Historical Approaches to Designing Medical Systems for Space Exploration
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Kris Lehnhardt↗
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Results for “Design Space Exploration”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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PRECISION AND ACCURACY IN SPACE EXPLORATION
System constraints and environmental factors in design of instrumentation for space exploration
SPACECRAFT INSTRUMENTATION↗
Magnetour: Surfing Planetary Systems on Electromagnetic and Multi-Body Gravity Fields
In this NIAC Phase One study, we propose a new mission concept, named Magnetour, to facilitate the exploration of outer planet systems and address both power and propulsion challenges. Our approach would enable a single spacecraft to orbit and travel between multiple moons of an outer planet, with no propellant required. Our approach would enable a single spacecraft to orbit and travel between multiple moons of an outer planet, with no propellant nor onboard power source required. To achieve this free-lunch _Grand Tour', we exploit the unexplored combination of magnetic and multi-body gravitational fields of planetary systems, with a unique focus on using a bare tether for power and propulsion. The main objective of the study is to develop this conceptually novel mission architecture, explore its design space, and investigate its feasibility and applicability to enhance the exploration of planetary systems within a 10-year timeframe. Propellantless propulsion technology offers enormous potential to transform the way NASA conducts outer planet missions. We hope to demonstrate that our free-lunch tour concept can replace heavy, costly, traditional chemical-based missions and can open up a new variety of trajectories around outer planets. Leveraging the powerful magnetic and multi-body gravity fields of planetary systems to travel freely among planetary moons would allow for long-term missions and provide unique scientific capabilities and flagship-class science for a fraction of the mass and cost of traditional concepts. New mission design techniques are needed to fully exploit the potential of this new concept.This final report contains the results and findings of the Phase One study, and is organized as follows. First, an overview of the Magnetour mission concept is presented. Then, the research methodology adopted for this Phase One study is described, followed by a brief outline of the main findings and their correspondence with the original Phase One task plan. Next, an overview of the environment of outer planets is provided, including magnetosphere, radiation belt and planetary moons. Then performance of electrodynamic tethers is assessed, as well as other electromagnetic systems. A method to exploit multi-body dynamics is given next. These analyses allow us to carry out a Jovian mission design to gain insight in the benefits of Magnetour. In addition, a spacecraft configuration is presented that fully incorporates the tether in the design. Finally technology roadmap considerations are discussed.
Gravity Fields↗
Universal stowage module for future space exploration
The development, design, and fabrication of a prototype storage module, with internal restraints, for the stowage of items that are normally launched to support a space mission are discussed. The primary design criteria was that the storage module be universal in accomodating most sizes and shapes of items that could be launched and returned in a shuttle payload. Mechanical drawings of various types of storage modules are provided.
Seccamp, V. A.↗
150 Passenger Commercial Aircraft
It has been projected that the need for a short-range mid-sized, aircraft is increasing. The future strategy to decrease long-haul flights will increase the demand for short-haul flights. Since passengers prefer to meet their destinations quickly, airlines will increase the frequency of flights, which will reduce the passenger load on the aircraft. If a point-to-point flight is not possible, passengers will prefer only a one-stop short connecting flight to their final destination. A 150-passenger aircraft is an ideal vehicle for these situations. It is mid-sized aircraft and has a range of 3000 nautical miles. This type of aircraft would market U.S. domestic flights or inter-European flight routes. The objective of the design of the 150-passenger aircraft is to minimize fuel consumption. The configuration of the aircraft must be optimized. This aircraft must meet CO2 and NOx emissions standards with minimal acquisition price and operating costs. This report contains all the work that has been performed for the completion of the design of a 150 passenger commercial aircraft. The methodology used is the Technology Identification, Evaluation, and Selection (TIES) developed at Georgia Tech Aerospace Systems Design laboratory (ASDL). This is an eight-step conceptual design process to evaluate the probability of meeting the design constraints. This methodology also allows for the evaluation of new technologies to be implemented into the design. The TIES process begins with defining the problem with a need established and a market targeted. With the customer requirements set and the target values established, a baseline concept is created. Next, the design space is explored to determine the feasibility and viability of the baseline aircraft configuration. If the design is neither feasible nor viable, new technologies can be implemented to open up the feasible design space and allow for a plausible solution. After the new technologies are identified, they must be evaluated to determine the physical compatibility of integrating multiple technologies and then the impact on the design, both improvements and degradations, must be determined. These technologies are assessed deterministically. Again, Response Surface Equations (RSEs) are developed to allow for a full factorial evaluation of the combinations of the technologies. The best combination of technologies is selected and then the design space is again reevaluated for feasibility and viability.
Bucovsky, Adrian↗
HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters
Popular HPC network interconnection simulators such as SST/macro provide a variety of configurable parameters to explore the design space of hardware components such as network interface cards (NIC), switches, and links among them. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for matching configurations of the existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or intentionally omitted. In this work, we propose a framework to autotune the multiple network models’ simulation configurations within SST/macro using Tree-structured Parzen Estimator-based Bayesian optimization to observe the effect on simulation accuracy across different message regimes. These regimes consist of small to large message sizes and latency to bandwidth-bound messages. We provide a detailed analysis of the simulation error for four representative HPC systems. Our Bayesian optimization based autotuning framework for network models achieves a maximum of 5x improvement in accuracy over best-effort manual configurations based on available hardware specifications.
Simulation, autotuning↗
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data
The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. Here, we demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Optimization of Microelectronic Devices for Sensor Applications
The NASA/JPL goal to reduce payload in future space missions while increasing mission capability demands miniaturization of active and passive sensors, analytical instruments and communication systems among others. Currently, typical system requirements include the detection of particular spectral lines, associated data processing, and communication of the acquired data to other systems. Advances in lithography and deposition methods result in more advanced devices for space application, while the sub-micron resolution currently available opens a vast design space. Though an experimental exploration of this widening design space-searching for optimized performance by repeated fabrication efforts-is unfeasible, it does motivate the development of reliable software design tools. These tools necessitate models based on fundamental physics and mathematics of the device to accurately model effects such as diffraction and scattering in opto-electronic devices, or bandstructure and scattering in heterostructure devices. The software tools must have convenient turn-around times and interfaces that allow effective usage. The first issue is addressed by the application of high-performance computers and the second by the development of graphical user interfaces driven by properly developed data structures. These tools can then be integrated into an optimization environment, and with the available memory capacity and computational speed of high performance parallel platforms, simulation of optimized components can proceed. In this paper, specific applications of the electromagnetic modeling of infrared filtering, as well as heterostructure device design will be presented using genetic algorithm global optimization methods.
Cwik, Tom↗
Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process
Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases.1 Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission.2 Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an Advanced Design Methods (ADM) based approach. This approach applies the concepts of Design of Experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development e ort. In order to t a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of Ground Rules and Assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated \what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are t to the outputs of each environment and are integrated via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any \what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will have the potential to lower LCC of launch vehicle designs originating from ACO.
Zwack, Mathew R.↗
Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process
Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are fit to the outputs of each environment and are "integrated" via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any ''what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will help ensure lower LCC of launch vehicle designs originating from ACO.
Zwack, Mathew R.↗
Parametric Mixer Design Methods for Acoustic Explorations of Internally Mixed Nozzles
As part of a jet noise reduction effort under the Commercial Supersonics Technology project, a process for the design and optimization of a parametric dual-stream lobe mixer is presented. The lobe mixer is fully defined from five independent parameters, including mixer length, lobe count, lobe penetration, lobe bias, and trailing edge slope. The mixers are inserted into an academic, constant-area nozzle with an external plug. The full geometry is meshed in Pointwise v18.4, and the RANS equations are solved in FUN3D v13.6. The results of a DoE study, involving ~80 different mixer designs, are presented, along with an associated parametric model for various performance response quantities. With a design space fully explored, observations and recommendations are given, and future work is discussed to further improve the design.
Dual-stream Mixer↗
Evolution and Control of 2219 Aluminum Microstructural Features Through Electron Beam Freeform Fabrication
The layer-additive nature of the electron beam freeform fabrication (EBF3) process results in a tortuous thermal path producing complex microstructures including: small homogeneous equiaxed grains; dendritic growth contained within larger grains; and/or pervasive dendritic formation in the interpass regions of the deposits. Several process control variables contribute to the formation of these different microstructures, including translation speed, wire feed rate, beam current and accelerating voltage. In electron beam processing, higher accelerating voltages embed the energy deeper below the surface of the substrate. Two EBF3 systems have been established at NASA Langley, one with a low-voltage (10-30kV) and the other a high-voltage (30-60 kV) electron beam gun. Aluminum alloy 2219 was processed over a range of different variables to explore the design space and correlate the resultant microstructures with the processing parameters. This report is specifically exploring the impact of accelerating voltage. Of particular interest is correlating energy to the resultant material characteristics to determine the potential of achieving microstructural control through precise management of the heat flux and cooling rates during deposition.
Taminger, Karen M.↗
Evolution and Control of 2219 Aluminum Microstructural Features through Electron Beam Freeform Fabrication
Electron beam freeform fabrication (EBF3) is a new layer-additive process that has been developed for near-net shape fabrication of complex structures. EBF3 uses an electron beam to create a molten pool on the surface of a substrate. Wire is fed into the molten pool and the part translated with respect to the beam to build up a 3-dimensional structure one layer at a time. Unlike many other freeform fabrication processes, the energy coupling of the electron beam is extremely well suited to processing of aluminum alloys. The layer-additive nature of the EBF3 process results in a tortuous thermal path producing complex microstructures including: small homogeneous equiaxed grains; dendritic growth contained within larger grains; and/or pervasive dendritic formation in the interpass regions of the deposits. Several process control variables contribute to the formation of these different microstructures, including translation speed, wire feed rate, beam current and accelerating voltage. In electron beam processing, higher accelerating voltages embed the energy deeper below the surface of the substrate. Two EBF3 systems have been established at NASA Langley, one with a low-voltage (10-30kV) and the other a high-voltage (30-60 kV) electron beam gun. Aluminum alloy 2219 was processed over a range of different variables to explore the design space and correlate the resultant microstructures with the processing parameters. This report is specifically exploring the impact of accelerating voltage. Of particular interest is correlating energy to the resultant material characteristics to determine the potential of achieving microstructural control through precise management of the heat flux and cooling rates during deposition.
Taminger, Karen M.↗
Environmental Controls and Life Support System (ECLSS) Design for a Multi-Mission Space Exploration Vehicle (MMSEV)
Engineers at Johnson Space Center (JSC) are developing an Environmental Control and Life Support System (ECLSS) design for the Multi-Mission Space Exploration Vehicle (MMSEV). The purpose of the MMSEV is to extend the human exploration envelope for Lunar, Near Earth Object (NEO), or Deep Space missions by using pressurized exploration vehicles. The MMSEV, formerly known as the Space Exploration Vehicle (SEV), employs ground prototype hardware for various systems and tests it in manned and unmanned configurations. Eventually, the system hardware will evolve and become part of a flight vehicle capable of supporting different design reference missions. This paper will discuss the latest MMSEV ECLSS architectures developed for a variety of design reference missions, any work contributed toward the development of the ECLSS design, lessons learned from testing prototype hardware, and the plan to advance the ECLSS toward a flight design.
Stambaugh, Imelda↗
Environmental Controls and Life Support System (ECLSS) Design for a Multi-Mission Space Exploration Vehicle (MMSEV)
Engineers at Johnson Space Center (JSC) are developing an Environmental Control and Life Support System (ECLSS) design for the Multi-Mission Space Exploration Vehicle (MMSEV). The purpose of the MMSEV is to extend the human exploration envelope for Lunar, Near Earth Object (NEO), or Deep Space missions by using pressurized exploration vehicles. The MMSEV, formerly known as the Space Exploration Vehicle (SEV), employs ground prototype hardware for various systems and tests it in manned and unmanned configurations. Eventually, the system hardware will evolve and become part of a flight vehicle capable of supporting different design reference missions. This paper will discuss the latest MMSEV ECLSS architectures developed for a variety of design reference missions, any work contributed toward the development of the ECLSS design, lessons learned from testing prototype hardware, and the plan to advance the ECLSS toward a flight design.
Stambaugh, Imelda↗
Tough Errors are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience
This project builds toward a comprehensive error-mitigating toolkit that makes quantum programming more robust and adaptive to the noisy, resource-limited nature of today’s quantum hardware. To that end, it integrates established error-mitigation methods — such as zero-noise extrapolation and dynamical decoupling — directly into compiler infrastructures. These techniques will be packaged as modules that can automatically adjust and combine based on performance analysis, enabling compilers to explore large design spaces and produce optimized, low-noise quantum programs with minimal manual intervention. In parallel, this project also explores new approaches to analog quantum programming or quantum simulation, and has developed the programming language SimuQ which treats quantum Hamiltonian evolution as the central object.
97 MATHEMATICS AND COMPUTING↗
Design Space Issues for Intrinsic Evolvable Hardware
This paper discusses the problem of increased programming time for intrinsic evolvable hardware (EM) as the complexity of the circuit grows. As the circuit becomes more complex, then more components will be required and a longer programming string, L, is required. We develop equations for the size of the population, n, and the number of generations required for the population to converge, based on L. Our analytical results show that even though the design search space grows as 2L (assuming a binary programming string), the number of circuit evaluations, n*ngen, only grows as O(Lg3), or slightly less than O(L). This makes evolvable techniques a good tool for exploring large design spaces. The major hurdle for intrinsic EHW is evaluation time for each possible circuit. The evaluation time involves downloading the bit string to the device, updating the device configuration, measuring the output and then transferring the output data to the control processor. Each of these steps must be done for each member of the population. The processing time of the computer becomes negligible since the selection/crossover/mutation steps are only done once per generation. Evaluation time presently limits intrinsic evolvable hardware techniques to designing only small or medium-sized circuits. To evolve large or complicated circuits, several researchers have proposed using hierarchical design or reuse techniques where submodules are combined together to form complex circuits. However, these practical approaches limit the search space of available designs and preclude utilizing parasitic coupling or other effects within the programmable device. The practical approaches also raise the issue of why intrinsic EHW techniques do not easily apply to large design spaces, since the analytical results show only an O(L) complexity growth.
Hereford, James↗
Heat Exchanger Performance Impacts on Optimum Cost Conditions in Thermoelectric Energy Recovery Designs
Cost is just as important as power density or efficiency for the adoption of waste heat recovery thermoelectric generators (TEG). Prior work [1] has shown that the system design that minimizes cost (e.g., the $/W value) can be close to the designs that maximize the system’s efficiency or power density, however, it is important to understand the relationship between those designs to optimize TEG performance-cost compromises. Expanding on recent work [1, 2, 3] the impact of heat exchanger conditions on the optimum TEG fill factors and cost scaling of a waste heat recovery thermoelectric generator with a detailed treatment of the hot side exhaust heat exchanger has been investigated further. The effect of the heat lost to the environment and updated relationships between the hot-side and cold-side conductances [4] that maximize power output are considered. The optimum fill factor to minimize TEG energy recovery system costs is strongly dependent on the heat leakage fraction, σ, the mass flow rate of the exhaust, the hot-side heat exchanger effectiveness, heat exchanger UAh, and heat flux. These relationships are explored and characterized for typical exhaust gas-flow conditions to show the inherent design complexities. The heat exchanger costs often dominate the TEG cost equation and it is critical to fully understand the tradeoff between heat exchanger performance, optimum TEG fill factors, and cost to establish potentially optimum design points within the cost-performance design space. This work will explore the design tradeoffs and relationships within the cost-efficiency-power density design space for a typical thermoelectric energy recovery system application. The interplay between optimum TEG fill factors and heat exchanger design can impact system footprint, volume, and mass in weight-sensitive applications. Less-effective, low-cost heat exchangers may outperform higher cost alternatives from a market adoption perspective. This shift of emphasis acknowledging the interdependence of optimum TEG fill factors and heat exchanger performance has significant implications on thermoelectric waste heat recovery systems designs and their operation. In addition, preferred TEG design regimes exist that accommodate reasonable compromises in TE performance and cost. This effort highlights how the optimum fill factor–heat exchanger performance relations couple to these optimum TEG performance-cost domains based on TEG-system-level analyses and provides a focus for future system research and development efforts.
Energy recovery↗