Simulating Hydraulic Fracture Stimulations at the EGS Collab: Model Validation from Experiments 1 and Design-Phase Simulation for Experiment 2
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Steps for Initiating FEEED Explore the FEEED Process Key Activities Key Deliverables Provide an update on FEEED Progress with FEEED Developers Radiant USNC Westinghouse
Integral Experiments are a key aspect of validating nuclear data behavior and simulation capabilities. Further, integral experiments are the earliest form of validation of equations and codes. They have been used extensively since the 1940s. Today, simulation and predictive capabilities have improved greatly from 75 years ago, but nonetheless, integral experiments are still needed. As simulation capabilities improve, their uncertainties get much smaller. The current focus of many new experiments is understanding the intermediate energy range. The intermediate energy range does not have a wide application space and it can be challenging to design experiments that are sensitive to neutron energies in this region. The Zeus experiments evaluated highly enriched uranium (HEU) in the intermediate energy region. Building on the success and knowledge gained from the Zeus experiments, the Critical Unresolved Region Integral Experiment (CURIE) experiments were designed to evaluate HEU in the narrower unresolved resonance region (URR). These experiments were executed during June and July 2020 at the National Criticality Experiments Research Center (NCERC). The National Criticality Experiments Research Center (NCERC), operated by Los Alamos National Laboratory, is the only general purpose critical experiments facility in the United States of America. NCERC regularly designs and executes critical experiments and other measurements useful to a wide variety of fields including nuclear criticality safety, commercial nuclear energy, nonproliferation, and nuclear data.
Magnetic fields are ubiquitous in the universe; however, their origin is not fully understood. Cosmologists and astrophysicists have proposed a variety of ways in which small seed magnetic fields could be created. It is widely thought that the much larger values of the cosmic magnetic fields that we observe are a result of the amplification of these seed fields by the nonlinear turbulent dynamo mechanism. Such a mechanism had not yet been demonstrated in a controlled laboratory environment. We conceived experiments designed to demonstrate and study the nonlinear turbulent dynamo mechanism in the laboratory. These experiments characterize the distribution of turbulent energy among the velocity, magnetic field, and density fluctuations, providing a comprehensive picture of the energy cascade in a magnetized, turbulent plasma. The experiments build on the pathfinder experiments we have conducted on the Vulcan laser at the Rutherford-Appleton Laboratory in the UK and the OMEGA laser at Laboratory for Laser Energetics at Rochester. They utilize the high-intensity lasers at the Omega Laser Facility, the National Ignition Facility (NIF) at LLNL and the Laser Megajoule (LMJ) Facility in France – the largest laser facilities in the world. The goal of this project was to design and model the highly demanding experiments through simulation campaigns using FLASH, a highly capable radiation-MHD code we have developed, and large-scale 3D simulations on the Mira supercomputer at ANL. The simulations were vital to ensuring the experiments achieve the strong turbulence and large magnetic Reynolds numbers required for the nonlinear turbulent dynamo mechanism to operate. The simulations were also critical to determining when to fire the diagnostics, since the experiments last tens of nanoseconds but the signals of the strongly amplified magnetic fields last only a few nanoseconds. Finally, high-fidelity, validated FLASH simulations were crucial to interpreting the results of the experiments. By combining theory, experiments, and simulations, we were able to succeed in demonstrating and characterizing the nonlinear turbulent dynamo mechanism. The effort leveraged (1) the emergence of the Flash Center for Computational Science as a leader in numerical modeling of academic High Energy Density Physics (HEDP) experiments; (2) the FLASH code, which has been developed by the Flash Center and is a leadership-class, multi- physics, publicly available community code that has been applied successfully in many laser-driven experiments; (3) the expertise acquired through pathfinder experiments we performed on the Vulcan laser at the Rutherford-Appleton Laboratory in the UK and on the Omega laser facility; (4) the experience we have gained in designing and interpreting these experiments using validated simulations done with FLASH; and (5) the close collaborations we have established with outstanding experimental groups in the US and abroad. The broader impacts of the project are two-fold: (1) the work furthered the transformation of the academic community’s ability to design and analyze HEDP experiments at large laser facilities that is happening through the availability of FLASH and its widespread adoption by the academic community – a community that previously had had limited access to open and validated hydro/MHD simulation tools for HEDP experiments; and (2) the work trained young scientists to design and interpret HEDP experiments using validated simulations – a critical national need.
Over the previous decade, numerous experiments have been performed using a laser to drive a strong, quasi-static magnetic field. Field strength and energy density measurements of these experiments have varied by many orders of magnitude, painting a confusing picture of the effectiveness of these laser-driven coils (LDCs) as tools for generating consistent fields. At the higher end of the field energy spectrum, kilotesla field measurements have been used to justify future experimental platforms, theoretical work, and inertial confinement fusion concepts. In this paper, we present the results from our own experiments designed to measure magnetic fields from LDCs as well as a review of the body of experiments that have been undertaken in this field. Here, we demonstrate how problems with prior diagnostic analyses have led to overestimations of the magnetic fields generated from LDCs.
This evaluation documents highly enriched uranium (HEU) experimental critical configurations with polyethylene moderators and sodium chloride absorbers conducted as part of the United States Nuclear Criticality Safety Program’s Thermal/Epithermal eXperiments (TEX) program. HEU-MET-MIXED-021 provides the benchmark evaluation of five TEX experiments designed to establish baseline configurations with HEU Jemima plates moderated by high density polyethylene (HDPE). The TEX-HEU experiments were designed to cover five different fission energy regimes by varying the thickness of the interstitial HDPE moderator, with varying fractions of thermal, intermediate, and fast fissions, and to be easily modified to accommodate test materials of interest. HEU-MET-INTER-013 documents the first TEX-HEU variation, incorporating hafnium in seven different experimental configurations. This evaluation covers an additional variant that incorporates absorber plates of compacted high-purity sodium chloride salt. These experiments were motivated by a criticality safety need for validation data for uranium purification by means of electrorefining with chloride salts, especially thermal and intermediate energy configurations resulting from moderator upset conditions, and their design was optimized by matching sensitivity profiles from application cases. All three experimental configurations are judged to be acceptable as benchmark cases. The main parameter varied between the configurations is the thickness of the polyethylene moderators and the sodium chloride absorbers between the HEU plates. Varying the thickness of the polyethylene tunes the neutron energy spectrum between majority thermal (Case 1 and 2) and intermediate (Case 3). The fission fractions, presented in Table 1, are determined calculationally. Case 3 is cross listed as HEU-MET-INTER-014.
Idaho National Laboratory (INL) is developing a first-of-a-kind leadout instrumented capsule experiment design to enable in-situ measurement capabilities in the Advanced Test Reactor (ATR) core. The Ceramic Advanced Thermal Evolution Research (CRATER) experiment supports the aLEU program objective to accelerate fuel performance irradiation testing for identifying alternative high-assay, low enriched uranium (HALEU) fuel systems. CRATER is a fueled, instrumented capsule experiment to measure in-situ temperature and thermal conductivity of ceramic fuels. Two ceramic fuel types will be used, uranium mono-nitride (UN) and uranium mono-carbide (UC), with a third metallic fuel used for comparison (UMo). The three fuel specimens will use a stainless-steel cladding. Programmatic objectives include linear heat generation rates (LHGR) of 210 ± 25 Watts per cm. and an inner clad temperature of 300-450 °C. The evolution of fuel thermal conductivity during irradiation has never been successfully measured in-situ for these systems and this experiment is designed to use advances in measurement sciences to characterize how thermal transport properties evolve while in reactor. Neutronic simulations of the experiment and its surrounding reactor environment were conducted using the Monte Carlo N-Particle Transport code (MCNP) and result in optimized fuel enrichment to meet target linear heat generation rates (LHGRs) influencing fuel temperatures, and fuel burnup requirements. Fabrication research and development (R&D) efforts are underway to produce annular right cylinder UC and UN pellets using carbothermic reduction and nitridation (or hydride-dehydride-nitride) synthesis methods, followed double-action die cold isostatic pressing.
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.
Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.
The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. Furthermore, we demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.
Abstract There are many challenges of commissioning a hydrogen combustor into future gas turbine engines; especially regarding achieving emissions goals. Previously, Escudero et al. and Tran et al. conducted a study to adapt the liquid fuel Lean Direct Injection (LDI) concept from Jet-A to gaseous natural gas-hydrogen blends and pure hydrogen [1], [2]. Experimental data was collected at atmospheric conditions using a Box Behnken design of experiments. The design of experiments suggested that biasing the air split in favor of the inner air circuit and increasing the swirl strength of this inner air passage resulted in improved NOx emissions, while the inverse was true for stability, which was quantified by studying the lean blowoff point (LBO) [1], [2]. The trends revealed by the original experiment [1], [2] provided a design direction for further iterations of the experimental hardware. The study presented herein describes the further investigation of such LDI injectors through experimental methods and computational fluid dynamic (CFD) simulations at atmospheric conditions, which were used to identify potential flow behaviors driving enhanced emissions performance. Further evaluation of select injectors from both studies was then conducted at elevated pressures up to 6 atmospheres. The results from both experiments are presented in this study, which include flame observations, emissions measurements, and operational challenges. NOx emissions results are reported on a volume basis in ppmvd corrected to 15% O2 and corrected for fuel. A predictive model for relating NOx emissions to test conditions at atmospheric conditions show high significance to adiabatic flame temperature while little to no significance to fuel composition for the best performing configurations. The results illustrate the connection between atmospheric testing and testing elevated pressures. The design direction indicated by the initial tests and CFD results in promising configurations for implementation into a Multi-point LDI array.
The DNCSH, through the HALEU Availability Program, needs publicly available benchmarks supporting Advanced Reactor Fuel Cycle and Transportation. LANL is uniquely positioned to support this goal, with its past experiments at TA-18, planned experiments at NCERC, experiment design capability, and ties to industry. The Deimos experiment will be executed soon and serve as a premier benchmark for the program. LANL expects to submit proposals to the call when issued (soon). Additional experiments supporting DNCSH have already been designed
New developments in automated optimal experimental design within the PSE+ software ecosystem. Advancements in user experience (to reduce the time taken to perform optimal experiment design) and computational capabilities (allowing more diverse experimental design) are shown with an example relevant to critical minerals and materials. Also, a small tutorial on science-based optimal experimental design and novel contributions therein are presented.