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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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Multipurpose analog pulse height computer performs real-time arithmetic operations on relatively fast pulses. This computer can be used for identification of charged particles, pulse shape discrimination, division of signals from position sensitive detectors, and other on-line data reduction techniques.
At the Ames Aeronautical Laboratory some flight and analog computer studies have been made on the final attack phase of automatic interceptions. The ultimate objective of these studies is to define the behavior of various types of automatic control systems as influenced by a wide range of aerodynamic characteristics. This is a continuing program still in its initial stages; however, it is felt that conclusions of general significance can be drawn from these preliminary studies. One point of particular importance is the necessity for including a gain charger or computer in the azimuth loop of any bank-to-turn airplane or missile. This device translates azimuth error signals into appropriate roll commands. The results to date have shown that the characteristics of this component have a predominant effect on the behavior of a automatic system. It is the purpose of this paper to discuss considerations of importance in the design of a suitable roll-angle computer. In this regard, the present paper complements the analytical work reported in NACA RM L54E27, 1954.
Experimental studies and computer simulations of water immersion are summarized and discussed with regard to their utility as analogs of weightlessness. Emphasis is placed on describing and interpreting the renal, endocrine, fluid, and circulatory changes that take place during immersion. A mathematical model, based on concepts of fluid volume regulation, is shown to be well suited to simulate the dynamic responses to water immersion. Further, it is shown that such a model provides a means to study specific mechanisms and pathways involved in the immersion response. A number of hypotheses are evaluated with the model related to the effects of dehydration, venous pressure disturbances, the control of ADH, and changes in plasma-interstitial volume. By inference, it is suggested that most of the model's responses to water immersion are plausible predictions of the acute changes expected, but not yet measured, during space flight. One important prediction of the model is that previous attempts to measure a diuresis during space flight failed because astronauts may have been dehydrated and urine samples were pooled over 24-hour periods.
Control and trajectory optimization involves the minimization of a performance index (PI) of integral form where some optimal control law exists in a dynamic system. In this paper, a hybrid minicomputer with an adaptive random-search algorithm implements an iterative search for the optimal control. The search assumes that some initial control is randomly perturbed and a fast analog computer generates respective PI from the analog response of the dynamic system. An improved PI informs the digital computer to utilize the perturbed control as a basis for the next iteration; otherwise a new perturbation replaces the old perturbation in the next iteration. The search terminates when no further improvements occur.
The objectives of this research were to develop techniques of controlling a dc-motor driven flywheel which would apply torque to the structure to which it was mounted. The motor control system was to be implemented using a microprocessor based controller. The purpose of the torque applied by this system was to dampen oscillations of the structure to which it was mounted. Before the work was terminated due to the unavailability of equipment, a system was developed and partially tested which would provide tight control of the flywheel velocity when it received a velocity command in the form of a voltage. The procedure followed in this development was to first model the motor and flywheel system on an analog computer. Prior to the time the microprocessor development system was available, an analog control loop was replaced by the microprocessor and the system was partially tested.
The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific and engineering applications. This paper investigates the potential of memristive crossbars in implementing matrix inversion algorithms. We explore both static and dynamic approaches, emphasizing the advantages of analog and in-memory computing for matrix operations beyond multiplication. In particular, we demonstrate that the electrical properties of memristive crossbars uniquely suit them for the evolution of a family of matrix exponentials, which can be exploited for the efficient computation of matrix inverses and online solutions for linear problems. Our results demonstrate that memristive arrays can reduce computational complexity. We also study power consumption and show a tradeoff between precision and energy. Furthermore, we address the challenges of device variability, precision, and scalability, providing insights into the practical implementation of these algorithms.
A survey is presented of shock and vibration technology for electrical systems developed by the aerospace programs. The shock environment is surveyed along with new techniques for modeling, computer simulation, damping, and response analysis. Design techniques based on the use of analog computers, shock spectra, optimization, and nonlinear isolation are discussed. Shock mounting of rotors for performance and survival, and vibration isolation techniques are reviewed.
Analog and digital computer simulation of proposed X-15 flight conditions effect on safe component operation
Data channel interfaces for digital and analog computers and applications to computerized simulation of variable time lags
Abstract The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.
The maximum principle is applied to minimum-time optimal-control problems, and an optimization algorithm is presented which can be implemented on a hybrid computer. The state and adjoint equations are set up on ASTRAC 2, a high-speed analog computer capable of 1000 differential equation solutions per second. The optimization algorithm is implemented on a PDP-9, an 18-bit, digital computer. The optimization scheme has global and local search phases and uses a vector optimization criterion. Second and third-order bang-bang control systems are studied as examples.
The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.
Hybrid computer determines the probability distributions of instantaneous and peak amplitudes of random signals. This combined digital and analog computer system reduces the errors and delays of manual data analysis.
Human describing functions measured in flight by T-33 variable-stability airplane, and by ground simulation of instrument flight of same aircraft and on analog computer with contact display
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML) and silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.
The Langley Research Center uses radio-controlled, scaled aircraft models to study the spin-entry characteristics of aircraft. Recent spin-entry studies required the use of an electronic proportional-control system for manipulating model control surfaces. In order to meet control system requirements, a special-purpose analog computer was designed to simulate the coupling between roll and pitch controls. A digital encoder was designed to encode the voltage analogs of control-surface position into a special pulse format for transmission to the model. This paper describes the two special developments and their relationship to the functions of the overall control system.
A wind-tunnel investigation was made to determine the longitudinal- and lateral-stability derivatives of a flat-top wing-body configuration at Mach numbers from 0.22 to 0.90 and Reynolds numbers of 3.5 and 17 million. The wing had a leading-edge sweepback of 78.9 deg and a cathedral of 45 deg on the outer panels. The tests included the determination of the effectiveness of elevon and rudder controls and also an investigation of ground effects. The model was tested at angles of attack up to 28 deg and angles of sideslip up to 18 deg. The dynamic response of this configuration has been determined from the wind-tunnel data for a simulated airplane having a wing loading of 17.7 pounds per square foot. The longitudinal data show a forward shift in aerodynamic center of 10 percent of the mean aerodynamic chord as the lift coefficient is increased above 0.1. Although flown in the lift range of decreasing stability, the simulated airplane did not encounter pitch-up in maneuvers initiated from steady level flight with zero static margin unless a load factor of 2.2 was exceeded. This maneuver margin was provided by a large value of pitching moment due to pitching velocity. The number of cycles to damp the Dutch roll mode to half amplitude, the time constants of the roll subsidence and spiral divergence modes, and control effectiveness in roll are computed. The lateral stability is shown to be positive but is marginal in meeting the military specifications for today's aircraft. An analog computer study has been made in five degrees of freedom (constant velocity) which illustrates that the handling characteristics are satisfactory. Several programed rolling maneuvers and coordinated turns also illustrate the handling qualities of the airplane.
A real-time hybrid computer simulation of the F100-PW-100 augmented turbofan is presented. The digital portion of the hybrid computer is used to perform the bivariate function generation associated with modeling the performance of the engine's rotating components. The remaining calculations are performed on the analog computer. Steady state simulation data along with sea level, static, transient data are presented to show that the real-time simulation matches baseline digital simulation results over a wide range of power settings and flight conditions. Steady state simulation data are compared with sea level, experimental data to show that the real-time hybrid and baseline digital simulations do adequately predict the performance of the actual engine. FORTRAN listings and analog patching diagrams are provided.