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At least 1,513 records · Page 84

Toward Hybrid Physics-Machine Learning to improve Land Surface Model predictions

A critical challenge for Land Surface Models (LSMs) is to simulate processes at the surface and the subsurface and their feedbacks to the atmosphere. Even using the same climate forcings, different LSMs predict different surface fluxes and soil moisture conditions due to differences in the formulations of individual processes, parameterizations, and representation of spatial heterogeneity. Ultimately, these differences contribute to the LSM prediction errors and uncertainty. This research seeks to address this challenge by coupling physics-based modeling with state-of-the-art machine learning (ML) techniques to describe complex physical and biogeochemical processes and narrow the gap between model predictions and observations.

58 GEOSCIENCES↗

New Laboratory and Astrophysical Probes of Physics Beyond the Standard Model (Final Technical Report)

The first of these directions centers on new ideas in dark matter detection. As experimental scrutiny narrows the window for new physics at the weak scale, it has become apparent that the solution to the dark matter mystery may not reside there. Theoretical developments such as the hidden sector/valley paradigm have, at the same time, shown that compelling theories of dark matter reside below the weak scale. Searching for dark matter at lower mass scales requires looking beyond the current paradigm of dark matter direct detection based on nuclear recoils. It is the goal of this project to provide new ideas to guide the experimental program looking for light dark matter. I am actively proposing ideas for detecting dark matter as light as a meV, well beyond the current focus of GeV-TeV scale dark matter. Some of the ideas I have proposed, such as superconducting, superfluid, and polar material targets, are actively being developed into experiments. My group will provide the relevant calculations to determine dark matter reach, providing crucial input to experiment on which targets should be developed. We will also provide an effective field theory framework to understand which types of experiments are most sensitive to each interaction type. The second of these directions is on probes of dark matter substructure as a mean to constrain, or observe, models of dark matter. The standard ΛCDM paradigm assumes that the dark matter density perturbations are adiabatic, scale invariant, and produced during inflation. However, many standard models of dark matter will produce modifications of this prediction, such as axion models with symmetry broken below the inflationary scale. Remarkably, we have limited direct measurements of the dark matter clumpiness at mass scales below dwarf galaxies. This allows for the possibility of observing modifications from vanilla ΛCDM due to particle dynamics. In many cases, simulations of dark matter structure in the presence of non-scale invariant fluctu- ations are understudied or completely lacking. I plan to develop both theoretical predictions for small scale structure in models with additional matter power on small scales, as well as observa- tional probes of small scale halos or clumps. One idea I have been recently focused on is pulsar timing, though my group will pursue a variety of lensing and astrometric probes. Lastly, the most risky direction involves spacetime fluctuations from quantum gravity. I showed that if one assumes that metric fluctuations in the Minkowski vacuum, at a surface separating a region in and out of causal contact (which we call a “horizon”), are determined by standard thermodynamic considerations at horizon, these metric fluctuations are large enough to observe in an interferometer with similar sensitivity to LIGO. In a follow-up paper we showed that these assumption holds for the vacuum in AdS/CFT. In future work, I plan to connect this work to recent soft graviton results, to frame this work in a concrete Randall-Sundrum model, and to work out concrete phenomenological predictions from the model. This work unquestionably takes a less trodden path, and works well as part of a well-rounded portfolio of less and more risky ideas. Particle physics is currently at a juncture which requires bold exploration of qualitatively new directions. This proposal outlines some of my plans over the coming years in these directions, leaving room also for surprises.

79 ASTRONOMY AND ASTROPHYSICS↗

LANL Weapons Physics Directorate: Mission Scope and Opportunities [Slides]

The Weapons Physics Directorate stewards a number of critical capabilities to meet the LANL mission. To execute our mission, ALDX develops and applies cutting-edge theory, computational models and multi-physics simulation codes, and designs, executes, and analyzes complex experiments. Our mission is to sustain the current stockpile, provide future stockpile options, and help shape a globalized nuclear world.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Associate Laboratory Directorate for Weapon Physics (ALDX) [Slides]

ALDX is one of seven technical Directorates at the Laboratory and resides within the Weapons Program. The Weapons Physics Directorate stewards a number of critical capabilities to meet its mission. Our mission is to sustain the resilience of the US nuclear stockpile and help shape a globalized nuclear world. Sustaining the aging stockpile poses scientific challenges to assess and certify the stockpile without testing. We are helping to solve major technical challenges in the nuclear global security regime. Geopolitical realities demand the Laboratory’s excellence in providing solutions to national security problems. High Performance Computing at Los Alamos opens the doors to many sophisticated modeling techniques. We use many experimental facilities to probe weapons physics. The diverse skill set required to execute ALDX’s mission has benefited greatly from the Postdoc program.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Simulating physical and biogeochemical dynamics of the Arctic Ocean [Slides]

Ocean physics (currents, mixing, air-sea transfer of heat and gases) and biogeochemistry (carbon and oxygen cycles, ecosystem dynamics, nutrient distributions) are tightly coupled. We used the DOE-developed Energy Exascale Earth System Model (E3SM) to simulate physical and biogeochemical dynamics of the global ocean, with an emphasis on the western North Pacific. In particular, we used the grid refinement capability of E3SM’s ocean component (LANL-developed MPASOcean) to quantify the effect of model resolution on the carbon and oxygen cycles using a pair of century-long simulations that differ only in the western North Pacific grid. Regional grid refinement has a noticeable impact on biogeochemistry. Distribution of important nutrients (such as nitrate) changes the amount of photosynthesis by phytoplankton, which in turn affects air-sea transfer of CO2 and the local carbon cycle.

58 GEOSCIENCES↗

Cyber-Physical Dynamic System (CPDS) Modeling for Frequency Regulation and AGC Services of Distributed Energy Resources

The substantial integration of renewable energy brings significant challenges to balance the system in real time because of the variability and intermittency of renewable power. For the reliable system operation, the frequency regulation service is used to stabilize the system frequency through automatically balancing the system generation and load. On one hand, with the substantially increasing deployment of renewable energy in electricity system, the requirement of frequency regulation (FR) services increases significantly. On the other hand, the current main resource of FR services, the controllable conventional generation, is continuously decreasing in the system generation mix. This means that in the future high renewable penetration power system, additional and alternative reliable FR services providers such as distributed energy storage (DES) resources should be explored. Although the capability of utility-scale energy storage to provide FR services has been demonstrated, the integrated control and dynamic modeling of distributed energy resource (DER) providing frequency regulation grid services has been rarely explored. There are several challenges to adopt DERs to provide reliable grid services as illustrated in FERC 755. First, the distributed installation of DES requires a comprehensive cyber-physical dynamic system (CPDS) modeling to fully consider the impacts of the communication latency variability on its real time FR provision capability. Unlike the conventional generators, there are two-layer communication between DERs and system operators which increase the communication delay. Second, unlike the conventional generators whose dynamic models have been comprehensively studied, the difference among individual DER components' power-dynamic characteristic brings challenges in the accurate dynamic AGC modeling of its power-frequency relationship. Third, the temporal dependent state of charge uncertainty of DERs challenges DERs' power and frequency regulation capacities scheduling in the look-ahead generation scheduling. Therefore, the aggregator should optimize the frequency regulation provision from individual DER in real time. To overcome these challenges, this project proposes a cyber-physical dynamic system (CPDS) model to handle the uncertainty of DERs two-layer communication latency and power dynamics. The variability of DERs' communication delay and dynamic constraints will be comprehensively modeled. The DERs' AGC model with communication delay is designed to validate DESs' frequency regulation services. Like current performance-based frequency regulation evaluation, the delivery of DERs' frequency regulation will be assessed through post-analysis of the actual AGC response with respect to the AGC control signal from system operators. Consequently, the reliability improvement with DERs providing reliable frequency regulation services can be evaluated from a comprehensive perspective considering all the dynamics of communication and power.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗

Particle Accelerators at the Intensity Frontier for Elementary Particle Physics Research

In 1897, Prof. J. J. Thomson used a cathode ray tube that operated at a few kiloelectron volts (keV) to discover the electron and measured its charge-to-mass ratio. In this case, the electrons were not created, but ejected from hot filament. This experiment is considered to be one of the earliest uses of particle accelerators in elementary particle physics research. Imparting a few keV of energy to a beam of electrons was considered as high energy at that time. The meaning of the phrase High Energy Physics (HEP) has changed significantly over the last century.

43 PARTICLE ACCELERATORS↗

Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics—Near-Real-Time Cyber-Physical Resiliency Through Machine Learning

The Situational Awareness of Grid Anomalies (SAGA) project built upon foundational power system tools developed at the National Renewable Energy Laboratory (NREL) integrated with an ever-increasing set of Gridmetrics data extracted from the cable television (CATV) broadband network infrastructure while assimilating other time-series geospatial data and information, such as weather and cyber-physical phenomena, to demonstrate a disruptive technology for power system data analytics relying on existing infrastructure. Three research thrusts supported (1) visual analytics, (2) cyber-physical power system simulation, and (3) anomaly detection. SAGA created technology that leverages, couples, and fortifies two vastly different realms - power and broadband - to increase the resiliency of the power grid in the face of increasing cyberattacks and operational challenges related to integrating DERs. The exploration of potential synergies of broadband-enabled grids resulted in identifying a mutually beneficial symbiosis that can increase the resiliency of both power and broadband services. Broadband networks perform better with reliable power and are good at providing real-time measurements that identify where the grid is under attack, is failing, or is weak. Likewise, sensor-starved distribution grids perform better and can be more reliable when their operation is buttressed with observations of broadband-detected anomalies. Future research can explore broadband's contribution to continuing to improve grid resiliency, reliability, and cost-effective operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Operational Uncrewed Aircraft Systems Development for Meteorology and Atmospheric Physics Field Campaign Report

The overarching goal of the project is to develop integrated small, unmanned aircraft systems (SUAS) capabilities for enhanced atmospheric physics measurements. This team included atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use SUAS in atmospheric physics. The flight campaigns at the Southern Great Plains (SGP) U.S. Department of Energy Atmospheric Radiation Measurement (ARM) observatory were an integrated evaluation of the systems in coordinated field tests. This enabled sensor testing, and validation of SUAS technology and integration of unmanned aircraft into the airspace. The set of experiments enabled evaluation and progression of remote UAS deployment operations with observing atmospheric sensors. Each flight test endeavor was notable for the significant lessons learned during every deployment event. Team size and roles, equipment use, aircraft instruments, meteorological instruments, and deployment logistics, were all evaluated and improved at every flight-testing day. The details of these are further discussed in the Results section.

54 ENVIRONMENTAL SCIENCES↗

2022 Annual Site Environment Report, Princeton Plasma Physics Laboratory

This report provides the U.S. Department of Energy (DOE) and the public with information on the level of radioactive and non-radioactive pollutants (if any) that are added to the environment as a result of Princeton Plasma Physics Laboratory’s (PPPL) operations. This report fulfills the annual public reporting requirements of DOE Order 231.1B. The results of PPPL’s 2022 environmental surveillance and monitoring program are presented and discussed. The report also summarizes environmental initiatives, assessments, and community involvement programs that were undertaken in 2022. PPPL’s on-site operations started to be restored in 2022, following curtailments in 2020 and 2021 for the global coronavirus (COVID-19) pandemic. PPPL has engaged in fusion energy research since 1951 and at its current locations since 1958. The Laboratory’s mission is to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world, and to develop the understanding of plasmas from the nano- to the astrophysical scale. PPPL’s primary experiment, the National Spherical Torus Experiment-Upgrade (NSTX-U) is a collaboration among national laboratories, universities, and national and international research institutions and is a major element in the US Fusion Energy Sciences Program. Its design tests the physics principles of spherical torus (ST) plasmas, playing an important role in the development of smaller, more economical fusion reactors. Due to previous operational issues, NSTX-U did not operate in 2022. PPPL is engaged in a project to replace key NSTX-U components and systems to enable the operation of this international magnetic fusion user facility. In 2022, PPPL’s radiological environmental monitoring program measured tritium in the air at onsite sampling stations. Using highly sensitive air monitors, PPPL is capable of detecting small changes in the ambient levels of tritium. The operation of monitors located on D-site is used to demonstrate compliance with the National Emission Standard for Hazardous Air Pollutants (NESHAPs) regulations. Also included in PPPL’s radiological environmental monitoring program, are water monitoring – ground, surface, and waste waters. PPPL’s radiological monitoring program characterized the background levels of tritium in the environment and those data are presented in this report. Ground water monitoring continued under New Jersey Department of Environmental Protection’s (NJDEP) Site Remediation Program regulations. PPPL monitored for non-radiological contaminants, mainly volatile organic compounds (components of common degreasing solvents). In 2022, PPPL complied with permit limits for surface water and sanitary wastewater discharges. PPPL was honored with an award for EPEAT-certified electronics purchasing from the Global Electronics Council.

54 ENVIRONMENTAL SCIENCES↗

Cyber Physical Subsystems under Adverse Conditions

The United States power grid is built upon complex cyber-physical systems (CPS), including many distributed energy resources (DERs) and microgrid structures. Due to cross-disciplinary domains that govern the interplay between cyber and physical technologies that sustain DERs, it is difficult to confidently predict how these systems will react under adverse conditions such as faults and cyberattacks. This project utilized power hardware in the loop (PHiL) to gain system behavior knowledge and robust understanding through experimentation and observational science. It also produced a large volume of datasets to feed into other efforts to advance high-fidelity model simulations and to support the design of novel control approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Scorpius Beam Physics Overview and a Few Remaining Concerns [Slides]

With so many new faces on board, I thought to take the opportunity to review some of the many beam physics issues that might cause problems after Scorpius is built and accelerating electron beams. Mitigation measures for all of these have been incorporated in the Scorpius design, wherever possible. In this talk, I’ll quickly review the physics issues that I feel are most problematic for Scorpius, based on my own experience, and that documented by other workers in the field of linear induction accelerators.

43 PARTICLE ACCELERATORS↗

Trustworthy Physics-Informed Deep Learning for Predictive Scientific Computing

This project has developed powerful trustworthy physics-informed deep learning (TPiDL) models and methods to fundamentally enhance the scale and power of computational modeling in the scientific and engineering domains. Deep learning (DL) has radically advanced the state-of-the-art in machine learning, computer vision, natural language processing, and also scientific computing. Nevertheless, progress has been driven almost entirely by empirical observations, hacks, and tricks. Under the support of this project, the graph operator learning tools and advanced trustworthy physical informed neural networks have been developed. In addition, stochastic gradient replica-exchange Markov Chain Monte Carlo (MCMC) sampling algorithms have been designed to quantify the uncertainties and speed up the training of large-scale neural networks.

97 MATHEMATICS AND COMPUTING↗

Coupled SAM/Griffin Model of a Reference Pebble Bed High-Temperature Gas Cooled Reactor for Multi-Physics Simulations

An effort has been dedicated to developing a reference model for multi-physics coupled simulation of the pebble bed high-temperature gas-cooled reactor (PB-HTGR) with SAM and Griffin computer codes for safety analysis and licensing purpose. The reference problem of a prototypical reactor design serves as the foundation for the U.S. NRC (Nuclear Regulatory Commission) to verify the adequacy of computer codes and evaluation models for specific reactor types. In this work, a SAM model of the HTR-PM reactor has been developed based on publicly available design information and the multi-dimensional Pronghorn model developed by Idaho National Laboratory. The SAM HTR-PM model includes a multi-dimensional core region and 0- D/1-D fluid components. Additionally, a simplified air RCCS loop is modeled for decay heat removal. The Griffin model is based on a recent work by Idaho National Laboratory. The coupling between the models is achieved through the MOOSE MultiApp system. Both steady-state and transient scenarios were simulated to demonstrate the coupled model’s capability for multi-physics simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Artificial Intelligence in Nuclear Physics

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly developing fields providing data-driven algorithms to predict, classify, and make decisions based on data. Nuclear Physics Research is data-driven and AI/ML techniques have been implemented for experiment and accelerator control, in theoretical applications, and in data processing and analysis. These algorithms open possibilities for automation, thereby augmenting human capabilities. Additionally, Open Science is enabled by simultaneous analyses of multiple data sources, leading to scientific knowledge. This talk will summarize current applications of AI/ML in nuclear physics, as well as accelerator applications, and will cover upcoming initiatives and research in AI/ML.

Jeske, Torri↗

A multi-Physics Experiment for Low-Yield Nuclear Explosion Monitoring

A series of multi-physics experiments, referred to as Physics Experiment 1 (PE1) is underway at the United States’ Nevada National Security Site (NNSS). The PE1 series includes detonations of three underground chemical explosions in P-tunnel, with fully coupled (PE1 A), partially decoupled (PE1 D L ), and fully decoupled (PE1 B) emplacements. Canisters with gas tracers are imbedded in the explosives, and the tracers are released when the canister is destroyed by the detonation. A dedicated electromagnetic (EM) experiment (EMX) generates well-characterized EM signals at an underground location near the chemical explosive experiments. A series of atmospheric experiments (METEX, REACT, and METREX) release smoke and radioactive tracers around Aqueduct Mesa to test gas transport in complex topography. Each of the chemical explosive experiments includes a network of sensors to record seismic, acoustic, and electromagnetic waves, measurement of atmospheric conditions, and air sample collection for measurement of tracer concentration. EMX records EM signals underground and on the surface of Aqueduct Mesa. METEX, REACT, and METREX include measurement of atmospheric condition, as well as tracking smoke releases. REACT and METREX add low-level radioactive gas tracers to the atmospheric releases.

58 GEOSCIENCES↗

Enabling a Physical Twin for Control Methods Evaluation

Advanced nuclear reactors play an important role in the energy future of the United States and the rest of the world. They are designed and operated based on a different model than that of the current operating fleet, thus enabling deployment in remote locations and allowing for safe semi-autonomous or autonomous operations. Such characteristics require the development of a new reactor control paradigm. A significant factor in the development of control technologies and methods is integration of the various technologies and methods with each other and with hardware (both reactor system hardware and control hardware). A recent workshop on control of advanced reactors identified the lack of a flexible, expandable software/hardware infrastructure to enable such integration as a key gap. A previous phase of the current effort involved developing and demonstrating the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) platform, which is capable of integrating autonomous-control-enabling technologies and methods, without the constraints imposed by existing software solutions. This platform will enable advanced reactor developers to deploy and test any developed technologies and methods by employing a common framework, and to couple them with their own models and hardware. The present phase of this effort entails using the Microreactor Automated Control System (MACS) platform, which was developed by the U.S. Department of Energy (DOE) Microreactor Program, to serve as a control method testbed. MACS can be used by advanced reactor developers to integrate their control related research activities with any reactor system. For the present effort, MACS was customized to mirror Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and COMMAND was leveraged to enable MACS to emulate the physics of MARVEL, thus positioning MACS as a physical twin of MARVEL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗