Search NASA⌕ Search

SEARCH · Search NASA

Results for “Transfer Learning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

WWAO-WSWC Workshop Report 2019 Final

EXECUTIVE SUMMARY The Western States Water Council (WSWC) and the NASA Western Water Applications Office (WWAO) hosted a joint workshop on technology transfer for water management in the Western U.S. The goals of the workshop were to understand how different agencies approach the technology transfer and research to operations (R2O) process, identify best practices, and discuss existing barriers to successful technology infusion into operational water resource management systems at the state and federal level. The workshop took place August 7-9, 2019 in Irvine, CA. Key outcomes of the meeting include the following:• U.S. Rep. Grace Napolitano provided opening remarks for the workshop, where she highlighted the critical value of water data and the importance of collaboration between state and federal agencies in working to advance the use of water data in water management, planning and policy. • A total of 33 participants (including remote participants) were part of the workshop. They included principal investigators and project teams supported by NASA (Cyanobacteria Assessment Network, Evapotranspiration for Western States, Evaporative Stress Index, the Airborne Snow Observatory, Satellite-based Snow Water Equivalent in the Sierra Nevadas, and Fallowed Area Mapping) as well as representatives from federal (USGS, NOAA, USBR, EPA) and state (CA, WY, OR, NE) agency partners. • One main outcome of the meeting was the consensus that successful transitions of new applications and new technologies into operations require careful planning, effective communication within and across institutions, resources and considerable time investments. In addition, there was broad agreement that significant lead time is often required to allow for identification of financial and technical resources to sustain operational use of new data, information and tools.• The meeting included remarks from U.S. Rep. Napolitano and discussions during presentations and breakout groups about key opportunities to develop best practices and streamline the technology transfer process. • For example, one key set of best practices that emerged revolved around the the importance of building trust and establishing clear lines of communication between the research and operational institutions. The conversations led to defining two key components of trust-building. The first aspect is purely technical. It requires effectively demonstrating that the proposed application meets the end user’s needs in terms of accuracy, format, resolution, latency, metadata and documentation. The second aspect of building trust involves developing sustained, productive and mutually-beneficial relationships with the partner operational agency. The best practices presented here span both the technical as well as the relational aspects of cultivating trust. • This workshop served as a first step in developing a broader community discussion around R2O in western water management. Many of the best practices and lessons learned described in this report represent starting places for action within the WWAO, WSWC and our colleagues’ institutions. • Effective implementation of the best practices that emerged from this workshop will require sustained investments of time, resources and transition planning. In recognition of this, the WSWC and the WWAO proposed continuation of discussions begun at the workshop through a series of semi-annual or annual workshops.

Wilardson, Tony↗
NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗
NASA NTRS2017

Development and Hot-fire Testing of Additively Manufactured Copper Combustion Chambers for Liquid Rocket Engine Applications

NASA and industry partners are working towards fabrication process development to reduce costs and schedules associated with manufacturing liquid rocket engine components with the goal of reducing overall mission costs. One such technique being evaluated is powder-bed fusion or selective laser melting (SLM), commonly referred to as additive manufacturing (AM). The NASA Low Cost Upper Stage Propulsion (LCUSP) program was designed to develop processes and material characterization for GRCop-84 (a NASA Glenn Research Center-developed copper, chrome, niobium alloy) commensurate with powder-bed AM, evaluate bimetallic deposition, and complete testing of a full scale combustion chamber. As part of this development, the process has been transferred to industry partners to enable a long-term supply chain of monolithic copper combustion chambers. To advance the processes further and allow for optimization with multiple materials, NASA is also investigating the feasibility of bimetallic AM chambers. In addition to the LCUSP program, NASA has completed a series of development programs and hot-fire tests to demonstrate SLM GRCop-84 and other AM techniques. NASA's efforts include a 4K lbf thrust liquid oxygen/methane (LOX/CH4) combustion chamber and subscale thrust chambers for 1.2K lbf LOX/hydrogen (H2) applications that have been designed and fabricated with SLM GRCop-84. The same technologies for these lower thrust applications are being applied to 25-35K lbf main combustion chamber (MCC) designs. This paper describes the design, development, manufacturing and testing of these numerous combustion chambers, and the associated lessons learned throughout their design and development processes.

Gradl, Paul R.↗
DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗
NASA NTRS2003

Spinoff 2003: 100 Years of Powered Flight

Today, NASA continues to reach milestones in space exploration with the Hubble Telescope, Earth-observing systems, the Space Shuttle, the Stardust spacecraft, the Chandra X-Ray Observatory, the International Space Station, the Mars rovers, and experimental research aircraft these are only a few of the many initiatives that have grown out of NASA engineering know-how to drive the Agency s missions. The technical expertise gained from these programs has transferred into partnerships with academia, industry, and other Federal agencies, ensuring America stays capable and competitive. With Spinoff 2003, we once again highlight the many partnerships with U.S. companies that are fulfilling the 1958 Space Act stipulation that NASA s vast body of scientific and technical knowledge also benefit mankind. This year's issue showcases innovations such as the cochlear implant in health and medicine, a cockpit weather system in transportation, and a smoke mask benefiting public safety; many other products are featured in these disciplines, as well as in the additional fields of consumer/home/recreation, environment and resources management, computer technology, and industrial productivity/ manufactacturing technology. Also in this issue, we devote an entire section to NASA s history in the field of flight and showcase NASA s newest enterprise dedicated to education. The Education Enterprise will provide unique teaching and learning experiences for students and teachers at all levels in science, technology, engineering, and mathematics. The Agency also is committed, as never before, to engaging parents and families through NASA s educational resources, content, and opportunities. NASA s catalyst to intensify its focus on teaching and learning springs from our mission statement: to inspire the next generation of explorers as only NASA can.

Source record↗
NASA NTRSDate not supplied

Ground Test Stand Development for One-step Electrochemical Production of High-Pressure Oxygen

NASA designed a test rig, High-Pressure Oxygen Generation Assembly (HPOGA) to test a novel method of electrolysis as an alternative technology to the state-of-the-art Oxygen Generation Assembly (OGA) currently on the International Space Station (ISS). This approach involves utilizing a specialized electrolysis cell stack designed to both generate and compress high-pressure oxygen up to 3600 PSIG. The design eliminates the need for an external compressor and hydrogen separator, streamlining the oxygen generation process. HPOGA tests the viability of an electrolysis cell stack for extended life usage. Within the system, there are several key subsystems: nitrogen, high/low pressure oxygen, hydrogen, water recirculation, and water injection. The water injection subsystem supplies deionized (DI) water to a centralized reservoir for consumption. Subsequently, a water recirculation loop transfers the DI water across the cathode side of the electrolysis cell stack with constant flow rate. The result is a supply of hydrogen in the water loop and oxygen accumulation on the anode side of the cell stack. Hydrogen is evaporated out as part of the hydrogen sub-system which also measures hydrogen/oxygen concentration for safety. The oxygen subsystem is responsible for managing high pressure oxygen, O2 exhaust, and conducting hydrogen/oxygen concentration measurements. The nitrogen subsystem serves dual functions, purging the hydrogen/oxygen subsystems, and regulating pressure in both high/low-pressure O2 sections via pneumatic back pressure regulators. Various sensors monitor pressure, temperature, humidity, gas concentration and flow rate in each subsystem. This paper presents the design and testing of a comprehensive test stand, which serves to operate and functionally verify the performance of the electrolysis cell while offering valuable lessons learned.

electrolysis↗
DOE OSTI2026

Operational Evolution of FTS3: A DevOps Driven Approach to Elastic Operations

The File Transfer Service (FTS3) is a distributed data movement service developed at CERN and widely used to transfer data across the Worldwide LHC Computing Grid (WLCG). At Fermilab, FTS3 supports data transfers for multiple experiments, including Intensity Frontier experiments such as DUNE, enabling reliable data movement between WebDAV endpoints in Europe and the Americas.​ At CHEP 2021, we reported on the initial containerized deployment of FTS3 on OKD, the community Kubernetes distribution of Red Hat OpenShift. In this work, we present the subsequent evolution of this deployment, focusing on new operational capabilities introduced to improve scalability, robustness, and long-term maintainability.​ We describe the adoption of more secure and reproducible container build workflows, the integration of DevOps-driven operational practices, and enhancements in monitoring and automation. A key new result is the introduction of horizontal scaling and elastic resource management, allowing FTS3 components to dynamically adapt to workload variations while maintaining service reliability. We also discuss improvements in fault tolerance and operational procedures derived from production experience.​ Finally, we summarize lessons learned from operating FTS3 as a Kubernetes-native service and outline how these developments have improved the resilience and efficiency of data movement operations at Fermilab.

Munoz Flores, Victor Leopoldo [Fermilab]↗
NASA NTRS2000

A Computer Learning Center for Environmental Sciences

In the fall of 1998, MacMillan Hall opened at Brown University to students. In MacMillan Hall was the new Computer Learning Center, since named the EarthLab which was outfitted with high-end workstations and peripherals primarily focused on the use of remotely sensed and other spatial data in the environmental sciences. The NASA grant we received as part of the "Centers of Excellence in Applications of Remote Sensing to Regional and Global Integrated Environmental Assessments" was the primary source of funds to outfit this learning and research center. Since opening, we have expanded the range of learning and research opportunities and integrated a cross-campus network of disciplines who have come together to learn and use spatial data of all kinds. The EarthLab also forms a core of undergraduate, graduate, and faculty research on environmental problems that draw upon the unique perspective of remotely sensed data. Over the last two years, the Earthlab has been a center for research on the environmental impact of water resource use in and regions, impact of the green revolution on forest cover in India, the design of forest preserves in Vietnam, and detailed assessments of the utility of thermal and hyperspectral data for water quality analysis. It has also been used extensively for local environmental activities, in particular studies on the impact of lead on the health of urban children in Rhode Island. Finally, the EarthLab has also served as a key educational and analysis center for activities related to the Brown University Affiliated Research Center that is devoted to transferring university research to the private sector.

Mustard, John F.↗
DOE OSTI2024

Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images

Machine learning models are known to be vulnerable to adversarial attacks, but prior works have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, these multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions.

Daw, Arka [ORNL] (ORCID:0009000633191271)↗
DOE OSTI2025

Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
NASA NTRS2016

Design, Development and Hotfire Testing of Monolithic Copper and Bimetallic Additively Manufactured Combustion Chambers

NASA and industry partners are working towards fabrication process development to reduce costs and schedules associated with manufacturing liquid rocket engine components with the goal of reducing overall mission costs. One such technique being evaluated is powder-bed fusion or selective laser melting (SLM) otherwise commonly referred to as additive manufacturing. The NASA Low Cost Upper Stage Propulsion (LCUSP) program was designed to develop processes and material characterization for the GRCop-84 copper-alloy commensurate with powder bed additive manufacturing, evaluate bimetallic deposition and complete testing of a full scale combustion chamber. As part of this development, the process has been transferred to industry partners to enable a long-term supply chain of monolithic copper combustion chambers. As a direct spin off of this program, NASA is working with industry partners to further develop the printing process for the GRCop-84 material in addition to the C-18150 (CuCrZr) material. To advance the process further and allow for optimization with multiple materials, NASA is also investigating the feasibility of bimetallic additively manufactured chambers. A 1.2k sized thrust-chamber was designed and developed to compare the printing process of the GRCop-84 and C-18150 SLM materials. A series of similar MCC liners also completed development with an Inconel 625 jacket bonded to the GRcop-84 liner evaluating direct metal deposition (DMD) laser and arc-based techniques. This paper describes the design, development, manufacturing and testing of these combustion chambers and associated lessons learned throughout the design and development process.

Gradl, Paul↗
NASA NTRS1999

Using the World Wide Web for GIDEP Problem Data Processing at Marshall Space Flight Center

Since April 1997, Marshall Space Flight Center has been using electronic transfer and the web to support our processing of the Government-Industry Data Exchange Program (GIDEP) and NASA ALERT information. Specific aspects include: (1) Extraction of ASCII text information from GIDEP for loading into Word documents for e-mail to ALERT actionees; (2) Downloading of GIDEP form image formats in Adobe Acrobat (.pdf) for internal storage display on the MSFC ALERT web page; (3) Linkage of stored GRDEP problem forms with summary information for access from the MSFC ALERT Distribution Summary Chart or from an html table of released MSFC ALERTs (4) Archival of historic ALERTs for reference by GIDEP ID, MSFC ID, or MSFC release date; (5) On-line tracking of ALERT response status using a Microsoft Access database and the web (6) On-line response to ALERTs from MSFC actionees through interactive web forms. The technique, benefits, effort, coordination, and lessons learned for each aspect are covered herein.

McPherson, John W.↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
NASA NTRS1991

Spacecraft rendezvous operational considerations affecting vehicle systems design and configuration

One lesson learned from Orbiting Maneuvering Vehicle (OMV) program experience is that Design Reference Missions must include an appropriate balance of operations and performance inputs to effectively drive vehicle systems design and configuration. Rendezvous trajectory design is based on vehicle characteristics (e.g., mass, propellant tank size, and mission duration capability) and operational requirements, which have evolved through the Gemini, Apollo, and STS programs. Operational constraints affecting the rendezvous final approach are summarized. The two major objectives of operational rendezvous design are vehicle/crew safety and mission success. Operational requirements on the final approach which support these objectives include: tracking/targeting/communications; trajectory dispersion and navigation uncertainty handling; contingency protection; favorable sunlight conditions; acceptable relative state for proximity operations handover; and compliance with target vehicle constraints. A discussion of the ways each of these requirements may constrain the rendezvous trajectory follows. Although the constraints discussed apply to all rendezvous, the trajectory presented in 'Cargo Transfer Vehicle Preliminary Reference Definition' (MSFC, May 1991) was used as the basis for the comments below.

Prust, Ellen E.↗
NASA NTRS2022

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

machine learning/artificial intelligence↗
NASA NTRS2014

Development Testing and Subsequent Failure Investigation of a Spring Strut Mechanism

Commodities are transferred between the Multi-Purpose Crew Vehicle (MPCV) crew module (CM) and service module (SM) via an external umbilical that is driven apart with spring-loaded struts after the structural connection is severed. The spring struts must operate correctly for the modules to separate safely. There was no vibration testing of strut development units scoped in the MPCV Program Plan; therefore, any design problems discovered as a result of vibration testing would not have been found until the component qualification. The NASA Engineering and Safety Center (NESC) and Lockheed Martin (LM) performed random vibration testing on a single spring strut development unit to assess its ability to withstand qualification level random vibration environments. Failure of the strut while exposed to random vibration resulted in a follow-on failure investigation, design changes, and additional development tests. This paper focuses on the results of the failure investigations including identified lessons learned and best practices to aid in future design iterations of the spring strut and to help other mechanism developers avoid similar pitfalls.

Dervan, Jared↗
DOE OSTI2025

Electron Bifurcating Hydrogenases

The importance of electron-bifurcating enzymes is manifest by their ability to maximize energy efficiency. Specifically, they couple a downhill oxidation-reduction (redox) reaction with an uphill redox reaction. Since the rapid increase in the discovery of bifurcating enzymes starting in 2008, there has been interest in incorporating their mechanistic principles into artificial/semiartificial systems to drive chemically challenging reactions. This has yet to be achieved, partly because the details of electron bifurcation, i.e. mechanisms, are largely elusive. Nevertheless, much progress has been made in understanding reactivities, structures, and some mechanistic aspects of these enzymes. Notable examples are electron-bifurcating hydrogenases, which are the focus of this chapter. The chapter is organized as follows. Section 11.1 provides an overview of hydrogenases and electron bifurcation. In Section 11.2, some physiological roles of electron-bifurcating hydrogenases are highlighted. Additionally, electron-bifurcating subunit compositions and biochemical reactivities are comprehensively tabulated, and some key points/considerations about these are noted. In Section 11.3, we discuss the known structures of these enzymes, which provide insight into their complex arrangements of redox cofactors, such as iron-sulfur clusters. Also provided are tabulations and discussions of some biophysical properties of the cofactors. In Section 11.4, we discuss the mechanistic proposals of these enzymes, which are primarily based on structural information. Areas of research that are much needed are outlined in Section 11.5. We conclude on the note that what is learned from electron-bifurcating hydrogenases has applicability to other bifurcating enzymes, nonbifurcating analogs, and mechanistic enzymology at large.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗