Results for “Rapid Process Design” — Page 10

NASA NTRSDate not supplied

Large Scale and Multi-Alloy Rocket Engine Component Development using Various Metal Additive Manufacturing Techniques

The NASA Marshall Space Flight Center (MSFC) has been involved with various forms of metallic additive manufacturing (AM) for use in liquid rocket engine component design, development, and testing since 2010. These AM techniques have been demonstrated to significantly reduce hardware cost, shorten fabrication schedules, increase reliability by reducing the number of joints, and improve hardware performance by allowing fabrication of designs not feasible by conventional means. The focus at the NASA MSFC for these metal additive manufacturing techniques include laser powder-bed fusion (L-PBF), blown powder directed energy deposition (DED), arc-based deposition, and Laser Wire Direct Closeout (LWDC). A variety of components have been evaluated and tested including thrust chamber injectors, injector components such as faceplates, regeneratively-cooled combustion chambers, regeneratively-cooled nozzles, gas generator and preburner hardware, and augmented spark igniters. To support these component applications in harsh environments, NASA has advanced a variety of “standard” additive manufacturing alloys such as those in the superalloy-family and also evolved new alloys including GRCop-84, GRCop-42, NASA HR-1, and JBK-75. The purpose of this presentation is to discuss the various component programs at the NASA MSFC using AM to develop, fabricate, and test combustion devices hardware and the evolution of the new additive alloys. One of these projects that will be highlighted is Rapid Analysis and Manufacturing Propulsion Technology (RAMPT), which includes new process development for large scale AM components, multi-metallic AM components, including unique component designs using additive manufacturing. Additional information will be provided on the development of other components, hot-fire testing, post-processing of AM techniques including surface enhancements (polishing) techniques, material and process characterization, future development programs, and dissemination of data to industry partners.

Additive Manufacturing↗

NASA NTRS2007

Design and Simulation of a MEMS Structure for Electrophoretic and Dielectrophoretic Separation of Particles by Contactless Electrodes

Rapid identification of pathogenic bacterial species is an important factor in combating public health problems such as E. coli contamination. Food and waterborne pathogens account for sickness in 76 million people annually (CDC). Diarrheagenic E. coli is a major source of gastrointestinal illness. Severe sepsis and Septicemia within the hospital environment are also major problems. 75 1,000 cases annually with a 30-50% mortality rate (Crit Care Med, July '01, Vol. 29, 1303-10). Patient risks run the continuum from fever to organ failure and death. Misdiagnosis or inappropriate treatment increases mortality. There exists a need for rapid screening of samples for identification of pathogenic species (Certain E. coli strains are essential for health). Critical to the identification process is the ability to isolate analytes of interest rapidly. This poster discusses novel devices for the separation of particles on the basis of the dielectric properties, mass and surface charge characteristics is presented. Existing designs involve contact between electrode surfaces and analyte medium resulting in contamination of the electrode bearing elements Two different device designs using different bulk micromachining MEMS processes (PolyMUMPS and a PyrexBIGold electrode design) are presented. These designs cover a range of particle sizes from small molecules through eucaryotic cells. The application of separation of bacteria is discussed in detail. Simulation data for electrostatic and microfluidic characteristics are provided. Detailed design characteristics and physical features of the as fabricated PolyMUMPS design are provided. Analysis of the simulation data relative to the expected performance of the devices will be provided and subsequent conclusions discussed.

Shaw, Harry C.↗

DOE OSTI2026

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2001

Imaging Electron Spectrometer (IES) Electron Preprocessor (EPP) Design

The Aerospace Corporation developed the Electron PreProcessor (EPP) to support the Imaging Electron Spectrometer (IES) that is part of the RAPID experiment on the ESA/NASA CLUSTER mission. The purpose of the EPP is to collect raw data from the IES and perform processing and data compression on it before transferring it to the RAPID microprocessor system for formatting and transmission to the CLUSTER satellite data system. The report provides a short history of the RAPID and CLUSTER programs and describes the EPP design. Four EPP units were fabricated, tested, and delivered for the original CLUSTER program. These were destroyed during a launch failure. Four more EPP units were delivered for the CLUSTER II program. These were successfully launched and are operating nominally on orbit.

Fennell, J. F.↗

NASA NTRS1983

Impact of digital systems technology on man-vehicle systems research

The present study, based on a NASA technology assessment, examines the effect of new technologies on trends in crew-systems design and their implications from the vantage point of man-vehicle systems research. Those technologies that are most relevant to future trends in crew-systems design are considered along with problems associated with the introduction of rapidly changing technologies and systems concepts from a human-factors point of view. The technologies discussed include information processing, displays and controls, flight and propulsion control, flight and systems management, air traffic control, training and simulation, and flight and resource management. The historical evolution of cockpit systems design is used to illustrate past and possible future trends in man-vehicle systems research.

Bretoi, R. N.↗

DOE OSTI2024

IDAES-PSE 2.5.0 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, most environmentally sustainable 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 2.5.0 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 diagnostics check for near-parallel variables and constraints. New diagnostics tools for identifying causes of infeasibility in models. New example for creating a custom model of a liquid-liquid extractor unit operation. Bug Fixes Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom. 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. Testing and Robustness Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries. Additional robustness tests for core model, and some associated improvements in the converge tester class. Fixed a number of issues that were causing unexpected warnings to be emitted during testing. Deprecations and Removals Removed examples for RIPE tool which has not been supported for a number of releases.

AS↗

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↗

NASA NTRS1978

Trajectory module of the NASA Ames Research Center aircraft synthesis program ACSYNT

A program was developed to calculate trajectories for both military and commercial aircraft for use in the aircraft synthesis program, ACSYNT. The function of the trajectory module was to calculate the changes in the vehicle's flight conditions and weight, as fuel is consumed, during the flying of one or more missions. The trajectory calculations started with a takeoff, followed by up to 12 phases chosen from among the following: climb, cruise, acceleration, combat, loiter, descent, and paths. In addition, a balanced field length was computed. The emphasis was on relatively simple formulations and analytic expressions suitable for rapid computation since a prescribed trajectory had to be calculated many times in the process of converging an aircraft design, or finding an optimum configuration. The trajectory module consists of about 2500 cards and operational on a CDC 7600 computer.

Tauber, M. E.↗

NASA NTRS1984

Ring particles - Collisional interactions and physical nature

Attention is given to the properties of, and dynamical processes affecting individual particles of Saturn's rings. Because particles tend to be gravitationally bound when located on the surfaces of larger particles, and since net tidal stresses within the particles are small, particle collisions should produce accretion in Saturn's rings. Rapid accretionary processes within the rings are counterbalanced by tidal disruption of the larger accreted aggregates, which are presently designated 'dynamic ephemeral bodies'. The coefficient of restitution is probably very low, implying that the large particles containing most of the rings' mass are in a monolayer, although the small particles responsible for most of the rings' visible cross section form a layer many particles thick. Kinematic viscosity and interparticle erosive process models should incorporate these properties.

Weidenschilling, S. J.↗

NASA NTRS1993

Effect of aeroelastic-propulsive interactions on flight dynamics of a hypersonic vehicle

The desire to achieve orbit-on-demand access to space with rapid turn-around capability and aircraft-like processing operations has given rise to numerous hypersonic aerospace plane design concepts which would take off horizontally from a conventional runway and employ air-breathing scramjet propulsion systems for acceleration to orbital speeds. Most of these air-breathing hypersonic vehicle concepts incorporate an elongated fuselage forebody to act as the aerodynamic compression surface for a scramjet combustor module. This type of airframe-integrated scramjet propulsion system tends to be highly sensitive to inlet conditions and angle-of-attack perturbations. Furthermore, the basic configuration of the fuselage, with its elongated and tapered forebody, produces relatively low frequency elastic modes which will cause perturbations in the combustor inlet conditions due to the oscillation of the forebody compression surface. The flexibility of the forebody compression surface, together with sensitivity of scramjet propulsion systems to inlet conditions, creates the potential for an unprecedented form of aeroelastic-propulsive interaction in which deflections of the vehicle fuselage give rise to propulsion transients, producing force and moment variations that may adversely impact the longitudinal flight dynamics and/or excite the elastic modes. These propulsive force and moment variations may have an appreciable impact on the performance, guidance, and control of a hypersonic aerospace plane. The objectives of this research are to quantify the magnitudes of propulsive force and moment perturbations resulting from elastic deformation of a representative hypersonic vehicle, and to assess the potential impact of these perturbations on the vehicle's longitudinal flight dynamics.

Raney, David L.↗

NASA NTRS2000

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for future research are identified.

Shyy, Wei↗

DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗

NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗

NASA NTRS2003

Team Software Development for Aerothermodynamic and Aerodynamic Analysis and Design

A collaborative approach to software development is described. The approach employs the agile development techniques: project retrospectives, Scrum status meetings, and elements of Extreme Programming to efficiently develop a cohesive and extensible software suite. The software product under development is a fluid dynamics simulator for performing aerodynamic and aerothermodynamic analysis and design. The functionality of the software product is achieved both through the merging, with substantial rewrite, of separate legacy codes and the authorship of new routines. Examples of rapid implementation of new functionality demonstrate the benefits obtained with this agile software development process. The appendix contains a discussion of coding issues encountered while porting legacy Fortran 77 code to Fortran 95, software design principles, and a Fortran 95 coding standard.

Alexandrov, N.↗

NASA NTRS2002

Rapid Modeling and Analysis Tools: Evolution, Status, Needs and Directions

Advanced aerospace systems are becoming increasingly more complex, and customers are demanding lower cost, higher performance, and high reliability. Increased demands are placed on the design engineers to collaborate and integrate design needs and objectives early in the design process to minimize risks that may occur later in the design development stage. High performance systems require better understanding of system sensitivities much earlier in the design process to meet these goals. The knowledge, skills, intuition, and experience of an individual design engineer will need to be extended significantly for the next generation of aerospace system designs. Then a collaborative effort involving the designer, rapid and reliable analysis tools and virtual experts will result in advanced aerospace systems that are safe, reliable, and efficient. This paper discusses the evolution, status, needs and directions for rapid modeling and analysis tools for structural analysis. First, the evolution of computerized design and analysis tools is briefly described. Next, the status of representative design and analysis tools is described along with a brief statement on their functionality. Then technology advancements to achieve rapid modeling and analysis are identified. Finally, potential future directions including possible prototype configurations are proposed.

Knight, Norman F., Jr.↗

NASA NTRS2003

Right-Rapid-Rough

IDEO (pronounced 'eye-dee-oh') is an international design, engineering, and innovation firm that has developed thousands of products and services for clients across a wide range of industries. Its process and culture attracted the attention of academics, businesses, and journalists around the world, and are the subject of a bestselling book, The Art of Innovation by Tom Kelley. One of the keys to IDEO's success is its use of prototyping as a tool for rapid innovation. This story covers some of IDEO's projects, and gives reasons for why they were successful.

Lawrence, Craig↗