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The Role of Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology: Proceedings of a Workshop

Quantum concepts hold the potential to enable significant advances in sensing and imaging technologies that could be vital to the study of biological systems. The workshop Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology, held online March 8–10, 2021, was organized to examine the research and development needs to advance biological applications of quantum technology. Hosted by the National Academies of Sciences, Engineering, and Medicine, the event brought together experts working on state-of-the-art, quantum-enabled technologies and scientists who are interested in applying these technologies to biological systems. Through talks, panels, and discussions, the workshop facilitated a better understanding of the current and future biological applications of quantum-enabled technologies in fields such as microbiology, molecular biology, cell biology, plant science, mycology, and many others. The workshop was organized around three main themes. The first, quantum in biology, examined quantum concepts that are hypothesized to be important for life processes and that researchers are working to observe through biological imaging and sensing. The second, quantum for biology, addressed ways to use quantum concepts to enhance technologies for biological imaging and sensing. The third, biology for quantum, offered a wider discussion of how the frontiers of biological imaging and sensing could enable future study using quantum concepts, tools, or technologies. Throughout the workshop, participants identified a wide range of emerging approaches and opportunities at the intersection of quantum physics and biological sensing and imaging. During the workshop, there were some differences in how each speaker defined the term quantum. During one of the panels, Prem Kumar offered thoughts on what phenomena are classical versus quantum, explaining that techniques get progressively more quantum as you move from just having superposition to having superposition with measurement and entanglement. Another explanation from Clarice Aiello delineates the definition into several levels. This includes a base level of “quantum-ness,” which reflects that all matter is made of atoms, and when these particles are isolated they behave based on quantum mechanical principles. A second level is related to quantum coherence, where a single quantum object might be found in a coherent superposition state. A final level, which she described as the quantum-entangled level, involves multiple quantum systems which are entangled among themselves. Overall, the workshop touched on concepts such as superposition, entanglement, and squeezing and their potential implications for communication, computing, and simulation, in addition to the workshop’s main focal area, biological sensing and imaging. At the opening of the workshop, Thorsten Ritz of the University of California, Irvine, identified two questions at the heart of quantum biology: Is the machinery of life quantum mechanical, and can quantum mechanics be used to study the machinery of life in new ways? Participants highlighted systems in which researchers have explored these questions, from the vast array of molecular interactions involved in biological processes such as photosynthesis, to the mechanics involved in cellular functions such as differentiation and aggregation, to the role of oscillating magnetic fields in flight orientation among birds. Sensing and imaging technologies are crucial to biological research; these technologies could both enhance the study of quantum effects and be enhanced by quantum concepts. A critical challenge in biological research is to develop imaging and sensing tools that do not damage or interfere with the often fragile and fleeting systems being studied. Attendees discussed a variety of established and emerging technologies that could enhance noninvasive biological imaging, including single- and two-photon spectroscopy, single-molecule spectroscopy, quantum illumination, ghost imaging, and cryo-electron microscopy. One example came from Marlan Scully who gave a keynote address on the first day of the workshop. Scully emphasized the use of different laser technologies, which exhibit coherence and other quantum properties, in moving toward real-world biological applications, such as the detection of SARS-CoV-2. Both tools and theory will play an important role in advancing quantum biology research and applications. Several participants suggested theorists and experimentalists should work in tandem to understand and model biological processes. While physics often reduces systems to their simplest forms for fundamental insights, participants also noted the value of observing and understanding biological systems in all their “messiness,” capturing both the inner workings of biological systems and the complex interactions that occur within and between organisms. In discussions among participants, several attendees stressed the need to match emerging tools with the right scientific questions. Rather than developing quantum technologies as “a hammer looking for a nail,” participants emphasized a focus on exploring the problems these technologies are best suited to address. For example, it is important to consider the size of the phenomenon being studied, the timescales that are important in answering the scientific question, and other relevant considerations. Every tool along the spectrum from classical to quantum involves its own set of trade-offs. For example, Ted Laurence of the Lawrence Livermore National Laboratory said that quantum measurements, such as single photon counting, fluorescence transitions, and lasers, can take longer to produce the same results as classical measurements. These quantum measurements, however, do not require calibration and can enable new research questions to be answered. Prem Kumar, Northwestern University, noted that, despite their promise, quantum approaches should not be used simply for the sake of using quantum, especially in situations where classical approaches better meet the needs of the researcher. Understanding and applying quantum concepts could enable advances in a wide range of application areas including energy, synthetic biology, medicine, and sustainability. For example, Michelle O’Malley, University of California, Santa Barbara, described how improved noninvasive imaging approaches could help capture the complex interactions and functions involved in the breakdown of organic matter by microbial communities and lead to new technologies for capturing valuable products from plant waste. Several other participants discussed needs in tracking the movement of metabolites and molecules in microbial communities for insights into nutrient cycling in environments such as soil. Margaret Ahmad, Sorbonne University, discussed potential opportunities to leverage the magnetic properties of cryptochromes to advance new treatment approaches for diseases such as COVID-19 and cancer. Looking toward the future development of the field, participants discussed challenges to advancing quantum biology that arise from disciplinary disconnects between physicists and biologists. The siloing of academic research disciplines represents a significant barrier to progress. Disconnects in terminology, motivations and priorities, and structural barriers to collaborative work underscore the need for concerted efforts to bridge these divides. Attendees and speakers offered suggestions for resolving these divergences, establishing a shared language, moving the field forward, and fostering meaningful feedback between disciplines. Overall, participants stressed a need for balance, open communication, collaboration, unity, and clear dialogue on trade-offs between quantum and classical approaches. Keiko Torii, The University of Texas at Austin, said that the best collaborations happen when the project provides mutual advantages that can show off everyone’s talents, each team finds the work interesting, and partners develop a camaraderie to pursue new knowledge. To enable near- and long-term opportunities in this space, participants suggested that exploratory, high-risk funding could improve existing instrumentation to explore quantum enhancement collaboratively. They also emphasized the need for collaboration between quantum physicists and sensing/imaging scientists, which could be advanced through a dedicated quantum biology investigator program. People, even more than technology, will be crucial to the future of quantum biology. Participants explored training, education, and workforce needs to further develop this burgeoning field and cultivate the next generation of scientists. While many programs are still in their nascent stages, participants highlighted examples of approaches and programs being developed at various types of institutions to engage students and professional scientists in quantum biology research. Several participants stressed the need for an inclusive approach, spanning disciplines as well as communities to foster a diverse field fueled by the intellectual contributions of a wide range of people, including historically under-resourced schools and students. To increase awareness and excitement about quantum physics and related areas of biology, attendees suggested capitalizing on the “buzz” around quantum. Several participants emphasized the need to start early, introducing students to quantum concepts and their appealing “weirdness” in K–12 education. Engaging students early—before they become entrenched in traditional disciplinary siloes as typically happens in graduate school—could help to galvanize interest in the area and foster a generation of scientists with the interdisciplinary mindset and skills needed to advance this interdisciplinary field. While these efforts could be advanced at many levels and across multiple sectors, several participants suggested a national quantum biology center could be a valuable hub to coordinate and support quantum biology education and workforce development across academia, industry, and government.

59 BASIC BIOLOGICAL SCIENCES↗

Rheo-Structural Spectroscopy: Fingerprinting the In Situ Response of Fluids to Arbitrary Flow Fields

The objectives of this project were to develop new sample environments, measurement methodologies and associated modeling tools for characterizing the structural response to arbitrarily complex processing flows using small angle scattering, and to apply these new tools for understanding the fundamental physics governing the structuring of anisotropic particulate and polymeric materials under flow histories and conditions relevant to industrial processing flows. The research resulted in the development and implementation of a new sample environment, the fluidic four roll mill (FFoRM), for in situ small angle neutron and X-ray scattering (SANS/SAXS) measurements. These measurements are capable of generating large data sets that “fingerprint” how a complex fluid responds to a wide range of flow histories involving time variations in deformation type and rate. New modeling tools were developed to extract detailed microstructural information from such data sets, including orientation distribution functions and interparticle correlation functions, as well as reduced-order parametric descriptors of these high-dimensional functions that can be used to readily map, visualize and interpret a fluid’s structural response to its flow history. These new tools were applied to a range of model materials involving elongated particle suspensions in order to provide new insights into the physics of how flow couples with orientational and structural order in complex flows, particularly under non-dilute conditions for which no accurate theories currently exist. Using these investigations, we elucidated a number of new insights into the fundamental phenomena driving such process-structure-property relationships. These findings provide guidance for the further development of rheological models, and ultimately can inform the rational and model-based design of flow processes to achieve optimized orientational ordering that is key to the properties and function of a wide range of energy-relevant materials.

36 MATERIALS SCIENCE↗

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

toughio v1.2.2

TOUGH is a widely used general purpose numerical simulator designed for fluid and heat flows of multiphase, multicomponent fluid mixtures in porous and fractured media, which has been applied to many real-world problems such as underground geological storage, geothermal reservoir engineering or nuclear waste disposal, to name a few. `toughio` is a lightweight, object-oriented and vectorized Python library that aims to provide user-friendly routines to facilitate pre- and post-processing of a TOUGH simulation. Although its primary target is the latest version TOUGH3, `toughio` can also be used to read/write TOUGH2 input/output files. Currently, to the best of our knowledge, only PyTOUGH offers an exhaustive number of features to carry out a complete TOUGH simulation using a scripting language. `toughio` and PyTOUGH share the same objectives, yet with different approaches and philosophies. On the one hand, a PyTOUGH mesh is represented as a MULGRAPH geometry where elements can be unstructured horizontally but only layered vertically. It also checks that the model parameters are valid to ensure that a TOUGH simulation runs smoothly. On the other hand, although it provides basic meshing features, `toughio` mostly relies on common third-party softwares (e.g. Abaqus, FLAC3D, Gmsh, LaGriT) to generate the mesh by importing and converting it to a TOUGH mesh, which also conveniently facilitates the coupling of TOUGH with any other simulator that also supports the same mesh formats. Besides, `toughio` is designed to be just a TOUGH input and output file reader/writer. It assumes that the user knows what he is doing and does not verify the validity of the model parameters (`toughio` does verify the functional arguments).

Luu, Keurfon↗

Lawrence Livermore National Laboratory Experimental Test Site, Site 300: Soil Study Institutional Review Board #1081 Soil Sampling and Analysis Plan

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) to collect soil quality data at an area located at Lawrence Livermore National Laboratory’s (LLNL’s) Experimental Test Site, Site 300 (Site 300) (project) (Figures 1 and 2). The purpose of the project is to determine the chemical and radiological quality of soil in the project area to support feasibility and design of future improvements. This SAP identifies chemicals of concern and describes the procedures for collection and analysis of environmental samples, and evaluation of analytical data (chemical and radiological) to determine the reuse potential of shallow soil at the project area. This SAP follows the criteria established in LLNL’s Soils Screening and Management Plan (SSMP) (LLNL 2022), which is consistent with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗

Manufacturing of Fabric Electrodes using a High-Throughput Screening Platform for Redox Flow Batteries

The objective of this project is to establish a new manufacturing methodology with machine learning- based high-throughput screening for the design and development of hierarchical structured, high-performance fabric electrodes for redox flow batteries (RFBs). The end goal of the project is to design and manufacture fabric electrodes for RFB applications that can provide 250 mA/cm2 current density operation for 100-cycles with 80% average energy efficiency. This was accomplished by first examining the structure-performance-property linkages of the electrodes provided by our partner, AvCarb. The electrodes’ microstructure was characterized by determining their pore size distribution, tortuosity, specific surface area, and porosity. The ohmic, charge transfer and mass transfer resistances were then calculated using electrochemical impedance spectroscopy. Carbon cloth electrodes showed the greatest resistance, which was dominated by charge transfer resistance, which we believe is related to the surface functionalization. Full cell cycling was used in order to determine the area specific resistance and energy efficiency of the cells. All of this experimental data and the results of the mathematical model (to increase the amount of inputs with parametric sweeping) were used to develop a machine learning-based model for the design of high-performance fabric electrodes. Using the results from the machine learning tool, optimized electrodes were fabricated by AvCarb. The ohmic, charge transfer and mass transfer resistances for these new electrodes were measured, and both performed better than any of the initial samples which had been provided by AvCarb.

25 ENERGY STORAGE↗

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗

Science Applications of Phased Array Radars

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near instantaneous sampling of the atmosphere with flexible beam forming, multi-functionality, low operational and maintenance costs, and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors including i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.

54 ENVIRONMENTAL SCIENCES↗

Characterization of Particle and Heat Losses from a High-Temperature Particle Receiver (2 nd Ed)

High - temperature particle receivers are being pursued to enable next - generation concentrating solar thermal power (CSP) systems that can achieve higher temperatures (> 700 C) to enable more efficient power cycles, lower overall system costs, and emerging CSP - based process - heat applications. The objective of this work was to develop characterization methods to quantify the particle and heat losses from the open aperture of the particle receiver. Novel camera - based imaging methods were developed and applied to both laboratory - scale and larger 1 MW t on - sun tests at the National Solar Thermal Test Facility in Albuquerque, New Mexico. Validation of the imaging methods was performed using gravimetric and calorimetric methods. In addition, conventional particle - sampling methods using volumetric particle - air samplers were applied to the on - sun tests to compare particle emission rates with regulatory standards for worker safety and pollution. Novel particle sampling methods using 3 - D printed tipping buckets and tethered balloons were also developed and applied to the on - sun particle - receiver tests. Finally, models were developed to simulate the impact of particle size and wind on particle emissions and concentrations as a function of location. Results showed that particle emissions and concentrations were well below regulatory standards for worker safety and pollution. In addition, estimated particle temperatures and advective heat losses from the camera - based imaging methods correlated well with measured values during the on - sun tests.

14 SOLAR ENERGY↗

Beam-ion Studies in NSTX and NSTX-U (Final Technical Report)

The confinement of high energy "fast ions" is crucial for the success of magnetic fusion as a practical energy source. The research performed by UC Irvine on NSTX and NSTX-U provided new information about this important topic in the configuration known as a "spherical tokamak." Injected neutral beams provided the fast ions (also known as "beam ions"). There were five overarching goals of the research. One objective was to measure the confinement of beam ions in order to ascertain if they were accomplishing their desired purpose of transferring their energy to the bulk plasma. A second goal was to better understand instabilities that are driven unstable by the fast ions. A related goal was to use the similarities and differences between NSTX and the DIII-D conventional tokamak to better understand the fast-ion driven instabilities. A fourth goal was to develop new instruments to measure fast ions and techniques that facilitate analysis of the data. The fifth objective was to measure and understand acceleration of beam ions by RF waves. Much was accomplished in all of these five areas. In the first, it was shown that the large magnetic moment of the beam ions did not harm confinement but instabilities known as the "sawtooth" and "long-lived mode" do. In the second area, much attention was devoted to the fast-ion instabilities known as "Alfvén eigenmodes." In particular, Alfvén eigenmode "avalanches" can cause ~ 30% of the fast ions to be lost in a single explosive burst. The comparative studies with DIII-D showed that an instability discovered on NSTX is also important in conventional tokamaks. In the fourth category, several instruments were developed, a number of effects that complicate interpretation of the data were understood, and progress toward a new method to infer the fast-ion distribution function from the data was made. In the fifth category, the measured profile of accelerated fast ions was initially much broader than theoretical predictions but subsequent improvements in the theoretical modeling achieved good agreement with the data.

43 PARTICLE ACCELERATORS↗

Harnessing Condorcet Methods to Improve Decision-making Based on Ranked Data

Decision-makers must often choose between multiple alternatives based on their relative merits across a variety of criteria. These multiple-criteria decision-making (MCDM) problems must be approached in an objective, measurable, and transparent fashion to obtain well-supported outcomes. The challenge of using sets of ranked data to identify the optimal choice is not unlike the challenge of using ranked ballots to identify the winner of an election in a preference voting system. As such, the methods of rank aggregation used in elections can be applied to help resolve MCDM problems. Previous research into the design of elections and ballots has yielded many algorithms with well-understood properties. A subset of these, called Condorcet methods, reliably identify the “Condorcet winner,” if it exists, giving the result that would defeat any other in a pairwise comparison. That, in addition to several other desirable qualities, makes Condorcet methods like the Schulze method, Ranked Pairs, and Copeland’s method, effective and scalable means for tackling MCDM problems. These rank aggregation methods have been implemented in JavaScript functions for incorporation into a web-based MCDM decision tool. These algorithms can be applied in a wide variety of contexts (for example, determining the “best” environmental remediation method or selecting a subcontractor) to facilitate effective decision-making. The performance of the JavaScript Condorcet prototype tool was compared across the implemented algorithms and with both a non-Condorcet rank aggregation method as well as an implementation of the Simple Multi-Attribute Rating Technique (SMART) algorithm.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Compaction of crushed salt for safe containment – overview of the KOMPASS project

Abstract. In Germany, rock salt formations are possible host rock candidates for a repository for heat-emitting radioactive waste. The safety concept of a repository in salt bases on a multibarrier system consisting mainly of the geological barrier salt and geotechnical seals ensuring safe containment. Crushed salt will be used for backfilling of cavities and sealing measures in drifts and shafts due to its favourable properties and its easy availability (mined-off material). The creep of the rock salt leads to crushed salt compaction with time. Thereby, the crushed salts' porosity is reduced from the initial porosity of 30 %–40 % to a value comparable to the porosity of undisturbed rock salt (≤1 %). In such low porosity ranges, technical impermeability is assumed. The compaction behaviour of crushed salt is rather complex and involves several coupled THM processes (Kröhn et al., 2017; Hansen et al., 2014). It is influenced by internal properties like humidity and grain size distribution, as well as boundary conditions such as temperature, compaction rate or stress state. However, the current process understanding has some important gaps referring to the material behaviour, experimental database and numerical modelling. It needs to be extended and validated, especially in the low porosity range. The objective of the KOMPASS project was development of methods and strategies for the reduction of deficits in the prediction of crushed salt compaction leading to an improvement of the prognosis quality. Key results are as follows (KOMPASS Phase 1, 2020): selection of an easily available and permanently producible synthetic crushed salt mixture, acting as a reference material for generic investigations; development and proof of different techniques for producing pre-compacted samples for further investigations; establishment of a tool of microstructure investigation methods to demonstrate the comparability of grain structures of pre-compacted samples with in-situ compacted material for future investigations; execution of various laboratory experiments using pre-compacted samples, e.g. long-term creep tests which deliver reliable information about time- and stress-dependent compaction behaviour; development of a complex experimental investigation strategy to derive necessary model parameters considering individual functional dependencies. Its technical feasibility was successfully verified; benchmarking with various existing numerical models using datasets from three different triaxial long-term tests. The result was not entirely satisfactory; however, the number of influencing factors is small and further validation work has to be done. Overall, the KOMPASS project has made significant progress in the approaches to solving the outstanding question, building the basis for further investigations.

Friedenberg, Larissa↗

Anomalies in the space of coupling constants and their dynamical applications I

It is customary to couple a quantum system to external classical fields. One application is to couple the global symmetries of the system (including the Poincaré symmetry) to background gauge fields (and a metric for the Poincaré symmetry). Failure of gauge invariance of the partition function under gauge transformations of these fields reflects ’t Hooft anomalies. It is also common to view the ordinary (scalar) coupling constants as background fields, i.e. to study the theory when they are spacetime dependent. We will show that the notion of ’t Hooft anomalies can be extended naturally to include these scalar background fields. Just as ordinary ’t Hooft anomalies allow us to deduce dynamical consequences about the phases of the theory and its defects, the same is true for these generalized ’t Hooft anomalies. Specifically, since the coupling constants vary, we can learn that certain phase transitions must be present. We will demonstrate these anomalies and their applications in simple pedagogical examples in one dimension (quantum mechanics) and in some two, three, and four-dimensional quantum field theories. An anomaly is an example of an invertible field theory, which can be described as an object in (generalized) differential cohomology. We give an introduction to this perspective. Also, we use Quillen’s superconnections to derive the anomaly for a free spinor field with variable mass. In a companion paper we will study four-dimensional gauge theories showing how our view unifies and extends many recently obtained results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Advanced Laboratory and Field Arrays: Debris Modeling, Detection,& Mitigation (Task 1)

The statement of project objectives for this task was: develop tools, methods and models to assess, and mitigate the risk of damage to MHK infrastructure from woody debris. Develop the capability to detect woody debris using sonar and/or physical methods for purposes of characterizing debris statistics in river (at UAF’s Tanana River Test Site) and near-shore wave (at Yakutat, AK) environments and to activate debris mitigation measures. Develop debris impact risk maps and tables using statistics on debris size, geometry, type, prevalence, mobility and location. Improve and apply the COUPi discrete element method (DEM) to develop models of debris movement and impact on MHK infrastructure to evaluate risk of damage, and interference, to operations from debris. The proposed final deliverable for the task was a set of tools or techniques for providing estimates of the probability of debris impact, and resulting impact forces, on MHK infrastructure as a function of debris size, type, wave regime, and current velocity. Such estimates are required to assess damage risks to operational MHK infrastructure.

13 HYDRO ENERGY↗

First Analysis of Earth's Stratospheric γ ‐Ray Emission in Geographical Coordinates With Fermi LAT

Abstract From low‐Earth orbit, the Earth is an extremely bright γ ‐ray source compared to distant astrophysical objects. The high‐energy γ ‐ray emission, from the Earth's upper atmosphere, is produced by the interactions between cosmic rays (CRs) and air molecules. As CRs are predominantly charged particles, the geomagnetic field acts as a spectrometer, forbidding CRs below a certain energy to arrive and interact with the atmosphere at a given geomagnetic location from a given direction. We use photon data between 0.2 and 20 GeV as observed by the Fermi Large Area Telescope (LAT) to create, for the first time, stratospheric γ ‐ray emission maps of the Earth in the geographical coordinate system, for different viewing directions from LAT altitude. These maps confirm a strong association between measured γ ‐ray fluxes and geomagnetic cutoffs for CRs. We also report the spectrum of the Earth's stratospheric γ ‐ray emission in cardinal directions and use the geomagnetic spectrometer effect to infer the atmosphere's yield function of γ rays per incoming CR proton as a function of CR rigidity at the altitude of the Fermi spacecraft. This work is relevant to the study of near‐Earth radiation exposure for astronauts and spacecraft, modeling the geomagnetic field, and CR‐air interactions.

Madlee, S.↗

Continuous Integration, In-Code Documentation, and Automation for Nuclear Quality Assurance Conformance

The Multiphysics Object Oriented Simulation Environment (MOOSE) is an open-source, finite element framework for solving highly coupled sets of nonlinear equations. The development of the framework and applications occurs concurrently using an agile, continuous-integration software package. Included in the framework is an in-code, extensible documentation system. Using these two tools in union with the repository management tools GitHub and GitLab, a software quality plan was created and followed such that MOOSE and a MOOSE-based application (BISON) have been shown to meet the American Society of Mechanical Engineers’ Nuclear Quality Assurance-1 standard. The approach relies heavily on automation for both testing and documentation. The resulting effort demonstrates that a rigorous software quality plan may be implemented that incurs a minimal impact on day-to-day development of the software, satisfying the stringent guidelines necessary to operate the software in a safety function within a nuclear facility.

97 MATHEMATICS AND COMPUTING↗

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Automated Process Planning for Embossing and Functionally Grading Materials via Site-Specific Control in Large-Format Metal-Based Additive Manufacturing

The potential for site-specific, process-parameter control is an attribute of additive manufacturing (AM) that makes it highly attractive as a manufacturing process. The research interest in the functionally grading material properties of numerous AM processes has been high for years. However, one of the issues that slows developmental progress in this area is process planning. It is not uncommon for manual programming methods and bespoke solutions to be utilized for site-specific control efforts. This article presents the development of slicing software that contains a fully automated process planning approach for enabling through-thickness, process-parameter control for a range of AM processes. The technique includes the use of parent and child geometries for controlling the locations of site-specific parameters, which are overlayed onto unmodified toolpaths, i.e., a vector-based planning approach is used in which additional information, such as melt pool size for large-scale metal AM processes, is assigned to the vectors. This technique has the potential for macro- and micro-structural modifications to printed objects. A proof-of-principle experiment is highlighted in which this technique was used to generate dynamic bead geometries that were deposited to induce a novel surface embossing effect, and additional software examples are presented that highlight software support for more complex objects.

36 MATERIALS SCIENCE↗