • to develop dynamic load-rebalancing algorithms and efficient semi-implicit time-integration schemes for enabling exascale computing, • to validate the computational submodels against experiments and direct numerical simulation (DNS) data in conjunction with sensitivity analysis, and • to support the direct transition of the resulting sub-models into other engine-simulation tools through the development of self-contained modules. All sub-models will be integrated into the open-source exascale engine-simulation platform Nek5000.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Hardware-Based Demonstration of Temperature Control Functions for Reactor Systems

Establishing autonomy in reactor control systems has become essential for the expansion of nuclear technologies. Thermal regulation in particular remains crucial for maintaining stable operation and ensuring the integrity of fuel. To alleviate public skepticism of the safety of nuclear reactors, demonstrating control over this key factor is pivotal. Utilizing electric heat pads to simulate the heat released in a reactor core, thermocouples for temperature monitoring, and an Arduino micro programmable logic controller (PLC) for control, a hardware-based demonstration of a reactor heating system validates the efficacy of reactor control over this key parameter. To improve precision, a proportional-integral-derivative (PID) algorithm was implemented in the heating control loop to ensure meticulous control of reactor functions. In addition, the integration of this physical system with a digital simulator tool such as RELAP5-3D establishes a foundation for a comprehensive testing environment. This allows for a refinement of temperature control under various simulated reactor conditions, bringing another layer of reliability to the operation of the system. By facilitating a physical demonstration of reactor thermal management and control strategies, this project provides a foundation for expanded testing and educational outreach. Ultimately, this system advances the broader goal of demonstrating the safety and viability of autonomous reactor operations, contributing to public trust and future reactor deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10 13 floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10 18 floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

biophysically detailed models↗

DOE OSTI2021

Understanding Type Ia Supernova Distance Biases by Simulating Spectral Variations

In the next decade, transient searches from the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope will increase the sample of known Type Ia supernovae (SNe Ia) from ~10 3 to 10 5 . With this reduction of statistical uncertainties on cosmological measurements, new methods are needed to reduce systematic uncertainties. Here, characterizing the underlying spectroscopic evolution of SN Ia remains a major systematic uncertainty in current cosmological analyses, motivating a new simulation tool for the next era of SN Ia cosmology: Build Your Own Spectral Energy Distribution (byosed). byosed is used within the SNANA framework to simulate light curves by applying spectral variations to model SEDs, enabling flexible testing of possible systematic shifts in SN Ia distance measurements. We test the framework by comparing a nominal Roman SN Ia survey simulation using a baseline SED model to simulations using SEDs perturbed with byosed, and investigating the impact of ignoring specific SED features in the analysis. These features include semiempirical models of two possible, predicted relationships: between SN ejecta velocity and light-curve observables, and a redshift-dependent relationship between SN Hubble residuals and host-galaxy mass. We analyze each byosed simulation using the SALT2 and BEAMS with Bias Corrections framework, and estimate changes in the measured value of the dark-energy equation-of-state parameter, w. We find a difference of Δw = –0.023 for SN velocity and Δw = 0.021 for redshift-evolving host mass when compared to simulations without these features. By using byosed for SN Ia cosmology simulations, future analyses (e.g., the Rubin and Roman SN Ia samples) will have greater flexibility to constrain or reduce such SN Ia modeling uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2011

Predicting the Consequences of Workload Management Strategies with Human Performance Modeling

Human performance modelers at the US Army Research Laboratory have developed an approach for establishing Soldier high workload that can be used for analyses of proposed system designs. Their technique includes three key components. To implement the approach in an experiment, the researcher would create two experimental conditions: a baseline and a design alternative. Next they would identify a scenario in which the test participants perform all their representative concurrent interactions with the system. This scenario should include any events that would trigger a different set of goals for the human operators. They would collect workload values during both the control and alternative design condition to see if the alternative increased workload and decreased performance. They have successfully implemented this approach for military vehicle. designs using the human performance modeling tool, IMPRINT. Although ARL researches use IMPRINT to implement their approach, it can be applied to any workload analysis. Researchers using other modeling and simulations tools or conducting experiments or field tests can use the same approach.

Mitchell, Diane Kuhl↗