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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 217 records · Page 12

Operational Experience from Solar Thermal Energy Projects

Over the past few years, Sandia National Laboratories were involved in the design, construction, and operation of a number of DOE-sponsored solar thermal energy systems. Among the systems currently in operation are several industrial process heat projects and the Modular Industrial Solar Retrofit qualification test systems, all of which use parabolic troughs, and the Shenandoah Total Energy Project, which uses parabolic dishes. Operational experience has provided insight to both desirable and undesirable features of the designs of these systems. Features of these systems which are also relevant to the design of parabolic concentrator thermal electric systems are discussed. Other design features discussed are system control functions which were found to be especially convenient or effective, such as local concentrator controls, rainwash controls, and system response to changing isolation. Drive systems are also discussed with particular emphasis of the need for reliability and the usefulness of a manual drive capability.

Cameron, C. P.↗

Walking robot: A design project for undergraduate students

The objective of the University of Maryland walking robot project was to design, analyze, assemble, and test an intelligent, mobile, and terrain-adaptive system. The robot incorporates existing technologies in novel ways. The legs emulate the walking path of a human by an innovative modification of a crank-and-rocker mechanism. The body consists of two tripod frames connected by a turning mechanism. The two sets of three legs are mounted so as to allow the robot to walk with stability in its own footsteps. The computer uses a modular hardware design and distributed processing. Dual-port RAM is used to allow communication between a supervisory personal computer and seven microcontrollers. The microcontrollers provide low-level control for the motors and relieve the processing burden on the PC.

Source record↗

Lunar lander and return propulsion system trade study

This trade study was initiated at NASA/JSC in May 1992 to develop and evaluate main propulsion system alternatives to the reference First Lunar Outpost (FLO) lander and return-stage transportation system concept. Thirteen alternative configurations were developed to explore the impacts of various combinations of return stage propellants, using either pressure or pump-fed propulsion systems and various staging options. Besides two-stage vehicle concepts, the merits of single-stage and stage-and-a-half options were also assessed in combination with high-performance liquid oxygen and liquid hydrogen propellants. Configurations using an integrated modular cryogenic engine were developed to assess potential improvements in packaging efficiency, mass performance, and system reliability compared to non-modular cryogenic designs. The selection process to evaluate the various designs was the analytic hierarchy process. The trade study showed that a pressure-fed MMH/N2O4 return stage and RL10-based lander stage is the best option for a 1999 launch. While results of this study are tailored to FLO needs, the design date, criteria, and selection methodology are applicable to the design of other crewed lunar landing and return vehicles.

Hurlbert, Eric A.↗

Technology Challenges for Deep-Throttle Cryogenic Engines for Space Exploration

Historically, cryogenic rocket engines have not been used for in-space applications due to their additional complexity, the mission need for high reliability, and the challenges of propellant boil-off. While the mission and vehicle architectures are not yet defined for the lunar and Martian robotic and human exploration objectives, cryogenic rocket engines offer the potential for higher performance and greater architecture/mission flexibility. In-situ cryogenic propellant production could enable a more robust exploration program by significantly reducing the propellant mass delivered to low earth orbit, thus warranting the evaluation of cryogenic rocket engines versus the hypergolic bi-propellant engines used in the Apollo program. A multi-use engine. one which can provide the functionality that separate engines provided in the Apollo mission architecture, is desirable for lunar and Mars exploration missions because it increases overall architecture effectiveness through commonality and modularity. The engine requirement derivation process must address each unique mission application and each unique phase within each mission. The resulting requirements, such as thrust level, performance, packaging, bum duration, number of operations; required impulses for each trajectory phase; operation after extended space or surface exposure; availability for inspection and maintenance; throttle range for planetary descent, ascent, acceleration limits and many more must be addressed. Within engine system studies, the system and component technology, capability, and risks must be evaluated and a balance between the appropriate amount of technology-push and technology-pull must be addressed. This paper will summarize many of the key technology challenges associated with using high-performance cryogenic liquid propellant rocket engine systems and components in the exploration program architectures. The paper is divided into two areas. The first area describes how the mission requirements affect the engine system requirements and create system level technology challenges. An engine system architecture for multiple applications or a family of engines based upon a set of core technologies, design, and fabrication approaches may reduce overall programmatic cost and risk. The engine system discussion will also address the characterization of engine cycle figures of merit, configurations, and design approaches for some in-space vehicle alternatives under consideration. The second area evaluates the component-level technology challenges induced from the system requirements. Component technology issues are discussed addressing injector, thrust chamber, ignition system, turbopump assembly, and valve design for the challenging requirements of high reliability, robustness, fault tolerance, deep throttling, reasonable performance (with respect to weight and specific impulse).

Brown, Kendall K.↗

Software for Acoustic Rendering

SLAB is a software system that can be run on a personal computer to simulate an acoustic environment in real time. SLAB was developed to enable computational experimentation in which one can exert low-level control over a variety of signal-processing parameters, related to spatialization, for conducting psychoacoustic studies. Among the parameters that can be manipulated are the number and position of reflections, the fidelity (that is, the number of taps in finite-impulse-response filters), the system latency, and the update rate of the filters. Another goal in the development of SLAB was to provide an inexpensive means of dynamic synthesis of virtual audio over headphones, without need for special-purpose signal-processing hardware. SLAB has a modular, object-oriented design that affords the flexibility and extensibility needed to accommodate a variety of computational experiments and signal-flow structures. SLAB s spatial renderer has a fixed signal-flow architecture corresponding to a set of parallel signal paths from each source to a listener. This fixed architecture can be regarded as a compromise that optimizes efficiency at the expense of complete flexibility. Such a compromise is necessary, given the design goal of enabling computational psychoacoustic experimentation on inexpensive personal computers.

Miller, Joel D.↗

Biomimetic Fly Eye Sensor for Real-Time Target Tracking

The project objective is to design, build, and demonstrate a biomimetic (inspired by biology) smart sensor based on the common house fly eye for real-time target tracking in three dimensions (3-D). The sensor makes use of revolutionary optical sensor design resulting in significantly improved motion detection capabilities when compared with conventional optical sensors. The design is motivated by the parallel nature of the flys vision system and its demonstrated hyperacuity or precision of visual localization beyond the conventional resolution limit. This means that the sensor is able to accurately detect very fast motion and small changes in motion. The analog architecture of the sensor allows for real-time solution at adjustable bandwidths depending on the application. The low cost, exceptionally lightweight, small form factor, low power requirements, and low computation requirements of the sensor make it ideally suited to many aeronautics applications that have limitations on weight, power, and processing time. The sensor is modular and can be easily adapted in scale and manufactured for a wide variety of sensing applications. Advanced target tracking algorithms will be developed to allow the sensor to track a target in 3-D. These algorithms will be implemented in the smart sensors hardware to enable real-time target tracking.

wing position measurement system↗

Adapting the SpaceCube v2.0 Data Processing System for Mission-Unique Application Requirements

The SpaceCube (sup TM) v2.0 system is a superior high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. This paper provides an overview of the design architecture, flexibility, and the advantages of the modular SpaceCube v2.0 high performance data processing system for space applications. The current state of the proven SpaceCube technology is based on nine years of engineering and operations. Five systems have been successfully operated in space starting in 2008 with four more to be delivered for launch vehicle integration in 2015. The SpaceCube v2.0 system is also baselined as the avionics solution for five additional flight projects and is always a top consideration as the core avionics for new instruments or spacecraft control. This paper will highlight how this multipurpose system is currently being used to solve design challenges of three independent applications. The SpaceCube hardware adapts to new system requirements by allowing for application-unique interface cards that are utilized by reconfiguring the underlying programmable elements on the core processor card. We will show how this system is being used to improve on a heritage NASA GPS technology, enable a cutting-edge LiDAR instrument, and serve as a typical command and data handling (C&DH) computer for a space robotics technology demonstration.

SpaceCube↗

Adapting the SpaceCube v2.0 Data Processing System for Mission-Unique Application Requirements

The SpaceCubeTM v2.0 system is a superior high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. This paper provides an overview of the design architecture, flexibility, and the advantages of the modular SpaceCube v2.0 high performance data processing system for space applications. The current state of the proven SpaceCube technology is based on nine years of engineering and operations. Five systems have been successfully operated in space starting in 2008 with four more to be delivered for launch vehicle integration in 2015. The SpaceCube v2.0 system is also baselined as the avionics solution for five additional flight projects and is always a top consideration as the core avionics for new instruments or spacecraft control. This paper will highlight how this multipurpose system is currently being used to solve design challenges of three independent applications. The SpaceCube hardware adapts to new system requirements by allowing for application-unique interface cards that are utilized by reconfiguring the underlying programmable elements on the core processor card. We will show how this system is being used to improve on a heritage NASA GPS technology, enable a cutting-edge LiDAR instrument, and serve as a typical command and data handling (CDH) computer for a space robotics technology demonstration.

Xilinx FPGA↗

Mission Use of the SpaceCube Hybrid Data Processing System

The award-winning SpaceCube v2.0 system is a high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. This presentation provides an overview of the design architecture, flexibility, and the advantages of the modular SpaceCube v2.0 high performance data processing system for space applications. The current state of the proven SpaceCube technology is based on 11 years of engineering and operations. Eight systems have been successfully operated in space starting in 2008 with eight more to be delivered for payload integration in 2018 in support of various missions. This presentation will highlight how this multipurpose system is currently being used to solve design challenges of a variety of independent applications. The SpaceCube hardware adapts to new system requirements by allowing for application-unique interface cards that are utilized by reconfiguring the underlying programmable elements on the core processor card. We will show how this system is being used to improve on a heritage NASA GPS technology, enable a cutting-edge LiDAR instrument, and serve as a typical command and data handling (CDH) computer for a space robotics technology demonstration.Finally, this presentation will highlight the use of the SpaceCube v2.0 system on the Restore-L robotic satellite servicing mission. SpaceCube v2.0 is the central avionics responsible for the real-time vision system and autonomous robotic control necessary to find, capture, and service a national asset weather satellite.

Petrick, Dave↗

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗

A principled approach to the measurement of situation awareness in commercial aviation

The issue of how to support situation awareness among crews of modern commercial aircraft is becoming especially important with the introduction of automation in the form of sophisticated flight management computers and expert systems designed to assist the crew. In this paper, cognitive theories are discussed that have relevance for the definition and measurement of situation awareness. These theories suggest that comprehension of the flow of events is an active process that is limited by the modularity of attention and memory constraints, but can be enhanced by expert knowledge and strategies. Three implications of this perspective for assessing and improving situation awareness are considered: (1) Scenario variations are proposed that tax awareness by placing demands on attention; (2) Experimental tasks and probes are described for assessing the cognitive processes that underlie situation awareness; and (3) The use of computer-based human performance models to augment the measures of situation awareness derived from performance data is explored. Finally, two potential example applications of the proposed assessment techniques are described, one concerning spatial awareness using wide field of view displays and the other emphasizing fault management in aircraft systems.

Tenney, Yvette J.↗

Data processing at the University of Kansas

Two systems for processing remotely sensed image data have been discussed. The first system, IDECS, is a near real-time hardware system oriented towards processing multi-image data sets quickly and economically. The IDECS has convenient film input and color display output capabilities and implements simple kinds of decision rules. The second system, KANDIDATS, is a software system capable of performing many of the more sophisticated processing methods. Because of its monitor which handles all bookkeeping and its modular design, KANDIDATS easily allows the testing of new automatic processing techniques. After a new technique has been proven on KANDIDATS, it may be simplified and hard-wired in IDECS, thereby keeping the volume processing of remotely sensed data always up to the current state-of-the-art.

Haralick, R. M.↗

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗

MARIE: A Python-Based Framework for Comprehensive Fuel Recycling Modeling

One of the most pressing challenges to the continued deployment of nuclear energy systems is in the ultimate management and disposition of discharged fuel assemblies. While reprocessing and recovery of valuable materials from UNF assemblies has been considered as part of an overall strategy for minimization of the volume of reactor-based wastes to be managed, the deployment of commercial-scale reprocessing facilities presents an enormous economic challenge. The MARIE software package has been developed as a means of confronting this challenge. Representing components of a generic fuel reprocessing operation as individual physical processes, MARIE is designed as a modular framework intended to allow for analysis and cost-optimization for a hypothetical reprocessing facility while realistically accounting for the physical characteristics of the used fuel source term, such as decay heat, activity, and radiation dose (informing corresponding shielding requirements). Capabilities supported by MARIE include head-end operations such as fuel shearing, voloxidation, and dissolution; generic solvent extraction operations informed by available open-literature data; a suite of unit operations intended to represent electrochemical processing of used fuel assemblies (i.e., oxide reduction, electrorefining, and electrowinning); and finally, accounting for both costs and physical features of discharged waste streams, which can be used to inform follow-on analyses such as the feasibility of deep-borehole disposal of HLW. This paper presents an overview of the MARIE software capabilities, including how individual unit operations are implemented to enable a larger-scale optimization of a hypothetical reprocessing operation on aspects such as cost and recovery of valuable materials.

Skutnik, Steve [ORNL] (ORCID:000000016441135X)↗

Recent Progress in the Engineering of Polymeric Membranes for CO2 Capture from Flue Gas

CO2 capture from coal- or natural gas-derived flue gas has been widely considered as the next opportunity for the large-scale deployment of gas separation membranes. Despite the tremendous progress made in the synthesis of polymeric membranes with high CO2/N2 separation performance, only a few membrane technologies were advanced to the bench-scale study or above from a highly idealized laboratory setting. Therefore, the recent progress in polymeric membranes is reviewed in the perspectives of capture system energetics, process synthesis, membrane scale-up, modular fabrication, and field tests. These engineering considerations can provide a holistic approach to better guide membrane research and accelerate the commercialization of gas separation membranes for post-combustion carbon capture.

20 FOSSIL-FUELED POWER PLANTS↗

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

59 BASIC BIOLOGICAL SCIENCES↗

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗