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DOE OSTI2013

Soil Segregation Methods for Reducing Transportation and Disposal Costs - 13544

At Formerly Utilized Sites Remedial Action Program (FUSRAP) sites where the selected alternative for contaminated soil is excavation and off-site disposal, the most significant budget items of the remedial action are the costs for transportation and disposal of soil at an off-site facility. At these sites, the objective is to excavate and dispose of only those soils that exceed derived concentration guideline levels. In situ soil segregation using gross gamma detectors to guide the excavation is often challenging at sites where the soil contamination is overlain by clean soil or where the contaminated soil is located in isolated, subsurface pockets. In addition, data gaps are often identified during the alternative evaluation and selection process, resulting in increased uncertainty in the extent of subsurface contamination. In response, the U.S. Army Corps of Engineers, Buffalo District is implementing ex situ soil segregation methods. At the remediated Painesville Site, soils were excavated and fed through a conveyor-belt system, which automatically segregated them into above- and below-cleanup criteria discharge piles utilizing gamma spectroscopy. At the Linde Site and the Shallow Land Disposal Area (SLDA) Site, which are both in the remediation phase, soils are initially segregated during the excavation process using gross gamma detectors and then transported to a pad for confirmatory manual surveying and sampling. At the Linde Site, the ex situ soils are analyzed on the basis of a site-specific method, to establish compliance with beneficial reuse criteria that were developed for the Linde remediation. At the SLDA Site, the ex situ soils are surveyed and sampled based on Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) final status survey guidance to demonstrate compliance with the derived concentration guideline levels. At all three sites, the ex situ soils that meet the site- specific DCGLs are retained on-site and used as backfill material. This paper describes the ex situ soil segregation methods, the considerations of each method, and the estimated cost savings from minimizing the volume of soil requiring transportation and off-site disposal. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Scalable control program for multiprecursor flow-type atomic layer deposition system

The authors report the development and implementation of a scalable control program to control flow type atomic layer deposition (ALD) reactor with multiple precursor delivery lines. The program logic is written and tested in LABVIEW environment to control ALD reactor with four precursor delivery lines to deposit up to four layers of different materials in cyclic manner. The programming logic is conceived such that to facilitate scale up for depositing more layers with multiple precursors and scale down for using single layer with any one precursor in the ALD reactor. The program takes precursor and oxidizer exposure and purging times as input and controls the sequential opening and closing of the valves to facilitate the complex ALD process in cyclic manner. The program could be used to deposit materials from any single line or in tandem with other lines in any combination and in any sequence.

42 ENGINEERING↗

DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗

DOE OSTI2025

Methanol from Integrated Direct Air Capture and Ceramic Electrolysis (MIDACE)

The Methanol from Integrated Direct Air Capture and Ceramic Electrolysis (MIDACE) project advanced a novel system concept for integrating pressurized co-electrolysis of steam and crude carbon dioxide captured directly from ambient air using a sorbent technology. The co-electrolysis produced syngas provides feedstock for a gas-to-liquid methanol synthesis reactor. The system recovers waste heat from the reactor and electrolyzer into regenerating the direct air capture sorbent. The purpose of the project was to demonstrate critical integration elements of the design and perform a technical study illustrating how a large-scale installation could achieve the $800/ton program target for green methanol production. The concept addresses the program objective to consolidate operations by combining crude CO 2 cleanup, hydrogen production, and partial CO 2 reduction steps within a carbon tolerant high temperature electrolyzer. The design lowers costs by simplifying the methanol recovery cycle, reducing carbon losses from venting, reducing sensitivity to catalyst selectivity, and avoiding syngas compression.

10 SYNTHETIC FUELS↗

DOE OSTI2017

System and method of detecting cavitation in pumps

A system and method for detecting cavitation in pumps for fixed and variable supply frequency applications is disclosed. The system includes a controller having a processor programmed to repeatedly receive real-time operating current data from a motor driving a pump, generate a current frequency spectrum from the current data, and analyze current data within a pair of signature frequency bands of the current frequency spectrum. The processor is further programmed to repeatedly determine fault signatures as a function of the current data within the pair of signature frequency bands, repeatedly determine fault indices based on the fault signatures and a dynamic reference signature, compare the fault indices to a reference index, and identify a cavitation condition in a pump based on a comparison between the reference index and a current fault index.

42 ENGINEERING↗

DOE OSTI2019

Parallel point defect identification in molecular dynamics simulations without post-processing: A compute and dump style for LAMMPS

Two classes for the molecular dynamics program LAMMPS – one compute style and one dump style – are presented that are designed to identify, count, and output point defects in cascade damage and related molecular dynamics simulations. The calculations are done in parallel across multiple MPI processes, and can be done at the same time as the original simulation. This drastically reduces storage requirements by eliminating the need to post-process every atom in the system. Non-cubic lattices, free surfaces, and large voids can be eliminated from the output by suitable choices of the reference lattice. The classes are derived from LAMMPS’s Dump and Compute classes, and pose no additional overhead to LAMMPS if they are not used.

42 ENGINEERING↗

DOE OSTI2013

The Belgian program for low and intermediate short lived waste management: from 1985 to license application

On 31 January 2013, ONDRAF/NIRAS, the Belgian Agency for Radioactive Waste and Enriched Fissile Materials, has introduced the construction and operation license application for the surface disposal facility for category A waste at Dessel. The objective of this paper is to present the development of this national programme. First the key milestones and their impact on the development of the project will be highlighted. This includes in particular: 1) the turning point in 1994, when ONDRAF/NIRAS converts its purely technic-scientific work method into an approach of participation that allows for societal aspects to be taken into account and 2) the decision of the government in 2006 in favour of the surface disposal of category A waste in Dessel. Then the paper presents the pillars of the long-term safety of a surface disposal and the principles for site selection and how they were applied to the surface disposal facility for category A waste at Dessel. The pillars of the long-term safety of a surface disposal are: (1) the passive confinement and isolation provided by the Engineering Barrier System (2) the contribution to this passive isolation and confinement by the site (3) the limitation of the radioactive source term, and (4) the control and monitoring measures. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Geothermal Hydraulic Stimulation: Overview of Methods and Best Practices

This paper outlines important information related to the hydraulic stimulation of geothermal wells and considerations for regulators responsible for evaluating applications to conduct these operations. Hydraulic stimulation applied to geothermal wells is an evolving technology that is different from the hydraulic fracturing process widely used to complete unconventional oil and gas wells. Important differences relate to operating pressures, variations in rock and fracture processes, and characteristics and disposal of hydraulic fracture fluids. Hydraulic stimulation is not widely used in the geothermal industry; however, future development of enhanced geothermal systems (EGS) will require hydraulic stimulation in order to enhance and create fracture permeability to allow wells to deliver sufficient heat and fluid to power plants. Gigawatts of EGS potential have been identified (U.S. Department of Energy 2019), so it is expected that the use of hydraulic stimulation of geothermal wells will be more common in the future. At present, there are no formal regulations guiding drilling programs or sundry notices that propose hydraulic stimulation of geothermal wells. On federal lands, important constraints and oversight are embedded in the process for obtaining Geothermal Drilling Permits outlined in the Code of Federal Regulations (43 CFR Section 3262.11); in Geothermal Resource Order 2 (GRO 2) guidance for assessment and mitigation of impacts of geothermal operations as well as construction and testing of geothermal wells; and in a Bureau of Land Management (BLM) Induced Seismicity Instruction Memorandum (BLM 2018). Additionally, local knowledge from BLM field offices and expertise of BLM engineers, or that of state regulators for operations on private and state lands, will be applied to fully assess operator applications to conduct geothermal hydraulic stimulation activities. Significant research effort has been directed toward EGS, so geothermal hydraulic stimulation techniques, impacts, and results are evolving and becoming better understood, including adaptation of decades of oil and gas industry experience with hydraulic fracturing of unconventional reservoirs. Recent EGS activities in the United States provide details about hydraulic stimulation with respect to oversight, stimulation design, execution, and results. Based on experience to date, the important issues to address when hydraulically stimulating a geothermal well include the following: 1. Wellbore construction and integrity must be appropriate to protect groundwater and manage stimulation pressures. 2. Understanding of lithology, faults, fractures, and subsurface stress state is necessary to design stimulation plans and predict results. 3. Seismic monitoring allows for observation and mitigation of induced seismicity.

geothermal↗

DOE OSTI2014

Development of SFR Primary System Simulation Capability for Advanced System Codes

Under the Reactor Product Line (RPL) of DOE/NE’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, an SFR System Analysis Module is being developed at Argonne National Laboratory for whole-plant safety analysis. This tool will simulate tightly coupled physical phenomena – including nuclear fission, heat transfer, fluid dynamics, and thermal-mechanical response – in SFR structures, systems, and components. It is based on the MOOSE (Multi-physics Object-Oriented Simulation Environment) framework, which relies upon open-source libraries such as libMesh and PETSc for mesh generation, finite element analysis, and numerical solutions. This development is a coordinated effort along with the development of RELAP-7, which is an advanced safety analysis tool for light-water reactors developed at Idaho National Laboratory. The SFR Module is aimed to model and simulate the SFR systems with much higher fidelity and with well-defined and validated prediction capabilities. It will provide fast-running, modest-fidelity, whole-plant transient analyses capability, which is essential for fast turnaround design scoping and engineering analyses.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2013

U.S. Department of Energy Pacific Region Clean Energy Application Center (PCEAC)

The U.S. Department of Energy Pacific Region Clean Energy Application Center (PCEAC) was formed in 2009 by the U.S. Department of Energy (DOE) and the California Energy Commission to provide education, outreach, and technical support to promote clean energy -- combined heat and power (CHP), district energy, and waste energy recovery (WHP) -- development in the Pacific Region. The region includes California, Nevada, Hawaii, and the Pacific territories. The PCEAC was operated as one of nine regional clean energy application centers, originally established in 2003/2004 as Regional Application Centers for combined heat and power (CHP). Under the Energy Independence and Security Act of 2007, these centers received an expanded charter to also promote district energy and waste energy recovery, where economically and environmentally advantageous. The centers are working in a coordinated fashion to provide objective information on clean energy system technical and economic performance, direct technical assistance for clean energy projects and additional outreach activities to end users, policy, utility, and industry stakeholders. A key goal of the CEACs is to assist the U.S. in achieving the DOE goal to ramp up the implementation of CHP to account for 20% of U.S. generating capacity by 2030, which is estimated at a requirement for an additional 241 GW of installed clean technologies. Additional goals include meeting the Obama Administration goal of 40 GW of new CHP by 2020, key statewide goals such as renewable portfolio standards (RPS) in each state, California’s greenhouse gas emission reduction goals under AB32, and Governor Brown’s “Clean Energy Jobs Plan” goal of 6.5 GW of additional CHP over the next twenty years. The primary partners in the PCEAC are the Department of Civil and Environmental Engineering and the Energy and Resources Group (ERG) at UC Berkeley, the Advanced Power and Energy Program (APEP) at UC Irvine, and the Industrial Assessment Centers (IAC) at San Diego State University and San Francisco State University. The center also worked with a wide range of affiliated groups and industry, government, NGO, and academic stakeholders to conduct a series of CHP education and outreach, project technical support, and related activities for the Pacific region. Key PCEAC tasks have included: - Preparing, organizing and conducting educational seminars on various aspects of CHP - Conducting state baseline assessments for CHP - Working with state energy offices to prepare state CHP action plans - Providing technical support services including CHP/district energy project feasibility screenings - Working with state agencies on CHP policy development - Developing additional CHP educational materials The primary specific services that PCEAC has offered include: - A CHP “information clearinghouse “ website: http://www.pacificcleanenergy.org - Site evaluations and potential projects screenings - Assessment of CHP status, potential, and key issues for each state - Information and training workshops - Policy and regulatory guidance documents and other interactions These services were generally offered at no cost to client groups based on the DOE funding and additional activities supported by the California Energy Commission, except for the in-kind staff resources needed to provide input data and support to PCEAC assessments at host sites. Through these efforts, the PCEAC reached thousands of end-users and directly worked with several dozen organizations and potential CHP “host sites” from 2009-2013. The major activities and outcomes of PCEAC project work are described.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

SharP: Towards Programming Extreme-Scale Systems with Hierarchical Heterogeneous Memory

The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. Along with hierarchical-heterogeneous memory, the system typically has a high-performing network and a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecture supports the convergence of the Big-Compute and Big-Data, the programming models have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. In this work, we propose and develop the programming abstraction called SHARed data-structure centric Programming abstraction (SharP) to address both of these goals, i.e., provide (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications. To evaluate SharP, we implement a Stencil benchmark using SharP, port QMCPack, a petascale-capable application, and adapt Memcached ecosystem, a popular Big-Data framework, to use SharP, and quantify the performance and productivity advantages. Additionally, we demonstrate the simplicity of using SharP on different memories including DRAM, High-bandwidth Memory (HBM), and non-volatile random access memory (NVRAM).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Study on the financing mechanism and management for decommissioning of nuclear installations in Malaysia

The whole cycle of the decommissioning process development of repository requires the relevant bodies to have a financial system to ensure that it has sufficient funds for its whole life cycle (over periods of many decades). Therefore, the financing mechanism and management system shall respect the following status: the national position, institutional and legislative environment, technical capabilities, the waste origin, ownership, characteristics and inventories. The main objective of the studies is to focus on the cost considerations, alternative funding managements and mechanisms, technical and non-technical factors that may affect the repository life-cycle costs. As a conclusion, the outcomes of this paper is to make a good recommendation and could be applied to the national planners, regulatory body, engineers, or the managers, to form a financial management plan for the decommissioning of the Nuclear Installation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Evaluation of Engineered Barrier Systems in the Disposition of Spent Nuclear Fuel

This document is a summary of the R&D activities associated with the Engineered Barrier Systems Work Package. Multiple facets of Engineered Barrier Systems (EBS) research were examined in the course of FY18 activities. This report is focused on delivering an update on the status and progress of modelling tools and experimental methods, both of which are essential to understanding and predicting long-term repository performance as part of the safety case. Specifically, the work described herein aims to improve understanding of EBS component evolution and interactions. Utlimately, the EBS Work Package is working towards producing process models for distinct processes that can either be incorporated into performance assessment (PA), or provide critical information for implementing better constraints on barrier performance The main objective of this work is that the models being developed and refined will either be implemented directly into the Generic Disposal System Analysis platform (GDSA), or can otherwise be indirectly linked to the performance assessment by providing improved bounding conditions. In either the case, the expectation is that validated modelling tools will be developed that provide critical input to the safety case. This report covers a range of topics — modelling topics include: thermal-hydrologic-mechanical-chemical coupling (THMC) in buffer materials, comparisons of modelling approaches to optimize computational efficiency, thermal analysis for EBS/repository design, benchmarking of thermal analysis tools, and a preliminary study of buffer re-saturation processes. Experimental work reported, includes: chemical evolution and sorption behavior of clay-based buffer materials and high-pressure, high temperature studies of EBS material interactions. The work leverages international collaborations to ensure that the DOE program is active and abreast of the latest advances in nuclear waste disposal. This includes participation in the HotBENT Field Test, aimed at understanding near-field effects on EBS materials at temperatures above 100 °C, and the analysis of data and characterization of samples from the FEBEX Field Test. Both the FEBEX and HotBENT Field Tests utilize/utilized the Grimsel Test Site in Switzerland, which is situated in a granite host rock. These tests offer the opportunity to understand near field evolution of bentonite buffer at in situ conditions for either a relatively long timescale (18 years for FEBEX) or temperature above 100 °C (HotBENT). Overall, this report provides in depth descriptions of tools and capabilities to investigate nearfield performance of EBS materials (esp. bentonite buffer), as well as tools for drift-scale thermal and thermal-hydrologic analysis critical to EBS and repository design. For a more detailed description of work contained herein, please see Section 10 ("Conclusions") of this document.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Development of Models to Simulate Tracer Tests for Characterization of Enhanced Geothermal Systems

A recent report found that power and heat produced from enhanced (or engineered) geothermal systems (EGSs) could have a major impact on the U.S energy production capability while having a minimal impact on the environment. EGS resources differ from high-grade hydrothermal resources in that they lack sufficient temperature distribution, permeability/porosity, fluid saturation, or recharge of reservoir fluids. Therefore, quantitative characterization of temperature distributions and the surface area available for heat transfer in EGS is necessary for the design and commercial development of the geothermal energy of a potential EGS site. The goal of this project is to provide integrated tracer and tracer interpretation tools to facilitate this characterization. This project was initially focused on tracer development with the application of perfluorinated tracer (PFT) compounds, non-reactive tracers used in numerous applications from atmospheric transport to underground leak detection, to geothermal systems, and evaluation of encapsulated PFTs that would release tracers at targeted reservoir temperatures. After the 2011 midyear review and subsequent discussions with the U.S. Department of Energy Geothermal Technology Program (GTP), emphasis was shifted to interpretive tool development, testing, and validation. Subsurface modeling capabilities are an important component of this project for both the design of suitable tracers and the interpretation of data from in situ tracer tests, be they single- or multi-well tests. The purpose of this report is to describe the results of the tracer and model development for simulating and conducting tracer tests for characterizing EGS parameters.

EGS↗

DOE OSTI2023

Advanced Algal Biofoundries for the Production of Polyurethane Precursors

The primary goal of the BEEPs project was to develop a process that could accelerate the development of algae as bioproduction platforms, from initial chemical product concept to an economically viable market supply. Under this program we elected to develop strains of algae that could generate polyurethane precursors, while simultaneously developing basic genetic tools to enable improved algal production systems. This program was specifically designed to incorporate National Laboratories as a means to utilize the expertise and facilities for new bio-production platforms. To that end, we designed a program to collaborate with the Agile BioFoundry at Lawrence Berkeley National Laboratory (LBNL) and computational platforms at Pacific Northwest National Laboratory (PNNL). In addition to these National Laboratory partners, we also had academic partners from UC Davis and Georgia Tech, as well as commercial partners Algenesis Materials and BASF. To achieve these goals, we initially focused on developing the genetic tools and high throughput screening technologies necessary to generate and assess production of polymer precursors (succinic acid) in algae and cyanobacteria, including advanced promoters and biosensors. In parallel, we computationally identified potential production bottlenecks and then used the developed genetic tools to increase production rates and yields. Constant feedback of data was used in conjunction with machine learning, high-throughput cell sorting, and synthetic biology, for additional targeted metabolic engineering. Multiple rounds of tool design, building, testing, and learning were supplied to partners at PNNL and LBNL to develop new models and tools that could expedite bioproduction platform development and increase yield performance. We targeted, and achieved, the FOA requirement yield metric of 20 g/L, as a milestone and deliverable from at least one of our engineered strains for the production of succinic acid.

09 BIOMASS FUELS↗

DOE OSTI2018

Phenomena identification and ranking table study for thermal hydraulics for Advanced High Temperature Reactor

The Advanced High-Temperature Reactor (AHTR) is a conceptual design of a Fluoride salt-cooled High-temperature Reactor (FHR) utilizing 7 LiF-BeF 2 (66-34 mol%) as its primary coolant. To identify key phenomena that impose potential challenges on thermal hydraulics modeling and simulation of such a reactor system, a thermal hydraulics phenomena identification and ranking table (TH-PIRT) study was performed for the AHTR in a Department of Energy Nuclear Energy University Program (NEUP) integrated research project led by Georgia Institute of Technology. A panel of experts from regulators, industries, national laboratories, and academia was assembled for the study. In this paper, the TH-PIRTs identified by the panel for two events, namely, station blackout and simultaneous withdrawal of all control rods, are summarized and discussed in detail. In addition, the key phenomena that warrant further study and research for AHTR analysis are identified to support the validation of thermal hydraulics system-level analysis codes and computational fluid dynamics simulation tools, as well as future FHR reactor licensing.

42 ENGINEERING↗

DOE OSTI2023

Enhancing Monte Carlo Workflows for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗