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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23

A redetermination of the succinonitrile-water phase diagram

The phase diagram of the water/succinonitrile system often used in modeling binary metallic alloys is determined experimentally using a laser scattering method. Triply distilled and zone-refined succinonitrile is placed in a jacketed water-cooled vessel with a magnetic mixer and a copper-constantan thermocouple, and the melting point is determined under vacuum and under Ar by observing the response of a photodiode placed opposite a laser source as the chamber is cooled and reheated. Then water is added, and the eutectic and monotectic temperatures and the entire phase diagram are determined under Ar. The results are presented in a table and graph and compared to those of Schreinemakers (1897): significant discrepancies are noted and attributed to impurities in the succinonitrile used by Schreinemakers. The importance of accurate phase diagrams for modeling binary alloys is stressed.

Smith, J. E., Jr.↗

The Mathematics of Dispatchability, Revisited

Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatchable STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently, it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, dispatchability has considerable theoretical as well as practical significance. One thing that hampers further work in this area is the underdeveloped theory. Moreover, the existing foundation is inadequate in certain respects. In this paper, we develop a new mathematical theory of dispatchability and its relationship to execution. We also provide several characterizations of dispatchability, including characterizations in terms of the structural properties of the STN graph. This facilitates the potential application of the theory to other areas.

mathematical models↗

Theoretical limits on Jovian radio belts

An attempt is made to establish safety limits for space missions near Jovian radio belts. A proton belt model was constructed and analyzed in an effort to set these limits based on the possible stability of radiation belts. Calculation results are graphed.

Neubauer, F. M.↗

Evaluation of a three-processor architecture for a satellite packet switch

The architecture of a microprocessor-based packet switch using three processors for use on-board satellites is presented. The operation of the switch involves three distinct functions, namely, the input, the routing and the output functions. Each of these functions is performed by one of the three microprocessors. The operation of the switch along with those of the corresponding software routines are explained. An expression for the throughput is presented. A queue theoretic model of the switch is developed. Analytical expressions for the overall average response time at the various queues are obtained. A number of graphs showing the average response time as a function of some parameters of the switch are also presented.

Arozullakh, M.↗

Global surface-water-induced seasonal variations in the earth's rotation and gravitational field

The effects of seasonal changes in continental surface-water storage on the low-degree gravitational-field coefficients (J), the annual wobble excitation (Psi), and the seasonal length-of-day (LOD) variations are investigated by means of numerical simulations based on compiled meteorological data (Willmott et al., 1985) and satellite snow-load estimates (Chao et al., 1987). The formulation of the model equations and the overall characteristics of the data sets are discussed in detail, and the computation results are presented in tables and graphs. The effect on Psi is found to be relatively small due to longitudinal cancellation, but those on LOD and J are considered significant.

Chao, B. F.↗

Flight investigation of variations in rotorcraft control and display dynamics for hover

Results are presented from a flight test of experimental control/display systems for the U.S. Army/NASA CH-47B variable-stability research helicopter in hovering maneuvers, supplementing the preliminary report of Eshow et al. (1987). The display format was a simplified version of the pilot night-vision system for the Apache AH-64, and the tests measured pilot performance in hover-pad capture and hover-pad tracking tasks (under simulated zero visibility) using displays based on (1) gainlike and (2) integratorlike controlled-element responses to control inputs. The implementation of the vehicle-response dynamics through an explicit model-following control system and the derivation of the display laws are described, and the test results are presented in extensive tables and graphs. Display type (1) was found to be preferred by the pilots and to give generally better performance than type (2).

Eshow, Michelle M.↗

Prototyping user displays using CLIPS

CLIPS is being used as an integral module of a rapid prototyping system. The prototyping system consists of a display manager for object browsing, a graph program for displaying line and bar charts, and a communications server for routing messages between modules. A CLIPS simulation of a physical model provides dynamic control of the user's display. Currently, a project is well underway to prototype the Advanced Automation System (AAS) for the Federal Aviation Administration.

Kosta, Charles P.↗

ASK Magazine; No. 21

THIS ISSUE FEATURES A VISUAL DEPICTION OF THE ACADEMY of Program and Project Leadership (APPL). I imagine a variety of initial reactions to the drawing. One might be, "What is a cartoon doing in a magazine about project management?" Or perhaps, "Wow, nice colors-and fun." Another may be to closely search the image for signs, symbols and meaning. Still another, to read a new level of innovation and creativity into the picture. Undoubtedly, some readers will raise questions about the cost. Of course, any reaction is a sign of engagement. The stronger, the more energized the emotional and cognitive processing, the better. It is a sign of attention and interaction. For I've heard it said, "You only need to worry if they don t care one way or the other." So what is the point of the picture? To stimulate interest, raise questions, promote discussion, and maybe raise a smile.. .That, at least, was my initial reaction when I was introduced to the work of Nancy Hegedus, who helps to create these drawings for Root Learning Inc. At the NASA PM Conference, I was first shown the work Nancy had been doing with the help of Goddard s Knowledge Management Architect, Dr. Ed Rogers. I was immediately drawn into the power of visualization as a tool for more effective learning, communicating, and conveying complex knowledge concepts. We need new tools in today s world, where information and data overwhelms by sheer volume. There are articles, pamphlets, communications, and white papers-all aiming to convince and influence. Reactions to these tend to be either avoidance or mind-numbing, heavy-eyed consent; the message never registers or enters the soul. That s one of the reasons that APPL s Knowledge Sharing Initiative (KSI) has turned to storytelling as a memorable way of transfer- ring knowledge, inspiring imitation of best practices, and spurring reflection. ASK Magazine s recent fourth birthday marks an important milestone in APPL s continuing quest to provide ongoing support to project managers and to promote mission success. And similar to storytelling, the power of visualization is receiving increasing attention in recent years as a way to stimulate engagement. Pictures and visual graphs are viewed as one of the most effective ways for displaying, describing, and generating discussion about quantitative and technically complex information. Prototypes, models, and simulations are considered essential for stimulating innovation through open and engaging discussions. There has also been extensive writing on the use of visual graphics, pictures, and cartoons to facilitate memory, creativity, openness, attention-and even well-being. For many of these reasons, I am excited to have a colorful visual depiction of the APPL world included in ASK. Without the addition of text or slides, the intent is to invite people into the world of the APPL mission-as well as its products, services, customers, and partners- in a fun and engaging manner. As project leaders strive to find ways to encourage engagement, learning, and transmission of knowledge, traditional technologies are proving to be as valuable as modern technologies. (But for those who want more information in the form of texts and slide presentations, we certainly have an abundance of those as well.)

Laufer, Alexander↗

Thermomechanical Property Prediction of Amorphous and Crystal PEKK via Molecular Dynamics

Traditionally, advanced aerospace composites have been manufactured using thermoset resins. However, recently, thermoplastics have been investigated for use in the manufacturing of composite materials due to their unique manufacturing characteristics. Thermoplastic resins can be reshaped and formed, along with the added benefit of being recyclable, which thermoset resin cannot. Thermoplastic materials undergo a crystallization process during manufacturing which affects the percent crystallinity of the material. The crystallization needs to be understood better to maximize the potential of thermoplastic resins. PEKK is a thermoplastic material with good chemical, thermal, and mechanical loading resistance. PEKK is also a material NASA is interested in for developing new bonded joint technology. The crystalline microstructure of PEKK is at the micrometer length scale, and it is of interest to model the effects of the crystallinity structure on PEKK’s bulk properties. Molecular dynamics (MD) is a simulation tool that allows for property-structure relationships between atomistic structure and nanometer-length portions of a material. This makes MD a useful tool for developing the structure-property relationship of PEKK. However, the micrometer length scale of PEKK’s crystal structure is too large for MD. Thus, a hybrid approach to modeling PEKK’s microstructure is proposed in this work where MD models are built of both the amorphous and crystalline phases of PEKK. The engineering material properties can be obtained using MD at the nanometer length scale. A micromechanics approach can then generate the micrometer length scale of the crystallinity and the effective properties can be homogenized. The objective of this paper is to show the MD model workflow and the MD-predicted properties of PEKK. The properties can then be homogenized with different crystalline percentages to build design graphs that can be used to tailor PEKK for specific composite applications.

poly ether ketone ketone↗

Observed and model-calculated NO2/NO ratios in tropospheric air sampled during the NASA GTE/CITE-2 field study

Airborne measurements of NO2/NO ratios in the free troposphere, obtained using two-photon LIF (TP-LIF) and chemiluminescence (CL) detectors during the NASA Global Tropospheric Experiment Chemical Instrumentation Test and Evaluation 2 (CITE 2) program in summer 1986, are analyzed and compared with the predictions of a photochemical model. The derivation of the model is outlined, with an emphasis on the role of peroxy radicals in the fast photochemical cycling of NO and NO2 and the production of O3; the CITE 2 instruments and flight protocols are described; and the results are presented in extensive tables and graphs and discussed in detail. It was found that on three CITE 2 flights the CL data were systematically greater than the TP-LIF data; when these data were removed, the averaged values of the two instruments agreed to within 1 percent. The NO2/NO ratios predicted by the model are shown to be consistent with the measurements, although usually 20-25 percent lower.

Chameides, W. L.↗

The photochemistry of ammonia in the Jovian atmosphere

A reinvestigation of the ammonia photochemistry in the Jovian lower atmosphere above the cloud tops is conducted in connection with certain questions concerning the model proposed by Strobel (1973). Chemical reactions and their rate coefficients are considered along with the model atmosphere, solar fluxes, the continuity equations, and numerical details. Differences between the newmodel and Strobel's model are related to more moderate ammonia depletion relations. The height profiles of H, NH2, N2H3, N2H4, and NH3 concentrations are presented in a graph.

Prasad, S. S.↗

Mathematical modeling and simulation of the space shuttle imaging radar antennas

Simulations of space shuttle synthetic aperture radar antennas under the influence of space environmental conditions were carried out at L, C, and X-band. Mathematical difficulties in modeling large, non-planar array antennas are discussed, and an approximate modeling technique is presented. Results for several antenna error conditions are illustrated in far-field profile patterns, earth surface footprint contours, and summary graphs.

Campbell, R. W.↗

Resource envelope concepts for mission planning

Seven proposed methods for creating resource envelopes for Space Station Freedom mission planning are detailed. Four reference science activity models are used to illustrate the effect of adding operational flexibility to mission timelines. For each method, a brief explanation is given along with graphs to illustrate the application of the envelopes to the power and crew resources. The benefits and costs of each method are analyzed in terms of resource utilization. In addition to the effect on individual activities, resource envelopes are analyzed at the experiment level.

Ibrahim, K. Y.↗

Quantum-accelerated Global Constraint Filtering

Motivated by recent advances in quantum algorithms and gate-model quantum computation, we introduce quantum-accelerated filtering algorithms for global constraints in constraint programming. We adapt recent work in quantum algorithms for graph problems and identify quantum subroutines that accelerate the main domain consistency algorithms for the all different constraint and the global cardinality constraint (gcc). The subroutines are based on quantum algorithms for finding maximum matchings and strongly connected components in graphs, and provide speedups over the best classical algorithms. We detail both complete and bounded-probability frameworks for quantum-accelerated global constraint filtering algorithms within backtracking search.

Quantum algorithms↗

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗

Interpretable ML Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Shreyas J Honrao↗

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗

Machine Learning for the Validation of Expert-Elicited Causal Risk Diagrams

Exposure to spaceflight poses risk to human health in complex ways. To help manage this risk, the Human Systems Risk Board (HSRB) at the National Aeronautics and Space Administration (NASA) maintains a set of causal diagrams that attempt to explain how spaceflight hazards generate health risks and lead to adverse outcomes both in-mission, immediately post-mission, and over the long term. These causal risk diagrams are formulated as directed acyclic graphs (DAGs) and can function as knowledge graphs of connected risks and outcomes. These DAGs have proven useful for communication, and, through network analysis, have allowed for the identification of structurally important factors in the risk network. However, the utility these DAGs provide is directly proportional to their verisimilitude, making assessment of this trait using empirical data – whether from actual human spaceflight or various spaceflight analogue exposures and model organisms – a high priority. In this research we explore the use of machine learning algorithms to learn DAG structure from empirical data as a means of evaluating human-elicited DAG structures. To do so, we test several different graph structure-learning algorithms on data concerning changes in the bones of rats and mice after exposure to either spaceflight or a spaceflight analogue. We explore potential methods for indexing the similarity between each algorithm’s output DAG with all the others and with that of the expert-elicited DAG. We discuss next steps in this ongoing line of research and open science initiatives underway to complete them.

directed acyclic graphs↗