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DOE OSTI2019

Instrumentation for In situ/Operando X-ray Scattering Studies of Polymer Additive Manufacturing Processes

With new opportunities and challenges from additive manufacturing (AM), novel characterization approaches are needed to realize reliable and industry-transferrable material systems and processes. The Complex Scattering Program at the National Synchrotron Light Source II (NSLS-II), a Department of Energy (DOE) Office of Science User Research Facility located at Brookhaven National Laboratory, has capabilities on three beamlines to support in situ and operando soft material additive manufacturing measurements. In this article, we describe some of the additive manufacturing capabilities that have been implemented on these beamlines, including in-line 3D printers for Continuous-flow Direct Writing (CDW), Fused Filament Fabrication (FFF) printing, and an instrument for Electrospray Deposition (ESD). We also describe a development program in autonomous experimentation that will be critical for addressing the complexity involved in optimizing additive manufacturing processes and materials.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Optimizing the 8 Li yield for the IsoDAR Neutrino Experiment

The focus of this paper is on optimizing the electron-antineutrino source for the IsoDAR (Isotope Decay at Rest) experimental program. IsoDAR will perform sensitive short-baseline neutrino oscillation and electroweak measurements, among other Beyond Standard Model searches, in combination with KamLAND and/or other suitable detectors. IsoDAR will rely on the high-Q β– decay of the 8 Li isotope for producing electron-antineutrinos, created mainly via neutron capture in an isotopically enriched 7 Li sleeve surrounding the Be target. In particular, this paper examines the performance, defined in terms of absolute 8 Li (or, equivalently, electron-antineutrino) production rate, of various candidate sleeve materials, including a lithium-fluoride, beryllium-fluoride mixture (“FLiBe”) sleeve and a homogeneous mixture of lithium and beryllium (“Li-Be”). Furthermore, these studies show that the 8 Li yield can be increased substantially by employing a Li-Be sleeve and therefore motivate significant changes to the nominal IsoDAR design.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Learning Distribution Grid Topologies: A Tutorial

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. Here this tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

SU-F-T-138: Commissioning and Evaluating Dose Computation Models for a Dedicated Proton Line Scanning Beam Nozzle in Eclipse Treatment Planning System

Purpose: In this study, we present an effective method to derive low dose envelope of the proton in-air spot fluence at beam positions other than the isocenter to reduce amount of measurements required for planning commission. Also, we demonstrate commissioning and validation results of this method to the Eclipse treatment planning system (version 13.0.29) for a Sumitomo dedicated proton line scanning beam nozzle. Methods: The in-air spot profiles at five beam-axis positions (±200, ±100 and 0 mm) were obtained in trigger mode using a MP3 Water tank (PTW-Freiburg) and a pinpoint ionization chamber (model 31014, PTW-Freiburg). Low dose envelope (below 1% of the center dose) of the spot profile at isocenter was obtained by repeated point measurements to minimize dosimetry uncertainty. The double Gaussian (DG) model was used to fit and obtain optimal σ1, σ2 and their corresponding weightings through our in-house MATLAB (Mathworks) program. σ1, σ2 were assumed to expand linearly along the beam axis from a virtual source position calculated by back projecting fitted sigmas from the single Gaussian (SG) model. Absolute doses in water were validated using an Advanced Markus chamber at the depth of 2cm with Pristine Peak (BP) R90d ranging from 5–32 cm for 10×10 cm2 scanned fields. The field size factors were verified with square fields from 2 to 20 cm at 2cm and before BP depth. Results: The absolute dose outputs were found to be within ±3%. For field size factor, the agreement between calculated and measurement were within ±2% at 2cm and ±3% before BP, except for the field size below 2×2 cm2. Conclusion: The double Gaussian model was found to be sufficient for characterizing the Sumitomo dedicated proton line scanning nozzle. With our effective double Gaussian fitting method, we are able to save significant proton beam time with acceptable output accuracy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

A Cooperative Multi-Agent Deep Reinforcement Learning Framework for Real-Time Residential Load Scheduling

Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing numbers of household users with controllable loads to actively participate in smart grid energy management. Realizing an efficient real-time energy management system that takes advantage of these developments requires novel techniques for managing the increased complexity of the control action space in resolving multiple challenges such as the uncertainty in energy prices and renewable energy output along with the need to satisy physical grid constraints such as transformer capacity. Addressing these challenges, we develop a multi-household energy management framework for residential units connected to the same transformer and containing DERs such as PV, ESS and controllable loads. The goal of our framework is to schedule controllable household appliances and ESS such that the cost of procuring electricity from the utility over a horizon is minimized while physical grid constraints are satisfied at each scheduling step. Traditional energy management frameworks either perform global optimization to satisfy grid constraints but suffer from high computational complexity (for example Integer Program, Mixed Integer Programming frameworks and centralized reinforcement learning) or perform decentralized real-time energy management without satisfying global grid constraints (for example multi-agent reinforcement learning with no cooperation). In contrast, we propose a cooperative multiagent reinforcement learning (MARL) framework that i) operates in real-time, and ii) performs explicit collaboration to satisfy global grid constraints. The novelty in our framework is two fold. Firstly, our framework trains multiple independent learners (IL) for each household in parallel using historical data and performs real-time inferencing of control actions using the most recent system state. Secondly, our framework contains a low complexity knapsack based cooperation agent which combines the outputs of ILs to minimize cost while satisfying grid constraints. Simulation results show that our cooperative MARL approach achieves significant cost improvement over centralized reinforcement learning and day-ahead planning baselines. Moreover, our approach strictly satisfies physical constraints with no apriori knowledge of system dynamics while the baseline approaches have occasional violations. We also measure the training and inference time by ranging the number of households from 1 to 25. Results show that our cooperative MARL approach scales best among various approaches.

14 SOLAR ENERGY↗

DOE OSTI2014

Gasification Studies: Task 4 Topical Report, Utah Clean Coal Program

A key objective of the Task 4 activities has been to develop simulation tools to support development, troubleshooting and optimization of pressurized entrained-flow coal gasifiers. The overall gasifier models (Subtask 4.1) combine submodels for fluid flow (Subtask 4.2) and heat transfer (Subtask 4.3) with fundamental understanding of the chemical processes (Subtask 4.4) processes that take place as coal particles are converted to synthesis gas and slag. However, it is important to be able to compare predictions from the models against data obtained from actual operating coal gasifiers, and Subtask 4.6 aims to provide an accessible, non-proprietary system, which can be operated over a wide range of conditions to provide well-characterized data for model validation. Highlights of this work include: (1) Verification and validation activities performed with the Arches coal gasification simulation tool on experimental data from the CANMET gasifier (Subtask 4.1). (2) The simulation of multiphase reacting flows with coal particles including detailed gas-phase chemistry calculations using an extension of the one-dimensional turbulence model’s capability (Subtask 4.2). (3) The demonstration and implementation of the Reverse Monte Carlo ray tracing (RMCRT) radiation algorithm in the ARCHES code (Subtask 4.3). (4) Determination of steam and CO 2 gasification kinetics of bituminous coal chars at high temperature and elevated pressure under entrained-flow conditions (Subtask 4.4). In addition, attempts were made to gain insight into the chemical structure differences between young and mature coal soot, but both NMR and TEM characterization efforts were hampered by the highly reacted nature of the soot. (5) The development, operation, and demonstration of in-situ gas phase measurements from the University of Utah’s pilot-scale entrained-flow coal gasifier (EFG) (Subtask 4.6). This subtask aimed at acquiring predictable, consistent performance and characterizing the environment within the gasifier.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2016

Coupling the System Analysis Module with SAS4A/SASSYS-1

SAS4A/SASSYS-1 is a simulation tool used to perform deterministic analysis of anticipated events as well as design basis and beyond design basis accidents for advanced reactors, with an emphasis on sodium fast reactors. SAS4A/SASSYS-1 has been under development and in active use for nearly forty-five years, and is currently maintained by the U.S. Department of Energy under the Office of Advanced Reactor Technology. Although SAS4A/SASSYS-1 contains a very capable primary and intermediate system modeling component, PRIMAR-4, it also has some shortcomings: outdated data management and code structure makes extension of the PRIMAR-4 module somewhat difficult. The user input format for PRIMAR-4 also limits the number of volumes and segments that can be used to describe a given system. The System Analysis Module (SAM) is a fairly new code development effort being carried out under the U.S. DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM is being developed with advanced physical models, numerical methods, and software engineering practices; however, it is currently somewhat limited in the system components and phenomena that can be represented. For example, component models for electromagnetic pumps and multi-layer stratified volumes have not yet been developed. Nor is there support for a balance of plant model. Similarly, system-level phenomena such as control-rod driveline expansion and vessel elongation are not represented. This report documents fiscal year 2016 work that was carried out to couple the transient safety analysis capabilities of SAS4A/SASSYS-1 with the system modeling capabilities of SAM under the joint support of the ART and NEAMS programs. The coupling effort was successful and is demonstrated by evaluating an unprotected loss of flow transient for the Advanced Burner Test Reactor (ABTR) design. There are differences between the stand-alone SAS4A/SASSYS-1 simulations and the coupled SAS/SAM simulations, but these are mainly attributed to the limited maturity of the SAM development effort. The severe accident modeling capabilities in SAS4A/SASSYS-1 (sodium boiling, fuel melting and relocation) will continue to play a vital role for a long time. Therefore, the SAS4A/SASSYS-1 modernization effort should remain a high priority task under the ART program to ensure continued participation in domestic and international SFR safety collaborations and design optimizations. On the other hand, SAM provides an advanced system analysis tool, with improved numerical solution schemes, data management, code flexibility, and accuracy. SAM is still in early stages of development and will require continued support from NEAMS to fulfill its potential and to mature into a production tool for advanced reactor safety analysis. The effort to couple SAS4A/SASSYS-1 and SAM is the first step on the integration of these modeling capabilities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Development of Optimized Controls and Hardware for Synchrotron Hard X-ray Microscopes with Advanced Spectroscopic Capabilities

The technical objectives of the program were to develop an integrated, fully automated command and data interface between the TXM-Wizard developed at SLAC and the Xradia control software that is used on the Xradia Nano-TXM x-ray microscope with the goal of allowing non-experts including industry users to routinely access the advanced imaging modes that employ collection of images with spectroscopic information. The major accomplishment associated with this goal was the development of a plan to convert the TXM-Wizard software from MatLab to Python and the hiring of an expert Python programmer under contract to perform the conversion.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Development of Optimized Controls and Hardware for Synchrotron Hard X-ray Microscopes with Advanced Spectroscopic Capabilities

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Extend an innovative HPC-Compatible Multiple Temporal-spatial Resolution Concurrent Finite Element Modeling Approach to Guide Laser Powder Bed Fusion Additive

Laser power bed fusing (PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. However, the geometric complexity of PBF-AM technique also leads to highly non-uniform heating and cooling rate in the manufactured part, which may cause flaw formation and produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. In this research project, we developed an innovative Multi-Spatial-Temporal-Resolution Finite Element (MUST-FE) method and completed the corresponding high performance computation (HPC) platform-based in-house code, which enables high accuracy prediction of temperature and residual stress fields for component-scale PBF-AM manufacture in efficient computation time. The MUST-FE model is calibrated and validated with a “2D pad” AlSi10Mg experiments by matching the melt pool shape and dimension, and with a “XY-cross” AlSi10Mg experiment by matching the thermal distortion and residual stress. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures and contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Implementing a Laser Stabilization System for Trapping Ca+ Ions: an Internship Reflection

At Lawrence Livermore National Laboratory, I contributed to a project developing 3D printed micro ion traps for quantum computing. I designed, implemented, and assessed a laser stabilization system that locked lasers to the frequencies required for calibrating our High Finesse WS8-10 wavelength meter and for laser cooling and trapping of Ca+ ions. I also programmed a Python interface for hardware communication, data collection, and statistical analysis. Additionally, I optimized and aligned laser beam paths, and I implemented a closed digital feedback loop using Proportional, Integral, and Derivative (PID) control parameters. I analyzed both the long-term and short-term behavior of our locked lasers and adjusted PID parameters to enhance performance. Furthermore, I used COMSOL to simulate the capacitance of a linear Paul trap design and predict our trap’s performance. The procedures I developed for the interface, analysis, and simulations will continue to support the ion trapping experiment after my appointment. I strengthened my skills in data analysis, Python coding, and optical alignment for laser systems. My confidence as a researcher grew, particularly in communicating my research. This experience taught me the importance of careful planning and consideration in research and solidified my desire to continue exploring novel quantum technology as an undergraduate

42 ENGINEERING↗

DOE OSTI2017

High Performance Computing Based Parallel HIearchical Modal Association Clustering (HPAR HMAC)

For many applications, clustering is a crucial step in order to gain insight into the makeup of a dataset. The best approach to a given problem often depends on a variety of factors, such as the size of the dataset, time restrictions, and soft clustering requirements. The HMAC algorithm seeks to combine the strengths of 2 particular clustering approaches: model-based and linkage-based clustering. One particular weakness of HMAC is its computational complexity. HMAC is not practical for mega-scale data clustering. For high-definition imagery, a user would have to wait months or years for a result; for a 16-megapixel image, the estimated runtime skyrockets to over a decade! To improve the execution time of HMAC, it is reasonable to consider an multi-core implementation that utilizes available system resources. An existing imple-mentation (Ray and Cheng 2014) divides the dataset into N partitions - one for each thread prior to executing the HMAC algorithm. This implementation benefits from 2 types of optimization: parallelization and divide-and-conquer. By running each partition in parallel, the program is able to accelerate computation by utilizing more system resources. Although the parallel implementation provides considerable improvement over the serial HMAC, it still suffers from poor computational complexity, O(N2). Once the maximum number of cores on a system is exhausted, the program exhibits slower behavior. We now consider a modification to HMAC that involves a recursive partitioning scheme. Our modification aims to exploit divide-and-conquer benefits seen by the parallel HMAC implementation. At each level in the recursion tree, partitions are divided into 2 sub-partitions until a threshold size is reached. When the partition can no longer be divided without falling below threshold size, the base HMAC algorithm is applied. This results in a significant speedup over the parallel HMAC.

Patlolla, Dilip R↗

DOE OSTI2021

Development of MCNP Training Modules for Safeguards Practitioners [Abstract]

The Monte-Carlo N-Particle (MCNP) software developed at LANL is the most widely used neutron transport code in the world. It is an essential tool for a variety of applications including detector development and design, nuclear fuel burnup simulation, criticality safety, and nondestructive assay system optimization. For this reason, it is indispensable within the safeguards and materials control & accountability (MC&A) communities. Multiple MCNP training courses have been created and taught over the last several decades by the MCNP development team at LANL, however there are no existing courses that cover specialized topics considered fundamental to NDA and safeguards models. To fill this gap, the MCNP team and Safeguards Science and Technology group at LANL have co-created a set of training modules customized to meet the specialized needs of the safeguards and MC&A communities. The basic modules cover concepts such as NDA system optimization, He-specific and other capture tallies, and tools for improved theoretical understanding. An advanced module was also created to cover topics including variance reduction for active interrogation simulations, use of the LANL MCNPTools post-processor, PTRAC (particle tracking) and list-mode data simulations, and fuel burnup simulations. The training modules teach to the latest and most state-of-the-art MCNP features and tools released by the development team at LANL and are intended to be taught jointly by the developers and safeguards experts. Ultimately, we hope that creation of these modules will serve to capture and convey the safeguards modeling and MCNP expertise at LANL, and that we will be able to share the modules more broadly with the MC&A and safeguards communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2015

Robotic D and D: Smart robots - 15093

Project' managers continually seek an ever-greater optimization of time between remote handling operations and those carried out manually; therefore, new technological solutions must be deployed. Robotics offers a great opportunity in this new field of technology to carry out, for example, samplings or remediation in hostile, cluttered surroundings. Teams in charge of dismantling at the CEA have therefore first defined robotizable functions. These functions have been assembled from existing technological blocks to arrive at robots which are operating today [RICAIII, patent: FR 2925702]. Lessons learned, particularly from experience with the RICA robot, have enabled the operating technical specifications to be fine-tuned. A new study phase has been launched applying the same principle of adapting existing, proven means. The growing role of robotics today is unquestioned. Led by research and the academic world; robots such as those equipped with wheels, tracks, feet or even helicopter rotors, are today accessible to the general public, particularly via broadening of the 'open source' concept. Added to these we need tools able to manage large component deconstruction systems, like MAESTRO. Industrialization of such high-potential technological solutions has been aided by: - Easy use, - Increasing reliability, - Flexibility of 'open source' solutions, - Widening skill networks, and therefore greater technical support - Lower costs. Decontamination and dismantling (D and D) projects must be able to meet a number of special demands, increasing the number of unit designs, their costs and delivery times. The complexity of dismantling works sites mean that each is a special case to be dealt with almost independently. Such a way of approaching these projects is not on the same wavelength as industry, with tool and method standardization. The answer to the challenge of operations in difficult environments is an eco-system of functions, performed by a set of inter-connected robots. The first step towards the construction of such robot teams is devoted to functions where strength is not necessary: investigating and clean-up in hostile environments. With this in mind, the CEA Marcoule teams have been given the objective of merging the strengthening commercial robotic world with the needs of D and D, and thus to improve the transversal use of the systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Predictive Modeling and Uncertainty Quantification in Condition Monitoring of Active Components: A Reactor Coolant Pump Use Case

This work develops data-driven models for onset of thermal barrier leakage in reactor coolant pumps. It incorporates uncertainty quantification to enhance the reliability and robustness of pre- dictions. Using synthetic data generated by the Generic Pressurized Water Reactor simulator, realistic degradation scenarios were simulated across lifecycle stages—beginning, middle, and end of life. Key variables, including differential pressure, flow rate, vibration, and temperatures, were analyzed using machine learning framework. The fully connected neural network models demonstrated exceptional performance, achieving R2 scores exceeding 0.99 and root mean square errors as low as around 8.23 × 10-2 gallon per minute (gpm) for the three stages of the lifecy- cle. UQ analysis further validated the model’s robustness, with narrow uncertainty bounds during steady-state operations and appropriately wider bounds during transitional phases, reflecting the physical behavior of the system. This work addresses important gaps in real-time condition moni- toring and regulatory compliance by integrating advanced condition monitoring technologies with UQ into IST programs. The ability to detect thermal barrier leakage early and quantify prediction reliability supports optimizing maintenance strategies while ensuring nuclear power plants’ safe and reliable operation.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

Biomass to Liquid Fuels and Electrical Power

This research program provided data on immediate applicability of forest biomass production and logistics models. Also, the research further developed and optimized fractionation techniques that can be used to separate biomass feedstocks into their basic chemical constituents. Finally, additional research established systematic techniques to determine economically feasible technologies for production of biomass-derived synthesis gases that will be used for clean, renewable power generation and for production of liquid transportation fuels. Moreover, this research program continued our efforts to educate the next generation of engineers and scientists needed to implement these technologies.

09 BIOMASS FUELS↗

DOE OSTI2023

Primal-Dual Differentiable Programming for Distribution System Critical Load Restoration: Preprint

Swift and reliable critical load restoration (CLR) can help make a distribution system resilient towards extreme events. To optimally achieve that, alongside practical concerns such as limiting online computational burden, some studies leverage model-free reinforcement learning (RL) to train control policies. Despite the advantages provided by RL algorithms, these approaches suffer from two issues: 1) the lack of a proper mechanism for constraint enforcement, and 2) poor sample efficiency. Therefore, in this paper, a primal-dual differentiable programming (PDDP) method is developed for guiding the training leading to a constraint-satisfying policy. Additionally, the model-based nature of the proposed method aims at improving sample efficiency. The experiment on a CLR problem demonstrates that PDDP can effectively train a control policy that both achieves desirable performance and satisfies required constraints.

differentiable programming↗