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At least 1,279 records · Page 71

Bettye Washington Greene: An Industrial Chemist and Inventor Who Lit a Path for Innovation

American civil rights pioneer John Lewis once wrote: “You are a light. You are the light. Never let anyone— any person or any force—dampen, dim or diminish your light.” At a time when the civil rights movement was shedding light on inequalities for African Americans, Dr. Bettye Washington Greene (March 20, 1935 – June 16, 1995) was forging a path that would light the way–figuratively and literally—for generations to come. She was one of the few African American women to earn a Ph.D. in chemistry in the 1960s, an era marked by the passing of the Voting Rights Act of 1965. Washington Greene’s doctoral thesis evaluated light-scattering methods to determine the size of particles, which influences molecular effectiveness, activity, and stability. The fundamental knowledge she provided critically aided the pharmaceutical, catalyst and cosmetics industries [1]. Following her doctoral defense, Bettye went on to become the first African American female employed in a scientific research role at the Dow Chemical Company, where her innovations were awarded three patents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modern version of the uncited 1938 experiment that first observed DT fusion

Experiments are described, and results are provided, for the duplication of the first-ever triton-deuterium (colloquially referred to as DT) fusion experiment accidentally performed by A.J. Ruhlig in 1938, but forgotten in the published scientific literature. Here, we find that Ruhlig overestimated the ratio of the triton-deuterium over deuteron-deuterium neutron yields in his secondary reaction (Reaction-in-Flight) experiment compared to modern theoretical calculations and our duplication of his experiment using modern neutron detection methods. Nevertheless, Ruhlig’s observation provided the motivation for the application of DT fusion after World War II and its more recent importance in peaceful energy production at DT fusion facilities around the world. Additionally, the experimental technique used in the present work provides a novel approach for checking on low-energy triton stopping powers in deuterium containing compounds.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗

Evaluation of the CEAS trend and monthly weather data models for soybean yields in Iowa, Illinois, and Indiana

The CEAS models evaluated use historic trend and meteorological and agroclimatic variables to forecast soybean yields in Iowa, Illinois, and Indiana. Indicators of yield reliability and current measures of modeled yield reliability were obtained from bootstrap tests on the end of season models. Indicators of yield reliability show that the state models are consistently better than the crop reporting district (CRD) models. One CRD model is especially poor. At the state level, the bias of each model is less than one half quintal/hectare. The standard deviation is between one and two quintals/hectare. The models are adequate in terms of coverage and are to a certain extent consistent with scientific knowledge. Timely yield estimates can be made during the growing season using truncated models. The models are easy to understand and use and are not costly to operate. Other than the specification of values used to determine evapotranspiration, the models are objective. Because the method of variable selection used in the model development is adequately documented, no evaluation can be made of the objectivity and cost of redevelopment of the model.

French, V.↗

The virtual library: Coming of age

With the high speed networking capabilities, multiple media options, and massive amounts of information that exist in electronic format today, the concept of a 'virtual' library or 'library without walls' is becoming viable. In virtual library environment, the information processed goes beyond the traditional definition of documents to include the results of scientific and technical research and development (reports, software, data) recorded in any format or media: electronic, audio, video, or scanned images. Network access to information must include tools to help locate information sources and navigate the networks to connect to the sources, as well as methods to extract the relevant information. Graphical User Interfaces (GUI's) that are intuitive and navigational tools such as Intelligent Gateway Processors (IGP) will provide users with seamless and transparent use of high speed networks to access, organize, and manage information. Traditional libraries will become points of electronic access to information on multiple medias. The emphasis will be towards unique collections of information at each library rather than entire collections at every library. It is no longer a question of whether there is enough information available; it is more a question of how to manage the vast volumes of information. The future equation will involve being able to organize knowledge, manage information, and provide access at the point of origin.

Hunter, Judy F.↗

Optimization of Ocean Color Algorithms: Application to Satellite Data Merging

The objective of our program is to develop and validate a procedure for ocean color data merging which is one of the major goals of the SIMBIOS project. The need for a merging capability is dictated by the fact that since the launch of MODIS on the Terra platform and over the next decade, several global ocean color missions from various space agencies are or will be operational simultaneously. The apparent redundancy in simultaneous ocean color missions can actually be exploited to various benefits. The most obvious benefit is improved coverage. The patchy and uneven daily coverage from any single sensor can be improved by using a combination of sensors. Beside improved coverage of the global Ocean the merging of Ocean color data should also result in new, improved, more diverse and better data products with lower uncertainties. Ultimately, ocean color data merging should result in the development of a unified, scientific quality, ocean color time series, from SeaWiFS to NPOESS and beyond. Various approaches can be used for ocean color data merging and several have been tested within the frame of the SIMBIOS program. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances (L(sub WN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) it works with single or multiple data sources regardless of their specific bands, 2) it exploits band redundancies and band differences, 3) it accounts for uncertainties in the (L(sub WN)(lambda)) data and, 4) it provides uncertainty estimates for the retrieved variables.

Maritorena, Stephane↗

Risk Management Implementation Tool

Continuous Risk Management (CM) is a software engineering practice with processes, methods, and tools for managing risk in a project. It provides a controlled environment for practical decision making, in order to assess continually what could go wrong, determine which risk are important to deal with, implement strategies to deal with those risk and assure the measure effectiveness of the implemented strategies. Continuous Risk Management provides many training workshops and courses to teach the staff how to implement risk management to their various experiments and projects. The steps of the CRM process are identification, analysis, planning, tracking, and control. These steps and the various methods and tools that go along with them, identification, and dealing with risk is clear-cut. The office that I worked in was the Risk Management Office (RMO). The RMO at NASA works hard to uphold NASA s mission of exploration and advancement of scientific knowledge and technology by defining and reducing program risk. The RMO is one of the divisions that fall under the Safety and Assurance Directorate (SAAD). I worked under Cynthia Calhoun, Flight Software Systems Engineer. My task was to develop a help screen for the Continuous Risk Management Implementation Tool (RMIT). The Risk Management Implementation Tool will be used by many NASA managers to identify, analyze, track, control, and communicate risks in their programs and projects. The RMIT will provide a means for NASA to continuously assess risks. The goals and purposes for this tool is to provide a simple means to manage risks, be used by program and project managers throughout NASA for managing risk, and to take an aggressive approach to advertise and advocate the use of RMIT at each NASA center.

Wright, Shayla L.↗

Implementation of a High-Fidelity Interface Resolving Method in Nek5000

The development and utilization of computational fluid dynamics (CFD) models for large, high-temperature electric melters in the Waste Treatment and Immobilization Plant (WTP) in eastern Washington State have proven to be valuable for various purposes. These models allow for a better understanding of the physio-chemical processes occurring within the melter vessels and can contribute to improving operational efficiency, throughput, and addressing operational issues related to vitrification. The CFD models employed for these melter vessels incorporate multiphase fluid flow and heat transfer simulations in different regions, including the plenum, cold cap, and molten glass regions. As the tank waste and glass formers are introduced into the melter, a reacting batch layer known as the cold cap forms on top of the molten glass. To enhance the melt rate, forced convection bubblers located at the bottom of the melters generate convection currents that help homogenize the molten glass and provide heat to the cold cap. As the bubbles rise through the highly viscous glass, they adopt a spherical-cap shape [1]. Meanwhile, the conversion of the batch to glass generates significant amounts of gases (such as water vapor, carbon dioxide, sulfur dioxide, and NOx) due to thermal decomposition [2]. These gases become trapped between the cold cap and molten glass, forming a foam layer [3]. For modeling multiphase flow in CFD and heat transfer simulations of waste glass melters at different scales, efforts are underway to augment the capabilities of the Nek5000 [4] and NekRS [5] open-source codes [6]. Nek5000/NekRS is a scalable and efficient spectral element code that has been successfully applied to a wide range of fluid dynamics problems. By leveraging the Nek5000/NekRS software, it becomes possible to model the melter systems more affordably and with lower computational requirements compared to currently utilized commercial CFD software. The specific objectives of this ongoing effort include: 1. Implementation of a level set method in Nek5000/NekRS: The level set method is a numerical technique commonly employed in CFD simulations to track and represent the interface between different phases or materials accurately. By incorporating this method into Nek5000/NekRS, the ability to simulate multiphase flows in waste glass melters at a high level of fidelity can be achieved. 2. Demonstration of capability for air bubbling through molten glass: As part of the development process, a specific case of air bubbling through molten glass will be simulated using the augmented Nek5000/NekRS code. This demonstration aims to showcase the ability of the software to accurately capture and analyze the complex phenomena involved in the multiphase flow within waste glass melters. By achieving these objectives, the improved Nek5000/NekRS code will offer a powerful computational tool for simulating and analyzing waste glass melter systems, enabling better understanding, optimization, and troubleshooting of these vitrification processes. The ability to accurately model and simulate multiphase flows has broad relevance across many industries and scientific domains, and the improved functionality can contribute to advancements in various fields beyond waste glass melter simulations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

54 ENVIRONMENTAL SCIENCES↗

Macro vs. Micro: Relating the Spectral Properties of Vesta and the HED Meteorite

We present the main results obtained comparing the visible-near infrared (VIS-NIR) spectra Vesta s surface with howardites, eucrites, diogenites (HED). HEDs are commonly associated with Vesta s composition based on spectral similarities. Because of such association, much effort is being made to merge the information from HEDs as well as Vestoids - with that from Vesta to characterize the lithologic diversity of the surface of this asteroid and to infer clues regarding its thermal history. However, while the HEDs are a class of meteorites well studied in the laboratory, the only spectral data available for Vesta until now were telescopic observations which are limited in terms of observation conditions, spatial resolution and Signal to Noise Ratio. The Dawn spacecraft, orbiting around Vesta since July 2011, is performing detailed observations of this body and thus improving our knowledge of its properties. Dawn s scientific payload includes an imaging spectrometer, VIR-MS, sensitive to the VIS-NIR spectral range. VIR-MS began acquiring spectra during the approach phase started in May 2011 and will continue its observations through July 2012 when the spacecraft will depart Vesta to travel to Ceres. The VIR-MS spatial resolution depends upon the mission phase (approach, survey, high altitude, low altitude). However, spectra acquired by VIR-MS have already exceeded the spatial resolution of ground-based telescopic observations, with resolution in the approach phase ranging from 2.5 up to 0.8 km/pixel. Moreover, the observations are uniformly distributed in latitude and longitude allowing us to have a global view of Vesta s crust spectral properties. Using the information provided by VIR spectra, we studied the distribution of the spectral heterogeneities on the surface and we used our findings to perform a comparison with HEDs spectra in the VIS-NIR spectral range searching for analogies and/or incompatibilities. In our analysis, we focused on a method to compare the results obtained at microscopic scale on HEDs samples and the one obtained at macroscopic scale on the surface of Vesta. Finally, we discuss our results in the context of vestan thermal history. The intent of this study is to improve our understanding of the connection between Vesta and the HED samples, which is one of the primary Dawn scientific objectives. This work is supported by an Italian Space Agency (ASI) grant and by NASA through the Dawn project and a Dawn at Vesta Participating Scientist grant.

Ammannito, E.↗

GC/MS Method Development for Separating Lunar Volatile Ice Simulant Headspace Gases

Various investigators propose the lunar surface contains widely distributed volatiles, especially water- like species, i.e. OH and H2O. Surface volatiles are theorized to exist as a hydrated regolith layer, concentrated in extremely cold polar permanently shadowed regions (PSR), and/or solar wind implantation reservoirs in lunar glasses. The proposed sources of lunar surface volatiles range from cometary impacts, solar wind, or a supply present during moon formation. Future Artemis missions aim to collect and return the samples containing volatiles collected near lunar polar craters or PSRs. We, as advanced curation scientists, are responsible for developing techniques and methodologies for preserving returned sample integrity as much as possible. Pristine volatile-bearing samples are invaluable to the scientific community seeking to unravel the history of the solar system. Realistically, a sample will experience alteration during collection, transportation back to earth, and storage. The Planetary Exploration and Astromaterials Research Lab (PEARL) seeks to understand temperature and pressure effects on high-fidelity volatile-containing regolith simulants, the foundation for the future of cold curation. This abstract outlines the separation, identification, and quantification of headspace gases over volatile ice feed stock material using gas chromatography/mass spectrometry (GC/MS). Preliminary objectives concentrated on sample handling, reproducibility, and understanding the elution characteristics for each analyte. Initial GC/MS method development experiments utilized diluted static headspace sample preparation. Diluted samples were used because sampling headspace gases directly from a vial containing liquid analyte resulted in overloading of the column and detector. Overloading is evident based on chromatogram peak shapes and instrument contamination, or carry over, between experiments. A mixture of three alcohols were used for a majority of the sample handling and reproducibility studies. Reproducibility was tested via multiple users, calibration curves, and check standards. Stock solutions of condensed lunar volatile analytes included methanol, ammonia in methanol, hydrogen sulfide in water, and an equal volume mixture of methanol, ethanol, and isopropanol. Current samples use room air as the headspace sample matrix, however future experiments will incorporate an inert purge gas, such as argon or nitrogen. Three mL of each analyte solution were capped in separate 20 mL crimp top GC vials. Dilutions were carried out by removing an aliquot of headspace gases with a calibrated 1 mL gastight syringe and immediately transferring to a 20 mL capped crimp top vial. The GC/MS is a Thermo Fisher Trace 1310/ISQ 7000 with a TriPlus RSH autosampler and split/splitless injector module. The experiments outlined in this abstract use the following hardware: a 2.5 mL gastight headspace syringe tool, 1 mm ID x 78.5 mm length ultra-inert straight injection liner, and a TG-BondQ 30 m × 0.32 mm × 10 μm column. Various parameters, such as hardware selection and the temperature, pressure, and split ratio set points, continue to evolve as the overall experiment is refined. Diluted headspace chromatograms were collected for the individual stock solutions. Retention times, peak shapes, and mass spectra were evaluated and added to the data processing method for each molecule of interest. Figure 1 shows the total ion chromatograms for the three major lunar volatile simulant stock solutions: methanol, 7 N ammonia in methanol, and 0.4% hydrogen sulfide in water. Tailing peak shapes for ammonia (2.98 min rt) and water (4.06 min rt) indicate the molecules are not properly eluting from the selected column with the current separation method. Additionally, hydrogen sulfide and ammonia have overlapping peak windows, which could impact quantification. Ongoing experiments aim to address the peak shape and overlapping via the separation method and hardware selection. Sample preparation reproducibility experiments used stock solution containing equal volumes of a non- interactive mixture of methanol, ethanol, and isopropanol. Mass spectrum ion traces were used to identify and quantify all three alcohols. Peaks were automatically detected, identified, and integrated through the mass spectra detection and processing parameters. Calibration response curves and check standards were used to evaluate the validity of the sample preparation procedure. Figure 2 shows the methanol chromatogram peak area versus total headspace dilution volume transferred from the alcohol mixture vial. The calibration response curves and check standards validate sample preparation procedure. Continuing data analysis efforts are working towards correlating the peak area and instrument response factor to the headspace analyte concentration and condensed phase composition. Static headspace gas chromatography theory relies on Dalton’s law, Raoult’s law, Henry’s Law, and the Kolb and Ettre equation to associate peak area to the analyte composition in a non-ideal solution. Equation 1 is a simplified expression derived from the aforementioned theories. Future experiments involve liquid injections of the individual stock solutions, liquid and headspace analysis of various stock solution combinations, and the addition of regolith simulants to the mixtures. Temperature is another variable expected to affect reaction rates and will be explored.

Cecilia L. Amick↗

Mechanical analysis of heterogeneous materials with higher-order parameters

Abstract Even though heterogeneous porous materials are widely used in a variety of engineering and scientific fields, such as aerospace, energy-storage technology, and bio-engineering, the relationship between effective material properties of porous materials and their underlying morphology is still not fully understood. To contribute to this knowledge gap, this paper adopts a higher-order asymptotic homogenization method to numerically investigate the effect of complex micropore morphology on the effective mechanical properties of a porous system. Specifically, we use the second-order scheme that is an extension of the first-order computational homogenization framework, where a generalized continuum enables us to introduce length scale into the material constitutive law and capture both pore size and pore distribution. Through several numerical case studies with different combinations of porosity, pore shapes, and distributions, we systematically studied the relationship between the underlying morphology and effective mechanical properties. The results highlight the necessity of higher-order homogenization in understanding the mechanical properties and reveal that higher-order parameters are required to capture the role of realistic pore morphologies on effective mechanical properties. Furthermore, for specific pore shapes, higher-order parameters exhibit dominant influence over the first-order continuum.

42 ENGINEERING↗

Hydraulic Modeling of Induced and Propagated Fractures: Analysis of Flow and Pressure Data From Hydromechanical Experiments in the COSC-1 Deep Borehole in Crystalline Rock Near Åre, Sweden

To characterize the coupled hydromechanical behavior of rock fractures, the step-rate injection method for fracture in-situ properties (SIMFIP) was conducted with a specialized downhole probe developed by Guglielmi et.al. In June 2019, a field campaign was carried out near Åre, Sweden, where the SIMFIP probe was applied in the Collisional Orogeny in the Scandinavian Caledonides-1 scientific borehole to understand the dynamics of injection-induced fracture initiation, fracture opening, and shearing due to water injection-withdrawal in a borehole interval isolated by two packers. Three intervals were investigated at ~500 m depth: (a) an unfractured section (intact rock), (b) a section with non-conductive fractures, and (c) a section with hydraulically conductive fractures. Pressure, injection flow rate, and borehole wall displacement were simultaneously measured during the tests. In the present study, the geometry of the induced fracture and deformation of existing fractures at different time stages of the tests are determined based on a hydrologic model by using the measured pressure and flow data during each time stage of the experiment. A numerical model for the fluid flow within the fracture and the packed-off borehole interval is implemented within COMSOL Multiphysics. By matching model simulations with observed data for all three sections, estimates of the induced and propagated fractures' radius and aperture at successive time stages have been obtained in each case. We could also determine the non-linear relationship between fracture aperture and pressure for values above fracture opening pressures. The model results provide insights for the understanding of pressure-induced fracture initiation and propagation in crystalline rock.

58 GEOSCIENCES↗

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

36 MATERIALS SCIENCE↗

Machine learning models for PDE constrained optimization

Partial differential equation (PDE)-constrained optimization problems arise in a variety of scientific and engineering applications, such as topology optimization, electrodynamics, fluid dynamics, and structural dynamics. However, these problems are often challenging and computationally expensive to solve, due to the need to solve the PDEs within the optimization loop. One approach to reducing the computational cost of these methods while providing convergence guarantees is through inexact trust region methods; this method uses lower fidelity solutions of the PDE at early stages of the optimization and adjusts the required accuracy of inexact PDE solvers as the optimization progresses. In this work, we explore the use of machine learning based surrogate models with these inexact trust region methods. We first demonstrate the potential of this approach by using Gaussian processes as the surrogate model and test this on a simple PDE-constrained optimization problem. We then document explorations into improving the computational costs of evolutional deep neural network / neural Galerkin methods, with the eventual goal of using these methods with the inexact trust region algorithms. We are able to speed up these approaches, albeit at the cost of lower accuracy.

97 MATHEMATICS AND COMPUTING↗

Solar Radiation Research Laboratory (SRRL) Core Project Final Report: Fiscal Years 2022-2024

The Solar Radiation Research Laboratory (SRRL) at the National Laboratory of the Rockies (NLR) is a world-leading solar calibration and measurement facility and maintains and disseminates the World Radiation Reference (essentially the W/m2) for the United States that is essential for traceable and accurate measurements of solar radiation at all solar generation facilities. SRRL operates two calibration facilities that meet International Standards Organization-17025 (ISO-17025) standards and provide unique high-quality calibrations to NREL and other U.S. Department of Energy laboratories. The Baseline Measurement System (BMS) at SRRL provides a high-quality record of solar irradiance and surface meteorological conditions. SRRL capabilities are used to develop (1) improved methods for the calibration of solar radiometers; (2) new standards through the ISO, the International Electrotechnical Commission (IEC), and the American Standards for Testing of Materials (ASTM) International; (c) solar radiation and meteorological models; and (d) advanced instrumentation and methods for operating solar measurement stations. The SRRL datasets are also critical for the validation of new models and datasets, such as the National Solar Radiation Database (NSRDB). The research and development of solar radiation measurement systems and resource modeling techniques are essential for advancing the scientific basis for producing reliable resource data. Specifically, the spatial, temporal, and spectral (wavelength dependency) characteristics of the solar resource are required in several different time frames for various project phases.

14 SOLAR ENERGY↗

A Technique for Simulating Conditions of Walking and Performing Other Self-Locomotive Activities on the Lunar Terrain

One of the most important and probably the most interesting phase of a manned lunar mission will be the time the astronauts spend outside their vehicle on the moon's surface taking scientific measurements, exploring the surface features, surveying possible sites for a lunar base, inspecting their vehicle and preparing it for their return trip. Because the lunar gravity is only one-sixth that of the earth gravity, the explorers undoubtedly will have to adjust their accustomed methods of walking, climbing, jumping and performing other self-locomotive activities in order to carry out these various tasks. In as much as the over-all success of the lunar mission will depend to a large extent upon the self-reliance of the explorers, it will be necessary to have extensive knowledge of the effects of the moon's reduced gravity on the physical capabilities of man and of man's ability to adopt to the new environment prior to the planning and execution of the mission. At the present time there is a dearth of information on this subject due primarily to the lack of a practical technique for simulating the reduced gravity. Several techniques such as immersion in water and riding in an airplane flying a Keplerian trajectory have been used for zero-g or weightlessness studies to determine the physical capabilities of man but these techniques are limited in their usefulness either by restrictions imposed by the viscous effect of the water or by the short duration and small test area available in an airplane. Consequently, an effort was made at the NASA Langley Research Center to devise a new technique that would provide a realistic simlation of a reduced gravity for unlimited periods of time and allow freedom of movement over considerable distances. This paper concerns itself with a discussion of the newly developed simulation technique and a presentation of some preliminary results which were obtained utilizing a working model based on this scheme.

Hewes, Donald E.↗

Digital processing of stereoscopic image pairs.

The problem under consideration is concerned with scene analysis during robot navigation on the surface of Mars. In this mode, the world model of the robot must be continuously updated to include sightings of new obstacles and scientific samples. In order to describe the content of a particular scene, it is first necessary to segment it into known objects. One technique for accomplishing this segmentation is by analyzing the pair of images produced by the stereoscopic cameras mounted on the robot. A heuristic method is presented for determining the range for each point in the two-dimensional scene under consideration. The method is conceptually based on a comparison of corresponding points in the left and right images of the stereo pair. However, various heuristics which are adaptive in nature are used to make the algorithm both efficient and accurate. Examples are given of the use of this so-called range picture for the purpose of scene segmentation.

Levine, M. D.↗