Deep Space Habitat Research Articles

Characterizing the Impact of Emergent Technologies on Earth Communications Reliance for Crewed Deep Space Missions

Future human expeditions into deep space will encounter unique design challenges posed by the increasing distance from Earth, one of which is the inability to maintain near-continuous communication with ground support teams. As a result, the habitat and crew will require a higher level of operational self-sufficiency informed by onboard self-awareness and decision-making capabilities to accomplish functions that have historically involved extensive support from mission control personnel. These include, for example, task planning and anomaly detection, diagnosis and correction, as well as monitoring of consumable usage rates and system performance. Novel emergent technologies in the domain of ‘smart’ systems and digital twins can be employed to enable the onboard capabilities needed to function with minimal Earth communication. However, these technologies are generally low-TRL and difficult to incorporate through traditional systems engineering methods.This work proposes an extension of utility theory to assess the potential reduction in Earth communication reliance between baseline and emergent technologies, scored through a summation of weighted attributes and evaluated against future deep space mission needs and constraints. Case studies are defined from technology under development in the NASA Habitats Optimized for Missions of Exploration (HOME) Space Technology Research Institute (STRI), a project charged with developing and evaluating technologies to enable highly autonomous deep space habitats, and compared to the current baseline technologies used onboard the International Space Station. Notional deep space mission needs and constraints are informed from current NASA plans for the Moon and Mars. These in turn are broken out into higher resolution attributes that are used to assess the operational capabilities of each emergent technology and whether it offers a potentially viable solution for deep space. These technology attributes are scored through a series of semi-structured subject-matter interviews and compared to notional deep space mission constraints. Additional considerations for technology maturation and integration are also discussed. The design analysis paradigm described and demonstrated here can be adapted to assess the degree to which other emergent technologies rely on Earth-based communication, and is intended to provide trade space considerations for future research and development towards deep space habitat self-sufficiency.

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach

The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases.

Designing Visual Displays for Deep Space Habitats: Challenges and Opportunities from a Cognitive Task Analysis with ISS Mission Control

This paper explores some challenges and opportunities in designing visual interfaces for deep space habitats with communication delays to Earth. The study first examines how Mission Control for the International Space Station (ISS) operates in detecting and responding to anomalous events. Interviews with participants who have held roles as flight controllers, backroom engineers, astronauts, or flight instructors for ISS and shuttle missions were conducted to gain insights into the responses to anomalous events. The collected anomalous scenarios were then re-considered under the context of deep space missions, where communication delays and other environmental factors are substantially different. Findings and insights on data analysis of the anomalous scenarios were then presented subsequently. Based on the findings, four areas of challenges and opportunities for visual interfaces design were identified. The paper suggests that these design challenges and opportunities be considered in more detail in future research.

Life Cycle Cost Model for Life Support Systems of Crewed Autonomous Transport for Deep Space Habitation

Intelligent transport systems are used in various transport systems, among which a special place is occupied by crewed autonomous transport systems such as space stations for deep space habitation. These objects have a complex and critical requirement for life support systems (LSSs) to maintain safe and habitable conditions for the crew in the isolated environment. This paper explores the different architectural options for life support systems (LSSs) in autonomous transport systems, specifically focusing on space stations. Three alternative LSS architectures are discussed: Open LSS (OLSS), Closed LSS (CLSS), and Mixed LSS (MLSS). Each architecture has its own advantages and disadvantages. OLSS relies on external resource delivery, reducing initial costs but increasing dependence on resupply missions. CLSS operates autonomously, generating resources onboard, but has higher initial costs and technological complexity. MLSS combines external delivery and onboard generation, providing flexibility and adaptability. The material emphasizes the importance of cost-effectiveness analysis at the early stages of design and identifies the boundary values of mission duration that determine the most effective LSS architecture choice. The material highlights the significance of striking the right balance between cost and performance to develop intelligent ecosystems of LSS for space stations and other autonomous transport systems.

Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search

The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must operate under continuous disruptive conditions arising from extreme environments like meteoroid impacts, extreme temperature fluctuations, galactic cosmic rays, destructive dust, and seismic events. Loss of air or atmospheric leakage from a habitat poses safety challenges that demand proper attention. Such leakage may arise from micro-meteoroid impacts, crack growth, bolt/rivet loosening, and seal deterioration. In this paper, leakage estimation in deep space habitats is posed as an inverse problem. A forward pressure-based dynamical model is formulated for atmospheric leakage. Experiments are performed on a small-scaled pressure chamber where different leakage scenarios are emulated and corresponding pressure values are measured. An exponentially-weighted adaptively-refined search (EWARS) algorithm is developed and validated for the inverse problem of real-time leakage estimation. It is demonstrated that the proposed methodology can achieve real-time estimation and tracking of constant and variable leaks with accuracy.

Defining and characterizing self-awareness and self-sufficiency for deep space habitats

Future deep-space crewed exploration plans include long duration missions (>1000 days) that will be constrained by lengthy transmission delays and potential occultations in communications, as well as infrequent resupply opportunities and likely periods of habitat unoccupancy. In order to meet the high level of autonomy needed for these missions, many essential capabilities and knowledge previously accomplished through ground support and human operators must now be designed into onboard systems to enable increasing self-reliance. Emergent technologies, including autonomous systems, have the potential to be mission enabling in deep space; however, as these technologies are often low-TRL and without defined mass, power, or volume, their net impact to the design must be assessed through alternative means, especially during the early planning phases. This paper proposes the concept of designing for self-reliant space habitats as the foundation for assessing potential contributions from the integration of emergent technologies. The term ‘self-reliance’ can be thought of as a combination of the spacecraft system and onboard crew's knowledge (self-awareness) and capabilities (self-sufficiency) independent of external intervention. In order to provide context for human spaceflight, these terms are first derived from related terrestrial applications. Subsequently, a methodology for characterizing the degree of self-awareness and self-sufficiency in a space habitat is outlined to provide designers with logic for assessing the contributions of emergent technologies to the overall self-reliance of the habitat as needed to allow future Earth-independence. The definitions and characterization logic provided in this work offer a systematic process for designing toward self-reliance in future deep space missions.

8.4 M Deep Space Habitat– Medical Bay Concept Design and Human Factors Engineering Analysis

Maintaining crew health throughout long duration deep space missions presents significant new design challenges. Since communication with Earth will be limited and it is not possible to return in the event of a medical emergency it is critical that medical care on a deep space mission be as autonomous as possible. The goal of this project was to design and assess a medical system concept for the Deep Space Habitat Concept Demonstrator derived from the core stage of the Space Launch System. The Medical Bay was designed as a primary location to support the medical care of 4 crew members on a 1000 day mission. It includes workspace, stowage, and direct access to all necessary medical equipment and supplies. A Human Factors Engineering (HFE) Analysis was conducted to test ergonomic effectiveness and system requirement compliance. This initial design will continue to be used by the HFE Team and the Advanced Concepts Office (ACO) at NASA’s Marshall Spaceflight Center (MSFC) for further demonstration, analysis, and design conception for deep space travel.

Central Nervous System Responses to Simulated Galactic Cosmic Rays.

In preparation for lunar and Mars missions it is essential to consider the challenges to human health that are posed by long-duration deep space habitation via multiple stressors, including ionizing radiation, gravitational changes during flight and in orbit, other aspects of the space environment such as high level of carbon dioxide, and psychological stress from confined environment and social isolation. It remains unclear how these stressors individually or in combination impact the central nervous system (CNS), presenting potential obstacles for astronauts engaged in deep space travel. Although human spaceflight research only within the last decade has started to include the effects of radiation transmitted by galactic cosmic rays to the CNS, radiation is currently considered to be one of the main stressors for prolonged spaceflight and deep space exploration. Here we will review the current knowledge of CNS damage caused by simulated space radiation with an emphasis on neuronal and glial responses along with cognitive functions. Furthermore, we will present novel experimental approaches to integrate the knowledge into more comprehensive studies, including multiple stressors at once and potential translation to human functions. Finally, we will discuss the need for developing biomarkers as predictors for cognitive decline and therapeutic countermeasures to prevent CNS damage and the loss of cognitive abilities.

Human Assisted Robotic Vehicle Studies - A conceptual end-to-end mission architecture

With current space exploration roadmaps indicating the Moon as a proving ground on the way to human exploration of Mars, it is clear that human-robotic partnerships will play a key role for successful future human space missions. This paper details a conceptual end-to-end architecture for an exploration mission in cis-lunar space with a focus on human-robot interactions, called Human Assisted Robotic Vehicle Studies (HARVeSt). HARVeSt will build on knowledge of plant growth in space gained from experiments on-board the ISS and test the first growth of plants on the Moon. A planned deep space habitat will be utilised as the base of operations for human-robotic elements of the mission. The mission will serve as a technology demonstrator not only for autonomous tele-operations in cis-lunar space but also for key enabling technologies for future human surface missions. The successful approach of the ISS will be built on in this mission with international cooperation. Mission assets such as a modular rover will allow for an extendable mission and to scout and prepare the area for the start of an international Moon Village.

Computer Aided Design and Analysis of Robotics for the NASA Deep Space Habitat

Computer Aided Design and Analysis of Robotics for the NASA Deep Space Habitat

A Minimal Architecture for Human Journeys to Mars

Abstract Proposed architectures for human journeys to Mars need to take note of the two competing constraints of an executable program: the annual NASA human spaceflight budget will likely remain constrained (possibly growing with inflation), and going to Mars and landing on Mars need to happen within the interest horizon of the various stakeholders, including the public. In this article we describe a stepwise approach for human journeys to Mars using a minimal architecture. We refer to this architecture as minimal because it would minimize large new development efforts and rely largely on elements currently being developed or planned by NASA, such as SLS, Orion, a deep space habitat, and a 100-kWe-class SEP tug. In the architecture proposed here, human missions to Mars would begin with a crewed landing on Phobos in 2033, followed by a short-stay landing on Mars in 2039, and continue with a one-year stay in 2043. Each mission campaign would build on previous campaigns, leaving a legacy and new capabilitie...

Wireless Power and Data System for Integrated System Health Management of Systems Operating in the Harsh Environment of Deep Space

Large and complex deep space platforms such as the Deep Space Habitat (DSH) being developed by NASA will require a robust, on-platform, Integrated System Health Management (ISHM) function. Currently the DSH is contemplated to be stationed at the L2 Lagrangian point outbound from the lunar orbit. This will provide a vantage point of the back side of the moon as well as to serve as a jumping off platform for manned trips to Mars, the Moon, or near Earth asteroids. The ISHM function includes the monitoring, diagnostics, prognostics, and failure mitigation strategies and capabilities for any viable failure modes of the DSH. To evaluate a prototype of this approach, NASA has assembled a full scale, ISS derived, DSH prototype at the Marshall Space Flight Center (MSFC), involving a wired ISHM sensor network of over 80 sensors located at various points where early system failure mechanisms may be detected and analyzed. However, it is anticipated that a wired, distributed architecture could involve many pounds of complex cable harnesses and connectors, along with the commonly encountered problems of accessibility, flexibility and maintainability. In the high likelihood that modifications or upgrades are needed, these complexities result in higher design and build cost along with increased operational costs as in-flight anomalies occur that could require the addition of different sensors or different sensor locations. To address these issues, the ISHM team at MSFC is studying a wireless, distributed architecture with on- platform, advanced prognostic and diagnostic capabilities. The approach being considered is based on the X-33 ISHM system which consisted of hardware identical remote health nodes (RHN) and a central vehicle health management computer. Each RHN was very flexible and reprogrammable to enable it to interface directly with all the health monitoring sensors. For application on the DSH, modifications to the RHN are being considered. These changes and resulting upgraded capabilities are described in this paper. As ISHM sensor technology gets smaller, more robust, and includes wireless interfaces for communication and power, the approach will be to connect these wireless sensors by adding state-of-the-art wireless technology to the X-33 developed RHN. This wireless approach eliminates connectors and cables, thus reducing development, installation and life cycle costs while improving reliability and flexibility of the ISHM systems.

Desert RATS 2011: Human and robotic exploration of near-Earth asteroids

The Desert Research and Technology Studies (D-RATS) 2011 field test involved the planning and execution of a series of exploration scenarios under operational conditions similar to those expected during a human exploration mission to a near-Earth asteroid (NEA). The focus was on understanding the operations tempo during simulated NEA exploration and the implications of communications latency and limited data bandwidth. Anchoring technologies and sampling techniques were not evaluated due to the immaturity of those technologies and the inability to meaningfully test them at D-RATS. Reduced gravity analogs and simulations are being used to fully evaluate Space Exploration Vehicle (SEV) and extravehicular (EVA) operations and interactions in near-weightlessness at a NEA as part of NASA's integrated analogs program. Hypotheses were tested by planning and performing a series of 1-day simulated exploration excursions comparing test conditions all of which involved a single Deep Space Habitat (DSH) and either 0, 1, or 2 SEVs; 3 or 4 crewmembers; 1 of 2 different communications bandwidths; and a 50-second each-way communications latency between the field site and Houston. Excursions were executed at the Black Point Lava Flow test site with a remote Mission Control Center and Science Support Room at Johnson Space Center (JSC) being operated with 50-second each-way communication latency to the field. Crews were composed of astronauts and professional field geologists. Teams of Mission Operations and Science experts also supported the mission simulations each day. Data were collected separately from the Crew, Mission Operations, and Science teams to assess the test conditions from multiple perspectives. For the operations tested, data indicates practically significant benefits may be realized by including at least one SEV and by including 4 versus 3 crewmembers in the NEA exploration architecture as measured by increased scientific data quality, EVA exploration time, capability assessment ratings, and consensus acceptability ratings provided by Crew, Mission Operations, and Science teams. A combination of text and voice was used to effectively communicate over the communications latency, and increased communication bandwidth yielded a small but practically significant improvement in overall acceptability as rated by the Science team, although the impact of bandwidth on scientific strategic planning and public outreach was not assessed. No effect of increased bandwidth was observed with respect to Crew or Mission Operations team ratings of overall acceptability.

Radiation effects on composites for long-duration lunar habitats

Fiber-reinforced composites are of great interest to NASA for deep-space habitation missions due to the specific strength, modulus and potential radiation shielding properties. However, the durability of these materials on long-duration missions has not been evaluated. Few studies have been conducted on the radiation effects of fiber-reinforced composites in space and even fewer have been conducted with high-energy protons, which replicate portions of the deep-space radiation environment. Furthermore, previous studies of carbon fiber-reinforced composites focused on pure epoxy composites, and aerospace composites in use today include toughening agents to increase the toughness of the material. These toughening agents are typically either rubber particles or thermoplastics, known to be susceptible to ionizing radiation, and could affect the overall composite durability when exposed to high-energy protons. Thus, NASA has undertaken a study to understand the long-term radiation effects on one such potential composite for use in deep-space habitats (boron fiber, carbon fiber and semi-toughened epoxy). Samples were irradiated with 200 MeV protons in air to different doses and evaluated via tensile tests, differential scanning calorimetry, Fourier transform infrared spectroscopy and scanning electron microscopy. The results showed evidence of a weakened matrix due to scission effects and interfacial failure as a result of resin debonding from the boron fibers.