Results for “Process Systems Engineering” — Page 6

NASA NTRS2012

Integrating Engineering Data Systems for NASA Spaceflight Projects

NASA has a large range of custom-built and commercial data systems to support spaceflight programs. Some of the systems are re-used by many programs and projects over time. Management and systems engineering processes require integration of data across many of these systems, a difficult problem given the widely diverse nature of system interfaces and data models. This paper describes an ongoing project to use a central data model with a web services architecture to support the integration and access of linked data across engineering functions for multiple NASA programs. The work involves the implementation of a web service-based middleware system called Data Aggregator to bring together data from a variety of systems to support space exploration. Data Aggregator includes a central data model registry for storing and managing links between the data in disparate systems. Initially developed for NASA's Constellation Program needs, Data Aggregator is currently being repurposed to support the International Space Station Program and new NASA projects with processes that involve significant aggregating and linking of data. This change in user needs led to development of a more streamlined data model registry for Data Aggregator in order to simplify adding new project application data as well as standardization of the Data Aggregator query syntax to facilitate cross-application querying by client applications. This paper documents the approach from a set of stand-alone engineering systems from which data are manually retrieved and integrated, to a web of engineering data systems from which the latest data are automatically retrieved and more quickly and accurately integrated. This paper includes the lessons learned through these efforts, including the design and development of a service-oriented architecture and the evolution of the data model registry approaches as the effort continues to evolve and adapt to support multiple NASA programs and priorities.

Carvalho, Robert E.↗

NASA NTRS2011

Multi-Center Implementation of NPR 7123.1A: A Collaborative Effort

Collaboration efforts between MSFC and GRC Engineering Directorates to implement the NASA Systems Engineering (SE) Engine have expanded over the past year to include other NASA Centers. Sharing information on designing, developing, and deploying SE processes has sparked further interest based on the realization that there is relative consistency in implementing SE processes at the institutional level. This presentation will provide a status on the ongoing multi-center collaboration and provide insight into how these NPR 7123.1A SE-aligned directives are being implemented and managed to better support the needs of NASA programs and projects. NPR 7123.1A, NASA Systems Engineering Processes and Requirements, was released on March 26, 2007 to clearly articulate and establish the requirements on the implementing organization for performing, supporting, and evaluating SE activities. In early 2009, MSFC and GRC Engineering Directorates undertook a collaborative opportunity to share their research and work associated with developing, updating and revising their SE process policy to comply and align with NPR 7123.1A. The goal is to develop instructions, checklists, templates, and procedures for each of the 17 SE process requirements so that systems engineers will be a position to define work that is process-driven. Greater efficiency and more effective technical management will be achieved due to consistency and repeatability of SE process implementation across and throughout each of the NASA centers. An added benefit will be to encourage NASA centers to pursue and collaborate on joint projects as a result of using common or similar processes, methods, tools, and techniques.

Hall, Phillip B.↗

NASA NTRS2016

A Tailored Concept of Operations for NASA LSP Integrated Operations

An integral part of the Systems Engineering process is the creation of a Concept of Operations (ConOps) for a given system, with the ConOps initially established early in the system design process and evolved as the system definition and design matures. As Integration Engineers in NASA's Launch Services Program (LSP) at Kennedy Space Center (KSC), our job is to manage the interface requirements for all the robotic space missions that come to our Program for a Launch Service. LSP procures and manages a launch service from one of our many commercial Launch Vehicle Contractors (LVCs) and these commercial companies are then responsible for developing the Interface Control Document (ICD), the verification of the requirements in that document, and all the services pertaining to integrating the spacecraft and launching it into orbit. However, one of the systems engineering tools that have not been employed within LSP to date is a Concept of Operations. The goal of this project is to research the format and content that goes into these various aerospace industry ConOps and tailor the format and content into template form, so the template may be used as an engineering tool for spacecraft integration with future LSP procured launch services.

Systems↗

NASA NTRS2017

Tailoring a ConOps for NASA LSP Integrated Operations

An integral part of the Systems Engineering process is the creation of a Concept of Operations (ConOps) for a given system, with the ConOps initially established early in the system design process and evolved as the system definition and design matures. As Integration Engineers in NASA's Launch Services Program (LSP) at Kennedy Space Center (KSC), our job is to manage the interface requirements for all the robotic space missions that come to our Program for a Launch Service. LSP procures and manages a launch service from one of our many commercial Launch Vehicle Contractors (LVCs) and these commercial companies are then responsible for developing the Interface Control Document (ICD), the verification of the requirements in that document, and all the services pertaining to integrating the spacecraft and launching it into orbit. However, one of the systems engineering tools that have not been employed within LSP to date is a Concept of Operations. The goal of this paper is to research the format and content that goes into these various aerospace industry ConOps and tailor the format and content into template form, so the template may be used as an engineering tool for spacecraft integration with future LSP procured launch services. This tailoring effort was performed as the authors final Masters Project in the Spring of 2016 for the Stevens Institute of Technology and modified for publication with INCOSE (Owens, 2016).

Knowledge↗

NASA NTRS1994

Improving cost efficiency in large programs

This paper examines the question of cost, from the birth of a program to its conclusion, particularly from the point of view of large multi-center programs, and suggests how to avoid some of the traps and pitfalls. Emphasis is given to cost in the systems engineering process, but there is an inevitable overlap with program management. (The terms systems engineering and program management have never been clearly defined.) In these days of vast Federal budget deficits and increasing overseas competition, it is imperative that we get more for each research and development dollar. This is the only way we will retain our leadership in high technology and, in the long run, our way of life.

Hodge, John D.↗

NASA NTRS2022

Design Thinking, Lean Startup, and High-Technology Marketing for Human-Centered Systems Engineering

The use of design thinking, lean startup, and high-technology marketing in systems engineering are presented as a case study. All were used and evaluated for their potential to improve systems engineering processes and design, address the challenges of culture change, increase the adoption rate of new technology, and engage with potential users and sponsors more effectively. Multiple examples of training and use on a model-based systems engineering initiative at a large government agency showcase how these approaches were tailored and used successfully for significant benefits such as enhanced collaboration and better problem definition and design solutions. Challenges remain for use by quiet thinkers and with virtual teams. These new ways of thinking are well aligned with creating and using new technologies and are suitable for situations involving innovation, uncertainty, and change within organizations. This case study intends to familiarize systems engineers with the application of these human-centered approaches, to interest systems engineers in incorporating these approaches into their work processes ,and to inspire the systems engineering research community to investigate further.

Karen J. Weiland↗

DOE OSTI2024

IDAES-PSE 2.6.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.6.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New Intersphinx extension automatically linking Jupyter notebook examples to project documentation New end-to-end diagnostics example demonstrated on a real problem New complementarity formulation for VLE with cubic equations of state, backward compatibility for old formulation New solver interface with presolve (ipopt_v2) in support of upcoming changes to the initialization and APIs methods, with default set to ipopt to maintain backwards compatibility; this will deprecate once all examples have been updated New forecaster and parameterized bidder methods within grid integration library Updated surrogates API and examples to support Keras 3, with backwards compatibility for older formats such as TensorFlow SavedModel (TFSM) Updated costing base dictionary to include the 2023 cost year index value Updated ProcessBlock to include information on the constructing block class Updated Flowsheet Visualizer to allow visualize() method to return value and functions Bug Fixes Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Fixed typos flagged by June update to crate-ci/typos and removed DMF-related exceptions Minor corrections of units of measurement handling in power plant waste/transport costing expressions, control volume material holdup expressions, and BTX property package parameters Fixed throwing >7500 numpy deprecation warnings by replacing scalar value assignment with element extraction and item iteration calls Testing and Robustness Migrated slow tests (>10s) to integration, impacting test coverage but also yielding a nearly 30% decrease in local test runtime Pinned pint to avoid issues with older supported Python versions Pinned codecov versions to avoid tokenless upload behavior with latest version Bumped extensions to version 3.4.2 to allow pointing to non-standard install location Deprecations and Removals Python 3.8 is no longer supported. The supported Python versions are 3.9 through 3.12 The Data Management Framework (DMF) is no longer supported. Importing idaes.core.dmf will cause a deprecation warning to be displayed until the next release The SOFC Keras surrogates have been removed. The current version of the SOFC surrogate model in the examples repository is a PySMO Kriging model.

AS↗

DOE OSTI2024

IDAES-PSE 2.7.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.7.0 Release Highlights New features: AutoScaler and CustomScalerBase classes: Such tools are the core of the new scaling framework being implemented in IDAES. Wider adoption of scaling tools among users will result in quicker and more robust model solutions. Scaler for equilibrium reactor and saponification properties: These scaler models are examples to follow for how to use the new scaling tools. ONNX Surrogate support from Optimization & Machine Learning Toolkit (OMLT): ONNX is an open standard format to save and load ML/AI models that is widely supported by all major frameworks. This capability makes it easier for IDAES users to create surrogate models and use them without having to support each framework individually. 1D Membrane Model for CO2 Capture and Utilization: Supports ongoing efforts for modeling and optimizing polymer membrane processes for CO2 capture and conversion into formic acid. StreamScaler unit model: Unrelated to the CustomScalerBase, this unit model allows a stream’s extensive variables to be scaled by a fixed factor. This allows streams being processed by multiple units in parallel to be scaled down to unit scale and scaled back up to process scale. Bug fixes or improvements: Scaling, EoS, Diagnostics tool, Modular Properties, tests & documentation Deprecations: Old Cubic EoS

AS↗

DOE OSTI2025

IDAES-PSE 2.8.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.

AS↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗

NASA NTRS2006

Managing Analysis Models in the Design Process

Design of large, complex space systems depends on significant model-based support for exploration of the design space. Integrated models predict system performance in mission-relevant terms given design descriptions and multiple physics-based numerical models. Both the design activities and the modeling activities warrant explicit process definitions and active process management to protect the project from excessive risk. Software and systems engineering processes have been formalized and similar formal process activities are under development for design engineering and integrated modeling. JPL is establishing a modeling process to define development and application of such system-level models.

design↗

NASA NTRS2014

Tailoring Enterprise Systems Engineering Policy for Project Scale and Complexity

Space systems are characterized by varying degrees of scale and complexity. Accordingly, cost-effective implementation of systems engineering also varies depending on scale and complexity. Recognizing that systems engineering and integration happen everywhere and at all levels of a given system and that the life cycle is an integrated process necessary to mature a design, the National Aeronautic and Space Administration's (NASA's) Marshall Space Flight Center (MSFC) has developed a suite of customized implementation approaches based on project scale and complexity. While it may be argued that a top-level system engineering process is common to and indeed desirable across an enterprise for all space systems, implementation of that top-level process and the associated products developed as a result differ from system to system. The implementation approaches used for developing a scientific instrument necessarily differ from those used for a space station. .

Cox, Renee I.↗

NASA NTRS2000

2nd Generation RLV Risk Definition Program

The 2nd Generation RLV Risk Reduction Mid-Term Report summarizes the status of Kelly Space & Technology's activities during the first two and one half months of the program. This report was presented to the cognoscente Contracting Officer's Technical Representative (COTR) and selected Marshall Space Flight Center staff members on 26 September 2000. The report has been approved and is distributed on CD-ROM (as a PowerPoint file) in accordance with the terms of the subject contract, and contains information and data addressing the following: (1) Launch services demand and requirements; (2) Architecture, alternatives, and requirements; (3) Costs, pricing, and business cases analysis; (4) Commercial financing requirements, plans, and strategy; (5) System engineering processes and derived requirements; and (6) RLV system trade studies and design analysis.

Davis, Robert M.↗

NASA NTRS2004

Decision Support Tools Evaluation Report for FAS/PECAD, Version 2.0

Global agricultral intelligence is a key element of decision support eithin the U.S. Department of Agriculture (USDA). Estimeates of production and yield issued by the USDA for both foreign and domestic agriculture are primary sources of information for policy and management decision making. The USDA monitors the major global agricultural commodities through the Production Estimates and Crop Assessment Division (PECAD) of its Foreign Agricultural Service (FAS). Specifically, PECAD iintelligence focuses on global agricultural production and on conditions that affect food security. In conjunction with the USDA, NASA is evaluating the potential for products from NASA's Earth Science Enterprise (ESE) missions to add value to PECAD's decision support tools. NASA is usig a systems engineering approach to evaluate the potential enhancement of PECAD's decision support system (DSS)-first by understanding the components of the system and its input requirements, then by recommending NASA products that may be integrated as system inputs to improve the accuracy, quality, or efficiency of the DSS output. This report documents the evaluation phase of the systems engineering process and includes an examination of the system architecture, operations, and input requirements, as well as an initial assessment of specific ESE measurement systems and products that should be considered for their potential to enhance the PECAD DSS.

Ross, Kenton↗

NASA NTRS1997

Risk Analysis and System Trades inthe Mars Sample Return (MSR) Mission

Risk Managenent advocates have long sought to directly influence the early stages of the systems engineering process through a more effective role in system design trade studies. The principal obstacle to this has been the lack of credible ways to represent and quantify mission risk that is, missios return form a probablistic viewpoint-for the project manager and the rest of the design team.

Risk Analysis Mars Sample Return↗

NASA NTRS2001

Risk analysis simulation of rover operations for Mars surface exploration

Risk management advocates have long sought to directly influence the early stages of the systems engineering process through a more effective role in system design trade studies. The principal obstacle to this has been the lack of credible ways to represent and quantify mission risk—that is, a combination of the probability of mission success (“system safety”) and science value—for the project manager and the rest of the design team. If it were possible to quantify mission risk, then the effects of proposed mission and system design changes could be calculated, and along with life-cycle costs, could be used to explore the design space more extensively and select better designs. JPL has been working to build the capability to quantify the probability of mission success using a federation of diverse simulations and models, each of which contributes some vital piece of the puzzle. The initial institutional focus has been on Mars surface operations. This ensemble computing framework enables the diverse models and simulations to work together seamlessly. Recent work at JPL has demonstrated the capability to exercise this ensemble from end-to-end using an Oracle-based database to automatically move results from one model/simulation to the next stage in the analysis.

Shishko, Robert↗

NASA NTRS2024

A Collaborative Vision for Deep Space Human Exploration

Artemis will be the first cooperative campaign among nations for crewed planetary exploration. This paradigm shift reflects a collaborative vision for deep space human exploration that embraces the concept that, “If you want to go fast, go alone; If you want to go far, go together.” This paper elaborates on the way the National Aeronautics and Space Administration(NASA)is striving to make its Moon to Mars effort a collaborative endeavor for long-term human exploration of deep space and for the international space community. The agency is making great strides in unifying its workforce, mission directorates, technical authorities, and centers behind shared goals and processes. Enhanced communication and a reinvigoration of systems engineering ideals have been a hallmark of that effort, guided by the Moon to Mars Architecture, which defines what NASA aims to accomplish and how it will execute those goals. This interdisciplinary approach ensures the Artemis campaign supports NASA’s engineering, science, and technology development goals. As of September 2024, over 43 nations have signed the Artemis Accords, which establish a common set of principles for peaceful cooperation in civil space exploration. Building off these efforts, NASA has been engaging in separate and distinct discussions about partner contributions to Artemis. International partnerships for the Artemis campaign include long-standing partners as well as agencies with which NASA has only more recently begun collaborating. The Gateway Program is an early example of both –benefitting from partnerships with Canada, Japan, and Europe forged though decades of cooperation on the International Space Station, as well as a new partnership with the United Arab Emirates. The agency’s Moon to Mars Objectives and Architecture unify the NASA workforce with common goals and shared systems-engineering processes. The architecture offers pathways for broader participation from industry, academia, and international partners. The agency evolves this collaborative approach through its annual Architecture Concept Review cycle, seeking input from the NASA workforce, and through workshops, from industry, academia, and the international space community to refine NASA’s roadmap for exploration. Exploring together, NASA and its partners are setting humanity on the path to long-term presence at the Moon and our eventual journey to Mars

Objective↗