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The goal of HRP is to provide human health and performance countermeasures, knowledge, technologies, and tools to enable safe, reliable, and productive human space exploration. The Human Research Program was designed to meet the needs of human space exploration, and understand and reduce the risk to crew health and performance in exploration missions.
Space Exploration missions requires the design and implementation of space networking that differs from terrestrial networks. In a space networking architecture, interplanetary communication protocols need to be designed, validated and evaluated carefully to support different mission requirements. As actual systems are expensive to build, it is essential to have a low cost method to validate and verify mission/system designs and operations. This can be accomplished through simulation. Simulation can aid design decisions where alternative solutions are being considered, support trade-studies and enable fast study of what-if scenarios. It can be used to identify risks, verify system performance against requirements, and as an initial test environment as one moves towards emulation and actual hardware implementation of the systems. We describe the development of Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) and its use cases in supporting architecture trade studies, protocol performance and its role in hybrid simulation/emulation. The MACHETE environment contains various tools and interfaces such that users may select the set of tools tailored for the specific simulation end goal. The use cases illustrate tool combinations for simulating space networking in different mission scenarios. This simulation environment is useful in supporting space networking design for planned and future missions as well as evaluating performance of existing networks where non-determinism exist in data traffic and/or link conditions.
In order to provide meaningful impacts to exploration technology needs, the In-Space Manufacturing (ISM) Initiative must influence exploration systems design now. In-space manufacturing offers: dramatic paradigm shift in the development and creation of space architectures; efficiency gain and risk reduction for low Earth orbit and deep space exploration; and "pioneering" approach to maintenance, repair, and logistics leading to sustainable, affordable supply chain model. In order to develop application-based capabilities in time to support NASA budget and schedule, ISM must be able to leverage the significant commercial developments, which requires innovative, agile collaborative mechanisms (contracts, challenges, SBIR's, etc.); and NASA-unique investments to focus primarily on adapting the technologies and processes to the microgravity environment. We must do the foundational work - it is the critical path for taking these technologies from lab curiosities to institutionalized capabilities: characterize, certify, institutionalize, design for Additive Manufacturing (AM). Ideally, International Space Station (ISS) U.S. lab rack or partial rack space should be identified for in-space manufacturing utilization in order to continue technology development of a suite of capabilities required for exploration missions, as well as commercialization on ISS.
With the advent of the Space Exploration Initiative, the possibility of designing and using systems on scales heretofore unattempted presents exciting new challenges in systems design and space science. The environments addressed by the Space Exploration Initiative include the surfaces of the Moon and Mars, as well as the varied plasma and field environments which will be encountered by humans and cargo enroute to these destinations. Systems designers will need to understand environmental interactions and be able to model these mechanisms from the earliest conceptual design stages through design completion. To the end of understanding environmental interactions and establishing robotic precursor mission requirements, an Environmental Interactions Working Group was established as part of the Robotic Missions Working Group. The working group is described, and its current activities are updated.
NASA's Asteroid Redirect Crewed Mission (ARCM) is challenged with primary mission objectives of demonstrating deep space Extravehicular Activity (EVA) and tools, and obtaining asteroid samples to return to Earth for further study. Although the Modified Advanced Crew Escape Suit (MACES) is used for the EVAs, it has limited mobility which increases fatigue and decreases the crews' capability to perform EVA tasks. Furthermore, previous Shuttle and International Space Station (ISS) spacewalks have benefited from EVA interfaces which have been designed and manufactured on Earth. Rigid structurally mounted handrails, and tools with customized interfaces and restraints optimize EVA performance. For ARCM, some vehicle interfaces and tools can leverage heritage designs and experience. However, when the crew ventures onto an asteroid capture bag to explore the asteroid and collect rock samples, EVA complexity increases due to the uncertainty of the asteroid properties. The variability of rock size, shape and composition, as well as bunching of the fabric bag will complicate EVA translation, tool restraint and body stabilization. The unknown asteroid hardness and brittleness will complicate tool use. The rock surface will introduce added safety concerns for cut gloves and debris control. Feasible solutions to meet ARCM EVA objectives were identified using experience gained during Apollo, Shuttle, and ISS EVAs, terrestrial mountaineering practices, NASA Extreme Environment Mission Operations (NEEMO) 16 mission, and during Neutral Buoyancy Laboratory testing in the MACES suit. The proposed concept utilizes expandable booms and integrated features of the asteroid capture bag to position and restrain the crew at the asteroid worksite. These methods enable the capability to perform both finesse, and high load tasks necessary to collect samples for scientific characterization of the asteroid. This paper will explore the design trade space and options that were examined for EVA, the overall concept for the EVAs including translation paths and body restraint methods, potential tools used to extract the samples, design implications for the Asteroid Redirect Vehicle (ARV) for EVA, the results of early development testing of potential EVA tasks, and extensibility of the EVA architecture to NASA's exploration missions.
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.
With the advent of the Space Exploration Initiative, the possibility of designing and using systems on scales not heretofore attempted presents exciting new challenges in systems design and space science. The environments addressed by the Space Exploration Initiative include the surfaces of the Moon and Mars, as well as the varied plasma and field environments which will be encountered by humans and cargo enroute to these destinations. Systems designers will need to understand environmental interactions and be able to model these mechanisms from the earliest conceptual design stages through design completion. To the end of understanding environmental interactions and establishing robotic precursor mission requirements, an Environmental Interactions Working Group has been established as part of the Robotic Missions Working Group. The current paper describes the working group and gives an update of its current activities. Working group charter and operation are reviewed, background information on the environmental interactions and their characteristics is offered, and the current status of the group's activities is presented along with anticipations for the future.
Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.
Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary optimization where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The new approach to shape optimization resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.
Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The free-form boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.
The following topics are addressed: (1) the frequency of encountering boulders that represent hazards to lunar operations; (2) the ease of lunar soil excavation; (3) the use of explosives in excavation operation; (4) the trafficability of the regolith; (5) problems encountered in mining (probably strip mining) of the regolith; (6) the stable angle(s) of repose in excavation of the regolith; (7) the layering to be encountered in the subsurface; (8) knowledge of the regolith site and the possibility of its general application to any site on the lunar surface; (9) the data needed to characterize a site for a lunar base; (10) the influence of regolith properties on the design of geophysical experiments from the lunar base; and (11) terrestrial analogues for the geophysical properties of the lunar regolith.
Following a very successful year of manufacturing, assembly and testing in factories located around the globe, NASA and ESA are preparing to deliver the major Exploration Mission-1 (EM-1) Orion flight elements, including the Crew Module, ESA Service Module and Launch Abort System. This international effort to design and develop a deep space exploration capable human spacecraft is rapidly transitioning from the design, development and test phase to the early test flight and production phase. Two major flight tests, an Ascent Abort test and EM-1, Orion's first flight onboard NASA's new heavy lift Space Launch System, are planned for the near future. Further, Orion will play a crucial role in the ambitious new Deep Space Gateway human exploration Program. This paper gives a short overview of the system and subsystem configuration of the Orion spacecraft, including NASA and ESA contributions, a status of EM-1, AA-2 and EM-2 spacecraft production, and a look at Orion's role in the construction and operation of the Deep Space Gateway. The paper will also address the innovative international cooperation methods being employed to conduct Orion and Service Module integration.
High performance, parallel applications with irregular data accesses are becoming a critical workload class for modern systems. In particular, the execution of such workloads on emerging many-core systems is expected to be a significant component of applications in data mining, machine learning, scientific computing and graph analytics. However, power and energy constraints limit the capabilities of individual cores, memory hierarchy and on-chip interconnect of such systems, thus leading to architectural and software trade-os that must be understood in the context of the intended application’s behavior. Irregular applications are notoriously hard to optimize given their data-dependent access patterns, lack of structured locality and complex data structures and code patterns. We have ported two irregular applications, graph community detection using the Louvain method (Grappolo) and high-performance conjugate gradient (HPCCG), to the Tilera many-core system and have conducted a detailed study of platform-independent and platform-specific optimizations that improve their performance as well as reduce their overall energy consumption. To conduct this study, we employ an auto-tuning based approach that explores the optimization design space along three dimensions - memory layout schemes, GCC compiler flag choices and OpenMP loop scheduling options. We leverage MIT’s OpenTuner auto-tuning framework to explore and recommend energy optimal choices for different combinations of parameters. We then conduct an in-depth architectural characterization to understand the memory behavior of the selected workloads. Finally, we perform a correlation study to demonstrate the interplay between the hardware behavior and application characteristics. Using auto-tuning, we demonstrate whole-node energy savings and performance improvements of up to 49:6% and 60% relative to a baseline instantiation, and up to 31% and 45:4% relative to manually optimized variants.
NREL will develop meshing and modelling scripts to automate exploration of the design space for Maiden Wave Energy's Wave Energy Converter ("WEC") device. This will aid Maiden Wave Energy to assess a range of different device configurations and explore how different variables in the system affect the concept's performance.
Book on space probes and planetary exploration emphasizing space vehicle design, instrumentation, American missions and support systems
Designed to meet the stringent requirements of human exploration missions into deep space and to Mars, NASA's Space Launch System (SLS) vehicle represents a unique new launch capability opening new opportunities for mission design. NASA is working to identify new ways to use SLS to enable new missions or mission profiles. In its initial Block 1 configuration, capable of launching 70 metric tons (t) to low Earth orbit (LEO), SLS is capable of not only propelling the Orion crew vehicle into cislunar space, but also delivering small satellites to deep space destinations. The evolved configurations of SLS, including both the 105 t Block 1B and the 130 t Block 2, offer opportunities for launching co-manifested payloads and a new class of secondary payloads with the Orion crew vehicle, and also offer the capability to carry 8.4- or 10-m payload fairings, larger than any contemporary launch vehicle, delivering unmatched mass-lift capability, payload volume, and C3.
Designed to meet the stringent requirements of human exploration missions into deep space and to Mars, NASA's Space Launch System (SLS) vehicle represents a unique new launch capability opening new opportunities for mission design. While SLS's super-heavy launch vehicle predecessor, the Saturn V, was used for only two types of missions - launching Apollo spacecraft to the moon and lofting the Skylab space station into Earth orbit - NASA is working to identify new ways to use SLS to enable new missions or mission profiles. In its initial Block 1 configuration, capable of launching 70 metric tons (t) to low Earth orbit (LEO), SLS is capable of not only propelling the Orion crew vehicle into cislunar space, but also delivering small satellites to deep space destinations. With a 5-meter (m) fairing consistent with contemporary Evolved Expendable Launch Vehicles (EELVs), the Block 1 configuration can also deliver science payloads to high-characteristic-energy (C3) trajectories to the outer solar system. With the addition of an upper stage, the Block 1B configuration of SLS will be able to deliver 105 t to LEO and enable more ambitious human missions into the proving ground of space. This configuration offers opportunities for launching co-manifested payloads with the Orion crew vehicle, and a new class of secondary payloads, larger than today's cubesats. The evolved configurations of SLS, including both Block 1B and the 130 t Block 2, also offer the capability to carry 8.4- or 10-m payload fairings, larger than any contemporary launch vehicle. With unmatched mass-lift capability, payload volume, and C3, SLS not only enables spacecraft or mission designs currently impossible with contemporary EELVs, it also offers enhancing benefits, such as reduced risk and operational costs associated with shorter transit time to destination and reduced risk and complexity associated with launching large systems either monolithically or in fewer components. As this paper will demonstrate, SLS represents a unique new capability for spaceflight, and an opportunity to reinvent space by developing out-of-the-box missions and mission designs unlike any flown before.
This document is one of three. It describes the Operational Concept (OpsCon) for a generic space exploration communication architecture. The purpose of this particular document is to identify communication flows and data types. Two other documents accompany this document, a security policy profile and a communication architecture document. The operational concepts should be read first followed by the security policy profile and then the architecture document. The overall goal is to design a generic space exploration communication network architecture that is affordable, deployable, maintainable, securable, evolvable, reliable, and adaptable. The architecture should also require limited reconfiguration throughout system development and deployment. System deployment includes: subsystem development in a factory setting, system integration in a laboratory setting, launch preparation, launch, and deployment and operation in space.