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At least 559 records · Page 31

Qualification of a Hybrid Propulsion System for the Mars Ascent Vehicle

Technology for a hybrid based propulsion system is being developed to support a potential Mars Sample Return campaign. A Mars Ascent Vehicle (MAV) concept for launching samples off of Mars, and delivering them to orbit for further transport to Earth may utilize hybrid propulsion due to the predicted favorable low temperature characteristics and high performance of this option. However, the hybrid option is still undergoing technology development to demonstrate these capabilities. Once development of a capable hybrid propulsion system is proven, further work will be required. This will include environmental testing relative to the mission, and integration with the vehicle reaction control systems and payload. Qualification of such a system will be a significant effort. It will require specialized procurements for the propellants and environments involved, and further testing of the more specialized designs. This paper details an estimate of the tasks required to complete development efforts from Technical Readiness Level 5 (TRL5) through qualification. A success based program was formulated to reach the required performance metrics sufficient for a standard Preliminary Design Review (PDR). Using task level inputs from team members cost and schedule were conceived for continued progress to Critical Design Review (CDR), then through Qualification.

Prince, Andrew↗

A Hybrid Mars Ascent Vehicle Design and FY 2016 Technology Development

Hybrid propulsion is currently favored for a Mars Ascent Vehicle (MAV) concept from a thermal performance and Gross Lift Off Mass standpoint. However, it is at a relatively low level of maturity compared to conventional propulsion options. Technology development efforts are currently underway to bring hybrid propulsion to a technology readiness level that would enable its infusion into potential Mars Sample Return. A new propellant combination is being considered for this design that has excellent low temperature behavior. Preliminary results of two ground test campaigns are currently underway to characterize this propellant combination. Hotfire testing is being carried out in parallel at Parabilis Space Technologies and Space Propulsion Group. In addition to the new propellant combination, several other technologies are being pursued for a potential hybrid MAV: hypergolic ignition and Liquid Injection Thrust Vector Control. Both of these technologies have been applied in other rocket applications, e.g. liquid propulsion commonly uses hypergolic propellants and missiles, such as the Minuteman II, have used LITVC in the past. Hypergolic ignition, when oxidizer and fuel combust upon contact, is highly desirable for multiple starts required by the MAV concept. Therefore, testing at Penn State and Purdue is being completed in this area. An updated hybrid propulsion system design for a Mars Ascent Vehicle concept based on JPL’s current understanding of potential Mars Sample Return requirements will be presented, leveraging the advances in technology development as well as updated understanding of how requirements may evolve.

Karp, Ashley C.↗

Hybrid CMOS System-on-Chip / InP MMIC Systems for Deep-Space Planetary Exploration at mm-Wave and THz

This paper discusses the applicability of hybrid CMOS/InP-MMIC mm-Wave systems to remote sensing instrumentation for space exploration in Earth and Planetary science. We review the need for lower power and lighter weight instruments to accommodate the limited payload resources of exploration spacecraft, and then demonstrate how hybrid systems can address these challenges. An example hybrid CMOS-InP radiometer operating at 100 GHz is discoursed in detail including circuit & system design, interfacing and packaging techniques. Measurements are presented showing that the hybrid approach does not compromise instrument sensitivity.

Tang, Adrian↗

Mars Ascent Vehicle Hybrid Propulsion Development

Hybrid propulsion is being investigated as a propulsion method for a possible Mars Ascent Vehicle (MAV) application. MAV is part of a proposed larger Mars Sample Return (MSR) campaign plan to bring samples from Mars to earth for examination. The Mars Ascent Vehicle would launch Mars surface samples found and packaged by the Mars 2020 mission to orbit around Mars. This version of hybrid propulsion is based on a wax based solid fuel, called SP7A, and a Mixed Oxides of Nitrogen oxidizer, MON-25. SP7 is a new fuel formulation developed by Space Propulsion Group and was modified for this application to be resistant to Mars temperature extremes and modified again to lower the regression rate to become SP7A. MON-25 was chosen for its low freezing temperature. Due to cost constraints, MON-3 was the oxidizer used during testing through 2018. In 2019, full scale hybrid testing with MON-25 commenced in Mojave, CA by Whittinghill Aerospace. One flight motor will be subjected to thermal cycling in a vacuum and later fired in a vacuum to demonstrate the proposed Liquid Injection Thrust Vector Control system performance at White Sands Test Facility (WSTF). In addition, there will be MON-25 characterization work done at Purdue University and WSTF. Additional testing of subscale and full scale motors will be conducted with MON-3 with fuel grain stress, fuel grain support and case design test objectives by Space Propulsion Group Inc. of Butte, MT. This paper documents some of the testing, issues and accomplishments with the MAV hybrid propulsion option that is being considered (along with a two-stage solid propulsion option).

Story, G. T.↗

Technology Development and Design of a Hybrid Mars Ascent Vehicle Concept

Hybrid propulsion has been investigated as an enhancing technology for a Mars Ascent Vehicle (MAV) concept as part of potential Mars Sample Return (MSR) because of its high specific impulse, restartability, and the ability to operate and survive at extremely low temperatures. A new wax-based hybrid fuel formulation has been developed that could withstand the harsh and variable Mars environment protected solely by a minimal layer of passive insulation. This formulation could provide substantial energy savings for a notional lander and is critical for rover mobility. Preliminary thermal cycle testing has determined that the formulation can survive the expected temperature extremes and lifetime thermal testing is currently underway. A complete preliminary design using this new fuel formulation combined with a low temperature oxidizer such as Mixed Oxides of Nitrogen (MON30) is presented. Several key features associated with a complete hybrid MAV concept are investigated to determine their mission suitability (e.g. Thrust Vector Control and restartable ignition options). Potential challenges along a path towards developing such a system are outlined and future work is suggested as a means of technology maturation. The hybrid design presented here was the lowest Gross Lift Off Mass (GLOM) result of a 2015 Jet Propulsion Laboratory (JPL) led MAV concept study.

Karp, Ashley C.↗

Feasibility of a Solid Oxide Fuel Cell System Applied to Hybrid-Electric Regional Aircraft

NASA’s vision for the future of flight includes highly energy efficient aircraft that are environmentally friendly and have positive national economic impact. Although battery-powered electric aircraft are a viable alternative to reduce environmental impact, purely electric aircraft have drawbacks such as the low specific energy of batteries, which translates to an unfavorable weight scaling. Solid oxide fuel cell (SOFC) systems also have promise in providing environmentally friendly electrical power for new aircraft configurations at a higher efficiency than current methods, such as electrical generators. This study explores the use of the Fostering Ultra-Efficient, Low Emitting Aviation Power (FUELEAP) power system architecture for hybrid-electric regional aircraft. The FUELEAP power system utilizes a SOFC as part of a hybrid-electric power system architecture, enabling higher efficiency and specific power with a reduction in carbon and NOx emissions compared to state-of-the-art hybrid-electric SOFC architectures. The study includes comparisons of hybrid-electric power architectures and integration of the FUELEAP power system for two regional-sized passenger aircraft concepts. Results suggest that use of the FUELEAP power system as a replacement for onboard batteries may provide up to a 50% reduction in the weight of the electric d energy system for a typical regional aircraft mission.

Geuther, Steven↗

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗

Gas Turbine Hybrid Technology at NASA

NASA has been instrumental in shaping the research for Gas Turbine – Electric Hybrid Technology by in-house R&D and through industry partners. The Technology Readiness Level (TRL) is being advanced so the technology will find its way into products used by the flying public as well as developing the longer-term goals. The hybrid approach leads to having the advantage of both a gas turbine and electrification in addition to using both approaches in a synergistic way. This is a panel session and the presentation will describe the basics of hybrid, and what NASA is doing for Gas Turbine Hybrid Technology.

Gas Turbine↗

The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE)

NASA is planning to return to the Moon in the mid-2020s as a stepping stone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support. The software used in APACHE was created by the JSC Engineering Directorate, in partnership with Buendea, powered by a custom Unreal Engine 5 (UE5.3, Epic Games) project. APACHE currently utilizes the HTC Vive Pro Eye in a wireless configuration for VR simulations. There are two virtual environments that subjects can explore within APACHE, a Lunar and Martian surface. The virtual Lunar surface was created from LIDAR data of the Lunar South Pole to create roughly 16 sq km of explorable terrain. The virtual Martian surface contains roughly 400 sq km of explorable terrain derived from Mars Reconnaissance Orbiter LIDAR data of the Jezero Crater. The immersion and related cognitive burdens of conducting a planetary EVA is simulated through a series of EVA-relevant tasks performed in the VR environment, using these high-fidelity visual representations. Additionally, APACHE includes biosensor driven informatics, such as real-time heart rate monitoring and/or derived values from model simulations, for active monitoring by the EV crew and added cognitive demand. A “Wizard of Oz” control panel enables test operators to activate contingency events such as simulated spacesuit malfunctions, loss of communications, and/or limited visibility. Embedded performance measures such as accuracy, completeness, and execution time have been developed for various exploration tasks to objectively quantify crew performance during an EVA and compare impacts to performance when different environmental stressors, both physical and cognitive, are added to or removed from the simulation. Additionally, validated cognitive and operational performance measures such as the Digit Symbol Substitution Task have been recreated and embedded in VR for direct and relatively unobtrusive measurement of motor perception. The APACHE environment currently supports multiple research studies at NASA. Examples include the CHAPEA project, a series of simulated year-long missions on Mars by a 4-person crew; and the CO2 Contingency Walk Back Study, an investigation of elevated CO2 exposure on crew performance during a contingency EVA scenario. APACHE also provides a test environment to support the development of the Crew State and Risk Model, which is a collection of individualized, mathematical models of crew physical and cognitive state; and the Personalized EVA Informatics and Decision Support system, an operational tool for flight controllers, and eventually a self-reliant Martian crew, to make biomedically-informed decisions in real-time to optimize the EVA planning and execution with respect to crew health and performance. Some technical challenges associated with developing the APACHE environment, as well as current limitations, include VR limitless natural walking with a hybrid spacesuit simulator, optimizing performance for wireless PC VR streaming while maintaining a high degree of visual fidelity, and the integration of various physiological (metabolic masks) and psychometric (eye tracking) sensors with the VR headset.

Human Performance↗

Lattice Confinement Fusion Fast Fission – A Hybrid Power System for Accessing Icy World Oceans

Lattice Confinement Fusion (LCF) is an emerging power technology that can be combined with nuclear fission to produce a hybrid innovative power system. The proposed innovation is a compact, scalable nuclear energy source that does not use highly enriched uranium (HEU), high-assay enriched uranium (HALEU), low enriched uranium (LEU) nor plutonium-238. The nuclear energy source consists of a hybrid fusion-fast-fission method whereby neutrons generated from LCF are used to fission materials such as depleted uranium or thorium. LCF has been demonstrated by both NASA (published in Physical Review C) and by Lawrence Berkeley National Laboratory (published in the Journal of Applied Physics). Although these methods are reminiscent of Low Energy Nuclear Reactions (LENR), both methods operate at much higher energies than any attempt at cold fusion. This new hybrid energy source is sufficient to provide power and heat for melting or boring through icy caps with untethered, autonomous probes. These probes can be used for planetary (i.e., Pluto), lunar (i.e., Enceladus), or asteroid (i.e., Ceres) exploration where icy caps are encountered. Each world may have a liquid water ocean beneath their ice crust. A robotic probe exploring the oceans beneath must either melt or bore through the ice crust first. Such a mission requires a small, but robust and long lived, electrical energy and heat source such as the LCF Fast Fission hybrid power system.

Theresa L. Benyo↗

Hybrid Analytics Solution for Power Plant Operations

Expert Microsystems, Inc., MapEx Software Inc. and XMPLR Energy LLC have teamed to submit a proposal to the US Department of Energy’s Office of Fossil Energy to improve the efficiency, reliability and flexibility of existing coal-based power plants. The project, titled Hybrid Analytics Solution to Improve Coal Power Plant Operations (the “Hybrid Analytics” project), will develop, demonstrate and commercialize a novel approach that integrates two proven real-time monitoring techniques. The Hybrid Analytics approach integrates into a single, integrated solution: a) an established, advanced data-driven analytics solution that includes artificial intelligence, advanced pattern recognition and machine-learning techniques and b) a well-proven, first principle thermal heat balance model solution. The future role of coal plants will depend on the ability to cycle and follow loads to meet marginal power demands, accommodate renewables and support the grid. Essential to success in this new role is a plant’s ability to maintain high reliability, efficiency and flexibility. However, these changing operational demands put significant stresses on power plant components that directly impact the ability to deliver these requirements. In the past, plants have independently monitored performance using physics-based solutions (heat rate, boiler losses, etc.) and reliability using data-driven analytics (advanced pattern recognition). The Hybrid Analytics solution integrates both approaches to enhance fault detection, provide automated diagnostics and estimates of remaining time to act, and provide critical, enhanced and accurate real-time information to coal plant operators. As a result, coal plants can improve efficiency, reliability, and flexibility and can cost-effectively maintain their key role in the power delivery system.

01 COAL, LIGNITE, AND PEAT↗

Multi-mode power split hybrid transmission with two planetary gear mechanisms

A multi-mode, power-split hybrid transmission system having two planetary gear (PG) sets connected to one engine, two electric motors, one output shaft, and each other by several clutches, brakes, and direct connection elements. Depending on the specific location and actuation of the various clutch and brake elements, the multi-mode, power-split hybrid transmission system can be run in one of several modes (e.g. electric drive, power-split, parallel hybrid, series hybrid, electronic continuously variable transmission (eCVT), generator, neutral, and the like).

33 ADVANCED PROPULSION SYSTEMS↗

Analysis of Using a Hybrid ZIRCEX Process to Generate High-Assay Low-Enriched Uranium (HALEU) - 20158

Idaho National Lab (INL) is investigating the feasibility of providing High-Assay Low Enriched Uranium (HALEU) by recovering Highly Enriched Uranium (HEU) from spent nuclear fuel (SNF) and downblending the HEU with natural, low-enriched, or depleted uranium (NU, LEU, or DU). That investigation identified that significant quantities of HALEU could be produced with a Hybrid ZIRCEX process to further advanced reactor research while reducing INL's environmental liability. Many new advanced reactor designs are being developed with improved safety, efficiency, and economics. Most require nuclear fuel with U-235 enrichment between 5% and 20%, which is defined as HALEU. Although technically feasible, there is no current domestic capability to make HALEU in the US. While it is anticipated that industry will provide HALEU through commercial enrichment once advanced reactors mature, until a market is established, an interim source of HALEU is needed to enable research and demonstration. An interim source of HALEU could be provided by recovering the HEU in some DOE-managed SNF and downblending it to between 5% and 20% using the Hybrid ZIRCEX Process. ZIRCEX is a dry head-end process to remove cladding (zirconium or aluminum) from SNF. Drying of the SNF is needed prior to introduction to ZIRCEX. After cladding removal, uranium and fission products in the bed material are oxidized and elutriated. In a Hybrid ZIRCEX process, the HEU process stream is sent to a very compact, modular solvent extraction process for uranium purification. The uranyl nitrate from solvent extraction is denitrated and calcined to produce a HALEU product ready for fuel fabrication. The long-lived fission products from solvent extraction are immobilized in glass using a small in-can melt. By removing the cladding before dissolution, the volume of glass waste is reduced by a factor of up to 300 times (when compared to not removing the cladding from the fuel). Other radioactive wastes from the process could be disposed of as Class A or Class B low-level waste. In addition to producing HALEU for advanced reactors, consumption of SNF would reduce DoE's environmental liability. Incorporating low-enriched spent fuel (versus exclusive use of LEU, DU, or NU) into the downblending scheme could reduce the SNF inventory even further. Several downblending and enrichment scenarios using INL fuels were investigated in this study. The results of this analysis show that the Hybrid ZIRCEX process could make a significant contribution to needed HALEU feedstock and reduce the environmental liability of managing SNF at INL. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗

Hybrid power plant design for low-carbon hydrogen in the United States

In this study, we provide a nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. We leverage the open-source Hybrid Optimization Performance Platform (HOPP) tool to simulate the hourly performance of an off-grid wind-solar plant integrated with a 1-GW polymer exchange membrane electrolyzer system. The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to explore cost sensitivities independent of plant design, performance, and site selection. Our findings suggest that strategies for cost reduction include selecting sites with abundant wind resources, complementary wind and solar resources, and optimizing the sizing of wind and solar assets to maximize the hybrid plant capacity factor. These strategies are linked to increased hydrogen production and reduced electrolyzer stack replacements, thereby lowering the overall cost of hydrogen.

08 HYDROGEN↗

Opportunities and Challenges for Nuclear-Renewable Hybrid Energy Systems

Nuclear-renewable hybrid energy systems (also known as integrated nuclear-renewable systems) are conceptual systems that have two or more energy inputs and produce two or more products. They maximize profitability, and usually equipment utilization, by adjusting between the products depending upon the availability of resources and market value. For example a nuclear-renewable hybrid energy system that produces electricity and hydrogen would maximize sales of electricity when its price is high and shift to hydrogen production when the electricity price is low. This presentation summarizes the results of several economic analyses of nuclear-renewable hybrid energy systems and they key overall conclusions from those analyses.

economic analysis↗