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Set of charts used in technical portion of the meeting, covering NASA's work in hybrid electric aircraft.
Monolithic microwave integrated circuits (MMIC), which incorporate all the elements of a microwave circuit on a single semiconductor substrate, offer the potential for drastic reductions in circuit weight and volume and increased reliability, all of which make many new concepts in electronic circuitry for space applications feasible, including phased array antennas. NASA has undertaken an extensive program aimed at development of MMICs for space applications. The first such circuits targeted for development were an extension of work in hybrid (discrete component) technology in support of the Advanced Communication Technology Satellite (ACTS). It focused on power amplifiers, receivers, and switches at ACTS frequencies. More recent work, however, focused on frequencies appropriate for other NASA programs and emphasizes advanced materials in an effort to enhance efficiency, power handling capability, and frequency of operation or noise figure to meet the requirements of space systems.
Self-contained undersea working environments with applications to space station EVA environments are discussed. Physiological limitations include decompression, inert gas narcosis, high-pressure nervous system, gas toxicity, and thermal limitations. Work task requirements include drilling support, construction, inspection, and repair. Work systems include hyperbaric diving, atmospheric work systems, tele-operated work systems, and hybrid systems. Each type of work system is outlined in terms of work capabilities, special interface requirements, and limitations. Various operational philosophies are discussed. The evolution of work systems in the subsea industry has been the result of direct operational experience in a competitive market.
Micro-technology enables the manufacturing of all kinds of components for miniature systems or micro-systems, such as sensors, pumps, valves, and channels. The integration of these components into a micro-electro-mechanical system (MEMS) drastically decreases the total system volume and mass. These properties, combined with the increasing need for monitoring and control of small flows in (bio)chemical experiments, makes MEMS attractive for space applications. The level of integration and applied technology depends on the product demands and the market. The ultimate integration is process integration, which results in a one-chip system. An example of process integration is a dosing system of pump, flow sensor, micromixer, and hybrid feedback electronics to regulate the flow. However, for many applications, a hybrid integration of components is sufficient and offers the advantages of design flexibility and even the exchange of components in the case of a modular set up. Currently, we are working on hybrid integration of all kinds of sensors (physical and chemical) and flow system modules towards a modular system; the micro total analysis system (micro TAS). The substrate contains electrical connections as in a printed circuit board (PCB) as well as fluid channels for a circuit channel board (CCB) which, when integrated, form a mixed circuit board (MCB).
A number of coupled particle-element and hybrid particle-element methods have been developed for the simulation of hypervelocity impact problems, to avoid certain disadvantages associated with the use of pure continuum based or pure particle based methods. To date these methods have employed spherical particles. In recent work a hybrid formulation has been extended to the ellipsoidal particle case. A model formulation approach based on Lagrange's equations, with particles entropies serving as generalized coordinates, avoids the angular momentum conservation problems which have been reported with ellipsoidal smooth particle hydrodynamics models.
A recently conceived approach to automated real-time control of the actions of a robotic system enables an embedded real-time planning algorithm to develop plans that are more robust than they would otherwise be, without imposing an excessive computational burden. This approach occupies a middle ground between two prior approaches known in the art as the universal-plan and hybrid approaches. Ever since discovering the performance limitations of taking a sense-plan-act approach to controlling robots, the robotics community has endeavored to follow a behavior-based approach in which a behavior includes a rapid feedback loop between state estimation and motor control. Heretofore, system architectures following this approach have been based, variously, on algorithms that implement universal plans or algorithms that function as hybrids of planners and executives. In a typical universal-plan case, a set of behaviors is merged into the plan, but the system must be restricted to relatively small problem domains to avoid having to reason about too many states and represent them in the plan. In the hybrid approach, one implements actions as small sets of behaviors, each applicable to a limited set of circumstances. Each action is intended to bring the system to a subgoal state. A planning algorithm is used to string these actions together into a sequence to traverse the state space from an initial or current state to a goal state. The hybrid approach works well in a static environment, but it is inherently brittle in a dynamic environment because a failure can occur when the environment strays beyond the region of applicability of the current activity. In the present approach, a system can vary from the hybrid approach to the universal-plan approach, depending on a single integer parameter, denoted n, which can range from 1 to a maximum domain-dependent value of M. As illustrated in the figure, n = 1 represents the hybrid approach, in which each linked action covers a small part of the state space of the system. As n increases, the portion of state space associated with each action and its subgoal grows. When n reaches M, coverage extends over the full state space, so that the system contains a universal plan.
The integration of high-fidelity Computational Fluid Dynamics (CFD) analysis tools with the industrial design process benefits greatly from the robust implementations that are transportable across a wide range of computer architectures. In the present work, a hybrid domain-decomposition and parallelization concept was developed and implemented into the widely-used NASA multi-block Computational Fluid Dynamics (CFD) packages implemented in ENSAERO and OVERFLOW. The new parallel solver concept, PENS (Parallel Euler Navier-Stokes Solver), employs both fine and coarse granularity in data partitioning as well as data coalescing to obtain the desired load-balance characteristics on the available computer platforms. This multi-level parallelism implementation itself introduces no changes to the numerical results, hence the original fidelity of the packages are identically preserved. The present implementation uses the Message Passing Interface (MPI) library for interprocessor message passing and memory accessing. By choosing an appropriate combination of the available partitioning and coalescing capabilities only during the execution stage, the PENS solver becomes adaptable to different computer architectures from shared-memory to distributed-memory platforms with varying degrees of parallelism. The PENS implementation on the IBM SP2 distributed memory environment at the NASA Ames Research Center obtains 85 percent scalable parallel performance using fine-grain partitioning of single-block CFD domains using up to 128 wide computational nodes. Multi-block CFD simulations of complete aircraft simulations achieve 75 percent perfect load-balanced executions using data coalescing and the two levels of parallelism. SGI PowerChallenge, SGI Origin 2000, and a cluster of workstations are the other platforms where the robustness of the implementation is tested. The performance behavior on the other computer platforms with a variety of realistic problems will be included as this on-going study progresses.
This presentation further develops the orthogrid vehicle panel work. Employed Hybrid Module capabilities to assess both low/mid frequency and high frequency models in the VA One simulation environment. The response estimates from three modeling approaches are compared to ground test measurements. Detailed Finite Element Model of the Test Article -Expect to capture both the global panel modes and the local pocket mode response, but at a considerable analysis expense (time & resources). A Composite Layered Construction equivalent global stiffness approximation using SEA -Expect to capture response of the global panel modes only. An SEA approximation using the Periodic Subsystem Formulation. A finite element model of a single periodic cell is used to derive the vibroacoustic properties of the entire periodic structure (modal density, radiation efficiency, etc. Expect to capture response at various locations on the panel (on the skin and on the ribs) with less analysis expense
Presentation to Aerospace Electrical Systems Expo, providing an overview of NASA's work on hybrid electric and all electric propulsion and projecting technology needs.
The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.
This work was performed to establish a labscale hybrid rocket motor test and evaluation capability at NASA Marshall Space Flight Center. The scope included activation of a Labscale Hybrid Motor, determination of baseline burning rates for PMMA fuel, and replication of pressure oscillations for HTPB fuel. The 0.820-in.-diam port, 10-in.-long fuel grains were burned for two seconds with gaseous oxygen. PMMA fuels were tested at oxygen fluxes from 0.047 lbm/sec sq in. to 0.378 lbm/sec sq in., and the HTPB fuel was evaluated at 0.378 lbm/sec sq in. The results showed that the labscale hybrid motor replicated previously reported PMMA fuel regression rates. The results also replicated low-frequency (less than 100 Hz) pressure oscillations that have been observed for HTPB fuels. These results establish the Labscale Hybrid Motor facility at MSFC.
The use of analog environments in preparing for future planetary surface exploration is key in ensuring we both understand the processes shaping other planetary surfaces as well as develop the technology, systems, and concepts of operations necessary to operate in these geologic environments. While conducting fieldwork and testing technology in relevant terrestrial field environments is crucial in this development, it is often the case that operational testing requires a time-intensive iterative process that is hampered by the rigorous conditions (e.g. terrain, weather, location, etc.) found in most field environments. Additionally, field deployments can be costly and must be scheduled months in advance, therefore limiting the testing opportunities required to investigate and compare science operational concepts to only once or twice per year.
Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical model. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. Excellent agreement is achieved between the predicted and measured results, thereby quantitatively validating the numerical tool.
Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.
This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in off-nominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-in-the-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive Bight control approach.
Past work demostrates that strong lower hybrid (LH) waves can be excited by electromagnetic whistler mode waves throughout large regions of the topside ionosphere and magnetosphere. The effects of the excited LH waves upon the suprathermal ion population in the topside ionosphere and magnetosphere depend upon the distribution of LH wave amplitude with wavelength lambda. The present work reports plasma wave data from the DE-1 and COSMOS 1809 spacecraft which suggests that the excited LH wave spectrum has components for which lambda less than or equal to 3.5 m when excitation occurs at a frequency roughly equal to the local lower hybrid resonance frequency. This wavelength limit is a factor of approximately 3 below that reported in past work and suggests that the excited LH waves can interact with suprathermal H(+) ions with energy less than or equal to 6 eV. This finding supports recent work concerning the heating of suprathermal ions above thunderstorm cells.
Electrical actuator systems are being pursued as alternatives to hydraulic systems to reduce maintenance time, weight and costs while increasing reliability. Additionally, safety and environmental hazards associated with the hydraulic fluids can be eliminated. For most actuation systems, the actuation process is typically pulsed with high peak power requirements but with relatively modest average power levels. The power-time requirements for electrical actuators are characteristic of pulsed power technologies where the source can be sized for the average power levels while providing the capability to achieve the peak requirements. Among the options for the power source are battery systems, capacitor systems or battery-capacitor hybrid systems. Battery technologies are energy dense but deficient in power density; capacitor technologies are power dense but limited by energy density. The battery-capacitor hybrid system uses the battery to supply the average power and the capacitor to meet the peak demands. It has been demonstrated in previous work that the hybrid electrical power source can potentially provide a weight savings of approximately 59% over a battery-only source. Electrochemical capacitors have many properties that make them well-suited for electrical actuator applications. They have the highest demonstrated energy density for capacitive storage (up to 100 J/g), have power densities much greater than most battery technologies (greater than 30kW/kg), are capable of greater than one million charge-discharge cycles, can be charged at extremely high rates, and have non-explosive failure modes. Thus, electrochemical capacitors exhibit a combination of desirable battery and capacitor characteristics.
Many hybrid data assimilation systems currently used for NWP employ some form of dual-analysis system approach. Typically a hybrid variational analysis is responsible for creating initial conditions for high-resolution forecasts, and an ensemble analysis system is responsible for creating sample perturbations used to form the flow-dependent part of the background error covariance required in the hybrid analysis component. In many of these, the two analysis components employ different methodologies, e.g., variational and ensemble Kalman filter. In such cases, it is not uncommon to have observations treated rather differently between the two analyses components; recentering of the ensemble analysis around the hybrid analysis is used to compensated for such differences. Furthermore, in many cases, the hybrid variational high-resolution system implements some type of four-dimensional approach, whereas the underlying ensemble system relies on a three-dimensional approach, which again introduces discrepancies in the overall system. Connected to these is the expectation that one can reliably estimate observation impact on forecasts issued from hybrid analyses by using an ensemble approach based on the underlying ensemble strategy of dual-analysis systems. Just the realization that the ensemble analysis makes substantially different use of observations as compared to their hybrid counterpart should serve as enough evidence of the implausibility of such expectation. This presentation assembles numerous anecdotal evidence to illustrate the fact that hybrid dual-analysis systems must, at the very minimum, strive for consistent use of the observations in both analysis sub-components. Simpler than that, this work suggests that hybrid systems can reliably be constructed without the need to employ a dual-analysis approach. In practice, the idea of relying on a single analysis system is appealing from a cost-maintenance perspective. More generally, single-analysis systems avoid contradictions such as having to choose one sub-component to generate performance diagnostics to another, possibly not fully consistent, component.