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DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗

DOE OSTI2022

Efficient light harvesting and photon sensing via engineered cooperative effects

Abstract Efficient devices for light harvesting and photon sensing are fundamental building blocks of basic energy science and many essential technologies. Recent efforts have turned to biomimicry to design the next generation of light-capturing devices, partially fueled by an appreciation of the fantastic efficiency of the initial stages of natural photosynthetic systems at capturing photons. In such systems extended excitonic states are thought to play a fundamental functional role, inducing cooperative coherent effects, such as superabsorption of light and supertransfer of photoexcitations. Inspired by this observation, we design an artificial light-harvesting and photodetection device that maximally harnesses cooperative effects to enhance efficiency. The design relies on separating absorption and transfer processes (energetically and spatially) in order to overcome the fundamental obstacle to exploiting cooperative effects to enhance light capture: the enhanced emission processes that accompany superabsorption. This engineered separation of processes greatly improves the efficiency and the scalability of the system.

42 ENGINEERING↗

NASA NTRS2018

Biocene 2018 - Welcome to the Period of New Life

Biocene is the period of new life. When our descendants look back at this period in time, they will see evidence, in the geologic and electronic record, of anthropic climate change, growing population, and scarcity of resources. But they will also see the rebirth of human ingenuity as we overcame the challenges that faced us through nature-inspired exploration. The Periodic Table of Life (PeTaL) is a proposed tool and open source framework that uses artificial intelligence to aid in the systematic inquiry of biology for its application to human systems. This presentation defines the PeTaL concept and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields have been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. The primary objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence in problem solving. Specifically specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. Blockchain technology may be vital in allowing open source design tools such as PeTaL to democratize design and yet protect intellectual property. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools include text classification, thesaurus, data visualization, and analysis. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life.

Biocene↗

NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗

DOE OSTI2020

Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry

The U.S. Department of Energy-funded Mesoscale-to-Microscale Coupling (MMC) project team planned and conducted a virtual Workshop on Atmospheric Challenges for the Wind Energy Industry on October 19 and 20, 2020. The goal of the workshop was to forge a dialog with the community, including industry representatives, on how modeling tools are currently being used, the present active atmospheric modeling research in support of wind energy, and required advancements in capabilities and technology to continue to advance wind energy deployment. The workshop was planned in collaboration with an industry advisory panel that included representatives from wind power plant developers, turbine manufacturers, and companies that provide resource assessment and forecasting services. The format of the workshop included panels from government research sponsors, visionaries from industry, and mixed panels of researchers discussing research status and needs. A shared keynote presentation from the Technical University of Denmark experts anchored the second day of the workshop. An emphasis was placed on understanding the research needs in the offshore environment. In addition, breakout opportunities were provided each day. On the first day, the breakout discussions addressed predesigned questions configured to elicit participants’ thoughts on needed research directions. The second-day breakouts treated three important technical topics through a combination of presentations and group conversations. Each workshop participant chose their breakout preference from among downscaling details, modeling for turbines, and using artificial intelligence for atmospheric modeling. The discussions were robust and productive. The outcomes of the workshop include archiving a series of recommendations from industry and the research community on research directions required to further advance wind energy deployment. Discussions confirmed the need for high-fidelity modeling but that there are specific areas of applicability and other areas where the time and cost of computation is prohibitive. In those cases, the high-fidelity models can inform low-order models that are more practical for real-time or widely deployed applications. Industry must consider the financial cost of performing more expensive modeling approaches, but industry engineers and researchers are using these approaches where there appears to be a return on investment. An emerging type of low-order model is based on machine learning (ML). Participants confirmed that there are many atmospheric phenomena that need to be modeled better, including low-level jets, cold air outbreaks, land-sea induced circulations, diurnal variability, thin stable boundary layers, dynamic changes such as from frontal passage, interaction of wakes and blockage, and more. For the offshore environment, there is wide agreement that some level of ocean-wave-atmospheric coupling is necessary to capture variations in rotor-level winds needed to plan and operate offshore wind plants. Another recurring recommendation is that more observations are needed, particularly for the offshore environment. Those observations should consider the needs for model improvement, both for physically based models and for ML models. Observations must capture atmospheric profiles of variables that are important to understanding and modeling atmospheric and oceanic phenomena that impact boundary layer winds. Models must be validated with data and the uncertainty quantified, particularly those that are sensitive to initial and boundary conditions. Finally, a repeated request was to consider the holistic needs of hybrid plants of wind, solar, and storage resources because those types of plants are likely to be the wave of the future. In addition, industry wishes to understand impacts of the resource under a changing climate for long-term planning.

17 WIND ENERGY↗

NASA NTRS2018

A First Look at the Evolution of Flight Crew Requirements for Emerging Market Aircraft

This is an exciting time for aviation. New vehicle and airspace technologies promise large increases in the number of aircraft in operation. One critical technology for these emerging markets is the increased use of automated systems to reduce pilot skill, training, and proficiency requirements. While the use of these systems promises to reduce or eliminate pilot functions in the long-term, the technology development for the required functions will necessitate a phased transition. The transition to, and adoption of automated systems will generate new safety challenges. This paper is a first look at a model to help frame flight crew functions for evaluation of future operational requirements. The model is intended to provide required flight crew functions regardless of whether the functions are performed by human or artificial agent. It is hoped that the model will be useful in identifying safety challenges and enabling a safe transition for the new aviation markets. The paper presents some background for a model for framing the flight crew function model and some thoughts about next steps.

safety-critical automation↗

DOE OSTI2022

Management and Storage of Scientific Data

Scientific discoveries rely heavily on efficient access, search, and management of massive data sets. Data management technologies have, for decades, provided foundational capabilities for scientific computing. Just as storage, input/output (I/O), and data management have been fundamental to simulation-based science for many years, so too are capable data-management technologies key to the success of today’s scientific workflows utilizing data intensive and machine learning (ML) techniques. The Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program has invested broadly in data-management research focused on high-performance computing (HPC) systems, from parallel file systems that store data to application software that makes these systems more productive. Still, advances in technology combined with growing diversity of supported science strongly motivate continued investment in this area. In January 2022, ASCR convened a workshop to identify priority research directions in the area of data management for high-performance and scientific computing. Attendees were challenged to identify promising approaches that would support the breadth of the DOE mission, including the explosion of artificial intelligence (AI) uses and the growing needs of experimental and observational science. Technological and science drivers were identified and considered as they relate to key aspects of data management such as interfaces, architectural design, and FAIR principles (Findable, Accessible, Interoperable, and Reusable). The thoughts of the workshop participants were distilled into a set of four priority research directions with the potential for high impact on DOE science. These research directions are summarized in the following pages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1995

Kinetic Kelvin-Helmholtz instability at a finite sized object

Two-dimensional hybrid simulations with particle ions and fluid electrons are used to calculate the kinetic evolution of the self-consistent flow around a two-dimensional obstacle with zero intrinsic magnetic field. Plasma outlfow from the obstacle is used to establish a boundary layer between the incoming solar wind and the outgoing plasma. Because the self-consistent flow solution, a velocity shear is naturally set up at this interface, and since the magnetic field for these simulations is transverse to this flow, the Kelvin-Helmholtz (K-H) instability can be excited at low-velocity shear. Simulations demonstrate the existence of the instability even near the subsolar location, which normally is thought to be stable to this instability. The apparent reason for this result is the overall time dependence at the boundary layer, which gives rise to a Rayleigh-Taylor like instability which provides seed perturbations for the K-H instability. These results are directly applicable to Venus, comets, artificial plasma releases, and laser target experiments. This result has potentially important ramifications for the interpretation of observational results as well as for an estimation of the cross-field transport. The results suggest that the K-H instability may play a role in dayside processes and the Venus ionopause, and may exist within the context of more general situations, for example, the Earth's magnetopause.

Thomas, V. A.↗

NASA NTRS1989

Life without water

Anhydrobiosis, or life without water is commonly demonstrated by a number of plants and animals. These organisms have the capacity to loose all body water, remain dry for various periods, and then be revived by rehydration. While in the anhydrobiotic state, these organisms become highly resistant to several environmental stresses such as extremely low temperatures, elevated temperatures, ionizing radiation, and high vacuum. Since water is commonly thought to be essential for life, survival of anhydrobiotic organisms with an almost total loss of water is examined. A search of literature reveal that many anhydrobiotic organisms make large quantities of trehalose or other carbohydrates. Laboratory experiments have shown that trehalose is able to stabilize and preserve microsomes of sarcoplasmic reticulum and artificial liposomes. It was demonstrated that trehalose and other disaccharides can interact directly with phosopipid headgroups and maintain membranes in their native configuration by replacing water in the headgroup region. Recent studies show that trehalose is an effective stabilizer of proteins during drying and that it does so by direct interaction with groups on the protein. If life that is able to withstand environmental extremes has ever developed on Mars, it is expected that such life would have developed some protective compounds which can stabilize macromolecular structure in the absence of water and at cold temperatures. On Earth, that role appears to be filled by carbohydrates that can stabilize both membrane and protein stuctures during freezing and drying. By analog with terrestrial systems, such life forms might develop resistance either during some reproductive stage or at any time during adult existence. If the resistant form is a developmental stage, the life cycle of the organism must be completed with a reasonable time period relative to time when environmental conditions are favorable. This would suggest that simple organisms with a short life cycle might be most sucessful.

Crowe, Lois M.↗

NASA NTRS2002

Artificial Immune System Approaches for Aerospace Applications

Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

KrishnaKumar, Kalmanje↗

DOE OSTI2023

Bioinspired mineralizing microenvironments generated by liquid-liquid phase coexistence

Biominerals such as bones, teeth and shells exhibit improved mechanical properties and intricate morphologies not seen in nonbiologically-produced minerals of ostensibly the same composition. These remarkable properties of biogenic minerals are thought to arise due to precise local control over the mineral deposition process, including organic and inorganic inclusions. Understanding how Biology controls the local reaction environment during formation of these materials to control their composition, structure, and properties is a grand challenge that promises to enable new routes to high-performance materials. This project developed multi-compartment bioinspired microreactors as artificial mineralizing vesicles, and used them to understand and control formation of inorganic/organic composite solid materials. A major emphasis was on developing all-aqueous emulsions in which each droplet was a structured microreactor with two or more adjacent phases. This approach provided local control over reaction environment including availability of inclusions such as polypeptides and metal ions, while being sufficiently simple to produce large populations of essentially identical multiphase reactor droplets simultaneously within a batch. Organic/inorganic composite materials could be produced with very high organic content that stabilized the inorganic portions as amorphous materials (e.g., amorphous calcium carbonate) by preventing the typical conversion to more thermodynamically crystalline forms (e.g., calcite). These stabilized amorphous composites could be stored indefinitely and converted to crystalline forms later by removal of the organic inclusions via, for example, heating. Compositional gradients in the organic and inorganic components were embedded during synthesis due to the evolution of the reaction microenvironment, and despite the complexity of this process it occurred similarly across the population of reactive droplets and was hence encoded into the population of resulting composite particles. The approach developed here allows pre-structuring of reactive microenvironments to control the spatiotemporal reaction environment, which is an important step towards rational design and on-demand production of complex functional materials with desired composition, optical properties, and mechanical response.

36 MATERIALS SCIENCE↗

NASA NTRS2000

Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

Mjolsness, Eric↗

NASA NTRS1979

Boundary conditions for exterior acoustic problems

Problems of acoustics are generally posed in unbounded regions. Numerical computations require that the infinite regions be truncated with artificial finite boundaries. Boundary conditions must be developed at these artificial boundaries which approximate the conditions for outgoing waves. These conditions should be asymptotic in the distance of the boundaries from the source terms and should give rise to well posed problems interior to fairly arbitrary regions, which can be chosen on the basis of computational efficiency rather than for mathematical (i.e., well posedness) reasons. A family of such conditions is presented and their properties are discussed. These conditions can be thought of as extensions of the Sommerfeld radiation condition for outgoing waves.

Bayliss, A.↗

DOE OSTI2022

Protein folds vs. protein folding: Differing questions, different challenges

We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A five-year milestone: reflections on advances and limitations in GeoAI research

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2019

Controls and Automation Research in Space Life Support

A highly controlled and automated life support system has long been a NASA goal. It is usually assumed that life support for future long duration missions will use physical/chemical recycling systems that substantially close the oxygen and water circulation loops. Such a tightly coupled life support system has been thought to require an overall supervisory control system to minimize crew operation and maintenance activities. The International Space Station (ISS) Environmental Control and Life Support System (ECLSS) was at first expected to have supervisory control and automation. After this was found infeasible during the design of the ISS ECLSS in the early 1990's, it was then expected that the ISS or future mission systems would be upgraded to meet the original expectations. Since then NASA has extensively researched life support system controls and automation. Automation and Artificial Intelligence (AI) have gone through several cycles of enthusiasm and neglect before their recent great achievements, and NASA life support interest has similarly varied. Since the ISS ECLSS was launched, its on-board operational problems have led NASA to deemphasize system level controls and automation in favor of improving subsystem reliability and maintainability. Recent work has investigated supervisory control for a system similar to the ISS ECLSS. This paper reviews past planning and work on the supervisory control of closed, integrated physical/chemical life support systems similar to the ISS ECLSS and its precursors dating back to the 1960's.

life support↗

NASA NTRS1975

Criteria for identification of ablation debris from primitive meteoric bodies

Samples of ablated materials are analyzed to determine properties expected to be characteristic of particulates generated by the ablation of primitive meteoric bodies. Analyses of carbonaceous-chondrite fusion crusts and samples artificially ablated in the laboratory indicate that most meteor-ablation debris should consist of assemblages of silicate minerals, principally olivine, and micron-sized magnetic grains. It is expected that ablation debris of at least 10 microns should have abundances of Fe, Mg, Si, Ca, and Ni similar to those found in chondritic meteorites. Volatile species such as S, H2O, and Cl are lost during ablation and normally should not be found in ablated material. The major findings of this study are supported by analysis of spherules collected in the atmosphere which are thought, on separate grounds, to be genuine meteor-ablation products. The majority of meteoric bodies probably have cometary origins, and it is hoped that the ability to collect and identify meteor-ablation debris reliably will provide an opportunity to do laboratory analysis of cometary matter.-

Brownlee, D. E.↗

DOE OSTI2022

An optimization method for chaotic turbulent flow

Evidence indicates that quantities-of-interest in some turbulent flows can be controlled despite the overall chaotic dynamics. It is typically thought that this is via relatively deterministic, larger-scale components of the turbulence. However, finding such controls, if they exist, is challenging because chaos causes sensitivity gradients to explode and the search space to become intractably non-convex. This challenge is analyzed, and a penalty method is introduced to cope with it. In the new approach, the time domain is broken into segments approximately matching the chaos time scales, so that the solution within each segment is both physical and relatively deterministic. The initial condition of each segment is included in an adjoint-based gradient optimization, which temporarily introduces artificial Δq discontinuities in the overall solution. The optimization then proceeds in stages with increasing penalization of Δq. The method is developed and illustrated for a logistic map, the Lorenz Equation, and an advection augmented Kuramoto–Sivashinsky Equation. These examples show how the Δq temporarily increases the search scale prior to the strong Δq → 0 penalization that recovers a physical solution. It is then applied to turbulent Kolmogorov flow, for which it also far outperforms a standard adjoint-based gradient search. Finally, the utility of such an optimized chaotic solution is discussed.

97 MATHEMATICS AND COMPUTING↗