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Apollo 8 was launched from Cape Kennedy, Fla., at 7 :50 a.m., e.s.t., on December 21, 1968. Two hours 50 minutes later, translunar injection was performed; and astronauts Col. Frank Borman, the commander; Capt. James A. Lovell, Jr., the command module pilot; and Maj. (now Lt. Col.) William A. Anders, the lunar module pilot, were on their way to the Moon. The spacecraft was placed in an elliptical lunar orbit at 69 hours 8 minutes after liftoff. After flying two elliptical orbits of 168.5 by 60 nautical miles with an inclination of 12° to the Equator, the spacecraft was placed in a nearly circular orbit of 59.7 by 60.7 nautical miles, in which it remained for eight orbits. At 89 hours 19 minutes, trans earth injection was performed from behind the Moon. A nearly flawless mission was completed on the morning of December 27 when splashdown occurred in the Pacific Ocean after a total elapsed time of 147 hours. Lt. Gen. Sam C. Phillips, the Director of the Apollo Program, announced that such a mission was being considered at a press conference on August 19, 1968. Formal announcement that NASA was preparing Apollo 8 for an orbital flight around the Moon was released to the press on November 12, 1968. The primary purpose of this mission was to further progress toward the goal of landing men on the Moon by gaining operational experience and testing the Apolio systems. However, a great effort was also made to accomplish worthwhile scientific tasks with photography and visual observations by the astronauts. In planning the scientific tasks to be attempted on this mission, it was obvious that one of the prime tasks should be photography of the lunar surface. Such photography would furnish valuable information on the following : 1. Approach topography and landmarks for the early Apollo landings 2. The scientific merit and the roughness of areas for possible follow-on Apollo landings 3. The broad structure and characteristics of the lunar surface During the orbital part of the mission, a major portion of the lunar far side would be in sunlight. Although almost all of the far side of the Moon has been photographed by the automated Lunar Orbiter spacecraft, the photography generally was made with the spacecraft relatively far from the Moon, limiting the Lunar Orbiter photographs to an average resolution of approximately 100 meters. Thus, Apollo photographs of the far side would have much better resolution than existing pictures. Finally, it was recognized that contamination, both as it relates to window fogging (which did occur) and to contamination clouds around t he spacecraft, should be studied for both scientific and operational interests.

This note has been written to acknowledge the pervasive impact the Method of Universal Slopes (MUS) has had on the design and development of the Space Shuttle Main Engine (SSME) through its use in material fatigue properties definition for component analysis. Included is a brief historical perspective describing propulsion system development leading up to the SSME, the need for fatigue analysis capability in the SSME design, the role that MUS played in meeting that need as a material modeling tool and the engine's subsequent successful operating history.

Algorithms for high-performance parallel computers are applied to perform static analyses of large-scale Space Station finite-element models (FEMs). Several parallel-vector algorithms under development at NASA Langley are assessed. Sparse matrix solvers were found to be more efficient than banded symmetric or iterative solvers for the static analysis of large-scale applications. In addition, new sparse and 'out-of-core' solvers were found superior to substructure (superelement) techniques which require significant additional cost and time to perform static condensation during global FEM matrix generation as well as the subsequent recovery and expansion. A method to extend the fast parallel static solution techniques to reduce the computation time for dynamic analysis is also described. The resulting static and dynamic algorithms offer design economy for preliminary multidisciplinary design optimization and FEM validation against test modes. The algorithms are being optimized for parallel computers to solve one-million degrees-of-freedom (DOF) FEMs. The high-performance computers at NASA afforded effective software development, testing, efficient and accurate solution with timely system response and graphical interpretation of results rarely found in industry. Based on the author's experience, similar cooperation between industry and government should be encouraged for similar large-scale projects in the future.

The objective of this program is to develop generic load models with multiple levels of progressive sophistication to simulate the composite load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen (LOX) posts and system ducting. These models will be developed using two independent approaches. The first approach consists of using state-of-the-art probabilistic methods to describe the individual loading conditions and combinations of these loading conditions to synthesize the composite load spectra simulation. The methodology required to combine the various individual load simulation models (hot-gas dynamic, vibrations, instantaneous position, centrifugal field, etc.) into composite load spectra simulation models will be developed under this program. A computer code incorporating the various individual and composite load spectra models will be developed to construct the specific load model desired. The second approach, which is covered under the options portion of the contract, will consist of developing coupled models for composite load spectra simulation which combine the (deterministic) models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients will then be determined using advanced probabilistic simulation methods with and without strategically selected experimental data. This report covers the efforts of the third year of the contract. The overall program status is that the turbine blade loads have been completed and implemented. The transfer duct loads are defined and are being implemented. The thermal loads for all components are defined and coding is being developed. A dynamic pressure load model is under development. The parallel work on the probabilistic methodology is essentially completed. The overall effort is being integrated in an expert system code specifically developed for this project.

The objective of the Composite Load Spectra (CLS) project is to develop generic load models to simulate the composite load spectra that are included in space propulsion system components. The probabilistic loads thus generated are part of the probabilistic design analysis (PDA) of a space propulsion system that also includes probabilistic structural analyses, reliability, and risk evaluations. Probabilistic load simulation for space propulsion systems demands sophisticated probabilistic methodology and requires large amounts of load information and engineering data. The CLS approach is to implement a knowledge based system coupled with a probabilistic load simulation module. The knowledge base manages and furnishes load information and expertise and sets up the simulation runs. The load simulation module performs the numerical computation to generate the probabilistic loads with load information supplied from the CLS knowledge base.

The application is described of Composite Load Spectra (CLS) and Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) family of computer codes to the probabilistic structural analysis of four Space Shuttle Main Engine (SSME) space propulsion system components. These components are subjected to environments that are influenced by many random variables. The applications consider a wide breadth of uncertainties encountered in practice, while simultaneously covering a wide area of structural mechanics. This has been done consistent with the primary design requirement for each component. The probabilistic application studies are discussed using finite element models that have been typically used in the past in deterministic analysis studies.

A knowledge based system LDEXPT using the intelligent data base paradigm was developed for the Composite Load Spectra (CLS) project to simulate the probabilistic loads of a space propulsion system. The knowledge base approach provides a systematic framework of organizing the load information and facilitates the coupling of the numerical processing and symbolic (information) processing. It provides an incremental development environment for building generic probabilistic load models and book keeping the associated load information. A large volume of load data is stored in the data base and can be retrieved and updated by a built-in data base management system. The data base system standardizes the data storage and retrieval procedures. It helps maintain data integrity and avoid data redundancy. The intelligent data base paradigm provides ways to build expert system rules for shallow and deep reasoning and thus provides expert knowledge to help users to obtain the required probabilistic load spectra.

The following subject areas are covered: issues (liquid rocket propulsion - current development approach, current certification process, and costs of engineering changes); state of the art (DICE information management system, key government participants, project development strategy, quality management, and numerical propulsion system simulation); needs identified; and proposed program.

A conventional remotely piloted vehicle (RPV) was designed to operate in a fictional 'Aeroworld' as a 30 passenger aircraft. The topics addressed include: economic/cost analysis, aerodynamics, weight and structures, propulsion, stability and control, and performance.

The development was initiated of the Probabilistic Design Analysis (PDA) Process for rocket engines. This will enable engineers a quantitative assessment of calculated reliability during the design process. The PDA will help choose better designs, make them more robust, and help decide on critical tests to help demonstrate key reliability issues to aid in improving the confidence of the engine capabilities. Rockedyne's involvement with the Composite Loads Spectra (CLS) and Probabilistic Structural Analysis Methodology (PSAM) contracts started this effort and are key elements in the on-going developments. Internal development efforts and hardware applications complement and extend the CLS and PSAM efforts. The completion of the CLS option work and the follow-on PSAM developments will also be integral parts of this methodology. A brief summary of these efforts is presented.

Information on probabilistic structural analysis methods for space propulsion systems is given in viewgraph form. Information is given on deterministic certification methods, probability of failure, component response analysis, stress responses for 2nd stage turbine blades, Space Shuttle Main Engine (SSME) structural durability, and program plans. .

NASA-Lewis is actively involved in the general effort to research, develop, test, and evaluate advanced theoretical, analytical, experimental, and probabilistic analysis concepts required for life prediction of liquid rocket engines at the subcomponent, component, and engine system levels. The models developed are oriented toward use in advanced health monitoring systems of space propulsion systems. It is planned to demonstrate the methodology considering a representative set of three components such as a main injector element, a combustion chamber liner, and a turbopump blade. This paper describes the initial development and application of this method to a specific location in the main injector element of the SSME. Further enhancements and various elements of the framework will be completed as the work proceeds in subsequent years.

Probabilistic methods and generic load models capable of simulating the load spectra that are induced in space propulsion system components are being developed. Four engine component types (the transfer ducts, the turbine blades, the liquid oxygen posts and the turbopump oxidizer discharge duct) were selected as representative hardware examples. The composite load spectra that simulate the probabilistic loads for these components are typically used as the input loads for a probabilistic structural analysis. The knowledge-based system approach used for the composite load spectra project provides an ideal environment for incremental development. The intelligent database paradigm employed in developing the expert system provides a smooth coupling between the numerical processing and the symbolic (information) processing. Large volumes of engine load information and engineering data are stored in database format and managed by a database management system. Numerical procedures for probabilistic load simulation and database management functions are controlled by rule modules. Rules were hard-wired as decision trees into rule modules to perform process control tasks. There are modules to retrieve load information and models. There are modules to select loads and models to carry out quick load calculations or make an input file for full duty-cycle time dependent load simulation. The composite load spectra load expert system implemented today is capable of performing intelligent rocket engine load spectra simulation. Further development of the expert system will provide tutorial capability for users to learn from it.

The probabilistic structural analysis program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988) is applied to characterize the dynamic loading and response of the Space Shuttle main engine (SSME) LOX post. The design and operation of the SSME are reviewed; the LOX post structure is described; and particular attention is given to the generation of composite load spectra, the finite-element model of the LOX post, and the steps in the NESSUS structural analysis. The results are presented in extensive tables and graphs, and it is shown that NESSUS correctly predicts the structural effects of changes in the temperature loading. The probabilistic approach also facilitates (1) damage assessments for a given failure model (based on gas temperature, heat-shield gap, and material properties) and (2) correlation of the gas temperature with operational parameters such as engine thrust.

The dynamic behavior of the discharge duct of the high-pressure oxidizer turbopump of a cryogenic rocket motor is investigated analytically. The probabilistic analysis program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988) is used to treat the uncertainties due to random and harmonic excitation (e.g., pump noise, pump-induced harmonics, and combustion noise), variations in engine inlet pressure, and changes in system damping. The load modeling procedure, the variation in power-spectral density in different zones of the engine structure, and the dynamic structural-analysis technique are described, and the numerical results of the NESSUS analysis are presented in extensive tables and graphs and discussed in detail.

The Composite Load Spectra project develops probabilistic models to simulate the probabilistic loads for selected components of a generic space propulsion system. Tremendous information such as engine load variables and their distributions is needed by the simulation program. An intelligent data base system was constructed and integrated with the probabilistic load simulation program to manage and maintain the knowledge base of the Composite Load Spectra project. The intelligent data base system takes care of the data retrieval and storage functions and has expert knowledge on engine load models and associated engine variables. The integration of the intelligent data base into the load simulation program achieves a smooth coupling between the numeric processing (load simulation calculation) and the symbolic processing (intelligent load information management).