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At least 955 records · Page 53

Volumetrically Absorbing Thermal Insulator (VATI) for High-temperature Receivers

This project seeks to exploit volumetric absorption of concentrated solar irradiation in a thin, high-temperature, thermally conductive medium. High efficiency thermal conversion is achieved by volumetric absorption of both incoming solar irradiation (short wavelength) and emitted irradiation (long wavelength), demonstrating a volumetrically absorbing thermally insulating (VATI) effect. The thermalized energy is conducted to the back wall, enclosing the working fluid (molten salt or supercritical CO 2 ). Reliance on conductive thermal transport requires a thermally conductive medium, in the absence of which large temperature gradients drive losses due to emission. The project’s outcomes are important to realize a cost-effective approach to reduce optical and thermal losses from CSP receivers at high temperatures (720°C). High-temperature stable and commercially available porous SiC structures (open-cell foams) were explored as a volumetrically absorbing and thermally insulating layer. To the best of our knowledge, the current state-of-the-art for industrially deployed coatings is Pyromark. However, Pyromark suffers degradation at 700+°C and diurnal temperature changes. This necessitates periodic recoating, and the downtime increases the overall levelized cost of energy (LCOE) production. On the other hand, contemporary research activities have generated significant advances in the development of selective emitter coatings, which present challenges with costs, scalability, and stability. The pursued approach alleviates these concerns by developing a receiver that utilizes the inherent structure of high-temperature stable porous materials to enable robust and cost-effective receivers which require no periodic maintenance downtimes. The overall goal of the project is to experimentally demonstrate a figure of merit (FOM) of 0.92 at a temperature of 720°C and solar irradiation of 1000x concentration with porous receivers. The relevant crystallographic (phase) optical and thermal properties of porous SiC were first characterized. Second, the 3-D geometry of the porous SiC was analyzed using micro-X-ray tomography and converted to CAD data using image processing analysis. Utilizing this 3-D geometry and relevant optical/thermal properties, Monte Carlo- Ray Tracing (MCRT) analysis was performed to extract important parameters governing solar-thermal energy conversion such as extinction coefficient (β, 1/m), scattering albedo (ω) and the scattering phase function (Φ). These properties were then integrated into an in-house radiative and conduction transport model to solve for temperature and transport fluxes characterizing the solar-thermal energy conversion. This model was utilized to predict the FOM for various SiC porous geometries and to identify the highest possible FOM. Testing of the optimized porous structures will be accomplished with a custom-built high-accuracy (< ±4%) FOM measurement test-stand and a 1000x solar concentrator. When neglecting convective losses and resistance at the open boundary and the back wall, the optimized SiC foam leads to a FOM of 0.84. This FOM does not surpass the performance of Pyromark 2500. Yet, conversely to Pyromark 2500, SiC is stable at high temperatures and does not degrade over time. Also, it is possible to boost the FOM of SiC by engineering its effective thermal conductivity and its scattering albedo. A FOM of 0.92 is predicted for an optimized foam by accounting for convective losses and resistance at the open boundary and the back wall. This largely exceeds the FOM of Pyromark 2500 (~0.86) predicted by neglecting convective losses and resistance. Therefore, further engineering of the foam could lead to unprecedented FOMs.

14 SOLAR ENERGY↗

Method for designing a combustion system with reduced environmentally-harmful emissions

A method for designing a combustion system which emits less of at least one environmentally-harmful emission is presented. In a describing step, an injector which introduces a fuel into a combustion chamber is described via a CFD code. In a modeling step, combustion kinetics of the fuel are modeled via a pre-processing code as the fuel mixes and reacts with an oxidizer. In a first selecting step, at least one primary scalar is derived during the modeling of the combustion kinetics. In a performing step, a table look-up is performed to obtain at least one data from a look-up database based on the primary scalar. In a second selecting step, at least one secondary scalar is selected in addition to the primary scalar(s). In a specifying step, at least one chemical pathway of formation or destruction for the secondary scalar is specified via a chemistry manager wherein the secondary scalar is representative of the environmentally-harmful emission(s) of the chemical pathway(s). In a utilizing step, the data is utilized to evaluate the chemical pathway(s) to quantify the environmentally-harmful emission(s). In an identifying step, an improvement to the combustion system is identified which reduces the environmentally-harmful emission(s).

Zambon, Andrea C.↗

Using a Portfolio of Algorithms for Planning and Scheduling

The Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles, includes a subsystem that utilizes a portfolio of heuristic algorithms that work synergistically to solve problems. The nature of the synergy of the specific algorithms is that their likelihoods of success are negatively correlated: that is, when a combination of them is used to solve a problem, the probability that at least one of them will succeed is greater than the sum of probabilities of success of the individual algorithms operating independently of each other. In ASPEN, the portfolio of algorithms is used in a planning process of the iterative repair type, in which conflicts are detected and addressed one at a time until either no conflicts exist or a user-defined time limit has been exceeded. At each choice point (e.g., selection of conflict; selection of method of resolution of conflict; or choice of move, addition, or deletion) ASPEN makes a stochastic choice of a combination of algorithms from the portfolio. This approach makes it possible for the search to escape from looping and from solutions that are locally but not globally optimum.

Sherwood, Robert↗

A Novel Ion Exchange System to Purify Mixed ISS Waste Water Brines for Chemical Production and Enhanced Water Recovery

Current International Space Station water recovery regimes produce a sizable portion of waste water brine. This brine is highly toxic and water recovery is poor: a highly wasteful proposition. With new biological techniques that do not require waste water chemical pretreatment, the resulting brine would be chromium-free and nitrate rich which can allow possible fertilizer recovery for future plant systems. Using a system of ion exchange resins we can remove hardness, sulfate, phosphate and nitrate from these brines to leave only sodium and potassium chloride. At this point modern chlor-alkali cells can be utilized to produce a low salt stream as well as an acid and base stream. The first stream can be used to gain higher water recovery through recycle to the water separation stage while the last two streams can be used to regenerate the ion exchange beds used here, as well as other ion exchange beds in the ISS. Conveniently these waste products from ion exchange regeneration would be suitable as plant fertilizer. In this report we go over the performance of state of the art resins designed for high selectivity of target ions under brine conditions. Using ersatz ISS waste water we can evaluate the performance of specific resins and calculate mass balances to determine resin effectiveness and process viability. If this system is feasible then we will be one step closer to closed loop environmental control and life support systems (ECLSS) for current or future applications.

brine↗

A Novel Ion Exchange System to Purify Mixed ISS Waste Water Brines for Chemical Production and Enhanced Water Recovery

Current International Space Station water recovery regimes produce a sizable portion of waste water brine. This brine is highly toxic and water recovery is poor: a highly wasteful proposition. With new biological techniques that do not require waste water chemical pretreatment, the resulting brine would be chromium-free and nitrate rich which can allow possible fertilizer recovery for future plant systems. Using a system of ion exchange resins we can remove hardness, sulfate, phosphate and nitrate from these brines to leave only sodium and potassium chloride. At this point modern chlor-alkali cells can be utilized to produce a low salt stream as well as an acid and base stream. The first stream can be used to gain higher water recovery through recycle to the water separation stage while the last two streams can be used to regenerate the ion exchange beds used here, as well as other ion exchange beds in the ISS. Conveniently these waste products from ion exchange regeneration would be suitable as plant fertilizer. In this report we go over the performance of state of the art resins designed for high selectivity of target ions under brine conditions. Using ersatz ISS waste water we can evaluate the performance of specific resins and calculate mass balances to determine resin effectiveness and process viability. If this system is feasible then we will be one step closer to closed loop environmental control and life support systems (ECLSS) for current or future applications.

Water Brines↗

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

Mercury ion thruster technology

The Mercury Ion Thruster Technology program was an investigation for improving the understanding of state-of-the-art mercury ion thrusters. Emphasis was placed on optimizing the performance and simplifying the design of the 30 cm diameter ring-cusp discharge chamber. Thruster performance was improved considerably; the baseline beam-ion production cost of the optimized configuration was reduced to Epsilon (sub i) perspective to 130 eV/ion. At a discharge propellant-utilization efficiency of 95 percent, the beam-ion production cost was reduced to about 155 eV/ion, representing a reduction of about 40 eV/ion over the corresponding value for the 30 cm diameter J-series thruster. Comprehensive Langmuir-probe surveys were obtained and compared with similar measurements for a J-series thruster. A successful volume-averaging scheme was developed to correlate thruster performance with the dominant plasma processes that prevail in the two thruster designs. The average Maxwellian electron temperature in the optimized ring-cusp design is as much as 1 eV higher than it is in the J-series thruster. Advances in ion-extraction electrode fabrication technology were made by improving materials selection criteria, hydroforming and stress-relieving tooling, and fabrications procedures. An ion-extraction performance study was conducted to assess the effect of screen aperture size on ion-optics performance and to verify the effectiveness of a beam-vectoring model for three-grid ion optics. An assessment of the technology readiness of the J-series thruster was completed, and operation of an 8 cm IAPS thruster using a simplified power processor was demonstrated.

Beattie, J. R.↗

Mars Mission Optimization Based on Collocation of Resources

This paper presents a powerful approach for analyzing Martian data and for optimizing mission site selection based on resource collocation. This approach is implemented in a program called PROMT (Planetary Resource Optimization and Mapping Tool), which provides a wide range of analysis and display functions that can be applied to raw data or imagery. Thresholds, contours, custom algorithms, and graphical editing are some of the various methods that can be used to process data. Output maps can be created to identify surface regions on Mars that meet any specific criteria. The use of this tool for analyzing data, generating maps, and collocating features is demonstrated using data from the Mars Global Surveyor and the Odyssey spacecraft. The overall mission design objective is to maximize a combination of scientific return and self-sufficiency based on utilization of local materials. Landing site optimization involves maximizing accessibility to collocated science and resource features within a given mission radius. Mission types are categorized according to duration, energy resources, and in-situ resource utilization. Optimization results are shown for a number of mission scenarios.

G E Chamitoff↗

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗

Analysis of Various Polysulfone-nanodiamond Mixed-Matrix Membranes

Historically, coal combustion systems have contributed to the extensive network of energy production across the world. Oxycombustion has been shown to be more energy efficient and can facilitate carbon capture from fossil energy utilization. The challenge for small modular deployment of coal combustion or gasification systems is that current O2 enrichment technologies do not scale downward cost-effectively. Fortunately, membrane processes have been developed to offer a more viable polymer-based technology for this application. The central focus is to deliver a high-performance polymer membrane capable of enriching O2 to 90-95% in oxycombustion systems for small modular coal-based power or gasification plants. To accomplish this, various properties of polysulfone-nanodiamond mixed-matrix membrane are examined to determine the appropriate conditions for applicable use. The polymers are infused with nanoparticles to increase the mechanical functionality of the structure which in turn alters the permeability and selectivity of the membrane. Furthermore, the thermal history can also impact the functionality and characteristics of these polymer systems. This study primarily discusses the structure and function of these assorted membranes in terms of permeability and thermomechanical characteristics.

01 COAL, LIGNITE, AND PEAT↗

Practical Considerations for Understanding Surface Reaction Mechanisms Involved in Heterogeneous Catalysis

Acquiring useful knowledge about the active site(s) of a catalyst, nature of reactant–catalyst interactions, nature of reactive intermediates, rate-determining step, reaction rate orders that affect various process parameters, and reaction mechanism as a whole is exceedingly challenging. This is especially true in the case of heterogeneous catalysts due to the complexity of the nature of surface active sites and their nonstatic behavior. Here, we present our perspective on differentiating between various surface reaction mechanisms in light of pioneering studies by leaders in the field, with the aim of clarifying some of the confusion associated with these complex mechanisms, especially the Eley–Rideal mechanism. Using bibliometric analysis, we identify and discuss the following four reactions that most commonly invoke the Eley– Rideal mechanism: H 2 activation, CO oxidation, esterification of alcohols by acids, and selective catalytic reduction (SCR) of NO x with NH 3 . Our analysis of studies utilizing well-suited experimental and computational methodologies for differentiating surface reaction mechanisms suggests that the above-mentioned four reactions do not occur via the Eley–Rideal mechanism. Instead, each reaction occurs via the Langmuir–Hinshelwood mechanism with nonidealities present. Lastly, we highlight practical considerations regarding select experimental (characterization methods and differential kinetics) and computational modeling that we believe can provide useful insights to accurately discern between the various possible reaction mechanisms in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Recommendations to Improve the Nuclear Regulatory Commission Reactor Licensing and Approval Process

Due to the urgency around climate change and associated goals for clean energy transition, and Battelle Energy Alliance's (BEA) and Idaho National Laboratory's (INL) role as the nation's nuclear energy laboratory, numerous stakeholders have asked for BEA’s thoughts and recommendations to reduce the time and costs associated with licensing new reactors at the U.S. Nuclear Regulatory Commission (NRC). As an M&O contractor for an FFRDC, BEA is a long-term partner with the Government in seeking to achieve clean energy goals, yet has a level of independence needed to appropriately evaluate this topic. The views herein are informed by extensive BEA experience supporting nuclear energy endeavors including ongoing discussions with current and former regulators, applicants, and licensees. With this background in mind, the United States benefits from having an agency such as the NRC, which is viewed internationally as the leader in nuclear safety licensing and regulation. Nonetheless, while acknowledging the important nuclear safety role satisfied by the NRC, it is apparent that one of the most significant time and resource intensive activities for new reactor developers is the NRC licensing process. The time and cost to obtain NRC licenses add significant financial stress for new reactor projects and may result in abandonment of projects or failure to even begin new projects. The challenge is particularly acute for advanced reactors which may raise unique or new regulatory questions and may be smaller in size, resulting in a much higher proportional impact from regulatory and cost challenges. This situation presents a particularly troublesome risk for the nation given the urgency in which utilities are working to transition to clean, non-carbon-emitting energy sources like nuclear energy. Reforms to the NRC licensing process have the potential to greatly increase certainty and support the successful progress of new reactors. The NRC can retain its world-class nuclear safety reputation while becoming a world leader for regulatory efficiency and a critical enabler to the clean energy transition. This report describes potential NRC reforms, focusing on those with a statutory connection. Recognizing the potential tradeoffs with any proposed changes, the report attempts to highlight those considerations in the analysis of the reforms. The recommendations are presented as a set of options for consideration. Unless noted, they are independent options, offering stakeholders the option to select a subset for further consideration. Although difficult to calculate precise time improvements for some of the changes, the reforms have the potential for substantial improvements, perhaps even by a factor of two.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The Environment for Application Software Integration and Execution (EASIE), version 1.0. Volume 3: Program execution guide

The Environment for Application Software Integration and Execution, EASIE, provides a methodology and a set of software utility programs to ease the task of coordinating engineering design and analysis codes. EASIE was designed to meet the needs of conceptual design engineers that face the task of integrating the results of many stand-alone engineering analysis programs. EASIE provides access to these programs via a quick, uniform, user-friendly interface. In addition, EASIE provides utilities which aid in the execution of the following tasks: selection of application programs, modification and review of program data, automatic definition and coordination of data files during program execution and a logging of steps executed throughout a design study. Volume 3, the Program Execution Guide, describes the executive capabilities provided by EASIE and defines the command language and menus available under Version 1.0. EASIE provides users with two basic modes of operation. One is the Application-Derived Executive (ADE) which provides users with sufficient guidance to quickly review data, select menu action items, and execute application programs. The second is the Complete Control Executive (CCE), which provides a full executive interface allowing users in-depth control of the design process.

Schwing, James L.↗

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗

Novel method of recycling perovskite solar cells using iodide solutions

A novel recycling method utilizing aqueous iodide solutions has shown promise at selectively dissolving lead perovskite and preserving the glass substrate of lead perovskite solar cells (PSCs). Perovskite compounds are notorious for degrading upon contact with moisture into lead iodide which can be capitalized upon for recycling. When iodide ions reach a critical concentration in aqueous solutions, the solubility of lead iodide begins to rapidly increase as several complex ions are formed. Iodide solutions selectively interact with the perovskite material which dissolves from the edge inwards, allowing for exfoliation of the back contact and retention of the glass substrate and electron transport layer. PbI 2 solubility studies using KI, HI, and NH 4 I were conducted to determine an effective iodide source to develop a scalable recycling process. Further, retaining the coated glass substrate intact preserves the possibility to manufacture new solar cells from the same materials, by also omitting the use of organic solvents the costs as well as the human and environmental hazards of recycling PSCs can be minimized.

14 SOLAR ENERGY↗

Hybrid vision activities at NASA Johnson Space Center

NASA's Johnson Space Center in Houston, Texas, is active in several aspects of hybrid image processing. (The term hybrid image processing refers to a system that combines digital and photonic processing). The major thrusts are autonomous space operations such as planetary landing, servicing, and rendezvous and docking. By processing images in non-Cartesian geometries to achieve shift invariance to canonical distortions, researchers use certain aspects of the human visual system for machine vision. That technology flow is bidirectional; researchers are investigating the possible utility of video-rate coordinate transformations for human low-vision patients. Man-in-the-loop teleoperations are also supported by the use of video-rate image-coordinate transformations, as researchers plan to use bandwidth compression tailored to the varying spatial acuity of the human operator. Technological elements being developed in the program include upgraded spatial light modulators, real-time coordinate transformations in video imagery, synthetic filters that robustly allow estimation of object pose parameters, convolutionally blurred filters that have continuously selectable invariance to such image changes as magnification and rotation, and optimization of optical correlation done with spatial light modulators that have limited range and couple both phase and amplitude in their response.

Juday, Richard D.↗

Unsupervised Resource Allocation with Graph Neural Networks

We present an approach for maximizing a global utility function by learning how to allocate resources in an unsupervised way. We expect interactions between allocation targets to be important and therefore propose to learn the reward structure for near-optimal allocation policies with a GNN. By relaxing the resource constraint, we can employ gradient-based optimization in contrast to more standard evolutionary algorithms. Our algorithm is motivated by a problem in modern astronomy, where one needs to select-based on limited initial information-among $10^9$ galaxies those whose detailed measurement will lead to optimal inference of the composition of the universe. Our technique presents a way of flexibly learning an allocation strategy by only requiring forward simulators for the physics of interest and the measurement process. We anticipate that our technique will also find applications in a range of resource allocation problems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗