Design of Critical Experiments Targeting Epithermal Cross Sections of Tantalum.
Abstract not provided.
Ames, David↗
SEARCH · Engineering Papers
Results for “designed experiment”
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Expert‐in‐the‐loop design of integral nuclear data experiments
Abstract Nuclear data are fundamental inputs to radiation transport codes used for reactor design and criticality safety. The design of experiments to reduce nuclear data uncertainty has been a challenge for many years, but advances in the sensitivity calculations of radiation transport codes within the last two decades have made optimal experimental design possible. The design of integral nuclear experiments poses numerous challenges not emphasized in classical optimal design, in particular, constrained design spaces (in both a statistical and engineering sense), severely under‐determined systems, and optimality uncertainty. We present a design pipeline to optimize critical experiments that uses constrained Bayesian optimization within an iterative expert‐in‐the‐loop framework. We show a successfully completed experiment campaign designed with this framework that involved two critical configurations and multiple measurements that targeted compensating errors in 239 Pu nuclear data.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Transforming New Multifamily Construction to Zero: Strategies for Implementing Energy Targets and Design Pathways: Preprint
Creating zero energy (ZE) mid- to high-rise multifamily buildings represent significant challenges in the design world. Tenants often take responsibility for some of the utilities and are responsible for consumption of some of the largest end uses: hot water and plug loads. Furthermore, designers can experience risk in creating new sets of details and recommending new strategies to reach aggressive EUI targets. To assist in a more streamlined and successful design approach to ZE multifamily buildings, five major professional societies and experts from the multifamily space created a Zero Energy Advanced Energy Design Guide (AEDG) for multifamily buildings based on data driven results coupled with actual design experiences and case studies. Pathways to achieve ZE design were established including whole building energy use intensity targets coupled with details on how to achieve success in implementing individual efficiency measures. The guidance strives to overcome design barriers and this paper provides an overview of the recommended process to achieve ZE or ZE-ready status, along with details on selecting energy targets, and strategy pathways to achieve the targets.
advanced energy design guide↗
Design of a first-of-A-kind instrumented advanced test reactor irradiation Capsule experiment for In situ thermal conductivity measurements of metallic fuel
Metallic fuel undergoes dramatic microstructural changes early in life due to fission gas swelling until ~2–3 at% burnup which affects the conductivity of the material, however the evolution of metallic fuel thermal conductivity during this early phase burnup has never been successfully measured in situ. The Irradiated Material Properties Accelerated Characterization Test (IMPACT) experiment will be the first in a series of experiments to irradiate advanced nuclear metallic fuel specimens with novel embedded thermal conductivity probes in ATR. In the current work the IMPACT experiment final design and supporting analysis is reported in detail. Results are evaluated for various reactor operational conditions to meet the functional requirements of the experiment. Finally, the first iteration of this IMPACT experiment will provide data regarding thermal properties evolution in uranium-zirconium (U10Zr) fuel, but this experiment vehicle is envisioned for future advanced fuels and structural materials irradiations in ATR.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
The Deimos Experiment: Advanced Reactor Testbed
Advanced reactor initiatives are growing significantly through programs nationwide. This research area includes small modular reactors, microreactors, and space reactors. Many of the reactors being designed are untested concepts. They include unique moderators, varying fuel types, high temperatures, and compact configurations. The shift in fuel type, from highly enriched uranium (HEU) to high assay, low enriched uranium (HALEU), is particularly important as it has driven many of the other changes. For example, lower enrichment requires advanced moderators, which in turn require different reflectors to make the systems compact. The change in materials including the transition from HEU to HALEU affects the temperature feedback of the systems. Additionally, these advanced reactor concepts generally have a thermal neutron spectrum in contrast to earlier fast spectrum advanced reactor. With the extensive changes from previous reactor designs, validation experiments are needed. The National Criticality Experiments Research Center (NCERC) is uniquely equipped to perform such experiments. The Deimos experiment, designed for execution at NCERC, will serve as a testbed for advanced reactor concepts. It will use HALEU fuel in a graphite matrix, provide the ability to use advanced moderators, and allow measurements of temperature reactivity coefficients (TRCs).
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
CERBERUS: CEDT Phase-1 Preliminary Design for Cu Critical Experiment
The goal of the Critical Experiment Reflected By copper to bEtteR Understand Scattering [CERBERUS] is to design a critical experiment that maximizes sensitivities to Cu reactions, particularly in the intermediate energy region (0.625 eV – 100 keV). Despite the number of experiments evaluated in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook1, there is still a gap of benchmarks sensitive to neutrons in the intermediate energy region. Of the ICSBEP benchmarks sensitive to neutrons in the intermediate energy region, very few are also sensitive to Cu in that region. One of the primary intermediate energy benchmark evaluations is the ZEUS series, which uses a Cu reflector. However, concerns have been brought up about the nuclear data associated with Cu scattering in the reflector. Improving Cu nuclear data is important outside of the ZEUS series, because it is present in many bronze and aluminum alloys, which are used in various nuclear operations.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Integral Experiment Request 523 CED-2 Report
This report documents the final design phase of the Critical Experiment Design (CED-2) conducted as part of integral experiment request (IER) 523. The purpose of IER 523 is to determine critical configurations of 35 weight percent (wt%) enriched uranium dioxide beryllium oxide (UO 2 -BeO) material driven by an annular ring of Seven Percent Critical Experiment (7uPCX) fuel rods at Sandia National Laboratories (Sandia). The experiments will provide benchmark data on water moderated, intermediately enriched UO2 systems as well as Be nuclear data. The experiment will also provide partial validation for the beryllium oxide (BeO) thermal neutron scattering law (TSL) in the thermal energy range. Experiment design concepts, neutronic analysis results, and proposed paths for continuing the CED process are presented. This report builds on the feasibility and justification of experimental need report (CED-0) and preliminary experiment design report (CED-1) completed in December 2021 and September 2023, respectively [1, 2].
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Development of Conceptual Lead Cartridge Design to Perform Irradiation Experiments in VTR
This paper seeks to introduce the latest design of the Extended Length Test Assembly–Cartridge Lead (ELTA-CL) with associated thermal-hydraulic (TH) assessment and related experiment activities to support the critical component development performed by the ELTA-CL team (Los Alamos National Laboratory, Westinghouse Electric Company, and the University of New Mexico). The goal of the ELTA-CL program is to develop and validate an experimental capability to perform irradiation experiments in the Versatile Test Reactor (VTR) addressing Lead Fast Reactor (LFR) technology gaps, in support of the commercial development of advanced lead-cooled fast reactor concepts. Through a design maturation process and parametric study, a conceptual design is proposed to meet the requirements for material and corrosion testing. Thermal-hydraulic characteristics for the conceptual design at desired operating conditions are assessed with systems-level (one-dimensional) and computational fluid dynamics (three-dimensional) simulations. Along with the conceptual design work, experimental activities for the development of critical components such as the pump and flowmeter are undertaken. From both the modeling study and the experimental results, the design requirements of the Phase 1 ELTA-CL (e.g., 500°C and 2 m/s) are achievable with the current conceptual design. Additional design improvements and safety assessments at both steady-state and transient conditions for the final ELTA-CL design will be pursued.
42 ENGINEERING↗
The MU2E Experiment at FERMILAB: R&D, Design and Status
The Mu2e Experiment at Fermilab 1) will search for coherent, neutrinoless conversion of negative muons into electrons in the field of an aluminum nucleus, µ - + N (A, Z) → e - + N (A, Z). This is an example of Charged Lepton Flavour Violation (CLFV) never observed experimentally. The dynamics of such a process is well modelled by a two-body decay, resulting in a mono-energetic electron with an energy slightly below the muon rest mass (~104.967 MeV). If no events are observed in three years of running, Mu2e will set an upper limit on the ratio between conversion and capture rate R µe ≤ 6 × 10 -17 (@ 90% C.L.). This will improve the current limit of a factor of 10 4 over previous experiments. The experiment complements and extends the current/planned searches (µ → eγdecay at MEG , mu3e) as well as the direct searches for new physics at the LHC. Indeed, such CLFV searches in the muon sector probe new physics at a mass scale inaccessible with direct searches at either present or planned high-energy colliders. To detect the muon conversion process, a very intense pulsed beam of negative muons is produced by means of a S-shape Superconducting Solenoid Magnet System that is organized into three subsystems: the Production Solenoid, the Transport Solenoid and the Detector Solenoid. The beam is stopped at 10 GHz on an Aluminum target inside the Detector Solenoid. The Mu2e detectors, also installed inside the Detector Solenoid, are a high-precision tracker made on ~20000 straw tubes, and a calorimeter composed of ~1500 pure CsI crystals organized in two disks and readout by two large area UV-extended Silicon Photomultipliers (SiPMs). The Detector Solenoid region is surrounded by a Cosmic Ray Veto based on scintillators readout by SiPMs.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
A Refined BISON-Griffin Workflow For Designing Targeted TRISO Transient Experiments In The Transient Reactor Test Facility
U.S. industry is expected to deploy tristructural isotropic (TRISO) particle fuel technologies for commercial reactors within the next decade. In our previous work, we defined a preliminary transient design space for TRISO fuels, identified potential gaps in the available data, and began to develop multiphysics modeling tools that could be applied to design targeted Transient Reactor Test Facility (TREAT) experiments to fill these gaps. This work builds on that foundation by (1) updating BISON fuel performance and Griffin reactor physics models to reflect the current TREAT experiment tube and capsule designs,(2) coupling the codes to improve the accuracy and usability of the transient design analyses, and (3) demonstrating their use over an expanded design space that includes fuel burnup. The simulated mechanical responses of the TRISO particles were complex functions of fission product accumulation, fission gas release, and irradiation-induced dimensional change in the pyrolytic carbon layers. Predicted tangential stresses in particle silicon carbide layers were least compressive for preheated tests involving fresh fuels but remained compressive throughout the ranges of temperature, heat rate, and burnup considered in this work. Finally, comparisons between the potential TREAT transients and historical test reactor irradiations showed that the TREAT tests would produce significantly lower average energy deposition rates, yielding less severe transients with greater relevance to near-term commercial applications. Use of these predictive capabilities has the potential to increase the value of each test, improving the overall efficiency and cost effectiveness of transient testing for TRISO and other advanced fuels.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Design of Cylindrical Implosion Experiments to Demonstrate Scale-Invariant Rayleigh-Taylor Instability Growth
Radiation-hydrodynamics simulations are used to design laser-driven cylindrical implosion experiments to directly measure hydrodynamic instability growth in convergent geometry. Designs for two different size targets, varying in radial dimension by a factor of three, are presented. A set of beam pointings and powers are identified for each scale design that result in a nearly axially uniform implosion of an embedded marker layer. The implosion trajectories are shown to be scale-invariant between designs, with nearly identical scaled acceleration profiles. Finally, linear theory and radiation-hydrodynamics simulations predict that Rayleigh-Taylor instability growth of an azimuthal perturbation, machined on the inner surface of the embedded marker, is also scale-invariant between designs.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Refinement and demonstration of a coupled BISON-Griffin workflow for designing targeted TRISO transient experiments in TREAT
The U.S. nuclear industry is expected to deploy tristructural isotropic (TRISO) particle fuel technologies for commercial reactors within the next decade. In previous work, we defined a preliminary transient design space for TRISO fuels, identified potential gaps in the available data, and began to develop multiphysics modeling tools that could be applied to design targeted Transient Reactor Test Facility (TREAT) experiments to fill these gaps. Here, this work builds on that foundation by (1) updating BISON fuel performance and Griffin reactor physics models to reflect the current TREAT experiment tube and capsule designs, (2) coupling the codes to improve the accuracy and usability of the transient design analyses, and (3) demonstrating their use over an expanded design space that includes fuel burnup. The simulated mechanical responses of the TRISO particles were complex functions of fission product accumulation, fission gas release, and irradiation-induced dimensional change in the pyrolytic carbon layers. The predicted tangential stresses in the particles' silicon carbide layers were least compressive for preheated tests involving fresh fuels but remained compressive throughout the ranges of temperature, heat rate, and burnup considered in this work. Finally, comparisons between the potential TREAT transients and historical test reactor irradiations showed that the TREAT tests would produce significantly lower average energy deposition rates, yielding less severe transients with greater relevance to near-term commercial applications. The use of these predictive capabilities has the potential to increase the value of each test, improving the overall efficiency and cost-effectiveness of transient testing for TRISO and other advanced fuels.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A Verification of Flux Sensitivity Estimates Using the MCNP Tally Perturbation Tool
Nuclear data is commonly used in applications such as nuclear nonproliferation, safeguards, and criticality safety. More specifically, nuclear data is used in predictive simulation codes like the Monte-Carlo N-Particle (MCNP ® ) transport code, Serpent, and similar radiation transport codes. The improvement of nuclear data enables more precise and accurate simulations, which result in higher fidelity designs and reduced operational/procedural costs. Therefore, the improvement of nuclear data is of paramount importance across the nuclear community. Nuclear data is improved and validated through integral benchmark experiments. The design of benchmark experiments is an extensive process; therefore, these experiments are often optimized on multiple characteristics, including sensitivity to the nuclear data, during the design process. Sensitivity is a measure of how much a quantity changes due to changes in independent variables such as experimental configuration. An experimental design that has a larger sensitivity to the nuclear data of interest will have a larger impact on the accuracy and precision of the validated data. Past integral benchmark experiments have primarily used the effective multiplication factor ($k_{eff}$) as the predominant measured quantity; however, experiments designed with other quantities in mind would be able to optimize on validating different areas of the nuclear data. A primary goal of the EUCLID project is to design, constrain, and reduce compensating errors in experiments focused on quantities other than $k_{eff}$ to better validate nuclear data across the board. Currently, there is a capability in MCNP to easily calculate the sensitivity of $k_{eff}$ to specific nuclear data of numerous reactions types and isotopes (KSEN card); however, the sensitivity of other quantities must be estimated in more strenuous manners. For example, the perturbation feature (PERT card) of MCNP can be used to estimate first-order sensitivities of some response in fixed source simulations. A recent announcement revealed that the first- and second-order perturbation features in previous releases of MCNP contained a bug. It was identified that particles were being scored into the wrong energy bin. The bug is in the most recent public release (MCNP6.2); however, a patch has been added to the most up to date version (MCNP6.2.2) that has not been released publicly. A direct comparison of the PERT card results for an F4 (neutron flux averaged over a cell) tally before and after the patch are shown in figure 1. All simulations used in the sensitivity estimates in this report were performed with MCNP6.2.2. This work verifies the patched MCNP perturbation tool by comparing first order sensitivities made using the PERT card to estimates made using manual perturbation of the compact ENDF (ACE) files.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Enabling integrated AI control on DIII-D: a control system design with state-of-the-art experiments
We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learning (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode (TM) free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine TM predictor-controller. There is also discussion on guiding principles for real-time ML controller design and implementation.
machine learning↗
Constrained Bayesian Optimization of Criticality Experiments at LLNL [Slides]
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. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for critical ity safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.
42 ENGINEERING↗
Development of an Efficient Conjugate Heat Transfer Modeling Framework to Optimize Mixing-Limited Combustion of Ethanol in a Diesel Engine
Mixing controlled combustion of alcohol fuels has been identified as a promising technology based on their low propensity for particulate and NOx production, but the higher heats of vaporization and auto-ignition temperatures of these fuels make their direct use in diesel engine architectures a challenge. To realize the potential of alcohol-fueled combustion, a computational fluid dynamics (CFD) modeling framework is developed, validated, and exercised to identify designs that maximize engine thermal efficiency. To evaluate the use of thermal barrier coatings (TBCs), a simplified one-dimensional (1D) conjugate heat transfer (CHT) modeling framework is employed. The addition of the 1D CHT model only increases the computational expense by 15% relative to traditional approaches, yet offers more accurate heat transfer predictions over constant temperature boundary conditions. The validated model is then used to explore a range of injector orientations and piston bowl geometries. Using a design of experiments (DoE) approach, several designs were identified that improved fuel-air mixing, shortened the combustion duration, and increased thermal efficiency. The most promising design was fabricated and tested in a Caterpillar 1Y3700 single-cylinder oil test engine (SCOTE). Engine testing confirmed the findings from the CFD simulations and found that the co-optimized injector and piston bowl design yielded over 2-percentage point increase in thermal efficiency at the same equivalence ratio (0.96) and over 6-percentage point increase at the same engine load (10.1 bar indicated mean effective pressure (IMEP)), while satisfying design constraints for peak pressure and maximum pressure rise rate.
Magnotti, Gina M.↗
AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50
The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bias. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Reference 2 (at the end of the article).
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50
The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗