Results for “Engineering constraints” — Page 9

NASA NTRS2024

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗

NASA NTRS1992

Loss terms in free-piston Stirling engine models

Various models for free piston Stirling engines are reviewed. Initial models were developed primarily for design purposes and to predict operating parameters, especially efficiency. More recently, however, such models have been used to predict engine stability. Free piston Stirling engines have no kinematic constraints and stability may not only be sensitive to the load, but also to various nonlinear loss and spring constraints. The present understanding is reviewed of various loss mechanisms for free piston Stirling engines and how they have been incorporated into engine models is discussed.

Gordon, Lloyd B.↗

NASA NTRS2004

Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates whose consistency can be tracked across multiple time steps.

Narasimhan, Sriram↗

NASA NTRS1978

Advanced engine study for mixed-mode orbit-transfer vehicles

Engine design, performance, weight and envelope data were established for three mixed-mode orbit-transfer vehicle engine candidates. Engine concepts evaluated are the tripropellant, dual-expander and plug cluster. Oxygen, RP-1 and hydrogen are the propellants considered for use in these engines. Theoretical performance and propellant properties were established for bipropellant and tripropellant mixes of these propellants. RP-1, hydrogen and oxygen were evaluated as coolants and the maximum attainable chamber pressures were determined for each engine concept within the constraints of the propellant properties and the low cycle thermal fatigue (300 cycles) requirement. The baseline engine design and component operating characteristics are determined at a thrust level of 88,964N (20,000 lbs) and a thrust split of 0.5. The parametric data is generated over ranges of thrust and thrust split of 66.7 to 400kN (15 to 90 klb) and 0.4 to 0.8, respectively.

Mellish, J. A.↗

NASA NTRS1996

Seals Code Development Workshop

Seals Workshop of 1995 industrial code (INDSEAL) release include ICYL, GCYLT, IFACE, GFACE, SPIRALG, SPIRALI, DYSEAL, and KTK. The scientific code (SCISEAL) release includes conjugate heat transfer and multidomain with rotordynamic capability. Several seals and bearings codes (e.g., HYDROFLEX, HYDROTRAN, HYDROB3D, FLOWCONl, FLOWCON2) are presented and results compared. Current computational and experimental emphasis includes multiple connected cavity flows with goals of reducing parasitic losses and gas ingestion. Labyrinth seals continue to play a significant role in sealing with face, honeycomb, and new sealing concepts under investigation for advanced engine concepts in view of smct environmental constraints. The clean sheet approach to engine design is advocated with program directions and anticipated percentage SFC reductions cited. Future activities center on engine applications with coupled seal/power/secondary flow streams.

Robert C. Hendrick↗

NASA NTRSDate not supplied

Analysis of Integrated Spacecraft Performance Using A Rotating Detonation Rocket Engine

For spacecraft with especially stringent mass fraction constraints, the use of a rotating detonation rocket engine (RDRE) may yield potentially significant system mass savings. The pressure gain combustion inherent to RDREs provides higher thrust than traditional rocket engines for a given supply pressure and throat area. This opens multiple avenues for reducing system mass via higher specific impulse, reduced gravity losses, and reduced feed pressures. To highlight the potentially weight-saving characteristics of RDRE systems, the integrated performance of representative spacecraft designs using either a conventional rocket engine or an RDRE was assessed. This paper details the assumptions, methods, and results of this trade study. Two representative spacecraft were assessed. The first is a Liquid Mars Ascent Vehicle (MAV). This spacecraft was designed by NASA in 2011 for the Mars Sample Return Campaign but was not selected for flight due in part to it exceeding the mass requirements by 119 lbm. The second is MIURA-1, a kerolox sounding rocket developed by Payload Aerospace S.L.. The Liquid MAV assessment showed that an RDRE powered design met the mass requirements with a total vehicle mass 46 lbm below the limit. The MIURA-1 assessment showed that wall heat transfer and regenerative cooling are significant challenges for kerolox RDREs, but these challenges may be mitigated by reducing mixture ratio and using both propellants to cool the chamber. The assessment showed that an RDRE powered sounding rocket could achieve 12% more microgravity time than a conventional sounding rocket with the same payload and total vehicle mass. Alternately, if the payload mass and target apogee are held constant, then an RDRE powered sounding rocket can be 8% shorter and 11% lighter than a conventional sounding rocket. These results suggest that the advent of RDREs for in-space propulsion may unlock new missions for which conventional propulsion is not feasible.

Rotating Detonation Rocket Engine↗

NASA NTRSDate not supplied

Analysis of Integrated Spacecraft Performance Using Rotating Detonation Rocket Engine

For spacecraft with especially stringent mass fraction constraints, the use of a rotating detonation rocket engine (RDRE) may yield potentially significant system mass savings. The pressure gain combustion inherent to RDREs provides higher thrust than traditional rocket engines for a given supply pressure and throat area. This opens multiple avenues for reducing system mass via higher specific impulse, reduced gravity losses, and reduced feed pressures. To highlight the potentially weight-saving characteristics of RDRE systems, the integrated performance of representative spacecraft designs using either a conventional rocket engine or an RDRE was assessed. This paper details the assumptions, methods, and results of this trade study. Two representative spacecraft were assessed. The first is a Liquid Mars Ascent Vehicle (MAV). This spacecraft was designed by NASA in 2011 for the Mars Sample Return Campaign but was not selected for flight due in part to it exceeding the mass requirements by 119 lbm. The second is MIURA-1, a kerolox sounding rocket developed by Payload Aerospace S.L.. The Liquid MAV assessment showed that an RDRE powered design met the mass requirements with a total vehicle mass 46 lbm below the limit. The MIURA-1 assessment showed that wall heat transfer and regenerative cooling are significant challenges for kerolox RDREs, but these challenges may be mitigated by reducing mixture ratio and using both propellants to cool the chamber. The assessment showed that an RDRE powered sounding rocket could achieve 12% more microgravity time than a conventional sounding rocket with the same payload and total vehicle mass. Alternately, if the payload mass and target apogee are held constant, then an RDRE powered sounding rocket can be 8% short and 11% lighter than a conventional sounding rocket. These results suggest that the advent of RDREs for in-space propulsion may unlock new missions for which conventional propulsion is not feasible.

Rotating Detonation Rocket Engine↗

NASA NTRS2013

Goal-Function Tree Modeling for Systems Engineering and Fault Management

The draft NASA Fault Management (FM) Handbook (2012) states that Fault Management (FM) is a "part of systems engineering", and that it "demands a system-level perspective" (NASAHDBK- 1002, 7). What, exactly, is the relationship between systems engineering and FM? To NASA, systems engineering (SE) is "the art and science of developing an operable system capable of meeting requirements within often opposed constraints" (NASA/SP-2007-6105, 3). Systems engineering starts with the elucidation and development of requirements, which set the goals that the system is to achieve. To achieve these goals, the systems engineer typically defines functions, and the functions in turn are the basis for design trades to determine the best means to perform the functions. System Health Management (SHM), by contrast, defines "the capabilities of a system that preserve the system's ability to function as intended" (Johnson et al., 2011, 3). Fault Management, in turn, is the operational subset of SHM, which detects current or future failures, and takes operational measures to prevent or respond to these failures. Failure, in turn, is the "unacceptable performance of intended function." (Johnson 2011, 605) Thus the relationship of SE to FM is that SE defines the functions and the design to perform those functions to meet system goals and requirements, while FM detects the inability to perform those functions and takes action. SHM and FM are in essence "the dark side" of SE. For every function to be performed (SE), there is the possibility that it is not successfully performed (SHM); FM defines the means to operationally detect and respond to this lack of success. We can also describe this in terms of goals: for every goal to be achieved, there is the possibility that it is not achieved; FM defines the means to operationally detect and respond to this inability to achieve the goal. This brief description of relationships between SE, SHM, and FM provide hints to a modeling approach to provide formal connectivity between the nominal (SE), and off-nominal (SHM and FM) aspects of functions and designs. This paper describes a formal modeling approach to the initial phases of the development process that integrates the nominal and off-nominal perspectives in a model that unites SE goals and functions of with the failure to achieve goals and functions (SHM/FM). This methodology and corresponding model, known as a Goal-Function Tree (GFT), provides a means to represent, decompose, and elaborate system goals and functions in a rigorous manner that connects directly to design through use of state variables that translate natural language requirements and goals into logical-physical state language. The state variable-based approach also provides the means to directly connect FM to the design, by specifying the range in which state variables must be controlled to achieve goals, and conversely, the failures that exist if system behavior go out-of-range. This in turn allows for the systems engineers and SHM/FM engineers to determine which state variables to monitor, and what action(s) to take should the system fail to achieve that goal. In sum, the GFT representation provides a unified approach to early-phase SE and FM development. This representation and methodology has been successfully developed and implemented using Systems Modeling Language (SysML) on the NASA Space Launch System (SLS) Program. It enabled early design trade studies of failure detection coverage to ensure complete detection coverage of all crew-threatening failures. The representation maps directly both to FM algorithm designs, and to failure scenario definitions needed for design analysis and testing. The GFT representation provided the basis for mapping of abort triggers into scenarios, both needed for initial, and successful quantitative analyses of abort effectiveness (detection and response to crew-threatening events).

Patterson, Jonathan D.↗

NASA NTRS2013

Goal-Function Tree Modeling for Systems Engineering and Fault Management

The draft NASA Fault Management (FM) Handbook (2012) states that Fault Management (FM) is a "part of systems engineering", and that it "demands a system-level perspective" (NASAHDBK- 1002, 7). What, exactly, is the relationship between systems engineering and FM? To NASA, systems engineering (SE) is "the art and science of developing an operable system capable of meeting requirements within often opposed constraints" (NASA/SP-2007-6105, 3). Systems engineering starts with the elucidation and development of requirements, which set the goals that the system is to achieve. To achieve these goals, the systems engineer typically defines functions, and the functions in turn are the basis for design trades to determine the best means to perform the functions. System Health Management (SHM), by contrast, defines "the capabilities of a system that preserve the system's ability to function as intended" (Johnson et al., 2011, 3). Fault Management, in turn, is the operational subset of SHM, which detects current or future failures, and takes operational measures to prevent or respond to these failures. Failure, in turn, is the "unacceptable performance of intended function." (Johnson 2011, 605) Thus the relationship of SE to FM is that SE defines the functions and the design to perform those functions to meet system goals and requirements, while FM detects the inability to perform those functions and takes action. SHM and FM are in essence "the dark side" of SE. For every function to be performed (SE), there is the possibility that it is not successfully performed (SHM); FM defines the means to operationally detect and respond to this lack of success. We can also describe this in terms of goals: for every goal to be achieved, there is the possibility that it is not achieved; FM defines the means to operationally detect and respond to this inability to achieve the goal. This brief description of relationships between SE, SHM, and FM provide hints to a modeling approach to provide formal connectivity between the nominal (SE), and off-nominal (SHM and FM) aspects of functions and designs. This paper describes a formal modeling approach to the initial phases of the development process that integrates the nominal and off-nominal perspectives in a model that unites SE goals and functions of with the failure to achieve goals and functions (SHM/FM).

Johnson, Stephen B.↗

NASA NTRS2017

Implementation of a Space Communications Cognitive Engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).

reinforcement learning↗

NASA NTRS2017

Implementation of a Space Communications Cognitive Engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).

reinforcement learning↗

NASA NTRS2011

The Challenge of Configuring Model-Based Space Mission Planners

Mission planning is central to space mission operations, and has benefited from advances in model-based planning software. Constraints arise from many sources, including simulators and engineering specification documents, and ensuring that constraints are correctly represented in the planner is a challenge. As mission constraints evolve, planning domain modelers need help with modeling constraints efficiently using the available source data, catching errors quickly, and correcting the model. This paper describes the current state of the practice in designing model-based mission planning tools, the challenges facing model developers, and a proposed Interactive Model Development Environment (IMDE) to configure mission planning systems. We describe current and future technology developments that can be integrated into an IMDE.

model-based systems↗

DOE OSTI2025

A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions

Abstract Metabolism operates under physico-chemical constraints that result in multireaction dependencies. Understanding how multireaction dependencies affect metabolic phenotypes remains challenging, hindering their biotechnological applications. Here, we propose the concept of a forcedly balanced complex that allows to efficiently determine the effects of specific multireaction dependencies on metabolic network functions in constrained-based models. Using this concept, we found that the fraction of multireaction dependencies induced by forcedly balanced complexes in genome-scale metabolic networks followed power law with exponential cut-off. We identified forcedly balanced complexes that are lethal in cancer but have little effect on growth in healthy tissue models. In addition, these forcedly balanced complexes are largely specific to models of particular cancer types. Therefore, multireaction dependencies resulting from forced balancing of complexes represent an innovative means to control cancers that, we argue, can be implemented via transporter engineering. The presented constraint-based approaches pave the way for using multireaction dependencies in metabolic engineering for diverse biotechnological applications.

Küken, Anika↗

NASA NTRS1996

Cascade Optimization Strategy for Aircraft and Air-Breathing Propulsion System Concepts

Design optimization for subsonic and supersonic aircraft and for air-breathing propulsion engine concepts has been accomplished by soft-coupling the Flight Optimization System (FLOPS) and the NASA Engine Performance Program analyzer (NEPP), to the NASA Lewis multidisciplinary optimization tool COMETBOARDS. Aircraft and engine design problems, with their associated constraints and design variables, were cast as nonlinear optimization problems with aircraft weight and engine thrust as the respective merit functions. Because of the diversity of constraint types and the overall distortion of the design space, the most reliable single optimization algorithm available in COMETBOARDS could not produce a satisfactory feasible optimum solution. Some of COMETBOARDS' unique features, which include a cascade strategy, variable and constraint formulations, and scaling devised especially for difficult multidisciplinary applications, successfully optimized the performance of both aircraft and engines. The cascade method has two principal steps: In the first, the solution initiates from a user-specified design and optimizer, in the second, the optimum design obtained in the first step with some random perturbation is used to begin the next specified optimizer. The second step is repeated for a specified sequence of optimizers or until a successful solution of the problem is achieved. A successful solution should satisfy the specified convergence criteria and have several active constraints but no violated constraints. The cascade strategy available in the combined COMETBOARDS, FLOPS, and NEPP design tool converges to the same global optimum solution even when it starts from different design points. This reliable and robust design tool eliminates manual intervention in the design of aircraft and of air-breathing propulsion engines where it eases the cycle analysis procedures. The combined code is also much easier to use, which is an added benefit. This paper describes COMETBOARDS and its cascade strategy and illustrates the capability of the combined design tool through the optimization of a subsonic aircraft and a high-bypass-turbofan wave-rotor-topped engine.

Patnaik, Surya N.↗

NASA NTRS1984

X-ray emission from active galactic nuclei

The advantages of X-ray astronomy for studying active galactic nuclei (AGN) are discussed. If the X-ray emission from AGN arises from a region close to the central energy source X-ray observations may provide the best constraints on the central engine. The shape of the X-ray continuum gives information about the mechanism for photon generation; X-ray time variability data can constrain the size and mass of the continuum source; and X-ray occultation data gives constraints on the relative sizes of the continuum source and the intervening absorbing material. Since a fair fraction of the total energy of an AGN is emitted at X-ray wavelengths, direct measurement of the amount and spectral form of this radiation is important for modeling of the optically emitting clouds.

Mushotzky, R.↗

NASA NTRS1985

X-ray emission from active galactic nuclei

It is often held that the X-ray emission from active galactic nuclei (AGN) arises from a region close to the central energy source. Thus X-ray observations may provide the best constraints on the central engine. In particular, the shape of the X-ray continuum gives information about the mechanism for photon generation, X-ray time variability data can constrain the size and mass of the continuum source, and X-ray occultation data give constraints on the relative sizes of the continuum source and the intervening absorbing material (often assumed to be the broad line clouds). In addition, since a fair fraction of the total energy of an AGN is emitted at X-ray wavelengths, direct measurement of the amount and spectral form of this radiation is important for modeling of the optically emitting clouds.

Mushotzky, R.↗

NASA NTRS1987

Defining and representing events in a satellite scheduling system - The IEPS (Interactive Experimenter Planning System) approach

A methodology is described for defining and representing satellite events from the IEPS perspective. The task of doing this is divided into four categories and includes defining and representing resource windows, event parameters, event scheduling strategies, and event constraints. The description of each of these categories includes examples from the IEPS ERBS-TDRSS Contact Planning System. This is a system which is being used by the Earth Radiation Budget Satellite (ERBS) schedulers to request TDRSS contact times from the NCC. The system is written in the C programming language and uses a custom built inference engine (TIE1) to do constraint checking and a custom built strategies interpreter to derive the plan. The planning system runs on the IBM-PC/AT or on any similar hardware which has a C development environment and 640K of memory.

Mclean, David R.↗

NASA NTRS2003

Ultra-efficient Engine Diameter Study

Engine fan diameter and Bypass Ratio (BPR) optimization studies have been conducted since the beginning of the turbofan age with the recognition that reducing the engine core jet velocity and increasing fan mass flow rate generally increases propulsive efficiency. However, performance tradeoffs limit the amount of fan flow achievable without reducing airplane efficiency. This study identifies the optimum engine fan diameter and BPR, given the advanced Ultra-Efficient Engine Technology (UEET) powerplant efficiencies, for use on an advanced subsonic airframe. Engine diameter studies have historically focused on specific engine size options, and were limited by existing technology and transportation infrastructure (e.g., ability to fit bare engines through aircraft doors and into cargo holds). This study is unique in defining the optimum fan diameter and drivers for future 2015 (UEET) powerplants while not limiting engine fan diameter by external constraints. This report follows on to a study identifying the system integration issues of UEET engines. This Engine Diameter study was managed by Boeing Phantom Works, Seattle, Washington through the NASA Glenn Revolutionary Aero Space Engine Research (RASER) contract under task order 10. Boeing Phantom Works, Huntington Beach, completed the engine/airplane sizing optimization, while the Boeing Commercial Airplane group (BCA) provided design oversight. A separate subcontract to support the overall project was issued to Tuskegee University.

Daggett, David L.↗