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At least 1,171 records · Page 65

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗

Machine-learning-based inversion of nuclear responses

A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments. Nuclear quantum Monte Carlo methods infer the nuclear electroweak response functions from their Laplace transforms. Inverting the Laplace transform is a notoriously ill-posed problem; and Bayesian techniques, such as maximum entropy, are typically used to reconstruct the original response functions in the quasielastic region. In this work, we present a physics-informed artificial neural network architecture suitable for approximating the inverse of the Laplace transform. Utilizing simulated, albeit realistic, electromagnetic response functions, we show that this physics-informed artificial neural network outperforms maximum entropy in both the low-energy transfer and the quasielastic regions, thereby allowing for robust calculations of electron scattering and neutrino scattering on nuclei and inclusive muon capture rates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

MDP: Reliable File Transfer for Space Missions

This paper presents work being done at NASA/GSFC by the Operating Missions as Nodes on the Internet (OMNI) project to demonstrate the application of the Multicast Dissemination Protocol (MDP) to space missions to reliably transfer files. This work builds on previous work by the OMNI project to apply Internet communication technologies to space communication. The goal of this effort is to provide an inexpensive, reliable, standard, and interoperable mechanism for transferring files in the space communication environment. Limited bandwidth, noise, delay, intermittent connectivity, link asymmetry, and one-way links are all possible issues for space missions. Although these are link-layer issues, they can have a profound effect on the performance of transport and application level protocols. MDP, a UDP-based reliable file transfer protocol, was designed for multicast environments which have to address these same issues, and it has done so successfully. Developed by the Naval Research Lab in the mid 1990's, MDP is now in daily use by both the US Post Office and the DoD. This paper describes the use of MDP to provide automated end-to-end data flow for space missions. It examines the results of a parametric study of MDP in a simulated space link environment and discusses the results in terms of their implications for space missions. Lessons learned are addressed, which suggest minor enhancements to the MDP user interface to add specific features for space mission requirements, such as dynamic control of data rate, and a checkpoint/resume capability. These are features that are provided for in the protocol, but are not implemented in the sample MDP application that was provided. A brief look is also taken at the status of standardization. A version of MDP known as NORM (Neck Oriented Reliable Multicast) is in the process of becoming an IETF standard.

Rash, James↗

SIVB's 2024 In Vitro Biology Meeting Proceedings

SIVB's 2024 World Congress on In Vitro Biology took place in Saint Louis, Missouri, from June 8th to 12th, 2024. The conference featured renowned speakers from academic and non-academic institutions who will present recent advancements in critical areas like plant transformation, genome editing, synthetic biology, advanced breeding technologies, cellular agriculture, future food sources, chromosome engineering, epigenetics, artificial intelligence, and machine learning. The Society for In Vitro Biology (SIVB) has always considered the education and professional development of young researchers as an integral component of its mission. The 2024 World Congress program, along with SIVB’s student initiatives, was customized to foster scientific growth and professional development among students and young scientists empowering them in their professional journeys. The recording of the DOE supported "Single Cell RNA Sequencing" workshop was made publicly available at https://youtu.be/A0UnuYwefwg for easy retrieval and reference of all information shared during the live event, thereby increasing accessibility and knowledge transfer. Their are 14 articles in the proceedings and the full list of files is located at https://link.springer.com/journal/11626/volumes-and-issues/60-1/supplement.

10 SYNTHETIC FUELS↗

X‐Ray Absorption Studies of Local Structure of Dilute Ionic Species in Molten Salts

Molten salts are crucial materials with exceptional properties that make them suitable for applications such as heat transfer media, coolants, and liquid fuels in molten salt nuclear reactors and the concentrated solar power industry. Understanding their properties requires unraveling the intricate mysteries of their structure. To achieve this, advanced characterization tools are essential. Among various techniques, X‐ray absorption fine structure (XAFS) stands out as a versatile method capable of studying the structure of molten salts under in situ conditions. This review highlights recent advancements in the application of XAFS for investigating the local structure of molten salts, discusses its limitations and potential improvements, and explores complementary approaches such as simulations, machine learning, and correlative experimental methods.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Ice Phase Classification Made Easy with Score-Based Denoising

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

C-SAW: a framework for graph sampling and random walk on GPUs

Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies. Fortunately, recent research efforts find out graph sampling and random walk, which significantly reduce the size of original graphs, can benefit the tasks of learning, mining, analyzing and visualizing large graphs by capturing the desirable graph properties. This paper introduces C-SAW, the first framework that accelerates Sampling and Random Walk framework on GPUs. Particularly, C-SAW makes three contributions: First, our framework provides a generic API which allows users to implement a wide range of sampling and random walk algorithms with ease. Second, offloading this framework on GPU, we introduce warp-centric parallel selection, and two novel optimizations for collision migration. Third, towards supporting graphs that exceed the GPU memory capacity, we introduce efficient data transfer optimizations for out-of-memory and multi-GPU sampling, such as workload-aware scheduling and batched multi-instance sampling. Taken together, our framework constantly outperforms the state of the art projects in addition to the capability of supporting a wide range of sampling and random walk algorithms.

97 MATHEMATICS AND COMPUTING↗

The Space Shuttle focused-technology program - Lessons learned

The results of a focused technology program (FTP), its management structure, the development of the Space Shuttle, and lessons applicable to future space programs such as a space station are discussed. A committee was formed by NASA in 1969 to define the technologies necessary for a reusable spacecraft. Basic and applied research assessments were featured at the beginning of the process. Working groups were established to cover all necessary areas, e.g., Operations, Structures and Materials, Aerothermodynamics, etc., and tasks were distributed to appropriate NASA centers. Funding was drawn from existing budgets. The FTP proceeded successfully because of an understanding of the respective roles of industry and government, the willingness of industry to invest early in a new technology, and the unclassified status of information generated by the program. The in-house design and technology transfer methods that brought the project to a technology demonstration phase are explored, noting the necessity for users to take part in the development within their field.

Fitzgerald, P. E., Jr.↗

BASIN-3D: A brokering framework to integrate diverse environmental data

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Varadharajan, C↗

Dependence of the electronic structure of the EuS/InAs interface on the bonding configuration

The EuS/InAs interface has recently attracted attention for the possibility of inducing magnetic exchange correlations in a strong spin-orbit semiconductor, which could be useful for topological quantum devices. We use density functional theory with a machine-learned Hubbard U correction [Yu et al., npj Comput. Mater. 6, 180 (2020)] to elucidate the effect of the bonding configuration at the interface on the electronic structure. For all interface configurations considered here, we find that the EuS valence band maximum (VBM) lies below the InAs VBM. In addition, dispersed states emerge at the top of the InAs VBM at the interface, which do not exist in either material separately. These states are contributed mainly by the InAs layer adjacent to the interface. They are localized at the interface and may be attributed to charge transfer from the EuS to the InAs. The interface configuration affects the position of the EuS VBM with respect to the InAs VBM, as well as the dispersion of the interface states. For all interface configurations studied here, the induced magnetic moment in the InAs is small. Our results suggest that this interface, in its coherent form studied here, may not be promising for inducing equilibrium magnetic properties in InAs.

36 MATERIALS SCIENCE↗

MDP: Reliable File Transfer for Space Missions

This paper presents work being done at NASA/GSFC (Goddard Space Flight Center) by the Operating Missions as Nodes on the Internet (OMNI) project to demonstrate the application of the Multicast Dissemination Protocol (MDP) to space missions to reliably transfer files. This work builds on previous work by the OMNI project to apply Internet communication technologies to space communication. The goal of this effort is to provide an inexpensive, reliable, standard, and interoperable mechanism for transferring files in the space communication environment. Limited bandwidth, noise, delay, intermittent connectivity, link asymmetry, and one-way links are all possible issues for space missions. Although these are link-layer issues, they can have a profound effect on the performance of transport and application level protocols. MDP, a UDP (User Datagram Protocol)-based reliable file transfer protocol, was designed for multicast environments which have to address these same issues, and it has done so successfully. Developed by the Naval Research Lab in the mid 1990s, MDP is now in daily use by both the US Post Office and the DoD (Department of Defense). This paper describes the use of MDP to provide automated end-to-end data flow for space missions. It examines the results of a parametric study of MDP in a simulated space link environment and discusses the results in terms of their implications for space missions. Lessons learned are addressed, which suggest minor enhancements to the MDP user interface to add specific features for space mission requirements, such as dynamic control of data rate, and a checkpoint/resume capability. These are features that are provided for in the protocol, but are not implemented in the sample MDP application that was provided. A brief look is also taken at the status of standardization. A version of MDP known as NORM (Nack Oriented Reliable Multicast) is in the process of becoming an IETF (Internet Engineering Task Force) standard.

Rash, James↗

Solid Surface Combustion Experiment Yields Significant Observations

The spread of a flame over solid fuel is not only a fundamental textbook combustion phenomenon, but also the central element of destructive fires that cause tragic loss of life and property each year. Throughout history, practical measures to prevent and fight fires have been developed, but these have often been based on lessons learned in a costly fire. Since the 1960 s, scientists and engineers have employed powerful tools of scientific research to understand the details of flame spread and how a material can be rendered nonflammable. High-speed computers have enabled complex flame simulations, whereasand lasers have provided measurements of the chemical composition, temperature, and air velocities inside flames. The microgravity environment has emerged as the third great tool for these studies. Spreading flames are complex combinations of chemical reactions and several physical processes including the transport of oxygen and fuel vapor to the flame and the transfer of heat from the flame to fresh fuel and to the surroundings. Depending on its speed, air motion in the vicinity of the flame can affect the flame in substantially different ways. For example, consider the difference between blowing on a campfire and blowing out a match. On Earth, gravity induces air motion because of buoyancy (the familiar rising hot gases); this process cannot be controlled experimentally. For theoreticians, buoyant air motion complicates the problem modeling of flame spread beyond the capacity of modern computers to simulate. The microgravity environment provides experimental control of air motion near spreading flames, with results that can be compared with detailed theory. The Solid Surface Combustion Experiment (SSCE) was designed to obtain benchmark flame spreading data in quiescent test atmospheres--the limiting case of flames spreading. Professor Robert Altenkirch, Vice President for Research at Mississippi State University, proposed the experiment concept, and the NASA Lewis Research Center designed, built, and tested the SSCE hardware. It was the first microgravity science experiment built by Lewis for the space shuttle and the first combustion science experiment flown in space.

Sacksteder, Kurt R.↗

NLML: A Deep Neural Network Emulator for the Exact Nonlinear Interactions in a Wind Wave Model

Nonlinear wave interactions describe the resonant energy transfer between wave components, playing a fundamental role in the evolution of ocean wave spectra. Nonlinear wave interactions significantly influence wave growth and development, making them essential for accurate wave modeling. However, resolving the full six-dimensional Boltzmann integral of the exact nonlinear wave interactions (Webb-Resio-Tracy method, WRT) is computationally expensive, limiting its application in real-time operational wave forecasting and for research purposes. Current approximations, such as the Discrete Interaction Approximation (DIA), prioritize computational speed over accuracy, resulting in significant errors in wave mean parameters. Here, we introduce NLML, a machine learning (ML) emulator designed to approximate the exact nonlinear wave interactions within WAVEWATCH III (WW3), with the goal of achieving the accuracy of WRT while maintaining the stability and computational speed of DIA. By leveraging GPU capabilities such as half precision inference, we achieved substantial speedups, up to 136x mathematical equation faster than the WRT and only a modest 1.04x mathematical equation slowdown relative to DIA, while achieving 2x mathematical equation the accuracy of DIA in global wave spectral energy and mean wave parameters, with up to 7x mathematical equation higher accuracy in some regions. Unlike previous ML approaches, NLML maintained inherent stability throughout model integration in a standalone, year-long WW3 simulation, without requiring additional constraints. Our new ML parameterization bridges the gap between accuracy and efficiency, offering a promising alternative for improving wave modeling in operational settings and research purposes.

16 TIDAL AND WAVE POWER↗

Noble Gases in Samples Returned From Asteroids Ryugu and Bennu

Introduction: The asteroid sample return missions, JAXA’s Hayabusa2 [1] and NASA’s OSIRIS-REx [2], fromC-rich asteroids Ryugu and Bennu offer unique opportunities to sample pristine early solar system material, unaffected by terrestrial weathering and contamination, and with a known geological context. The comparison with most primi-tive members of carbonaceous –and inner solar system ordinary and enstatite– chondrite classes allows us to (i) assess the variation (or the lack of thereof) of the distribution of the most volatile elements throughout the entire early solar system, (ii) determine the effects of parent body thermal and aqueous alteration, and (iii) learn about the dynamic evolution and history of the various parent bodies, as sampled by both return missions and meteorite delivery. Here we will present noble gas data obtained from newly received particles from Ryugu and Bennu. Samples & Analysis: We received nine particles allocated by JAXA, with masses of 0.5-1.7 mg mass: four col-lected during Hayabusa2’s first touchdown (chamber A) and five from a small artificial crater (chamber C). We received three particles from Bennu of 0.07, 0.89 and 0.94 mg mass (OREX-800032-102/3/4). All particles were processed, allocated, weighed, and transferred into our UHV sample chamber within pure N2, to prevent any contact with air. We examined all He-Xe isotopes with our custom-built mass spectrometer “Albatros” ([3,4]). Gases were extracted individually from each particle by melting induced by a 1064 μm Nd:YAG IR laser in 2–4 heating steps. Large amounts of water and other reactive volatiles were released, consistent with the presence of abundant volatiles from both Ryugu and Bennu. Residues of Ryugu particles remaining after lasering, subsequently exposed to air, were additionally extracted in a crucible to verify complete gas extraction. Only the particle that still showed a “crystalline” structure after lasering contained detectable Ryugu noble gases, whereas all other particles turned immediately into glassy spheres by laser heating and lacked any gas. Results & Discussion: Ryugu and Bennu matter contains noble gases similar to the most pristine, unheated aqueously altered meteorites, e.g., CM or CI chondrites [5,6]. Remarkably, all four chamber A samples that originated from Ryugu’s surface show solar wind (SW) Ne (trapped (20Ne/22Ne)tr ~12–13), whereas all five chamber C samples do not ((20Ne/22Ne)tr ~9.0–10.7 consistent with the presence of non-solar trapped Ne components such as Q and HL). This demonstrates that JAXA’s sampling strategy succeeded: Chamber C samples analyzed in this work originated from areas shielded from SW, slightly below the surface, that was sampled after the impactor excavated material. The three Bennu particles populate the same range as the Ryugu particles in Ne isotope space with one particle containing SW suggesting that it was part of the uppermost surface layer of Bennu during sampling, while Ne in the smallest sample can be explained best by mixing Ne from presolar diamonds and SiC or graphite, and that in the third one by mixing of, e.g., Q and HL. Using cosmic ray production rates given by [7] yields preliminary exposure ages for Ryugu particles in the same range as determined in [7]. There is, as expected, also no systematic difference between samples from chambers A and C because both sample sets were likely collected within the uppermost layer of Ryugu, which were not shielded from cosmic rays. The concentration of cosmogenic (cos) Ne in the Bennu particle, where Necos was resolvable, is in the same range as found for Ryugu, suggesting roughly similar exposure time to cosmic rays for 5–8 Ma for all particles examined here. The Xe isotopic compositions of all particles are consistent with those of phase Q, with small additions of Xe-HL from presolar diamonds and excess 129Xe* from short-lived 129I decay. The comparably high 129Xe*/129XeQ ratio is similar in all samples, suggesting similar initial I concentrations and closure to Xe loss –or the incorporation of a well-mixed Xe reservoir. The Bennu material was in brief contact with air in the capsule, after atmospheric entry and before storage in pure N2 in a clean room. Both, the –most diagnostic– Xe isotopes and the Ar/Xe vs. Kr/Xe systematics prove that the noble gases are not affected by terrestrial contamination, in contrast to many CI, CY, and CM chondrites found on Earth [5,6], illustrating the importance of sample return. He-Xe concentrations in all particles unaffected bySW are comparable to but at the upper end of ranges reported [5-7]. In accordance with the strong aqueous alteration experienced on the parent bodies of Ryugu and Bennu [1,2], all samples lack the Ar-rich and water-susceptible noble gas components found in less aqueously altered CMs or CRs [e.g., 5,8] and all COs.

Henner Busemann↗

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗

Electronic structure theory on modeling short-range noncovalent interactions between amino acids

While short-range noncovalent interactions (NCIs) are proving to be of importance in many chemical and biological systems, these atypical bindings happen within the so-called van der Waals envelope and pose an enormous challenge for current computational methods. We introduce SNCIAA, a database of 723 benchmark interaction energies of short-range noncovalent interactions between neutral/charged amino acids originated from protein x-ray crystal structures at the “gold standard” coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory with a mean absolute binding uncertainty less than 0.1 kcal/mol. Subsequently, a systematic assessment of commonly used computational methods, such as the second-order Møller−Plesset theory (MP2), density functional theory (DFT), symmetry-adapted perturbation theory (SAPT), composite electronic-structure methods, semiempirical approaches, and the physical-based potentials with machine learning (IPML) on SNCIAA is carried out. It is shown that the inclusion of dispersion corrections is essential even though these dimers are dominated by electrostatics, such as hydrogen bondings and salt bridges. Overall, MP2, ωB97M-V, and B3LYP+D4 turned out to be the most reliable methods for the description of short-range NCIs even in strongly attractive/repulsive complexes. SAPT is also recommended in describing short-range NCIs only if the δMP2 correction has been included. The good performance of IPML for dimers at close-equilibrium and long-range conditions is not transferable to the short-range. We expect that SNCIAA will assist the development/improvement/validation of computational methods, such as DFT, force-fields, and ML models, in describing NCIs across entire potential energy surfaces (short-, intermediate-, and long-range NCIs) on the same footing.

Chemistry↗

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗

Flight Planning Branch Space Shuttle Lessons Learned

Planning products and procedures that allow the mission flight control teams and the astronaut crews to plan, train and fly every Space Shuttle mission have been developed by the Flight Planning Branch at the NASA Johnson Space Center. As the Space Shuttle Program ends, lessons learned have been collected from each phase of the successful execution of these Shuttle missions. Specific examples of how roles and responsibilities of console positions that develop the crew and vehicle attitude timelines will be discussed, as well as techniques and methods used to solve complex spacecraft and instrument orientation problems. Additionally, the relationships and procedural hurdles experienced through international collaboration have molded operations. These facets will be explored and related to current and future operations with the International Space Station and future vehicles. Along with these important aspects, the evolution of technology and continual improvement of data transfer tools between the shuttle and ground team has also defined specific lessons used in the improving the control teams effectiveness. Methodologies to communicate and transmit messages, images, and files from Mission Control to the Orbiter evolved over several years. These lessons have been vital in shaping the effectiveness of safe and successful mission planning that have been applied to current mission planning work in addition to being incorporated into future space flight planning. The critical lessons from all aspects of previous plan, train, and fly phases of shuttle flight missions are not only documented in this paper, but are also discussed as how they pertain to changes in process and consideration for future space flight planning.

Price, Jennifer B.↗