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In addition to Joe McMann’s duties as project engineer and technical manager, he was a test subject for spacesuits, life support systems, and other systems under his purview for over 18 years. During this Spacesuit Knowledge Capture event, he shared his experiences as a test subject during the Gemini, Apollo, and Space Shuttle Programs, which include both comical and serious lessons learned.
An Autonomous Offshore Power System (AOPS) provides in-situ power, energy storage, real-time data and communications support, asset management, and other capabilities at sea. It has applications for all offshore industries: energy, defense and security, aquaculture, science and research, and communications. Furthermore, this paper highlights how an AOPS can reduce cost, complexity, and carbon-intensity for existing offshore operations and enable new capabilities for offshore industry leaders. This new AOPS technology has two primary advantages. First, it unlocks the autonomous, electric future of the ocean economy via ‘local’ power generation and energy storage, in addition to real-time connection to the data cloud. Second, it helps enable a material change in the global energy mix through cost-effective, reliable generation and storage technology for use cases including mobile/static data-gathering and reporting systems, operating equipment, and charging networks for uncrewed surface vessels. AOPS technology will help transform the ocean economy, and thus has implications for offshore industry leaders as they push to reduce costs today and make an autonomous and decarbonized future possible.
This paper describes the results of a study into the feasibility of identifying, robotically capturing, and returning an entire Near-Earth Asteroid (NEA) to the vicinity of the Earth by the middle of the next decade. The feasibility of such an asteroid retrieval mission hinges on finding an overlap between the smallest NEAs that could be reasonably discovered and characterized and the largest NEAs that could be captured and transported in a reasonable flight time. This overlap appears to be centered on NEAs roughly 7 m in diameter corresponding to masses in the range of 250,000 kg to 1,000,000 kg. The study concluded that it would be possible to return a approx.500,000-kg NEA to high lunar orbit by around 2025. The feasibility is enabled by three key developments: the ability to discover and characterize an adequate number of sufficiently small near-Earth asteroids for capture and return; the ability to implement sufficiently powerful solar electric propulsion systems to enable transportation of the captured NEA; and the proposed human presence in cislunar space in the 2020s enabling exploration and exploitation of the returned NEA. Placing a 500-t asteroid in high lunar orbit would provide a unique, meaningful, and affordable destination for astronaut crews in the next decade. This disruptive capability would have a positive impact on a wide range of the nation's human space exploration interests. It would provide a high-value target in cislunar space that would require a human presence to take full advantage of this new resource. It would offer an affordable path to providing operational experience with astronauts working around and with a NEA that could feed forward to much longer duration human missions to larger NEAs in deep space. It represents a new synergy between robotic and human missions in which robotic spacecraft would retrieve significant quantities of valuable resources for exploitation by astronaut crews to enable human exploration farther out into the solar system. The capture, transportation, examination, and dissection of an entire NEA would provide valuable information for planetary defense activities that may someday have to deflect a much larger near-Earth object. Transportation of the NEA to lunar orbit with a total flight time of 6 to 10 years would be enabled by a ~40-kW solar electric propulsion system with a specific impulse of 3,000 s. The flight system could be launched to low-Earth orbit (LEO) on a single Atlas V-class launch vehicle, and return to lunar orbit a NEA with at least 28 times the mass launched to LEO. Longer flight times, higher power SEP systems, or a target asteroid in a particularly favorable orbit could increase the mass amplification factor from 28-to-1 to 70-to-1 or greater. The NASA GRC COMPASS team estimated the full life-cycle cost of an asteroid capture and return mission at approx.$2.6B.
With the NASA SBIR/STTR Program, Tendeg, LLC, has advanced its parabolic antenna technology. NASA’s Surface Deformation and Change (SDC) study is interested in antenna technology that could be used to capture images of Earth’s surface for science research. In 2021, Tendeg received investor funds from SDC to continue developing SBIR-related work through the Phase II-Extended (II-E) option. Tendeg’s technology has supported NASA and commercial space missions; in total, Tendeg attributes $45 million in revenue as a result of SBIR-related developments.
Direct electrochemical upgrading of CO 2 in capture media is an attractive approach to carbon capture that can bypass the energy requirement for the thermal release of pure CO 2 . Here we investigate the electrocatalytic activity of iron(tetraphenylporphyrin) in the presence of organic solvents that convert into ionic liquids upon exposure to CO 2 . Four different solvent systems were tested, all of which capture CO 2 in the form of an alkyl carbonate (or carbamate) anion and an acidic ammonium cation. The electrocatalytic selectivity exhibited a strong dependence on the acidity of the capture medium, with the most basic solvent affording a high selectivity for production of CO instead of H 2 . Experimental and computational studies support a canonical mechanism in which the catalyst reacts with free CO 2 in solution, as opposed to a reaction with the alkyl carbonate that is present in high concentration. Kinetic analysis indicates that the rate-limiting step is changed from C–O protonolysis in traditional solvents to the binding of CO 2 in the capture media. Quantitative 13 C– 13 C EXSY revealed that the dissociation of the alkyl carbonate into free, solvated CO 2 is very rapid (~15 s –1 ) compared to the interconversion of HCO 3 – /CO 2 in aqueous solution. These results underscore the need to understand the mechanism and kinetics for both the release of captured CO 2 and its electrocatalytic conversion.
To support development of Martian Ascent Vehicles, analysis tools are needed to support the development of Guidance, Navigation, and Control requirements. This paper presents a focused approach to Navigation analysis to capture development of requirements on initial state knowledge and inertial sensor capabilities. A simulation and analysis framework was used to assess the capability of a range of sensors to operate inertially along a range of launch trajectories. The baseline Martian Ascent Vehicle was used as the input for optimizing a set of trajectories from each launch site. These trajectories were used to perform Monte Carlo analysis dispersing error sensor terms and their effects on integrated vehicle performance. Additionally, this paper provides insight into the use of optical navigation techniques to assess state determination and the potential to use observations of local extraplanetary bodies to estimate state. This paper provides an initial level of performance assessment of navigation components to support continued requirements development of a Martian Ascent Vehicle with applications to both crew and sample return missions.
Direct air capture (DAC) represents a vital technology for atmospheric CO₂ remediation, but few studies have tested catalysts at dilute atmospheric CO₂ concentrations. Inspired by the carbonic anhydrase metalloenzyme, we report a catalytic DAC strategy employing robust zinc(II) enzyme mimics that enable efficient CO₂ sequestration pathways. A catalyst-mediated CO₂ hydration cycle in aqueous sorbents facilitates accelerated capture from dilute atmospheric air, thereby addressing the kinetic limitations observed in carbonate-based systems. Our developed complexes [ZnC1] and [ZnC2] enhance capture rates up to two-fold at millimolar concentrations and improve CO₂ mass transfer by 40-60% in 1 M K₂CO₃ sorbent under ambient conditions. These bench-stable, earth-abundant zinc catalysts operate effectively under dilute CO₂ concentrations, overcoming the kinetic limitations of conventional carbonate-based sorbents. As a result, mechanistic studies support a biomimetic catalytic cycle that facilitates rapid CO₂ conversion, demonstrating that catalyst-assisted DAC can enable energy-efficient, scalable carbon capture technologies.
With the need to maintain long-term reliable energy using nuclear power plants, there is an underlying demand to ensure that the maintenance of plant components and systems is also done in an efficient and cost-effective manner. One way to achieve this is by moving from time-based maintenance to condition-based maintenance. The research presented in this paper focuses on applying statistical and machine-learning-based methods to capture anomalies within data for fault detection to further develop into condition monitoring. This paper focuses on system data for a circulating water system (CWS) of a pressurized heavy-water reactor for detecting anomalies. The different methodologies used for detecting and capturing anomalies in the CWS data are matrix profile, density-based spatial clustering of applications with noise (DBSCAN), and support vector machines (SVMs). Matrix profile and DBSCAN are used to distinguish between normal data and anomalous data. This paper presents a hybrid method using DBSCAN and SVM when a portion of the data is used for DBSCAN to generate clusters. This portion of data is then used to train the SVM along with the clusters generated by DBSCAN as output. SVM is then tested on unseen data as a predictive tool, which can work in real time to categorize data points as either normal or anomalous. This paper presents results that show the high accuracies of DBSCAN and SVM in capturing anomalies within the data for a CWS for fault detection. Thus, the maintenance plan would be focused on component condition rather than a time-based schedule by switching to an automated system to identify and predict faults within a CWS.
This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.
Li 2 CuO 2 was evaluated as a possible catalyst for the NO selective catalytic reduction (NO SCR) by CO. The initial idea was supported by ab initio thermodynamic analysis. This analysis showed that NO SCR was favored by using Li 2 CuO 2 as a catalyst, based on the calculated heat of reaction and Gibbs free energy. The NO SCR reaction was evaluated under dynamic, isothermal and cyclic conditions using different NO : CO ratios (2 : 1, 1 : 1 and 1 : 2). The dynamic and isothermal experiments showed that Li 2 CuO 2 does reduce NO in the presence of CO to reach higher conversions (>90%) between 400 and 450 °C. However, it must be mentioned that the catalytic process was reduced once some of the produced CO 2 was captured on the ceramic. At that point, Li 2 CuO 2 acts as a bifunctional material, catalyzing the NO SCR reaction and capturing some of the produced CO 2 . Moreover, cyclic experiments evidenced high NO reduction. Finally, a different set of experiments showed that this catalytic reduction performed differently if NO and CO were previously fed. Finally, when lithium cuprate is previously carbonated (initial CO feed), the catalytic reaction does not complete. In contrast, when there was a previous nitrate formation (initial NO feed), the reaction reached 100% efficiency.
The Cockpit Interface Control Document defines the hardware interfaces between the X-57 cockpit and subsystems. It provides locational and operational information in support of ground and flight operations with details on controls and displays that include Modes of Operation, Start-Up and Shut- Down Sequence diagrams and captures the current state of the MOD II Avionics Power Architecture. There is also preliminary information of the MOD III and MOD IV configurations. Microsoft PowerPoint was chosen for the document as early development required frequent meetings with multiple customers including aircraft operators (pilots), ground operations, support contractors and power, instrumentation, and human systems integration engineers and PowerPoint enabled presentations that could be quickly modified based on customer and developer interaction. One of the driving requirements for the cockpit design was to keep the left side panel as close the stock Tecnam panel as possible to reduce the failure risk of flight critical indicators. The original annunciator panel in the left side panel was modified to alert the pilot to failures in critical X-57 subsystems and an operator audio alert capability was added for these subsystems. Power-Up switches for the aircraft low voltage 13.8 VDC systems are located at the bottom of the left side panel and center panel, the same location as the stock Tecnam 13.8 VDC switches. The switches for energizing the high voltage system were located in the overhead panel to reduce the risk of inadvertently energizing the high voltage system during the low voltage power-up sequence. The Cruise Motor ARM switches were also located in the overhead panel and correspond to the same location as the stock Tecnam ignition switches. The stock Tecnam throttle levers and prop pitch levers were retained for the X-57. The throttle levers were renamed torque levers since they controlled the commanded torque to the cruise motors. The prop pitch levers provide a commanded RPM signal to an electronic prop pitch controller. X-57 specific displays, located in the right-side panel, are driven by dedicated sensors that monitor right and left side cruise motor RPM, right and left high voltage “Traction Bus” A and B (voltage, current and power) and the Avionics Bus DC converters (A and B) voltage and current. An X-57 Multi-Function Display (MFD) located in the center panel displays CAN Bus parameters. CAN Bus architecture is not certified for flight so these displays could not be used for safety critical information but were designed to be used for test point information only.
We report combined synchrotron X-ray nanotomography imaging, cryogenic electron microscopy (cryo-EM) and modeling elucidate how potassium (K) metal-support energetics influence electrodeposit microstructure. Three model supports are employed: O-functionalized carbon cloth (potassiophilic, fully-wetted), non-functionalized cloth and Cu foil (potassiophobic, nonwetted). Nanotomography and focused ion beam (cryo-FIB) cross-sections yield complementary three-dimensional (3D) maps of cycled electrodeposits. Electrodeposit on potassiophobic support is a triphasic sponge, with fibrous dendrites covered by solid electrolyte interphase (SEI) and interspersed with nanopores (sub-10 nm to 100 nm scale). Lage cracks and voids are also a key feature. On potassiophilic support, the deposit is dense and pore-free, with uniform surface and SEI morphology. Mesoscale modeling captures the critical role of substrate-metal interaction on K metal film nucleation and growth, as well as the associated stress state.
Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.
The use of heat pipes in safety-critical systems such as nuclear microreactors dictates the development of generalized, practical, scalable performance and safety parameters. Traditional dimensional metrics, while informative, lack the universality required for comparative analysis across varying designs and operating regimes. Here, this work introduces a comprehensive set of non-dimensional parameters to characterize heat pipe performance and safety, including capillary performance, effective thermal conductivity, response time, exergetic efficiency, allowable temperature gradients, allowable rate of temperature change, priming coefficients, and factor of safety. A reference heat pipe design representative of microreactor applications was analyzed via the developed parameters using both traditional analytical models and Sockeye simulations under transient and steady-state conditions. Sodium, potassium, and water were evaluated as working fluids to demonstrate the applicability of the framework across a broad temperature range. The proposed non-dimensional parameters effectively captured key thermal-hydraulic behaviors and safety concerns, as was demonstrated via Sockeye simulations. This framework supports the development of design optimization strategies, operational protocols, and safety assurance practices for advanced reactor systems and other high-reliability applications.
The ongoing CCUS commercial projects are highly relied on the support of government incentives due to massive capital investment. Here, this study analyzes the economics of carbon capture utilization and sequestration (CCUS) projects, shows a state-wide CCUS deployment exercise, followed by simulation results of enhanced oil recovery (EOR) based CO 2 storage in unconventional reservoirs. The comprehensive economic analysis of capture, transportation, sequestration costs, enhanced 45Q tax credits, and EOR revenue implies the practicality of CO 2 -EOR to offset the high CCUS costs. With the economics understanding, we study the top CO 2 sources, existing CO 2 pipelines, and sequestration sinks in the state of Colorado, USA. This paper next presents results from EOR simulation in one section of the unconventional Denver-Julesburg (DJ) Basin Niobrara and Codell reservoirs. The simulation model is based on a geological static model, incorporated with hydraulic fracture stimulation, history matched to production, and calibrated to the microseismic and time-lapse surface seismic data. The CO 2 -EOR simulation results show that oil production can be increased and more CO 2 stored with: a longer primary production period; the presence of a shut-in period; higher injection rates; and multi-well injectors. The modeling results show that about 7–10 Mscf of CO 2 will be stored when recovering 1 stb of EOR oil. By adding the enhanced oil revenue and the carbon credits together, it is estimated that the most economic case can generate $\$13$ MM when oil price is assumed to be $\$80$/stb, and the EOR oil revenue is 3.4 times greater than that generated from 45Q incentives. It corresponds to the scenario that a five-year primary production is followed by CO 2 injection into four wells with the sequence of injection (4 MMscf/day for 6 months), shut-in (6 months) and production (12 months). The best practices in this study will provide valuable insights for similar CCUS projects in other unconventional fields. Furthermore, this study defines a term named “Carbon Neutrality Index (CNI)” by comparing the amount of CO 2 stored with that burned by EOR oil. The CNI value of 0 indicates the enhanced oil is carbon neutral; a negative CNI value implies there is a net reduction in carbon emission. The 4-year huff-n-puff (HnP) simulation leads to a positive CNI value, indicating that the EOR oil generated in this process is not carbon neutral yet.
Within the context of Ni photoredox catalysis, halogen atom photoelimination from Ni has emerged as a fruitful strategy for enabling hydrogen atom transfer (HAT)-mediated C(sp 3 )–H functionalization. Despite the numerous synthetic transformations invoking this paradigm, a unified mechanistic hypothesis that is consistent with experimental findings on the catalytic systems and accounts for halogen radical formation and facile C(sp 2 )–C(sp 3 ) bond formation remains elusive. We employ kinetic analysis, organometallic synthesis, and computational investigations to decipher the mechanism of a prototypical Ni-catalyzed photochemical C(sp 3 )–H arylation reaction. Our findings revise the previous mechanistic proposals, first by examining the relevance of SET and EnT processes from Ni intermediates relevant to the HAT-based arylation reaction. Our investigation highlights the ability for blue light to promote efficient Ni–C(sp 2 ) bond homolysis from cationic Ni III and C(sp 2 )–C(sp 3 ) reductive elimination from bipyridine Ni II complexes. However interesting, the rates and selectivities of these processes do not account for the productive catalytic pathway. Instead, our studies support a mechanism that involves halogen atom evolution from in situ generated Ni II dihalide intermediates, radical capture by a Ni II (aryl)(halide) resting state, and key C–C bond formation from Ni III . Oxidative addition to Ni I , as opposed to Ni 0 , and rapid Ni III /Ni I comproportionation play key roles in this process. Furthermore, the findings presented herein offer fundamental insight into the reactivity of Ni in the broader context of catalysis.
Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org
At the 2024 FECM/NETL Carbon Management Research Project Review Meeting, within the Carbon Transport and Storage Breakout Session 3, the presentation "Dynamic CCS-EJ-SJ Database and Web Application - What's New" highlights the critical tool designed to integrate environmental and social justice considerations into Carbon Capture and Storage (CCS) projects. Key features include an interactive dashboard for data access and visualization, which supports stakeholders in making informed decisions regarding CCS implementation, and updated data layers. The latest version enhances data integration and usability, providing a comprehensive resource for assessing the social and environmental impacts of CCS projects. There are 7 categories in the CCS EJSJ v2 database (released 03/31/2024): environmental justice, energy justice, economic justice, social justice, ecosystem assets, clean energy, and infrastructure. Most of the layers within each category have been updated in this version. As compared to the old database, there are 3 new categories in the v2 database: ecosystem assets, clean energy, and infrastructure.