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The aim of the present research program is the development of more efficient and reliable calculation schemes for the coupled momentum and continuity equations. The resulting schemes would significantly reduce the expense of computing complex flows such as those in combustion chambers, gas turbines, and heat exchangers.
As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.
As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.
The design of a new parallel-processing digital simulator is described. The new simulator has been developed specifically for analysis of wind energy systems in real time. The new processor has been named: the Wind Energy System Time-domain simulator, version 3 (WEST-3). Like previous WEST versions, WEST-3 performs many computations in parallel. The modules in WEST-3 are pure digital processors, however. These digital processors can be programmed individually and operated in concert to achieve real-time simulation of wind turbine systems. Because of this programmability, WEST-3 is very much more flexible and general than its two predecessors. The design features of WEST-3 are described to show how the system produces high-speed solutions of nonlinear time-domain equations. WEST-3 has two very fast Computational Units (CU's) that use minicomputer technology plus special architectural features that make them many times faster than a microcomputer. These CU's are needed to perform the complex computations associated with the wind turbine rotor system in real time. The parallel architecture of the CU causes several tasks to be done in each cycle, including an IO operation and the combination of a multiply, add, and store. The WEST-3 simulator can be expanded at any time for additional computational power. This is possible because the CU's interfaced to each other and to other portions of the simulation using special serial buses. These buses can be 'patched' together in essentially any configuration (in a manner very similar to the programming methods used in analog computation) to balance the input/ output requirements. CU's can be added in any number to share a given computational load. This flexible bus feature is very different from many other parallel processors which usually have a throughput limit because of rigid bus architecture.
Computationally complex primitive operations of an algorithm are executed concurrently in a plurality of functional units under the control of an assignment manager. The algorithm is preferably defined as a computationally marked graph contianing data status edges (paths) corresponding to each of the data flow edges. The assignment manager assigns primitive operations to the functional units and monitors completion of the primitive operations to determine data availability using the computational marked graph of the algorithm. All data accessing of the primitive operations is performed by the functional units independently of the assignment manager.
Runtime monitors for future-time unbounded temporal logics like RVLTL, LTL 3 and FLTL, have double-exponential (2^2^n) worst-case space complexity bounds in size of the input formula. The semantics of these logics require monitors to perform general satisfiability solving for LTL expressions, a well-studied problem whose computational complexity is NP-hard and PSPACE-complete. This paper introduces an unbounded future-time linear temporal logic defined over a lattice. We call our logic an incremental temporal logic as it can be viewed as incrementally constructing proofs about the trace. On this account, we view online runtime monitoring as a decision procedure for proofs systems about incrementally growing traces. We demonstrate that our incremental temporal logic allows monitor construction to void satisfiability solving while still soundly detecting when the property is violated in an online fashion. This enables asymptotic improvements in space complexity. As proof, we provide a procedure to construct monitors that utilize linear space and time in the size of the input formula, while remaining constant in the size of the input stream and suitable for online monitoring. We further demonstrate, through several examples, that our incremental temporal logic is straightforward to adopt and practical for runtime verification.
This chapter examines a class of human-computer interaction applications, specifically the design of human-computer interaction for the operators of complex systems. Such systems include space systems (e.g., manned systems such as the Shuttle or space station, and unmanned systems such as NASA scientific satellites), aviation systems (e.g., the flight deck of 'glass cockpit' airplanes or air traffic control) and industrial systems (e.g., power plants, telephone networks, and sophisticated, e.g., 'lights out,' manufacturing facilities). The main body of human-computer interaction (HCI) research complements but does not directly address the primary issues involved in human-computer interaction design for operators of complex systems. Interfaces to complex systems are somewhat special. The 'user' in such systems - i.e., the human operator responsible for safe and effective system operation - is highly skilled, someone who in human-machine systems engineering is sometimes characterized as 'well trained, well motivated'. The 'job' or task context is paramount and, thus, human-computer interaction is subordinate to human job interaction. The design of human interaction with complex systems, i.e., the design of human job interaction, is sometimes called cognitive engineering.
The use of N microprocessors in the SIMD mode of parallel processing to do classifications almost N times faster than a single microprocessor is discussed. Examples of contextual classifiers are given, uniprocessor algorithms for performing contextual classifications are presented, and their computational complexity is analyzed. The SIMD mode of parallel processing is defined and PASM is overviewed. The presented uniprocessor algorithms are used as a basis for developing parallel algorithms for performing computationally intensive contextual classifications.
Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.
A prototype decoder for a serially concatenated pulse position modulation (SCPPM) code has been implemented in a field-programmable gate array (FPGA). At the time of this reporting, this is the first known hardware SCPPM decoder. The SCPPM coding scheme, conceived for free-space optical communications with both deep-space and terrestrial applications in mind, is an improvement of several dB over the conventional Reed-Solomon PPM scheme. The design of the FPGA SCPPM decoder is based on a turbo decoding algorithm that requires relatively low computational complexity while delivering error-rate performance within approximately 1 dB of channel capacity. The SCPPM encoder consists of an outer convolutional encoder, an interleaver, an accumulator, and an inner modulation encoder (more precisely, a mapping of bits to PPM symbols). Each code is describable by a trellis (a finite directed graph). The SCPPM decoder consists of an inner soft-in-soft-out (SISO) module, a de-interleaver, an outer SISO module, and an interleaver connected in a loop (see figure). Each SISO module applies the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm to compute a-posteriori bit log-likelihood ratios (LLRs) from apriori LLRs by traversing the code trellis in forward and backward directions. The SISO modules iteratively refine the LLRs by passing the estimates between one another much like the working of a turbine engine. Extrinsic information (the difference between the a-posteriori and a-priori LLRs) is exchanged rather than the a-posteriori LLRs to minimize undesired feedback. All computations are performed in the logarithmic domain, wherein multiplications are translated into additions, thereby reducing complexity and sensitivity to fixed-point implementation roundoff errors. To lower the required memory for storing channel likelihood data and the amounts of data transfer between the decoder and the receiver, one can discard the majority of channel likelihoods, using only the remainder in operation of the decoder. This is accomplished in the receiver by transmitting only a subset consisting of the likelihoods that correspond to time slots containing the largest numbers of observed photons during each PPM symbol period. The assumed number of observed photons in the remaining time slots is set to the mean of a noise slot. In low background noise, the selection of a small subset in this manner results in only negligible loss. Other features of the decoder design to reduce complexity and increase speed include (1) quantization of metrics in an efficient procedure chosen to incur no more than a small performance loss and (2) the use of the max-star function that allows sum of exponentials to be computed by simple operations that involve only an addition, a subtraction, and a table lookup. Another prominent feature of the design is a provision for access to interleaver and de-interleaver memory in a single clock cycle, eliminating the multiple clock-cycle latency characteristic of prior interleaver and de-interleaver designs.
Issues associated with analysis of 'icing effects' on airfoil and wing performances are discussed, along with accomplishments and efforts to overcome difficulties with ice. Because of infinite variations of ice shapes and their high degree of complexity, computational 'icing effects' studies using available software tools must address many difficulties in geometry acquisition and modeling, grid generation, and flow simulation. The value of each technology component needs to be weighed from the perspective of the entire analysis process, from geometry to flow simulation. Even though CFD codes are yet to be validated for flows over iced airfoils and wings, numerical simulation, when considered together with wind tunnel tests, can provide valuable insights into 'icing effects' and advance our understanding of the relationship between ice characteristics and their effects on performance degradation.
A high speed common data buffer system is described for providing an interface and communications medium between a plurality of computers utilized in a distributed computer complex forming part of a checkout, command and control system for space vehicles and associated ground support equipment. The system includes the capability for temporarily storing data to be transferred between computers, for transferring a plurality of interrupts between computers, for monitoring and recording these transfers, and for correcting errors incurred in these transfers. Validity checks are made on each transfer and appropriate error notification is given to the computer associated with that transfer.
A precise statement of the relationship between sequential computation at one rate, parallel or pipeline computation at a much higher rate, the data movement rate between levels of memory, the fraction of inherently sequential operations or data that must be processed sequentially, the fraction of data to be moved that cannot be overlapped with computation, and the relative computational complexity of the algorithms for the two processes, scalar and vector, was developed. The relationship should be applied to the multirate processes that obtain in the employment of various new or proposed computer architectures for computational aerodynamics. The relationship, an efficiency measure that the single user of the computer system perceives, argues strongly in favor of separating scalar and vector processes, sometimes referred to as loosely coupled processes, to achieve optimum use of hardware.
It is often proposed that the redundancy in choosing a force distribution for multiple arms grasping a single object should be handled by minimizing a quadratic performance index. The performance index may be formulated in terms of joint torques or in terms of the Cartesian space force/torque applied to the body by the grippers. The former seeks to minimize power consumption while the latter minimizes body stresses. Because the cost functions are related to each other by a joint angle dependent transformation on the weight matrix, it might be argued that either method tends to reduce power consumption, but clearly the joint space minimization is optimal. A comparison of these two options is presented with consideration given to computational cost and power consumption. Simulation results using a two arm robot system are presented to show the savings realized by employing the joint space optimization. These savings are offset by additional complexity, computation time and in some cases processor power consumption.
The discretization of separable elliptic partial differential equations leads to linear systems with special block triangular matrices. Several methods are known to solve these systems, the most general of which is the Block Cyclic Reduction (BCR) algorithm which handles equations with nonconsistant coefficients. A method was recently proposed to parallelize and vectorize BCR. Here, the mapping of BCR on distributed memory architectures is discussed, and its complexity is compared with that of other approaches, including the Alternating-Direction method. A fast parallel solver is also described, based on an explicit formula for the solution, which has parallel computational complexity lower than that of parallel BCR.
In this article, we present a reduced order modeling approach suitable for active control of fluid dynamical systems based on proper orthogonal decomposition (POD). The rationale behind the reduced order modeling is that numerical simulation of Navier-Stokes equations is still too costly for the purpose of optimization and control of unsteady flows. We examine the possibility of obtaining reduced order models that reduce computational complexity associated with the Navier-Stokes equations while capturing the essential dynamics by using the POD. The POD allows extraction of certain optimal set of basis functions, perhaps few, from a computational or experimental data-base through an eigenvalue analysis. The solution is then obtained as a linear combination of these optimal set of basis functions by means of Galerkin projection. This makes it attractive for optimal control and estimation of systems governed by partial differential equations. We here use it in active control of fluid flows governed by the Navier-Stokes equations. We show that the resulting reduced order model can be very efficient for the computations of optimization and control problems in unsteady flows. Finally, implementational issues and numerical experiments are presented for simulations and optimal control of fluid flow through channels.
As part of its preparation for the operational Shuttle era, the Goddard Space Flight Center (GSFC) is currently replacing most of the mission operations computing complexes that have supported near-earth space missions since the late 1960's. Major associated systems include the Metric Data Facility (MDF) which preprocesses, stores, and forwards all near-earth satellite tracking data; the Orbit Computation System (OCS) which determines related production orbit and attitude information; the Flight Dynamics System (FDS) which formulates spacecraft attitude and orbit maneuvers; and the Command Management System (CMS) which handles mission planning, scheduling, and command generation and integration. Management issues and experiences for the resultant replacement process are driven by a wide range of possible future mission requirements, flight-critical system aspects, complex internal system interfaces, extensive existing applications software, and phasing to optimize systems evolution.
In the application of finite-difference methods to obtain numerical solutions of viscous compressible fluid flow about wing-fuselage bodies, it is advantageous to transform the governing equations to an idealized boundary-fitted coordinate system. The advantages are reduced computational complexity and added accuracy in the application of boundary conditions. The solution process requires that a grid be superimposed on the physical solution domain which corresponds to a uniform grid on a rectangular computational domain (uniform rectangular parallel-epiped). Grid generation is the determination of a one to one relationship between grid points in the physical domain and grid points in the computational domain. A technique for computing wing-fuselage surface grids using the Harris geometry and software for smooth-surface representation is described. Grid spacing control concepts which govern the relationship between the wing-fuselage surface and the computational grid are also presented.