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Deep Space Habitat ECLS Design Concept

Life support is vital to human spaceflight, and most current life support systems employ single-use hardware or regenerable technologies that throw away the waste products, relying on resupply to make up the consumables lost in the process. Because the long-term goal of the National Aeronautics and Space Administration is to expand human presence beyond low-earth orbit, life support systems must become self-sustaining for missions where resupply is not practical. From May through October 2011, the life support team at the Johnson Space Center was challenged to define requirements, develop a system concept, and create a preliminary life support system design for a non-planetary Deep Space Habitat that could sustain a crew of four in near earth orbit for a duration of 388 days. Some of the preferred technology choices to support this architecture were passed over as the mission definition also has an unmanned portion lasting 825 days. The main portion of the architecture was derived from technologies currently integrated on the International Space Station as well as upcoming technologies with moderate Technology Readiness Levels. The final architecture concept contains only partially-closed air and water systems, as the breakeven point for some of the closure technologies was not achieved with the mission duration.

Curley, Su↗

Deep Space Habitat ECLSS Design Concept

Life support is vital to human spaceflight, and most current life support systems employ single-use hardware or regenerable technologies that throw away the waste products, relying on resupply to make up the consumables lost in the process. Because the long-term goal of the National Aeronautics and Space Administration is to expand human presence beyond low-earth orbit, life support systems must become self-sustaining for missions where resupply is not practical. From May through October 2011, the life support team at the Johnson Space Center was challenged to define requirements, develop a system concept, and create a preliminary life support system design for a non-planetary Deep Space Habitat that could sustain a crew of four in near earth orbit for a duration of 388 days. Some of the preferred technology choices to support this architecture were passed over because the mission definition has an unmanned portion lasting 825 days. The main portion of the architecture was derived from technologies currently integrated on the International Space Station as well as upcoming technologies with moderate Technology Readiness Levels. The final architecture concept contains only partially-closed air and water systems, as the breakeven point for some of the closure technologies was not achieved with the mission duration.

Curley, Su↗

Proximity operations analysis: Retrieval of the solar maximum mission observatory

Retrieval of the solar maximum mission (SMM) observatory is feasible in terms of orbiter primary reaction control system (PRCS) plume disturbance of the SMM, orbiter propellant consumed, and flight time required. Man-in-loop simulations will be required to validate these operational techniques before the verification process is complete. Candidate approach and flyaround techniques were developed that allow the orbiter to attain the proper alinement with the SMM for clear access to the grapple fixture (GF) prior grappling. Because the SMM has very little control authority (approximately 14.8 pound-foot-seconds in two axes and rate-damped in the third) it is necessary to inhibit all +Z (upfiring) PRCS jets on the orbiter to avoid tumbling the SMM. A profile involving a V-bar approach and an out-of-plane flyaround appears to be the best choice and is recommended at this time. The flyaround technique consists of alining the +X-axes of the two vehicles parallel with each other and then flying the orbiter around the SMM until the GF is in view. The out-of-plane flyaround technique is applicable to any inertially stabilized payload, and, the entire final approach profile could be considered as standard for most retrieval missions.

Yglesias, J. A.↗

Human Factors in Space Flight

After forty years of experience with human space flight (Table 1), the current emphasis is on the design of space vehicles, habitats, and missions to ensure mission success. What lessons have we learned that will affect the design of spacecraft for future space exploration, leading up to exploring Mars? This chapter addresses this issue in four sections: Anthropometry and Biomechanics; Environmental Factors; Habitability and Architecture; and Crew Personal Sustenance. This introductory section introduces factors unique to space flight. A unique consideration for design of a habitable volume in a space vehicle is the lack of gravity during a space flight, referred to as microgravity. This affects all aspects of life, and drives special features in the habitat, equipment, tools, and procedures. The difference in gravity during a space mission requires designing for posture and motion differences. In Earth s gravity, or even with partial gravity, orientation is not a variable because the direction in which gravity acts defines up and down. In a microgravity environment the working position is arbitrary; there is no gravity cue. Orientation is defined primarily through visual cues. The orientation within a particular crew station or work area is referred to as local vertical, and should be consistent within a module to increase crew productivity. Equipment was intentionally arranged in various orientations in one module on Skylab to assess the efficiency in use of space versus the effects of inconsistent layout. The effects of that arrangement were confusion on entering the module, time spent in re-orientation, and conflicts in crew space requirements when multiple crew members were in the module. Design of a space vehicle is constrained by the three major mission drivers: mass, volume and power. Each of these factors drives the cost of a mission. Mass and volume determine the size of the launch vehicle directly; they can limit consumables such as air, water, and propellant; and they impact crew size and the types of activities the crew performs. Power is a limiting factor for a space vehicle. All environmental features (e.g., atmosphere, temperature, lighting) require power to maintain them. Power can be generated from batteries, from fuel cells, or from solar panels. Each of these sources requires lifting mass and volume from Earth, driving mission cost. All engineering decisions directly impact the design for habitation design and usage. For instance, if fuel cells are used they produce water, which is used for drinking and food preparation. If a different power source is used water has to be carried and stored on the vehicle which then directly impacts the food system choice as well as the launch weight of the vehicle.

Woolford, Barbara J.↗

Wet chemical synthesis and properties of argyrodite sulfide solid electrolytes for solid state lithium batteries

The commercialization of the lithium-ion battery (LIB) in 1991 was responsible for the explosion in portable electronic technologies that has been seen over the past 30 years. With the advent of electric vehicles and other high-powered technologies, there is tremendous demand for LIBs with higher energy density and high safety. To achieve this, new electrode materials must be explored. The obvious choice of anode material would be pure metal lithium, which has a theoretical specific capacity of 3860 mAh g-1 . Unfortunately, metal lithium anodes have not been widely commercialized due to their tendency to react violently with the flammable liquid electrolytes used in today’s batteries. Battery safety can best be achieved by adopting solid electrolytes in place of liquid electrolytes. Solid electrolytes are nonvolatile and nonflammable, safely allowing for the combination of high-capacity cathode materials with a Li metal anode. Argyrodite sulfide solid electrolytes such as halogen-doped Li6PS5X (X = Cl, Br, I) are noted for their high ionic conductivity. But before sulfides can be commercially adopted, they possess several disadvantages which must be addressed, including time- and energy-consuming synthesis processes, poor electrochemical stability, and intrinsically poor air stability. This dissertation seeks to address each of these challenges through materials design an synthesis strategies. In this work, we pioneer a solvent-based approach for the synthesis of argyrodite solid electrolytes Li7PS6 and Li6PS5Xinstead of a stringent solid-state synthesis. Nontoxic ethanol is employed as the solvent, enabling a rapid synthetic approach to produce argyrodite solid electrolytes with high phase purity and compositional flexibility. Compared with Li7PS6, halogen doping (i.e. X = F, Cl, Br, I) not only increases the ionic conductivity, but also enhances the electrochemical stability at the interface towards Li metal. Specifically, F-doped argyrodites produce a robust SEI layer containing LiF, contributing to enhanced interfacial stability. Finally, to address the air instability challenge, argyrodite-incorporated composite solid electrolytes (CSEs) are designed and prepared to produce stable and flexible membranes that are demonstrated in solid-state Li metal batteries. These advances push argyrodite sulfide solid electrolyte research further and pave the way for the proliferation of next generation lithium metal batteries.

25 ENERGY STORAGE↗

A split ribozyme system for in vivo plant RNA imaging and genetic engineering

RNA plays a central role in plants, governing various cellular and physiological processes. Monitoring its dynamic abundance provides a discerning understanding of molecular mechanisms underlying plant responses to internal (developmental) and external (environmental) stimuli, paving the way for advances in plant biotechnology to engineer crops with improved resilience, quality and productivity. In general, traditional methods for analysis of RNA abundance in plants require destructive, labour-intensive and time-consuming assays. To overcome these limitations, we developed a transformative innovation for in vivo RNA imaging in plants. Specifically, we established a synthetic split ribozyme system that converts various RNA signals to orthogonal protein outputs, enabling in vivo visualisation of various RNA signals in plants. We demonstrated the utility of this system in transient expression experiments (i.e., leaf infiltration in Nicotiana benthamiana ) to detect RNAs derived from transgenes and tobacco rattle virus, respectively. Also, we successfully engineered a split ribozyme-based biosensor in Arabidopsis thaliana for in vivo visualisation of endogenous gene expression at the cellular level, demonstrating the feasibility of multi-scale (e.g., cellular and tissue level) RNA imaging in plants. Furthermore, we developed a platform for easy incorporation of different protein outputs, allowing for flexible choice of reporters to optimise the detection of target RNAs.

59 BASIC BIOLOGICAL SCIENCES↗

Quantifying policy gaps for achieving the net-zero GHG emissions target in the U.S. light-duty vehicle market through electrification

The U.S. light-duty vehicle (LDV) industry, a major greenhouse gas (GHG) emitting sector, is embracing decarbonization. Considering only electrification pathways, this study uses publicly-available tools – MA3T and VISION on vehicle market penetration, fleet accounting and life-cycle analysis to quantify the policy gaps for LDVs to achieve the net-zero GHG emissions target in nine vehicle penetration cases under two electricity mix scenarios, including the U.S. administration's decarbonization strategy – 100% clean electricity by 2035. The MA3T model is a multinomial discrete choice model for market share projection by vehicle technology, and the VISION is a vehicle stocks and GHG emissions projection model by using vehicle and travel characteristics. This study projects the impacts of technology and policy enforcement on shaping the dynamics and decarbonization of the LDV market. Additionally, achieving the expected improvement of battery technology and charging infrastructure is critical but can only reduce the 2050 GHG emissions to 48–54% of the 2020 level under the electricity renewable mix scenario. It is almost impossible to achieve a 100% battery electric vehicle stock by 2050 and the 2050 net-zero target in the LDV industry unless ban of internal combustion engine technology is implemented starting in 2035 and under the 2035 100% clean electricity scenario. These extreme conditions also sacrifice most from the consumer welfare perspective. A greater policy forcing intensity accelerates plug-in electric vehicle penetration, while with declining marginal effect and reduced consumer welfare. Among the investigated policy scenarios, the policy forcing intensity equivalent to a fuel tax of $1–2 per gasoline gallon reduces the most GHG emissions while keeping a positive consumer welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Genesis Solar Wind Sample Curation Documentation

Introduction: A scientist with experience as a sample science analyst, provider of flight hardware for multiple missions, and senior engineer in an ISO 2000-rated manufacturing plant has described the timeline of key participants in any PI-led sample return mission, the breadth of the organizations involved [1,2], and, of interest to this meeting, choosing the types of data to preserve and issues of future data accessibility. This work broadens that perspective by giving similar lessons from Genesis sample curation point-of-view. Curation participation regarding data gathering was part of the mission review process from the beginning. Genesis’ story illustrates outcome of several choices about types of data to record and preserve. Precision analysis of solar wind atoms captured in pure, ultraclean substrates is the driving science goal; therefore, detailed documentation was captured from all mission and curation phases and from investigator laboratories because these processes affect the final analytical results [3]. Pre-flight: Design and fabrication of the spacecraft. Like many modern small sample return missions, Genesis was a tightly managed team integrated across science, engineering and curation. Communication across the team was excellent, and, for the most part, the hands-on engineering technicians understood the impacts of “small choices” they routinely make, and the eyes-on oversight of manufacturing processes by scientists was mindful of details. The payload was designed by the Jet Propulsion Laboratory and the spacecraft by Lockheed Martin. Solar wind collectors and instruments were fabricated by multiple vendors and laboratories. The main portion of the payload was assembled at JSC. Fabrication procedures and contamination-control data (with witness coupons) were stored primarily at JSC. The original composition, dimensions and configuration of components, results of thermal testing, etc. are still needed for interpretation of analytical data. At times, these must be estimated from secondary information acquired pre-flight. Moreover, some files (e.g., original 3-D models and early Powerpoint) cannot be opened using software. Archived curation data includes 2-D drawings, material usage lists, QA documentation and analyses of consumables used during fabrication. Important chemical information still resides in archived hardware, paints and lubricants, material coupons, cleaning coupons, environmental witness plates and reference materials from manufacturing facilities. Purity and cleanliness of collector substrates. Semi-conductor vendors provided surface cleanliness data and some purity data. Purity for specific elements of interest was verified by science team members in their laboratories [4]. Curation archived procurement and shipping records, analysis reports, and non-proprietary fabrication data. A physical archive of flight collector reference materials is maintained for future use so additional data can be collected as analytical techniques improve. These are of increased value due to the hard landing upon re-entry. Cleaning and cleanliness assessments of flight hardware. Cleaning of the science canister payload was performed at JSC in a dedicated ISO 4 cleanroom using ultrapure water (UPW). The cleanliness of this UPW was monitored throughout processing. The archive for the clean lab also includes airborne particle counts, airborne molecular and inorganic contamination measurements as well as cleanroom construction material coupons and witness coupons. Hardware cleanliness was assessed by particle counts in rinse water batches. This information is recorded in batch cleaning forms and logbooks, and are, perhaps, of decreased value due to the hard landing. Post-flight: Curation-generated data. The curation handling history of each Genesis sample is documented in a typical astromaterials sample database which captures sample location, physical description and characterization data. Samples have a “shelf life”. Crucial to the preservation of samples is ongoing documentation of the sample environment, initially under curatorial control but is now a separate facility function with requires coordination. PI-generated data. Data on sample characterization and cleaning techniques continues to be generated by sample users [5]. These are often captured in LPSC abstracts, but these “engineering” results often are not publishable as stand-alone papers. We are actively looking for ways to make this information more accessible to users. Ion implants into samples have aided science return and can be shared among investigators. These (and similar) materials should be added to the curatorial collection with appropriate process and characterization data generated externally. Summary: Complete data archives for returned astromaterial samples must be broad in types and formats, and inclusive of environmental monitoring.

Genesis↗

Remote Recession Sensing of Ablative Heat Shield Materials

Tests were performed to demonstrate the feasibility of a new method of measuring surface recession of a material sample during arc-jet testing in the NASA Ames mARC subscale developmental facility. The measurement principle was inspired through tracer elements such as Ca and Na which were seen in the spectra taken during the airborne observation campaign of the Stardust re-entry and which could be clearly observed standing out against the emission spectra emitted by postshock layer and glowing surface of the re-entry capsule. The measurement principle involves seeding of the heat shield materials at a defined depth with tracer elements which show strong and characteristic emission lines in the post shock plasma. Once the material recession reaches the seeding depth, these elements get into the hot plasma and show up in the emission spectra. The methodology was successfully demonstrated during arc-jet testing of phenolic impregnated carbonablator (PICA) material which was seeded in depth with a mixture of NaCl and MgCl in powder form. In the emission spectroscopy data, the emission lines of Mg and Na showed up about 1.5 seconds after probe insertion into the arc-jet plasma and vanished after another 2.5 seconds when recessionhad consumed the seeding material. From these data, a recession rate of about 1 mm/s is estimated.The heat flux during the test was measured to be 2575 W/sq cm on a hemispherical heat flux probewhich corresponds to a heat flux of 1036 W/sq cm on the rectangular test articles. An estimate for a lower limit of the surface temperature of 2800K during the test was obtained by fitting Planck radiation to the continuum spectra emitted by the PICA sample. Typical recession rates of PICA during testing in the large arc-jet facilities at similar test conditions are reported to be on the order of 0.05 to 0.1 cm/s which agrees well with the recession rates of 0.05-0.06 cm/s estimated from the emission spectroscopy data. Further tests under better controlled conditions are suggested to quantify this measurement method. Through a different choice of seeding materials with lower melting point, an extension of the measurement principle to monitor char depth seems feasible but was not yet demonstrated. Possible applications besides ground testing are recession and possibly char depth measurements during real re-entry. Detection through emission spectroscopy could be accomplished through ground based or airborne observation as performed during the Stardust and Hayabusa re-entries, or through on-board spectrometers. A suitable mission would be the re-entryof the OSIRIS-REX mission planned for late 2023. The measured data are presented and interpreted, the results and details of future applications are discussed.

instrumentation↗

Datum: A Scientific Metadata Catalog

The data catalog market is currently flooded with a myriad of different products, but none serve the scientific community well. There are cloud-native tools like Databricks, Snowflake,to on-premise solutions like Collibra and Datahub. The common failing of all these tools however, is their inability to serve the scientific data community directly. Most catalogs are targeted towards financial, health, or user data - not sensor or scientific domain data. They also prioritize integrations that often don’t exist or are just starting to be used in the scientific realm - all while ignoring common scientific tools and file types. Datum is a catalog which targets the scientific data directly, including the tools and networks in which those tools are used. We work with the producers and consumers of the data where they are, targeting cloud and on-premise with a focus on classified networks. Datum is an Erlang/Elixir application. Technical Features Note: The features listed below are still under development and may change, slightly, upon final delivery of the product. File Formats - Datum has the ability to read additional metadata and provides processing pipelines for the following file formats: Plain Text, PDF, LaTeX, HTML, Open Document Format (.odt), XML, CSV/TSV (and other standard delimiters), OpenDocument Database and Spreadsheets, Geo-Referenced TIFF, Common Data Format, HDF/HDF5, LabView TDMS, Excel, DeltaTables, Parquet, Apache Iceberg, Apache Hudi and many others. Metadata Collection - Scanners for the local and networked file systems and cloud storage providers. Network integration with common databases such as MSSQL and MySQL. User Plugin System - Users are able to provide either file processing, metadata extraction, or sampling plugins in the programming language of their choice. Authentication/Authorization -: OIDC integration, SCIM provisioning and EntraID integration out of the box. Full user and group management system with a “least privilege” operating mode. Governance - Customizable data governance platform; dictate and enforce required metadata, enforce data embargos, and enforce user agreements and NDAs before data access. Ability to create health checks on data, rejecting abandoned or poorly curated data and automatically removing it from the search index. Ability for users to submit corrections. Search - Semantic search is a first class citizen. No licenses to expensive, external software required. Integrated use of vectors and vector-based search allows for AI agent integration at all levels of operation. Metadata Model - Display and control data’s lineage and connections to other data and data directories. Data is modeled after a filesystem - an organization instantly recognizable and navigable by most any user. CLI and SDK - Ships with a Command Line Interface (CLI) tool and with a fully-featured Python SDK. This allows for rapid and programmatic use of Datum by every level of user. Minimal Infrastructure - Datum ships as a single executable file and can be run on any operating system and most CPU architectures. Datum has no reliance on external databases, search indexing tools, or other outside services - and it runs equally well on edge computing devices, cloud services, or in a clustered HPC environment.

darrington, john↗

The Effects of Chronic Sleep Restriction on Calorie and Macronutrient Intake

Introduction: Chronic sleep restriction (CSR) has been associated with increased calorie intake and increased consumption of fats and carbohydrates, with inconsistent changes in protein. However, the majority of studies have either been observational field studies with no sleep intervention, or laboratory-based studies where food availability may not have reflected participants’ real-world choices. We hypothesized that calorie, fat, and carbohydrate intake would increase during a week of imposed CSR compared to a week of sleep satiation (SS) among individuals living in their home environment. Methods: Twelve healthy participants (6 females) kept a fixed sleep-wake schedule, with a constant waketime, at home for four weeks (actigraphy confirmed compliance). During weeks one and three, participants maintained 9 hours in bed. During weeks two and four, participants were randomly assigned to experimental weeks of 5 and 9 hours of time-in-bed in a crossover design. Participants documented their food consumption during both experimental weeks using a picture-based meal logging application (MealLogger). Intake of calories and macronutrients were classified by two blinded evaluators. Descriptive statistics were calculated in SAS (Cary, NC). Results: Participants averaged 4.43 ± 0.33 (SD) hours of sleep per night during CSR compared to 7.42 ± 0.42 hours during SS. Participants consumed a daily average of 1812 ± 672 kilocalories, 71 ± 31 grams of total fat, 217 ± 69 grams of carbohydrates, and 84 ± 40 grams of protein during CSR, compared to 1682 ± 514 kilocalories, 68 ± 23 grams of total fat, 198 ± 61 grams of carbohydrates, and 77 ± 32 grams of protein during SS. Conclusion: Preliminary descriptive findings suggest that, on average, participants consumed more calories, from an increase in consumption of each macronutrient group, during a week of sleep restriction compared to a week of sleep satiation. Further analysis is needed to determine whether these differences are statistically different and to identify when calories were consumed in each of the experimental conditions.

chronic sleep restriction↗

Quantifying the Tangible Value of Public Electric Vehicle Charging Infrastructure

The lack of an extensive public recharging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Because charging infrastructure is likely to be underutilized during the early stages of market development, it is difficult for decision makers to decide how much to invest in public charging stations. Quantifying the value of public charging infrastructure to current and potential future owners of PEVs is essential for estimating the benefits of charging stations to current PEV owners and for predicting the impact on future PEV sales. This paper estimates consumers’ willingness to pay for public charging infrastructure in the context of utility maximization. The objective is to provide a method for valuing charging infrastructure that can inform investment decisions and be used in forecasting models to predict the impacts on future PEV sales. A basic theory of the tangible value of charging infrastructure is developed as a function of PEV type, range, recharging time and existing infrastructure. Existing simulation studies provide functional relationships that quantify the ability of charging infrastructure to enable additional miles of electrified travel. The enabled travel functions are used to predict impact of infrastructure deployment on incremental electrified travel for 1) plug-in hybrids and 2) intra-regional and 3) inter-regional travel by all-electric vehicles. The willingness to pay for increased electrified miles is derived from the willingness to pay for increased electric driving range, based on econometric studies of plug-in vehicle choice. The result is a set of three functions that can be used to calculate the marginal willingness-to-pay for public charging infrastructure as a function of vehicle attributes, existing charging infrastructure, energy prices and annual vehicle travel.

33 ADVANCED PROPULSION SYSTEMS↗

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗

Middleware Evaluation and Benchmarking for Use in Mission Operations Centers

Middleware technologies have been promoted as timesaving, cost-cutting alternatives to the point-to-point communication used in traditional mission operations systems. However, missions have been slow to adopt the new technology. The lack of existing middleware-based missions has given rise to uncertainty about middleware's ability to perform in an operational setting. Most mission architects are also unfamiliar with the technology and do not know the benefits and detriments to architectural choices - or even what choices are available. We will present the findings of a study that evaluated several middleware options specifically for use in a mission operations system. We will address some common misconceptions regarding the applicability of middleware-based architectures, and we will identify the design decisions and tradeoffs that must be made when choosing a middleware solution. The Middleware Comparison and Benchmark Study was conducted at NASA Goddard Space Flight Center to comprehensively evaluate candidate middleware products, compare and contrast the performance of middleware solutions with the traditional point- to-point socket approach, and assess data delivery and reliability strategies. The study focused on requirements of the Global Precipitation Measurement (GPM) mission, validating the potential use of middleware in the GPM mission ground system. The study was jointly funded by GPM and the Goddard Mission Services Evolution Center (GMSEC), a virtual organization for providing mission enabling solutions and promoting the use of appropriate new technologies for mission support. The study was broken into two phases. To perform the generic middleware benchmarking and performance analysis, a network was created with data producers and consumers passing data between themselves. The benchmark monitored the delay, throughput, and reliability of the data as the characteristics were changed. Measurements were taken under a variety of topologies, data demands, and data characteristics, using several middleware products. All results were compared to systems using traditional point-to-point socket communication. By comparing performance results under Merent settings, inferences could be made about each middleware's ability to meet certain requirements of the GPM mission. The second phase simulated a middleware-based mission operations center. Actual mission support tools were either used or simulated to create real world demands on the middleware. Network and computer demands were watched closely to verify that no specific idiosyncrasies of mission operations activities would prove unsupportable by the middleware. In our presentation, we will address some commonly accepted misconceptions concerning middleware in mission support architectures. Specifically, we will focus on the perception that middleware solutions are too slow or impose too much overhead for real-time mission operations, and that middleware solutions are too expensive for small

Antonucci, Rob↗

Brilliant Execution of Smart Labs: Employing Best Practices to Improve Safety and Reduce Energy for Sustainable Labs: Preprint

Laboratory buildings often dominate the energy use on research campuses, consuming three to four times the energy of a typical office building. In most cases, these facilities are defined as “mission critical,” with excessive energy use as simply an unfortunate truth that cannot be avoided without compromising safety. However, case studies indicate cost savings opportunities of up to 40% in laboratories (compared to standard practices) by employing energy efficient design strategies that also improve the safety of lab spaces (Better Buildings 2018). To help stakeholders implement improvement measures, the Smart Labs Toolkit describes a proven approach to optimizing performance of laboratories and critical control environments in new or existing facilities. The Toolkit outlines distinct phases for building a successful Smart Labs program that include: Plan, Assess, Optimize, and Manage. Each phase has specific tasks, training videos, and resources to educate users, support the efforts of key stakeholders, and maximize benefits for the organization in implementing high-performance labs. The Toolkit also includes case studies from the 17 Better Buildings Smart Labs Accelerator partners, sharing lessons learned and supporting the Smart Labs approach to safe, efficient world class science. This paper reviews the technology choices for implementing Smart Labs ventilation strategies, controls, and other efficiency measures. Better Building Smart Lab Accelerator partners provide case study examples of safety and energy-savings results of using Smart Labs approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗