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At least 91 records · Page 5

Advanced probabilistic risk analysis using RAVEN and RELAP-7

RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program [1], is advancing its capability to perform statistical analyses of stochastic dynamic systems. This is aligned with its mission to provide the tools needed by the Risk Informed Safety Margin Characterization (RISMC) path-lead [2] under the Department Of Energy (DOE) Light Water Reactor Sustainability program [3]. In particular this task is focused on the synergetic development with the RELAP-7 [4] code to advance the state of the art on the safety analysis of nuclear power plants (NPP). The investigation of the probabilistic evolution of accident scenarios for a complex system such as a nuclear power plant is not a trivial challenge. The complexity of the system to be modeled leads to demanding computational requirements even to simulate one of the many possible evolutions of an accident scenario (tens of CPU/hour). At the same time, the probabilistic analysis requires thousands of runs to investigate outcomes characterized by low probability and severe consequence (tail problem). The milestone reported in June of 2013 [5] described the capability of RAVEN to implement complex control logic and provide an adequate support for the exploration of the probabilistic space using a Monte Carlo sampling strategy. Unfortunately the Monte Carlo approach is ineffective with a problem of this complexity. In the following year of development, the RAVEN code has been extended with more sophisticated sampling strategies (grids, Latin Hypercube, and adaptive sampling). This milestone report illustrates the effectiveness of those methodologies in performing the assessment of the probability of core damage following the onset of a Station Black Out (SBO) situation in a boiling water reactor (BWR). The first part of the report provides an overview of the available probabilistic analysis capabilities, ranging from the different types of distributions available, possible sampling strategies, and post processing analysis capabilities. The first part of the report provides an extensive description of two major developments introduced this year: adaptive sampling for limit surface sampling and multi variate distributions. The document concludes with a description of the demo case (BWR-SBO) and a discussion of the results obtained.

97 MATHEMATICS AND COMPUTING↗

Finite Element Analysis of the TRUST Nonlinear Dynamics Testbed

This paper builds on prior work conducted within the Los Alamos National Laboratory (LANL) Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) program. Specifically, it builds on finite element (FE) modeling efforts for the TRUST program’s Nonlinear Dynamics (ND) testbed. Historically, the FE model for the ND testbed has exclusively utilized a Lanczos eigensolver that linearly extracts the system’s natural frequencies. This paper investigates the Abaqus 2024’s explicit dynamic solver, which implements a central difference explicit solver. The central difference method used in Abaqus can capture nonlinear material responses in structural dynamic simulations making it suitable for the ND FE model.

42 ENGINEERING↗

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

59 BASIC BIOLOGICAL SCIENCES↗

On the measurement of shape: With applications to lunar regolith

With the renewed commitment from NASA and other commercial entities for a presence on the Moon, the importance of understanding the characteristics of lunar regolith and how to utilize it have become the target of increasing scrutiny. Much of what is known about lunar regolith was collected during and immediately after the Apollo program, however, analytical techniques and instrumentation have advanced in leaps and bounds in the subsequent decades. Specifically, dynamic image analysis systems have advanced to the point that millions of particles can have morphological characteristics automatically determined in relatively short time frames. Particle morphological data was collected on several lunar samples and a pair of widely used regolith simulants to ascertain the accuracy of these simulants and to explore statistical analysis methods of these large datasets. It is found that these morphology datasets can vary widely depending on the particle size of the particles, and simple averaging of the data skews the results heavily towards the numerically abundant size ranges, the fines. Different reporting methods are suggested to ameliorate these problems. When applied to the lunar regolith, the particles are noted to be less morphologically complex than initially suspected. Compared to the lunar material, the simulants are found to contain some more morphological variability and angular grains. Such difference is likely due to the wildly different comminution processes that these different powder systems are subjected to.

2D shape↗

Abstracts of the 2025 51st Annual NATAS Conference

The North American Thermal Analysis Society (NATAS) is pleased to announce its 51st Annual Conference, held jointly with the IX International Baekeland Symposium. This premier event unites scientists, practitioners, and students from academia, industry, and government to explore the forefront of materials science. The NATAS conference provides a dynamic forum for attendees to delve into the latest advancements in thermal analysis, rheology, and materials characterization. The technical program will highlight new developments in instrumentation and software, alongside practical applications across a wide range of industries. Concurrently, the Baekeland Symposium will showcase cutting-edge scientific, technical, and industrial innovations in the field of high-performance thermosetting polymers. The synergy of this joint meeting creates a unique platform for cross-disciplinary collaboration, fostering the exchange of novel ideas and sparking new research opportunities. Featuring technical presentations, poster sessions, and plenary lectures from renowned experts and emerging graduate students, the conference offers an ideal environment for networking and professional development. We invite you to join us to discover state-of-the-art techniques, discuss groundbreaking research, and connect with peers and leaders in the thermal and materials community.

batteries↗

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Molecular dynamics (MD) simulations must take very small (femtosecond) integration steps in simulation-time to avoid numerical errors. Efficient use of parallel programming models and accelerators in state-of-the art MD programs now is pushing Moore’s limit for time-per-MD step. As a result, directly simulating timescales beyond milliseconds will not be attainable directly, even at exascale. However, concepts from statistical physics can be used to combine many parallel simulations to provide information about longer timescales and to adequately sample the simulation space, while preserving details about the dynamics of the system. Implementing such an approach requires a workflow program that allows adaptable steering of task assignments based on extensive statistical analysis of intermediate results. Here we report the implementation of such an adaptable workflow program to drive simulations on the Summit IBM Power System AC922, a pre-exascale supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). We compare to experiences on Titan, Summit’s predecessor, report the performance of the workflow and its components, and describe the porting process. We find that using a workflow program managed by a Mongo database can provide the fault tolerance, scalable performance, task dispatch rate, and reconfigurability required for robust and portable implementation of ensemble simulations such as are used in enhanced-sampling molecular dynamics. This type of workflow generator can also be used to provide adaptive steering of ensemble simulations for other applications in addition to MD.

Ossyra, John R.↗

Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing – A hierarchical data-driven approach

The power distribution system and urban transportation system are two networked system bare their own operation constraints, such peak load in power systems and traffic congestion in transportation system. With the increasing number of electrical vehicles and charging/discharging stations, two systems are become tightly coupled. However, to optimize the two systems target using electrical vehicles as decision control variables cannot be easily solved using a uniformed optimization frame work. Thus we propose a hierarchical optimization approach to address this problem, which consists of a higher and a lower level. In the higher level, the power distribution system and urban transportation system are treated together to minimize the social cost. Meanwhile, the electrical vehicles and the charging/discharging stations are treated as customers to minimize their own expenditures. Then, an equilibrium is designed to determine the optimal charging/discharging price. In the lower level, the models of power distribution system and urban transportation system are developed to provide a detailed analysis. Specifically, in power distribution system, the three-phase unbalanced optimal power flow problem is relaxed with the semidefinite relaxation programming, and solved with alternating direction method of multiplier. A dynamic user equilibrium problem is formulated for the urban transportation system. For electrical vehicles, the state of charge is considered to optimize the charging/discharging schedule and reduce the impacts of power distribution systems. We conducted the simulation and numerical analysis using the IEEE 8500-bus distribution system and the Sioux Falls system with about 10,000 cars. The results demonstrate the feasibility and effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Near-Optimal Performance of Stochastic Model Predictive Control

Here, this article presents a regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative uncertainties. Under a finite support assumption, the problem can be cast as a finite-dimensional quadratic program, but the problem becomes quickly intractable as the problem size grows exponentially in the horizon length. SMPC aims to compute approximate solutions by solving a sequence of problems with truncated prediction horizons and committing the solution in a receding-horizon fashion. Although this approach is widely used in practice, its performance relative to the optimal solution is not well understood. This article reports for the first time a rigorous near-optimal performance guarantee of SMPC: under stabilizability and detectability conditions, the regret of SMPC is exponentially small in the prediction horizon length, allowing SMPC to achieve near-optimal performance at a substantially reduced computational expense.

93E20, 93B45↗

Execution Strategy Analysis (ESA) Origin Sites Readiness Model - 19073

This is a technical paper that does not take into account the contractual limitations under the Standard Contract for Disposal of Spent Nuclear Fuel and/or High-Level Radioactive Waste (Standard Contract) (10 CFR Part 961). Under the provisions of the Standard Contract, DOE does not consider spent nuclear fuel in multi-assembly canisters to be an acceptable waste form, absent a mutually agreed to contract amendment. To the extent discussions or recommendations in this paper conflict with the provisions of the Standard Contract, the Standard Contract provisions prevail. The DOE Office of Nuclear Energy (DOE-NE) Office of Spent Fuel and Waste Disposition (SFWD) Integrated Waste Management System (IWMS) program is developing an Execution Strategy Analysis (ESA) Origin Sites Readiness Model using the Goldsim{sup TM} software application to provide information on potential future approaches for removing SNF and HLW from origin sites such as commercial nuclear power plant sites. The purpose of this ESA model is to apply integrated waste management system analysis, system engineering, and decision analysis principles to inform future decisions regarding potential future nuclear waste management system architectures. The ESA Origin Sites Readiness Model is a dynamic simulation modeling capability. It is developed for use in the analysis and comparison of potential future implementation strategies associated with an integrated nuclear waste management program. The model is designed to be independent of the destination of the SNF or HLW so the results from the Origin Sites Readiness Model are equally applicable to a wide range of scenarios involving shipments to generic destinations in an integrated waste management system. The model explicitly takes significant assumptions, uncertainties, and risks into account. Currently, the ESA Origin Sites Readiness Model includes 14 shutdown sites (Maine Yankee, Yankee Rowe, Connecticut Yankee, Vermont Yankee, La Crosse, Big Rock Point, Zion, Kewaunee, Fort Calhoun, Trojan, Rancho Seco, Humboldt Bay, San Onofre, and Crystal River) and is designed to be modular so that other sites can be added in the future. The model also includes multiple transportation modes (heavy haul truck, rail, and barge), activities related to the acquisition of transportation assets such as railcars and transportation casks, and site-specific considerations of on-site and near-site transportation infrastructure. The model was developed using the ESA process with Subject Matter Experts (SME); it incorporates uncertainty and risks and provides a structured, systematic methodology for evaluating potential SNF campaign scenarios. The model also complements other system-level logistics tools being developed by SFWD. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Software Quality Assurance Plan ANSYS LSDYNA Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic and electromagnetic simulation capabilities. ANSYS LS-DYNA is the most used explicit simulation program capable of simulating the response of materials to short periods of severe loading. Its many elements, contact formulations, material models, and other controls can be used to simulate complex models with control over all the details of the problem. ANSYS LS-DYNA has a vast array of capabilities to simulate extreme deformation problems using its explicit solver. Engineers can tackle simulations involving material failure and look at how the failure progresses through a part or through a system. Models with large amounts of parts or surfaces interacting with each other are also easily handled, and the interactions and load passing between complex behaviors are modeled accurately. Using computers with higher numbers of CPU cores can drastically reduce solution times. In addition, many consulting firms and hundreds of universities use ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. ANSYS has successfully passed over 100 customer quality system audits against American Society of Mechanical Engineers (ASME) NQA-1 and 10 CFR Part 50, Appendix B, since the company was founded, over 60 of which have been since 1997. ANSYS has successfully passed over 100 International Organization for Standardization (ISO) 9001 assessments. ANSYS design analysis software is the first created within a quality system with ISO 9001 certification, which is the internationally accepted quality standard. Product development, testing, maintenance, and support processes also meet the US Nuclear Regulatory Commission’s (NRC’s) quality requirements, as they have for nearly four decades. ANSYS staff perform more than 60,000 software verification tests before releasing each new product. ASME NQA-1-2012 (Subpart 2.7 is specific to software) is the industry- and NRC-accepted approach (consensus standard) for meeting 10 CFR Part 50, Appendix B, requirements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

XPlacer/Tracer

XPlacer/Tracer is a dynamic analysis tool that finds bad memory access patterns in heterogeneous codes. XPlacer/Tracer consists of two components. (1) A configurable ROSE plugin for source code instrumentation of heterogeneous code written in the C/C++ and CUDA programming languages. (2) A sample configuration that instruments memory accesses to dynamic memory in CPU and GPU codes, and a runtime library that tracks these memory accesses at runtime. The collected information is reported in form of a textual summary or as memory map that can be converted to images.

Pirkelbauer, PeterM.↗

Analysis of HolosGen Sub-Scale Simulator with Plant Dynamics Code

The Subcritical Power Module Sub-scale Simulator (SPM-SS) has been designed and constructed by HolosGen LLC under the ARPA-E MEITNER program to simulate the thermal-hydraulic and heat transfer behavior of the full-scale Holos-Quad Subcritical Power Modules (SPMs). Four coupled SPMs, each rated at 5.5MW, form the Holos-Quad gas-cooled microreactor design. The SPM-SS represents a substantially scaled-down system with a power rating less than 40 kW, equipped with an electrically heated fuel cartridge heat exchanger, an electrically heated compressor heat exchanger, and a valve actuated turbine heat exchanger, in addition to a recuperator and a cooler heat exchanger. The fuel cartridge represents a portion of the full-scale SPM core, the compressor heat exchanger mimics the temperature changes resulting from the compressor’s turbomachinery inefficiencies, the turbine heat exchanger mimics the expansion process normally occurring through the turbine, while the recuperator and cooler heat exchangers complete the subscale simulator loop. The heaters equipping the fuel cartridge and the compressor heat exchangers are electronically controlled to simulate normal and off-normal SPM operating conditions. The full-scale Holos-Quad SPM design eliminates the traditional balance of plant and executes thermal-to-electric energy conversion by means of an intercooled Brayton cycle with decoupled compressor-turbine turbomachinery. The Holos-Quad full-scale design is equipped with a multi-stage axial Low- and High-Pressure compressor, and a multistage axial turbine. The SPM-SS is designed for testing and validation of selected components which are instead coupled by a traditional balance of plant. The SPMSS is not equipped with turbo-machinery (compressor and turbine) as the development of these components were excluded from the scope of work under the ARPA-E MEITNER funding program. The SPM-SS balance of plant enables modifications, replacement and testing of individual components with different working fluids and is designed to include the turbo-machinery components that will be developed in future research . The SPM-SS can be operated with different gases, variable mass-flow-rates, pressures, and temperatures to obtain test data for selected components, whose performance can be scaled to validate the computer model of the full-scale SPM at various conditions (e.g., start-up, transients conditions). The SPM-SS can operate at the maximum Holos-Quad design pressure of 7 MPa, and a maximum temperature limited to 650 °C by the electrical heaters. Several SPM-SS tests have been conducted and analyzed with the Plant Dynamics Code (PDC) developed at the Argonne National Laboratory (ANL). These tests aimed at validating the PDC modeled predictions of the full-scale Holos-Quad design with data from selected SPM-SS components. In order to address SPM-SS specific characteristics, such as components heat losses and absence of turbomachinery components, some modifications to the PDC have been implemented to factor the design differences from the full-scale Holos-Quad SPM to the SPM-SS. As the PDC offers capabilities to analyze systems with different working fluids, air, nitrogen, and helium were utilized as the SPM-SS working fluids. Air was utilized to fine-tune the SPM-SS Systems Structures and Components (SSCs), nitrogen was utilized to pressure test the SPM-SS loop at the SPMs design maximum pressure of 7MPa. Helium was utilized as the working fluid circulating through the SPM-SS SSCs for specific tests to validate the PDC predictions of the fuel cartridge heat exchanger. SPM-SS tests data were also analyzed with both the steady-state and transient analysis capabilities offered by the PDC. This report describes the PDC analysis of the SPM-SS tests data, including the necessary code modifications and comparison of the code results with the experimental data. Based on the results, a discussion is presented on how the analysis supports design and transient calculations of the full-scale Holos-Quad microreactor. Also based on the results of this work, recommendations are made for future optimizations of the SPM-SS components and PDC model development needs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Accumulator Sizing for the Hybrid Hydraulic Electric Architecture (HHEA) Using Dynamic Programming

Abstract The Hybrid Hydraulic Electric Architecture (HHEA) is an approach to electrifying and improving that does not require very large electric machines. The majority power is provided hydraulically via a set of common pressure rails while small electric machines are used to modulate that power to meet exact demand. Each common pressure rail has an accumulator, which keeps the pressure of each rail near constant as long as the volume in the accumulator does not change much. A main pump adds or removes fluid from each accumulator as needed to reduce volume changes in the accumulators. It is the operation of this main pump which is investigated here. In previous analysis it was assumed that the size of the accumulators was large enough to account for any difference in fluid volume without significantly changing the pressure. In this paper, the required accumulator sizes are studied using a dynamic programming approach. At each time step the inlet and outlet of the main pump could be connected to any of the common pressure rails. These decisions for the main pump were optimized to minimize the required accumulator size. Case studies are conducted using drive cycles from a 22-ton excavator, a 20-ton wheel loader and a 5-ton excavator. The machines could be operated with total accumulator sizes of less than 20, 10, and 5 liters respectively. Required accumulator size was found to vary with pump flow rate, the frequency at which the pump/motor was able to change operating conditions, the tolerance level of pressure deviation and the accumulator’s polytropic index.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicity library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation. - Highlights: • Impedance-based monitoring of human iPSC-derived cardiomyocyte contractility • Limit cycle analysis of impedance data identifies aberrant oscillation patterns. • Nonlinear autocorrelation function quantifies beat irregularity. • Identification of hERG and non-hERG inhibitors with known risk of arrhythmia • Automated software processes limit cycle and autocorrelation analyses of 96w data.

60 APPLIED LIFE SCIENCES↗

FENIX: Towards a Fully Integrated Multiphysics Framework for Plasma Facing Component Modeling

Computational tools have a crucial role to play in accelerating the deployment of fusion as a clean, reliable, abundant, and sustainable energy source. Multiphysics, high-fidelity simulation capabilities can help model, study, and predict intricate interactions between materials performance, plasma exposure, neutron irradiation, and engineering processes. As such, they can assist in the resolution of scientific and engineering challenges underpinning design, construction, and commission of fusion power plants. To address these needs, ongoing efforts are leveraging the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework and delivering new computational tools for the fusion community. These tools inherit crucial attributes from MOOSE. They are open-source, modular, integrated with nuclear industry-standard software quality assurance processes, and enable multiphysics, multi-fidelity, fully integrated, zero- to three-dimensional, and massively parallel simulations. After a short overview of these capabilities, we will present the development of Fusion ENergy Integrated multiphys-X (FENIX), a MOOSE-based application designed to enable plasma facing component design and performance evaluation. Throughout their lifetime, plasma facing components are exposed to extreme thermal loads, repeated thermal shocks, and irradiation by plasma ions, neutral particles, and high-energy neutrons. Consequently, designing a plasma facing component with acceptable lifetime degradation is extremely challenging. FENIX aims to model the multiphysics environment in which plasma facing components evolve to accelerate their design studies. To that end, FENIX couples existing MOOSE capabilities such as heat transfer, thermomechanics, and thermal hydraulics, with tritium transport via the MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8), with neutronics via the MOOSE-based high-fidelity neutron-photon transport and fluid dynamics code Cardinal, and finally with Particle-in-Cell plasma simulation capabilities being developed in this project. In this study, we present the current FENIX capabilities and preliminary results of its application to model the Tritium Plasma Experiment set up at Idaho National Laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Individual Dynamical Masses of Ultracool Dwarfs

We present the full results of our decade-long astrometric monitoring programs targeting 31 ultracool binaries with component spectral types M7–T5. Joint analysis of resolved imaging from Keck Observatory and Hubble Space Telescope and unresolved astrometry from CFHT/WIRCam yields parallactic distances for all systems, robust orbit determinations for 23 systems, and photocenter orbits for 19 systems. As a result, we measure 38 precise individual masses spanning 30–115 M{sub Jup}. We determine a model-independent substellar boundary that is ≈70 M{sub Jup} in mass (≈L4 in spectral type), and we validate Baraffe et al. evolutionary model predictions for the lithium-depletion boundary (60 M{sub Jup} at field ages). Assuming each binary is coeval, we test models of the substellar mass–luminosity relation and find that in the L/T transition, only the Saumon and Marley “hybrid” models accounting for cloud clearing match our data. We derive a precise, mass-calibrated spectral type–effective temperature relation covering 1100–2800 K. Our masses enable a novel direct determination of the age distribution of field brown dwarfs spanning L4–T5 and 30–70 M{sub Jup}. We determine a median age of 1.3 Gyr, and our population synthesis modeling indicates our sample is consistent with a constant star formation history modulated by dynamical heating in the Galactic disk. We discover two triple-brown-dwarf systems, the first with directly measured masses and eccentricities. We examine the eccentricity distribution, carefully considering biases and completeness, and find that low-eccentricity orbits are significantly more common among ultracool binaries than solar-type binaries, possibly indicating the early influence of long-lived dissipative gas disks. Overall, this work represents a major advance in the empirical view of very low-mass stars and brown dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗

Rotating Detonation Combustion for Gas Turbines: Modeling and System Synthesis to Exceed 65% Efficiency Goal (Phase II)

As of May 2019, all RDE test hardware and test operations were completed for this program. Over 700 hot fire tests were conducted at 4 facilities with 7 RDE assemblies, with the data available for future analysis. In particular, measured static and dynamic pressure and computational fluid dynamics (CFD) provided basis for pressure gain combustion (PGC) analysis of the Engineering Scale RDE. It was demonstrated via measurements and CFD that a carefully designed diffuser can recover some total pressure of the highly oscillatory RDE exhaust. In general, the isolator/injector is largest contributor of pressure losses; however a low loss design was demonstrated that improves significantly on previous natural-gas/air RDEs. Also, a tailored injector and mixing technique was demonstrated that allowed NG-air RDE operation with overall equivalence ratio of less than 0.6. CFD models by AR, the UofM and Purdue University were extensively used throughout the program to refine diffuser design for improved pressure recovery, to simulate injection and mixing, to estimate turbine interaction, to estimate combustor pressure gain anchored with test data and to provide input to other design and analytical tasks. CFD analyses were completed for many of the configurations tested at Purdue with the 13.4” RDE including the shark fin and low area ratio isolators. Results were compared favorably to available hot fire test particle image velocimetry (PIV) measurements and wave speeds. The results of these CFD analyses suggest a modest pressure gain of 33% with a sufficiently low area ratio isolator. A thermodynamic power plant system model was set up which utilizes an RDE combustor. The performance of the RDE combustor module was scaled based on measurements from the Engineering Scale RDE tests with low loss injector and tailored fuel flow. This combined model indicates potential for significant plant efficiency improvement. The successful tailored fuel injection test result with lower than .6 equivalence ratio has implication that a power plant can use a RDE combustor configuration where all the air flows through the combustion zone thereby not requiring a bypass mixer. In this configuration, the results of the natural gas combined cycle (NGCC) plant system indicate net efficiency of 66.4%.

02 PETROLEUM↗