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At least 289 records · Page 16

Risk analysis simulation of rover operations for Mars surface exploration

Risk management advocates have long sought to directly influence the early stages of the systems engineering process through a more effective role in system design trade studies. The principal obstacle to this has been the lack of credible ways to represent and quantify mission risk—that is, a combination of the probability of mission success (“system safety”) and science value—for the project manager and the rest of the design team. If it were possible to quantify mission risk, then the effects of proposed mission and system design changes could be calculated, and along with life-cycle costs, could be used to explore the design space more extensively and select better designs. JPL has been working to build the capability to quantify the probability of mission success using a federation of diverse simulations and models, each of which contributes some vital piece of the puzzle. The initial institutional focus has been on Mars surface operations. This ensemble computing framework enables the diverse models and simulations to work together seamlessly. Recent work at JPL has demonstrated the capability to exercise this ensemble from end-to-end using an Oracle-based database to automatically move results from one model/simulation to the next stage in the analysis.

Shishko, Robert↗

Terminal Descent Radar System Testbed for Future Planetary Landers

Terminal Descent Radars (TDR), or landing radars, have been an integral element of Guidance, Navigation and Control (GN\&C) sensor suites of robotic exploration missions to the Moon and Mars. As plans for new, exciting exploration missions to the Moon, Mars and other planetary bodies are being developed, there is a need for a new generation of TDRs that are smaller, consume less power and are less expensive than previous sensors. The challenge of designing such a landing sensor is twofold: the first is to have well-vetted software tools that allow us to explore the design space for a particular mission scenario and analyze performance of relevant radar architectures. The second challenge is to reduce mass and power requirements of a landing radar without compromising reliability and performance. New design approaches that address these challenges need to be tested and demonstrated in realistic Entry-Descent-Landing (EDL)/Deorbit-Descent-Landing (DDL) scenarios. In this paper, we describe a TDR testbed developed at the Jet Propulsion Laboratory. The testbed is a closed-loop design, analysis and verification capability used to design and evaluate the next generation of landing radars for a variety of EDL/DDL scenarios.

Tope, Michael↗

Terminal Descent Radar System Testbed for Future Planetary Landers

Terminal Descent Radars (TDR), or landing radars, have been an integral element of Guidance, Navigation and Control (GN\&C) sensor suites of robotic exploration missions to the Moon and Mars. As plans for new, exciting exploration missions to the Moon, Mars and other planetary bodies are being developed, there is a need for a new generation of TDRs that are smaller, consume less power and are less expensive than previous sensors. The challenge of designing such a landing sensor is twofold: the first is to have well-vetted software tools that allow us to explore the design space for a particular mission scenario and analyze performance of relevant radar architectures. The second challenge is to reduce mass and power requirements of a landing radar without compromising reliability and performance. New design approaches that address these challenges need to be tested and demonstrated in realistic Entry-Descent-Landing (EDL)/Deorbit-Descent-Landing (DDL) scenarios. In this paper, we describe a TDR testbed developed at the Jet Propulsion Laboratory. The testbed is a closed-loop design, analysis and verification capability used to design and evaluate the next generation of landing radars for a variety of EDL/DDL scenarios.

Tope, Michael↗

Preliminary Assessment of a Distributed Electric Propulsion System for the SUSAN Electrofan

The SUSAN Electrofan is a new hybrid electric large regional jet aircraft concept being studied by NASA that leverages advanced propulsion system technologies such as distributed electric propulsion (DEP) and boundary-layer ingestion (BLI) to reduce fuel consumption and emissions. In order to evaluate the individual benefits of these technologies toward the SUSAN Electrofan’s wing-mounted propulsion systems, three configurations are proposed. The first consists of two underwing pylon-mounted podded propulsors and serves as a baseline, while the second features an underwing pylon-mounted DEP concept with 16 ducted fans in a mail-slot nacelle. The third and final configuration mounts the mail-slot nacelle directly onto the pressure side of the wing to also take advantage of BLI. This paper presents preliminary investigations into the design and performance of the first two propulsion system configurations. This begins with an initial propulsor and mail-slot design where the aeropropulsive design space is explored, and adverse effects are addressed through iterative geometry modifications. The propulsion system configurations are then installed onto a wing–body model to account for integration effects and assess the relative aerodynamic and shaft power performance of each concept. Results indicate the potential benefits of DEP, which come from significant reductions in total drag, provided by operation at much lower propulsor fan pressure ratios.

ARMD↗

Hardware-Efficient Quantum Optimization Layered Algorithms and Experiments

Quantum optimization algorithms, such as QAOA, that implement parametrized stochastic optimization solvers attempt to identify low-energy solutions of Ising systems by exploiting available quantum effects in noisy-intermediate scale machines. Engineering a well-performing parametrized quantum optimization circuit is indeed an exercise in balancing the trade-off between expressivity and implementation complexity. We show that, for MaxCut QAOA circuits defined on native hardware topology (Rigetti’s Aspen Quantum Processors), error-mitigation techniques recover simulated features of the noiseless theory. Moreover, we explore a design space for QAOA-like ansatze that perform well in theory as well as in hardware for fully-connected problems. We also discuss how efficient coherence and entanglement detection methods that could be coupled with quantum optimization experiments require only linear overhead in benchmarking time.

quantum computing↗

2013 R&D 100 Award: ‘Miniapps’ Bolster High Performance Computing

Two Livermore computer scientists served on a Sandia National Laboratories-led team that developed Mantevo Suite 1.0, the first integrated suite of small software programs, also called "miniapps," to be made available to the high performance computing (HPC) community. These miniapps facilitate the development of new HPC systems and the applications that run on them. Miniapps (miniature applications) serve as stripped down surrogates for complex, full-scale applications that can require a great deal of time and effort to port to a new HPC system because they often consist of hundreds of thousands of lines of code. The miniapps are a prototype that contains some or all of the essentials of the real application but with many fewer lines of code, making the miniapp more versatile for experimentation. This allows researchers to more rapidly explore options and optimize system design, greatly improving the chances the full-scale application will perform successfully. These miniapps have become essential tools for exploring complex design spaces because they can reliably predict the performance of full applications.

97 MATHEMATICS AND COMPUTING↗

Neutronic optimization in high conversion Th-{sup 233}U fuel assembly with simulated annealing

This paper reports on fuel design optimization of a PWR operating in a self sustainable Th-{sup 233}U fuel cycle. Monte Carlo simulated annealing method was used in order to identify the fuel assembly configuration with the most attractive breeding performance. In previous studies, it was shown that breeding may be achieved by employing heterogeneous Seed-Blanket fuel geometry. The arrangement of seed and blanket pins within the assemblies may be determined by varying the designed parameters based on basic reactor physics phenomena which affect breeding. However, the amount of free parameters may still prove to be prohibitively large in order to systematically explore the design space for optimal solution. Therefore, the Monte Carlo annealing algorithm for neutronic optimization is applied in order to identify the most favorable design. The objective of simulated annealing optimization is to find a set of design parameters, which maximizes some given performance function (such as relative period of net breeding) under specified constraints (such as fuel cycle length). The first objective of the study was to demonstrate that the simulated annealing optimization algorithm will lead to the same fuel pins arrangement as was obtained in the previous studies which used only basic physics phenomena as guidance for optimization. In the second part of this work, the simulated annealing method was used to optimize fuel pins arrangement in much larger fuel assembly, where the basic physics intuition does not yield clearly optimal configuration. The simulated annealing method was found to be very efficient in selecting the optimal design in both cases. In the future, this method will be used for optimization of fuel assembly design with larger number of free parameters in order to determine the most favorable trade-off between the breeding performance and core average power density. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

CanSat Competition: Contributing to the Development of NASA's Vision for Robotic Space Exploration

CanSat is an international student design-build-launch competition organized by the American Astronautical Society (AAS) and American Institute of Aeronautics and Astronautics (AIAA). The competition is also sponsored by the Naval Research Laboratory (NRL) and the National Aeronautics and Space Administration (NASA). The CanSat competition is designed for college, university and high school students wanting to participate in an applicable space-related competition. The objective of the CanSat competition is to complete space exploration missions by designing a specific system for a small sounding rocket payload which will follow and perform to a specific set of rules and guidelines for each year's competition. The competition encompasses a complete life-cycle of one year which includes all phases of design, integration, testing, judging and competition. The mission guidelines are based from space exploration missions and include bonus requirement options which teams may choose to participate in. The fundamental goal of the competition is to educate future engineers and scientists. This is accomplished by students applying systems engineering practices to a development project that incorporates an end-to-end life cycle, from requirements analysis, through preliminary design, integration and testing, an actual flight of the CanSat, and concluding with a post-mission debrief. This is done specifically with space related missions to bring a unique aspect of engineering and design to the competition. The competition has been progressing since its creation in 2005. The competition was originally meant to purely convey the engineering and design process to its participants, but through many experiences the competition has also undergone a learning experience with respect to systems engineering process and design. According

Berman, Joshua↗

Managing Analysis Models in the Design Process

Design of large, complex space systems depends on significant model-based support for exploration of the design space. Integrated models predict system performance in mission-relevant terms given design descriptions and multiple physics-based numerical models. Both the design activities and the modeling activities warrant explicit process definitions and active process management to protect the project from excessive risk. Software and systems engineering processes have been formalized and similar formal process activities are under development for design engineering and integrated modeling. JPL is establishing a modeling process to define development and application of such system-level models.

design↗

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection

Abstract Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I , two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.

36 MATERIALS SCIENCE↗

Space transportation options for the Space Exploration Initiative

The candidate architectures of space transportation vehicles designed for the Space Exploration Initiative are discussed together with the launch systems concepts. Special attention is given to the lunar mission and Mars mission scenarios and the launch- and transfer-vehicle options for Mars and moon missions.

Huber, William G.↗

Plans and Considerations for the Exploration of Space

The Mars Settlement Design Competition is a program for high school students and teachers to experience the process of mission and hardware design. It provides a top level view into how NASA plans to explore space. I will be involved with all three days of this competition. On Friday I plan to give two presentations, one to the employees of White Sands Test Facility and one to students and teachers. On Saturday, I will have a question and answer session with some of the teachers participating in the workshop. Sunday I will serve as one of the judges that will review the students projects created over the weekend. The main emphasis of my talk will focus on exploring the possibilities of the future of space exploration. I will discuss the Mars Reference Mission 3.0, as well as some of the current robotic missions being sent to Mars. Next, I will present a business model perfected by Hum Mandell, showing how the public, private, and commercial sectors all play a major role in sending humans to Mars. I will also discuss the work of the Integrated Design Team at JSC and how that working together approach is key for a successful design. Finally, I will present that the question of how humans can reach out beyond low earth orbit and place permanent settlements on Mars is really a function of the imagination of those who intend on going there.

Derkowski, Brian J.↗

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of !2 to an !5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Christopher J Sullivan↗

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of L2 to an L5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Christopher J. Sullivan↗

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of 𝐿2 to an 𝐿5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Mashiku, Alinda K.↗

Space in Space: Designing for Privacy in the Workplace

Privacy is cultural, socially embedded in the spatial, temporal, and material aspects of the lived experience. Definitions of privacy are as varied among scholars as they are among those who fight for their personal rights in the home and the workplace. Privacy in the workplace has become a topic of interest in recent years, as evident in discussions on Big Data as well as the shrinking office spaces in which people work. An article in The New York Times published in February of this year noted that "many companies are looking to cut costs, and one way to do that is by trimming personal space". Increasingly, organizations ranging from tech start-ups to large corporations are downsizing square footage and opting for open-office floorplans hoping to trim the budget and spark creative, productive communication among their employees. The question of how much is too much to trim when it comes to privacy, is one that is being actively addressed by the National Aeronautics and Space Administration (NASA) as they explore habitat designs for future space missions. NASA recognizes privacy as a design-related stressor impacting human health and performance. Given the challenges of sustaining life in an isolated, confined, and extreme environment such as Mars, NASA deems it necessary to determine the acceptable minimal amount for habitable volume for activities requiring at least some level of privacy in order to support optimal crew performance. Ethnographic research was conducted in 2013 to explore perceptions of privacy and privacy needs among astronauts living and working in space as part of a long-distance, long-duration mission. The allocation of space, or habitable volume, becomes an increasingly complex issue in outer space due to the costs associated with maintaining an artificial, confined environment bounded by limitations of mass while located in an extreme environment. Privacy in space, or space in space, provides a unique case study of the complex notions of privacy, the impact of design and others on achieving it, and the sensemaking that occurs when privacy is less than expected. The findings show that privacy is not just a personal, individual need but is also a need that is shared among teams and groups. Moreover, the case of space in space reveals the influence the design of the built and social environments have on privacy needs and on achieving privacy. When the level of privacy is less than expected, sensemaking occurs and the lack of privacy is dealt with by means of absencing the present. creating new social norms, and "making space" by manipulating the spatial, temporal, material aspects of the lived experience. Although the Mars habitat study represents an extreme case of privacy in the workplace, lessons learned from outer space are applicable to life in the Earth-bound workplace. A mini-case study was conducted to evaluate office space at the headquarters of a major American airline that illustrates the usefulness of building unexpected bridges between the unknown, unfamiliar Mars habitat and the everyday workplace. The comparative studies reveal insight into the interconnected, social nature of the spatial, temporal, and material aspects of the lived experience and how users of the habitat and office workspace view privacy, self, and others through an embodied, design interaction.

Akin, Jonie↗

System Analysis and Generative Design for Icenode, a Buoyant Vehicle for Measuring Melt Rate Under Ice Shelves

Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean, and their collapse could trigger a meter or more of global sea level rise by the end of the century. Current state-of-the-art predictions for ice shelf behavior in a warming climate have large uncertainty, significantly hindered by a lack of in situ melt rate observations under ice shelves, especially near grounding zones. IceNode is a novel robotic vehicle under development at the NASA Jet Propulsion Laboratory to acquire such measurements, but presents a complex design problem owing to the large design space and conflicting performance requirements. Evaluating a preliminary design requires several types of tedious analysis which prevents rapid iteration and exploration of the design space. We present the implementation of a custom system analysis framework which was used to automate analysis such as resource budgeting, mission simulation, mass and buoyancy balancing, and static landing stability analysis of a given design configuration. Using this framework, we selected parameters which had the largest effects on design success and conducted a generative design study in which we programmatically varied a handful of parameters to generate 1540 different design candidates and test them across 160 different environmental scenarios. The custom built system analysis tool enabled rapid development of critical analysis and automated exploration of a large design space to guide preliminary design of the IceNode vehicle.

Gebara, Christine↗

AI‐Accelerated Optimization of Self‐Assembled Organic Mixed Ionic‐Electronic Conductors (OMIEC) (Final Report)

This document describes research activities, products and outcomes of a DOE-funded program to help accelerate development of Organic Mixed Ionic Electronic Conductors (OMIECs) using neutron scattering and high-throughput experimentation. OMIECs are organic materials that conduct both ionic and electronic charge carriers. For these new OMIEC materials, self-assembling ion-conducting block copolymers (BCPs) are used as a structural template for electronic conducting polymers. This forms OMIECs with long-range structural order that can help facilitate long-range electronic transport. The conductive properties of OMIECs are closely associated with their structure, which is affected by solution conditions and polymer macromolecular designs. Thus high-throughput experimentation has been implemented to explore this large design space effectively. The BCP-CP OMIEC systems explored are composed of ion-conducting diblock or triblock copolymers containing polyethylene oxide (PEO), di(ethylene glycol) ethyl ether acrylate (DEGEEA) or poly(ethylene glycol) methyl ether acrylate) (PEGMEA) hydrophilic blocks. These blocks will be coupled with either polypropylene oxide (PPO) or polyheptafluorobutyl acrylate (PHFBA) hydrophobic blocks to drive assembly. The electronic conducting component consists of several different types of conjugated polymers. Small angle scattering of neutrons and X-rays (SANS/SAXS) as well as electrochemical analysis have been coupled with modern algorithms for autonomous research using artificial intelligence (AI).

36 MATERIALS SCIENCE↗