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This presentation of spacecraft power systems to solve planetary protection, includes topics such as Planetary Protection requirements, Enduring Authority of the Outer Space Treaty, Relationship to COSPAR, Planetary Protection Mission Categories (NASA/ESA/COSPAR Policy).
We present a non-interior point based, polynomial-time, algorithm for solving certain classes of rank minimization problem.
On the International Space Station today, the crew has the near real-time support of a large group of system experts on the ground when dealing with problems on-board. For exploration beyond Low Earth Orbit, however, intermittent and delayed communication with ground will force small crews to take the lead in responding to vehicle anomalies. Enabling a flight crew of roughly four astronauts to perform the job that has traditionally been done by a ground crew of over 80 experts will require a fundamental rethinking of human-systems integration. Through observations of anomaly resolution processes, interviews with system experts and astronauts, and analyses of problem-solving models, we have identified the capabilities that are not currently available on-board but will be needed to enable safe exploration further away from Earth. These include increased data access, just-in-time training tools and technologies, and troubleshooting decision support. Important questions remain on how these technologies can be designed and implemented for increased crew autonomy. We present this critical challenge for deep space exploration to the human-computer interaction research community to reflect on the areas identified by our needs analysis and contemplate how they might be manifested as solutions.
Contamination containment gloveboxes are sealed containers where operators outside the glovebox can safely manipulate hazardous or contamination-sensitive materials inside the glovebox through glove ports on the container. Containment gloveboxes are used in the space industry to study sample materials returned from space such as lunar, asteroid, and comet materials, to manufacture sensitive instruments and space ight hardware, and to perform contamination-sensitive experiments. A hands-on activity has been developed by contamination control engineer and STEM education experts as part of the Virginia Earth System Science Scholars (VESSS) summer academy program to teach high school students about gloveboxes and about problem solving in a collaborative environment. In this activity, student teams are given a list of common household materials (e.g. plastic bags, plastic straws, aluminum foil, tape, disposable gloves, etc.) for building their own glovebox. Students work together to sketch and design their glovebox before being provided a kit of materials, which they must purchase along with any extra materials with a mock team budget. They are given 30 minutes to build their glovebox, however, they do not know what will be placed inside the glovebox or what operations they will need to perform within the glovebox. At the end of the build time, teams are given a small, sealed paper envelope which they place in the glovebox. Each team selects a team member to be designated as the operator, and the operator places their hands in the gloves of the glovebox. The operator uses their gloves to open the envelope in the glovebox, and they discover they have 20 minutes to assemble a jigsaw puzzle. However, the puzzle is \contaminated" with talcum powder. After the 20 minute time period, teams are judged on how well they completed and cleaned the contaminated puzzle. In a post-activity debrief, teams discuss lessons learned such as how the actual built box di ered from the sketched design, the challenge of managing a budget for materials, how the team dealt with surprises, and if their glovebox allowed enough room for the operator to perform the task. This activity has been part of the VESSS summer academy since 2016, and teaches students teamwork, creativity, hands-on experimentation, communication, and reasoning skills. In a demonstration at IAC, a volunteer from the audience will be chosen to assemble a glovebox during the presentation using the same kit of materials and will demonstrate handling puzzle pieces in the completed glovebox.
Engineers from the National Aeronautics and Space Administration (NASA) and education experts from the Virginia Space Grant Consortium (VSGC) partnered together to create a hands-on student activity to teach students about problem solving, working in a collaborative environment, and about the unique career fields of contamination control and planetary protection. The activity focuses on contamination containment gloveboxes, which are sealed containers where operators outside the glovebox can safely manipulate hazardous or contamination-sensitive materials inside the glovebox through glove ports on the container. The activity utilizes common household materials and teams of students work together to design and build a glovebox using the materials provided. Once the glovebox has been constructed, students perform a task under a time constraint by using their glovebox to assemble a puzzle “contaminated” with corn starch. In a post-activity debrief, teams discuss lessons learned such as how the actual built glovebox differed from the sketched design, the challenge of managing a budget for materials, how the team dealt with surprises, and if their glovebox allowed enough room for the operator to perform the task. This activity has been part of VSGC’s Virginia Earth System Science Scholars (VESSS) summer academy program for high school students since 2016, and has been an engaging method to teach students teamwork, creativity, hands-on experimentation, communication, and reasoning skills while also teaching them about unique engineering fields such as contamination control and planetary protection.
Thermal protection system analysis of complex features or damage sites can sometimes require modeling of high temperature enclosures. Implementing efficient and accurate view-factor algorithms required to model such problems is complex. The current work leverages the Non-equilibrium Radiation (NERO) software, which solves the radiation transport equation in a finite-volume scheme, to alleviating challenges often faced with view-factor calculations. By assuming heat transfer occurs only between grey bodies and that the medium is non-participating, computational cost of the method is significantly reduced. The enclosure physics are modeled through emitting and reflecting boundary conditions in NERO. The emitted radiative flux is dependent on the wall temperature which is a solution to the material response, obtained from Icarus, in this context. The Ares framework manages the time-advancement and exchange of the necessary data between the solvers. The surface energy balance is modified to account for the enclosure terms within the material response boundary condition. The methodology was verified against analytical solutions including radiating parallel plates, a hollow cylinder (shown in Fig. 1), and a hemisphere. Application of the methodology to inform the design of components of the Dragonfly system will be shown.
Geosteering inversion, which can be viewed as a nonlinear inverse problem, is an important technique used by directional drilling. Traditional methods that rely on iterative procedures and regularization are sensitive to the selection of initial values and can be slow due to convergence issues. In industrial applications, a lookup table is used to produce fast predictions. However, performance is not guaranteed by this approach due to the limitation of the hardware. In this paper, we propose a novel physics-driven deep-learning framework for providing a fast, accurate surrogate to solve the inverse problem. Particularly, leveraged by the forward physical model and 1D convolutional neural network (1D-CNN), the proposed method provides more reliable solutions to the inverse problem with improved performance. In addition, a new physics-driven loss function is introduced to accommodate both the model misfit and the data misfit. In conclusion, our experiments demonstrate the effectiveness of our method.
This paper focuses on using variations of the Frank-Wolfe algorithm for solving unit commitment problems with high volumes of demand responsive loads on the power grid. We present a formulation of the unit commitment problem with demand responsive loads. We then show through reformulation and relaxations of the problem that variations of the Frank-Wolfe algorithm can be used to determine the time series decisions for the demand responsive loads. We show through computational experiments on the RTS-GMLC test system that the time series of demand responsive load decisions obtained through our approach are near optimal and provide details regarding how large scale parallel implementations of our approach can be highly computationally efficient.
AC optimal power flow has proven difficult to solve with interior point methods on GPUs. This is largely due to challenging linear algebra problems that current state of the art massively parallel linear solvers struggle with. However, the advent of Riemannian optimization techniques and the fact that the power flow equations form a smooth manifold present an alternative approach. In this talk, we present the basics of Riemannian optimization techniques in which optimization is done directly on a manifold. Then we present computational results showing that Riemannian techniques are capable of producing solutions of comparable quality as interior point methods.
Engineering education literature offers a variety of theoretical and conceptual frameworks for project-based learning. This study explores the implementation of real-world problem-solving projects in engineering education. The research team analyzed the motivations and methods behind professors' adoption of such projects through exploratory qualitative interviews with seven professor participants who integrated a nation-wide student competition into their courses. We analyzed the resulting data using a constructivist grounded theory approach to identify key themes of professor practices. Findings reveal that the real-world aspect of the projects and alignment with values and research interests were primary motivators for implementation. While implementation methods varied significantly based on context (i.e., university setting, course type), we found that these projects could be effectively integrated into various classroom settings. The findings support the recommendation for non-academic institutions to develop and manage competitions that can be integrated into classrooms and which offer a point of engagement that is available to professors from a wide range of disciplines.
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