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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 1,099 records · Page 61

All sources NASA NTRS DOE OSTI

NASA NTRS2011

Improving Data Collection and Analysis Interface for the Data Acquisition Software of the Spin Laboratory at NASA Glenn Research Center

In jet engines, turbines spin at high rotational speeds. The forces generated from these high speeds make the rotating components of the turbines susceptible to developing cracks that can lead to major engine failures. The current inspection technologies only allow periodic examinations to check for cracks and other anomalies due to the requirements involved, which often necessitate entire engine disassembly. Also, many of these technologies cannot detect cracks that are below the surface or closed when the crack is at rest. Therefore, to overcome these limitations, efforts at NASA Glenn Research Center are underway to develop techniques and algorithms to detect cracks in rotating engine components. As a part of these activities, a high-precision spin laboratory is being utilized to expand and conduct highly specialized tests to develop methodologies that can assist in detecting predetermined cracks in a rotating turbine engine rotor. This paper discusses the various features involved in the ongoing testing at the spin laboratory and elaborates on its functionality and on the supporting data system tools needed to enable successfully running optimal tests and collecting accurate results. The data acquisition system and the associated software were updated and customized to adapt to the changes implemented on the test rig system and to accommodate the data produced by various sensor technologies. Discussion and presentation of these updates and the new attributes implemented are herein reported

Abdul-Aziz, Ali↗

NASA NTRS2012

DROP: Durable Reconnaissance and Observation Platform

Robots have been a valuable tool for providing a remote presence in areas that are either inaccessible or too dangerous for humans. Having a robot with a high degree of adaptability becomes crucial during such events. The adaptability that comes from high mobility and high durability greatly increases the potential uses of a robot in these situations, and therefore greatly increases its usefulness to humans. DROP is a lightweight robot that addresses these challenges with the capability to survive large impacts, carry a usable payload, and traverse a variety of surfaces, including climbing vertical surfaces like wood, stone, and concrete. The platform is crash-proof, allowing it to be deployed in ways including being dropped from an unmanned aerial vehicle or thrown from a large MSL-class (Mars Science Laboratory) rover.

Parness, Aaron↗

NASA NTRS2014

Star-Shaped Fluid Flow Tool for Use in Making Differential Measurements

A fluid flow tool's plate-like structure has a ring portion defining a flow hole, a support portion extending radially away from the ring portion and adapted to be coupled to conduit wall, and extensions extending radially away from the ring portion such that a periphery of the plate-like structure is defined by the extensions and trough regions between adjacent extensions. One or more ports formed in the ring portion are in fluid communication with the flow hole. A first manifold in the plate-like structure is in fluid communication with each port communicating with the flow hole. One or more ports are formed in the periphery of the plate-like structure. A second manifold in the plate-like structure is in fluid communication with each port formed in the periphery. The first and second manifolds extend through the plate-like structure to terminate and be accessible at the conduit wall.

England, John Dwight↗

NASA NTRS2017

Autonomous Task Management and Decision Support Tools

For some time aircraft manufacturers and researchers have been pursuing mechanisms for reducing crew workload and providing better decision support to the pilots, especially during non-normal situations. Some previous attempts to develop task managers or pilot decision support tools have not resulted in robust and fully functional systems. However, the increasing sophistication of sensors and automated reasoners, and the exponential surge in the amount of digital data that is now available create a ripe environment for the development of a robust, dynamic, task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This presentation will discuss the many significant challenges and issues associated with the development and functionality of such a system for use on the aircraft flight deck.

decision support↗

NASA NTRS2016

Contour Crafting Simulation Plan for Lunar Settlement Infrastructure Build-Up

Economically viable and reliable building systems and tool sets are being sought, examined and tested for extraterrestrial infrastructure buildup. This project focused on a unique architecture weaving the robotic building construction technology with designs for assisting rapid buildup of initial operational capability Lunar and Martian bases. The project aimed to study new methodologies to construct certain crucial infrastructure elements in order to evaluate the merits, limitations and feasibility of adapting and using such technologies for extraterrestrial application. Current extraterrestrial settlement buildup philosophy holds that in order to minimize the materials needed to be flown in, at great transportation costs, strategies that maximize the use of locally available resources must be adopted. Tools and equipment flown as cargo from Earth are proposed to build required infrastructure to support future missions and settlements on the Moon and Mars.

Infrastructure↗

DOE OSTI2025

Revealing structure and shaping priorities in plant and fungal cell wall architecture via solid-state NMR

Plant and fungal cell walls are essential for growth, adaptation, and survival, with their intricate architectures dictating both resistance to stress and susceptibility to antifungal or biomass-degrading strategies. Understanding how these walls are built, remodeled, and function at the molecular level is therefore central to both clinical and biotechnological applications. Solid-state nuclear magnetic resonance (ssNMR) has emerged as a uniquely powerful tool for this purpose, as it reveals the structure, dynamics, and interactions of intact biopolymers without disrupting their native organization. Using this approach, recent studies have shown how structural polymorphism, polymer-polymer interactions, and species-specific remodeling govern mechanical integrity, drug resistance, and stress adaptation. Applications highlighted here include lignin-carbohydrate packing during plant stem maturation, fungal wall reorganization under treatment by wall-targeting antifungals such as echinocandin and nikkomycin, and the functional diversity of glucans, chitins, and mannans. Together, these insights uncover conserved principles of polymer assembly across kingdoms while informing new opportunities for antifungal development and biomass utilization. Ongoing advances in sensitivity and resolution are expected to broaden the reach of ssNMR and further accelerate its role in linking structural heterogeneity to biosynthetic complexity and biological function.

Antifungal↗

NASA NTRS2022

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, and Psychosocial Adaptions within a Team

The Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team (the Team Risk) is primarily performance-focused, with a secondary emphasis on behavioral health outcomes resulting from team performance and interpersonal interactions. Monitoring tools, measures, and countermeasures are aimed at enhancing team processes and team composition configurations to optimize team performance and functioning. Long-duration exploration missions (LDEMs) will include major challenges that could affect team performance, including social isolation, physical confinement, a small and diverse crew, communication delays between crew and ground, limited or no crew rotation or evacuation options, limited or no resupply, and a high-consequence environment. Each of these conditions will affect the crew’s coordination, cooperation, psychological well-being, and performance. Although the International Space Station (ISS) remains important for studies that require spaceflight testing and validation, the current conditions on the ISS do not adequately mimic the exploration environment that is required for National Aeronautics and Space Administration (NASA) teams research, and thus access to terrestrial or ground-based analogs of LDEM conditions is paramount. The emphasis on analogs for research is reflected in this updated evidence review of the Team Risk, and includes data from studies conducted at isolated, confined, extreme (ICE) environments (e.g., Antarctic stations), and from several mission simulation analogs such as the Human Exploration Research Analog (HERA) (HERA Experiment Information Package, 2014), also known as isolated, confined, controlled (ICC) environments. These studies have characterized many team factors regarding LDEMs, and the Team Risk has now matured from risk characterization to focusing more on countermeasure development. Because spaceflight evidence for team-level research is lacking, no reliable data is available to quantify the impact of team-level variables on individual and team-level outcomes during spaceflight missions. Until recently, no systematic attempt had been undertaken to measure the performance effects of team cohesion, team composition, team training, or team-related psychosocial adaptation during spaceflight. The Team Risk is a relatively young research area for NASA, with substantial growth only since the 2000s, and with limited access to spaceflight performance data. As a result, spaceflight evidence is lacking to identify specifically what team composition, level of training, amount of cohesion, or quality of psychosocial adaptation is necessary to reduce the risk of performance errors in space. However, astronaut journals and interviews and reports from spaceflight subject matter experts (SMEs) provide testimonies that team performance during spaceflight is important for mission success and to maintain crew health. Team spaceflight data is now being collected as part of the Spaceflight Standard Measures task (Clement, 2021)—a set of core measurements related to many human spaceflight risks that are collected from astronauts before, during, and after long-duration missions. The team-related standard measures focus on team cohesion, team performance, group living, team climate, and team processes. Collection of standard measures data is ongoing and published data is not yet available. Finally, although spaceflight evidence is lacking, evidence gleaned from ground studies and spaceflight analog studies will help close the gaps outlined in the Team Risk. Ground-based studies provide quantitative evidence for team functioning in ICE environments. Academic research on teams has produced dozens of meta-analyses that can be used to understand the general relationships among team inputs (e.g., team member characteristics and skills, job context), team processes, and emergent states (e.g., coordination, communication, cooperation, cohesion, trust, shared cognition), and team outcomes (e.g., effectiveness, errors, adaptation). Teams are complex, incorporating individual characteristics of team members, but also existing at a level that is greater than the sum of its parts. Therefore, the Team Risk must be integrated with other individual-focused NASA Human Research Program (HRP) risks, including Behavioral Medicine (BMed), Sleep, and Human-Systems Integration Architecture (HSIA), and emerging research indicates more integration may needed between the Team Risk and the physiologically oriented risks. Much of this integration occurs through the Human Systems Risk Board (HSRB). A lack of team functioning may be a stressor in some circumstances, but the team often acts as a countermeasure. For example, support for team leaders and teammates can facilitate individual functioning and encourage psychological and physically healthy behaviors and attitudes. However, more research is needed regarding teams during LDEMs and the remaining gaps in the research are described in the current report.

Lauren Blackwell Landon↗

NASA NTRS2016

Engineering Design Tools for Shape Memory Alloy Actuators: CASMART Collaborative Best Practices and Case Studies

The primary goal of the Consortium for the Advancement of Shape Memory Alloy Research and Technology (CASMART) is to enable the design of revolutionary applications based on shape memory alloy (SMA) technology. In order to help realize this goal and reduce the development time and required experience for the fabrication of SMA actuation systems, several modeling tools have been developed for common actuator types and are discussed herein along with case studies, which highlight the capabilities and limitations of these tools. Due to their ability to sustain high stresses and recover large deformations, SMAs have many potential applications as reliable, lightweight, solid-state actuators. Their advantage over classical actuators can also be further improved when the actuator geometry is modified to fit the specific application. In this paper, three common actuator designs are studied: wires, which are lightweight, low-profile, and easily implemented; springs, which offer actuation strokes upwards of 200 at reduced mechanical loads; and torque tubes, which can provide large actuation forces in small volumes and develop a repeatable zero-load actuation response (known as the two-way shape memory effect). The modeling frameworks, which have been implemented in the design tools, are developed for each of these frequently used SMA actuator types. In order to demonstrate the versatility and flexibility of the presented design tools, as well as validate their modeling framework, several design challenges were completed. These case studies include the design and development of an active hinge for the deployment of a solar array or foldable space structure, an adaptive solar array deployment and positioning system, a passive air temperature controller for regulation flow temperatures inside of a jet engine, and a redesign of the Corvette active hatch, which allows for pressure equalization of the car interior. For each of the presented case studies, a prototype or proof-of-concept was fabricated and the experimental results and lessons learned are discussed. This analysis presents a collection of CASMART collaborative best practices in order to allow readers to utilize the available design tools and understand their modeling principles. These design tools, which are based on engineering models, can provide first-order optimal designs and are a basic and efficient method for either demonstrating design feasibility or refining design parameters. Although the design and integration of an SMA-based actuation system always requires application- and environment-specific engineering considerations, common modeling tools can significantly reduce the investment required for actuation system development and provide valuable engineering insight.

Design tools↗

DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Bridging the gap between LCA, LCC and CBA as sustainability assessment tools

Increasing interest in sustainability has led to the development of sustainability assessment tools such as Life Cycle Analysis (LCA), Life Cycle Costing (LCC) and Cost–Benefit Analysis (CBA). Due to methodological disparity of these three tools, conflicting assessment results generate confusion for many policy and business decisions. In order to interpret and integrate assessment results, the paper provides a framework that clarifies the connections and coherence between the included assessment methodologies. Building on this framework, the paper further focuses on key aspects to adapt any of the methodologies to full sustainability assessments. Aspects dealt with in the review are for example the reported metrics, the scope, data requirements, discounting, product- or project-related and approaches with respect to scarcity and labor requirements. In addition to these key aspects, the review shows that important connections exist: (i) the three tools can cope with social inequality, (ii) processes such as valuation techniques for LCC and CBA are common, (iii) Environmental Impact Assessment (EIA) is used as input in both LCA and CBA and (iv) LCA can be used in parallel with LCC. Furthermore, the most integrated sustainability approach combines elements of LCA and LCC to achieve the Life Cycle Sustainability Assessment (LCSA). The key aspects and the connections referred to in the review are illustrated with a case study on the treatment of end-of-life automotive glass. - Highlights: • Proliferation of assessment tools creates ambiguity and confusion. • The developed assessment framework clarifies connections between assessment tools. • Broadening LCA, key aspects are metric and data requirements. • Broadening LCC, key aspects are scope, time frame and discounting. • Broadening CBA, focus point, timespan, references, labor and scarcity are key.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Evaluation of Load Analysis Methods for NASAs GIII Adaptive Compliant Trailing Edge Project

The Air Force Research Laboratory (AFRL), NASA Armstrong Flight Research Center (AFRC), and FlexSys Inc. (Ann Arbor, Michigan) have collaborated to flight test the Adaptive Compliant Trailing Edge (ACTE) flaps. These flaps were installed on a Gulfstream Aerospace Corporation (GAC) GIII aircraft and tested at AFRC at various deflection angles over a range of flight conditions. External aerodynamic and inertial load analyses were conducted with the intention to ensure that the change in wing loads due to the deployed ACTE flap did not overload the existing baseline GIII wing box structure. The objective of this paper was to substantiate the analysis tools used for predicting wing loads at AFRC. Computational fluid dynamics (CFD) models and distributed mass inertial models were developed for predicting the loads on the wing. The analysis tools included TRANAIR (full potential) and CMARC (panel) models. Aerodynamic pressure data from the analysis codes were validated against static pressure port data collected in-flight. Combined results from the CFD predictions and the inertial load analysis were used to predict the normal force, bending moment, and torque loads on the wing. Wing loads obtained from calibrated strain gages installed on the wing were used for substantiation of the load prediction tools. The load predictions exhibited good agreement compared to the flight load results obtained from calibrated strain gage measurements.

Aerodynamic Loads↗

DOE OSTI2023

Rapid in situ nutrient element distribution in plants and soils using laser-induced breakdown spectroscopy ($\mathrm{LIBS}$)

The aim of this study is to develop and test the applicability of a rapid in situ plant chemistry profiling technique to determine elemental composition of small-volume plant and soil samples obtained from a woody bioenergy crop species, Populus trichocarpa. Expanding the research tools available to characterize the nutrient element correlations among plant tissue types and soil depths is a critical need in the path of understanding productivity and adaptation of plants to variations in external abiotic and biotic factors and developing sustainable perennial bioenergy crops that are co-optimized for biomass valorization aboveground and carbon sequestration belowground. Several plant root, stem, and soil samples were tested using laser-induced breakdown spectroscopy (LIBS) to evaluate the presence and distribution of nutrient elements. Samples were tested as collected and after being dried and cross sectioned to evaluate the effectiveness of using LIBS for in situ analysis on plant samples. The collected LIBS spectra show the elemental peaks were the same in both the as collected and prepared samples for roots and stems. Qualitative amounts of elements such as H, C, N, O, Li, Na, Mg, K, Ca, Fe, Al, and Si were able to be identified rapidly in raw samples. Here in this report we demonstrate suitability of LIBS in obtaining rapid, in situ, elemental distribution in plant and soil samples, utilizing only small sample volumes and minimal sample preparation. This demonstration opens up a new rapid phenotyping avenue necessary to fill the asymmetrical knowledge gaps in belowground performance of plant systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models

We present Π4U,{sup 1} an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

TU-C-BRE-07: Quantifying the Clinical Impact of VMAT Delivery Errors Relative to Prior Patients’ Plans and Adjusted for Anatomical Differences

Purpose: There is increased interest in the Radiation Oncology Physics community regarding sensitivity of pre-treatment IMRT/VMAT QA to delivery errors. Consequently, tools mapping pre-treatment QA to the patient DVH have been developed. However, the quantity of plan degradation that is acceptable remains uncertain. Using DVHs adapted from prior patients’ plans, we developed a technique to determine the magnitude of various delivery errors required to degrade a treatment plan to outside the clinically accepted range. Methods: DVHs for relevant organs at risk were adapted from a population of prior patients’ plans using a machine learning algorithm to establish the clinically acceptable DVH range specific to the patient’s anatomy. We applied this technique to six low-risk prostate cancer patients treated with single-arc VMAT and compared error-induced DVH changes to the adapted DVHs to determine the magnitude of error required to push the plan outside of the acceptable range. The procedure follows: (1) Errors (systematic ' random shift of MLCs, gantry-MLC desynchronization, dose rate fluctuations, etc.) were simulated and degraded DVHs calculated using the Varian Eclipse TPS. (2) Adapted DVHs and acceptable ranges for DVHs were established. (3) Relevant dosimetric indices and corresponding acceptable ranges were calculated from the DVHs. Key indices included NTCP (Lyman-Kutcher-Burman Model) and QUANTEC’s dose-volume Objectives: s of V75Gy≤0.15 for the rectum and V75Gy≤0.25 for the bladder. Results: Degradations to the clinical plan became “unacceptable” for 19±29mm and 1.9±2.0mm systematic outward shifts of a single leaf and leaf bank, respectively. All other simulated errors fell within the acceptable range. Conclusion: Utilizing machine learning and prior patients’ plans one can predict a clinically acceptable range of DVH degradation for a specific patient. Comparing error-induced DVH degradations to this range, it is shown that single-arc VMAT plans for low-risk prostate are relatively insensitive to many potential delivery errors.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Brief Overview of the NCSP [Slides]

The mission of the NCSP is to provide sustainable expert leadership, direction, and the technical infrastructure necessary to develop, maintain, and disseminate the essential technical tools, training, and data required to support safe, efficient fissionable material operations within the Department of Energy. The vision of the NCSP is a continually improving, adaptable, and transparent program that communicates and collaborates globally to incorporate technology, practices, and programs to be responsive to the essential technical needs of those responsible for developing, implementing, and maintaining nuclear criticality safety.

2021 NCSP TPR Best Paper Award↗

DOE OSTI2020

Brief Overview of the NCSP [Slides]

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2013

Tackling Structures of Long Noncoding RNAs

RNAs are important catalytic machines and regulators at every level of gene expression. A new class of RNAs has emerged called long non-coding RNAs, providing new insights into evolution, development and disease. Long non-coding RNAs (lncRNAs) predominantly found in higher eukaryotes, have been implicated in the regulation of transcription factors, chromatin-remodeling, hormone receptors and many other processes. The structural versatility of RNA allows it to perform various functions, ranging from precise protein recognition to catalysis and metabolite sensing. While major housekeeping RNA molecules have long been the focus of structural studies, lncRNAs remain the least characterized class, both structurally and functionally. Here, we review common methodologies used to tackle RNA structure, emphasizing their potential application to lncRNAs. When considering the complexity of lncRNAs and lack of knowledge of their structure, chemical probing appears to be an indispensable tool, with few restrictions in terms of size, quantity and heterogeneity of the RNA molecule. Probing is not constrained to in vitro analysis and can be adapted to high-throughput sequencing platforms. Significant efforts have been applied to develop new in vivo chemical probing reagents, new library construction protocols for sequencing platforms and improved RNA prediction software based on the experimental evidence.

SHAPE↗

NASA NTRS1992

Reynolds number and cowl position effects for a generic sidewall compression scramjet inlet at Mach 10 - A computational and experimental investigation

Reynolds number and cowl position effects on the internal shock structure and the resulting performance of a generic three-dimensional sidewall compression scramjet inlet with a leading edge sweep of 45 degrees at Mach 10 have been examined both computationally and experimentally. Prior to the experiment, a three-dimensional Navier-Stokes code was adapted to perform preliminary parametric studies leading to the design of the present configuration. Following this design phase, the code was then utilized as an analysis tool to provide a better understanding of the flow field and the experimental static pressure data for the final experimental configuration. The wind tunnel model possessed 240 static pressure orifices distributed on the forebody plane, sidewalls, and cowl and was tested in the NASA Langley 31 Inch Mach 10 Tunnel.

Holland, Scott D.↗