Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Process design”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
NASA NTRS2006

G2 Flywheel Module Design

Design of a flywheel module, designated the G2 module, is described. The G2 flywheel is a 60,000 RPM, 525 W-hr, 1 kW system designed for a laboratory environment; it will be used for component testing and system demonstrations, with the goal of applying flywheels to aerospace energy storage and integrated power and attitude control (IPACS) applications. G2 has a modular design, which allows for new motors, magnetic bearings, touchdown bearings, and rotors to be installed without a complete redesign of the system. This design process involves several engineering disciplines, and requirements are developed for the speed, energy storage, power level, and operating environment. The G2 rotor system consists of a multilayer carbon fiber rim with a titanium hub on which the other components mount, and rotordynamics analysis is conducted to ensure rigid and flexible rotor modes are controllable or outside of the operating speed range. Magnetic bearings are sized using 1-D magnetic circuit analysis and refined using 3-D finite element analysis. The G2 magnetic bearing system was designed by Texas A&M and has redundancy which allows derated operation after the loss of some components, and an existing liquid cooled two pole permanent magnet motor/generator is used. The touchdown bearing system is designed with a squeeze film damper system allowing spin down from full operating speed in case of a magnetic bearing failure. The G2 flywheel will enable module level demonstrations of component technology, and will be a key building block in system level attitude control and IPACS demonstrations.

Jensen, Ralph H.↗
NASA NTRSDate not supplied

Using Systems Engineering to Develop an Integrated Crew Health and Performance System to Mitigate Risk for Human Exploration Missions

New space exploration missions are currently being designed to take humanity beyond Low EarthOrbit (LEO) to cis-lunar space, the lunar surface, and eventually to Mars. These missions carryincreased risks due to a number of factors, including distance from Earth, exposure to deep spacehazards, reduced capacity and ability to resupply and evacuate, and increased communication delays.As distance from Earth grows and mission length increases, a growing proportion of overall missionrisk can be attributed to the “human system.” Almost twenty years ago, the Institute of Medicine in theUnited States recommended the early and complete integration of the human system into thespacecraft and mission design process to mitigate this increased risk inherent in exploration missions.Exploration missions will require increasing levels of crew self-sufficiency that will challenge thecurrent operational paradigms established in LEO. Enabling progressive Earth independence requiresmanagement of the increasingly complex interactions among spacecraft systems and integration of allthe data and functions that affect human health and performance into one coordinated system – theCrew Health and Performance (CHP) system. This system is a critical spacecraft system that isanalogous to other systems such as propulsion, guidance and navigation, or avionics. Design andintegration of the CHP system requires the evidence-based merger of typically disparate disciplinessuch as medicine, human factors, and physiological support system design, using systems engineering(SE) practices. The approach described here enables spacecraft and mission designers to align thescope of a CHP system with mission specific requirements, decrease risk to the crew, and increase theprobability of mission success.

Kerry Mcguire↗
DOE OSTI2021

Valence Effects of Fe Impurity for Recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 Cathode Materials

Iron impurities are generally included in the obtained leaching liquor solution during the hydrometallurgical recycling method of spent lithium-ion batteries (LIBs) due to the usage of iron in battery casings and machinery parts of recycling equipment, which would definitely affect the physical and electrochemical features of the recovered active materials. In this paper, the effects of iron impurity with different valence states (Fe 2+ and Fe 3+ ) and gradient concentrations (0.2, 1.0, and 5.0 at. %) for the obtained LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathodes are fully studied. It is found that Fe3+ impurity could easily lower the tap density and average size of NCM622 particles and even introduce some impurity phases in the NCM622 structure at high concentration (5.0 at. %), leading to much lower specific capacity, worse rate capability, and cycling performance of the Fe 3+ -based NCM622 cathode. On contrast, with certain concentrations of Fe 2+ impurity (0.2 and 1.0 at. %), the NCM622 cathode material exhibits comparable and much better electrochemical properties compared with the virgin NCM622 materials. Based on these results, the valence of Fe impurity should be considered and controlled as well as its concentration during the recycling process design for spent LIBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
NASA NTRS2010

General Pressurization Model in Simscape

System integration is an essential part of the engineering design process. The Ares I Upper Stage (US) is a complex system which is made up of thousands of components assembled into subsystems including a J2-X engine, liquid hydrogen (LH2) and liquid oxygen (LO2) tanks, avionics, thrust vector control, motors, etc. System integration is the task of connecting together all of the subsystems into one large system. To ensure that all the components will "fit together" as well as safety and, quality, integration analysis is required. Integration analysis verifies that, as an integrated system, the system will behave as designed. Models that represent the actual subsystems are built for more comprehensive analysis. Matlab has been an instrument widely use by engineers to construct mathematical models of systems. Simulink, one of the tools offered by Matlab, provides multi-domain graphical environment to simulate and design time-varying systems. Simulink is a powerful tool to analyze the dynamic behavior of systems over time. Furthermore, Simscape, a tool provided by Simulink, allows users to model physical (such as mechanical, thermal and hydraulic) systems using physical networks. Using Simscape, a model representing an inflow of gas to a pressurized tank was created where the temperature and pressure of the tank are measured over time to show the behavior of the gas. By further incorporation of Simscape into model building, the full potential of this software can be discovered and it hopefully can become a more utilized tool.

Servin, Mario↗
DOE OSTI2021

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Using Systems Engineering to Develop an Integrated Crew Health and Performance System to Mitigate Risk for Human Exploration Missions

New space exploration missions are currently being designed to take humanity beyond low Earth orbit (LEO) to cis-lunar space, the lunar surface, and eventually to Mars. These missions carry increased risks due to a number of factors, including distance from Earth, exposure to deep space hazards, reduced capacity and ability to resupply and evacuate, and increased communication delays. As distance from Earth grows and mission length increases, a growing proportion of mission risk can be attributed to the “human system.” Almost twenty years ago, the Institute of Medicine in the United States recommended the early and complete integration of the human system into the spacecraft and mission design process to mitigate this increased risk inherent in exploration missions. Exploration missions will require increasing levels of crew self-sufficiency that will challenge the current operational paradigms established in LEO. Enabling progressive Earth independence requires management of the increasingly complex interactions among spacecraft systems and integration of all the data and functions that affect human health and performance into one coordinated system –the Crew Health and Performance (CHP) system. This system is a critical spacecraft system that is analogous to other systems such as propulsion, guidance and navigation, or avionics. Design and integration of the CHP system requires the evidence-based merger of typically disparate disciplines such as medicine, human factors, and life support system design, using systems engineering (SE) practices. SE provides traceability of spacecraft requirements and enables reliable and repeatable trade space analysis of the many competing options for hardware and software to be included in the mission architecture. The approach described here enables spacecraft and mission designers to align the scope of a CHP system with mission specific requirements, decrease risk to the crew, and increase the probability of mission success.

Kerry McGuire↗
NASA NTRS1990

Mars mission gravity profile simulation

A flight experiment designed to determine the need for artificial gravity for Mars mission architectures at earlier stages of the design process is proposed. The Soviet Mir space station, the NASA Space Shuttle, and the resources of NASA Ames Research Center would be used to duplicate in the terrestrial environment the complete Mars-mission gravity profile in order to assess the need for artificial gravity. All mission phases of 1 G would be on earth; all mission phases of zero or micro G would be in space aboard Mir; and all launch, ascent, orbit, deorbit, approach, departure, and descent G loads would be provided by actual spacecraft in operations that could be designed to simulate the actual G loads, while the Mars stay time would be simulated on earth or in a variable-gravity research facility in space. Methods of simulating activities on the Martian surface are outlined along with data monitoring, countermeasures, and launch site and vehicle selection criteria.

Kuznetz, Lawrence H.↗
NASA NTRS1992

The NASA/Industry Design Analysis Methods for Vibrations (DAMVIBS) Program - Boeing helicopters airframe finite element modeling

Finite-element modeling of the airframe vibration of the Army/Boeing CH-47D helicopter is conducted with comparisons to experimental data in an effort to improve the design process. A NASTRAN FEM is developed that is fully representative of the test configuration and includes the support fixture, shakers, and the aircraft/shaker suspension system. The analysis is conducted with specific attention given to the prediction of reasonable forced amplitudes throughout the airframe. Reasonable correlation is noted between the FEM and experimental results, although improved correlation can be obtained by including more accurate damping values and secondary effects such as stringer shear loading. It is shown that the general stress model does not provide an adequate dynamic analysis on which to base design improvements. A more detailed model is required that emphasizes highly detailed helicopter elements and employs a finer mesh particularly in the description of the mass distribution.

Gabel, R.↗
DOE OSTI2019

Integrating narratives into decision making for complex systems engineering design issues

Abstract Engineering decision making and design requires collaboration between groups from different disciplines, each with different tools, vocabulary, and concerns. Traditional engineering decision‐making tools are generally based on understanding the decision makers’ values, modeling uncertainty with probability, and selecting the alternative that maximizes utility. This rational approach to decision making may not be well understood or used by many stakeholders involved in the engineering design process. Constructing narratives, a basic means of human communication, may aid in engineering communication and comprehension and help with decision making. Narratives represent events by means of a story and usually include characters or agents who cause events and to whom events happen. This paper recommends three methods for how the use of narrative can be applied to the area of engineering decision making. These methods include connecting the decision maker to the analysis, creating narrative simulations for training decision makers, and fostering consensus in problems with multiple stakeholders. An illustrative example of designing a better cookstove for the developing world demonstrates the role that understanding narratives of various stakeholders can play for accomplishing complex systems engineering.

MacKenzie, Cameron A.↗
DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗
DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Review of a Draft - Avoiding the Impossible: Re-focusing a Non-Feasible Mission 2-Hrs into a 3-Day Engineering Session

Concurrent engineering offers a great many benefits to engineers and mission designers throughout the world of aerospace. The only downside of concurrent engineering, and this is somewhat unavoidable, is that you don’t know the results of a design session until the end when it is completed. Usually, this is not a problem – you wouldn’t start building a spacecraft before the design is finished. However, within mass and cost constrained systems, you may end up with a final design that although technically sound – is not feasible due to mass or cost limits. Employing in-session mass and cost models with flexible inputs that refine their estimates and variance as more detailed information comes in throughout a design session allows major design changes to be made when the probability of breaching a mass or cost cap exceeds a threshold level. This enables mission designers to re-focus the study, and avoid spending 3-days with 15 engineers designing a non-feasible mission. By understanding key correlations and nested relationships within mass or cost, and specifically mass or cost allocations per mission element by mission type, it’s possible to get flexible-input, statistically based mass and cost estimates very early in the design process. Baseline models are seeded using mission characteristics and general parameters (outer planetary orbiter-probe mission, $500M cost cap for example) to provide a rough estimate of the expected mass or cost. As information gets solidified during the session, it gets added to the model and the estimates are updated. Continuing the orbiter-probe mission example, modeling probe heat shield cost as a percent of total probe cost, and probe cost as a percent of total flight system cost, and total flight system cost as a percent of total mission cost allows a design team to roll-up solidified information to estimate the probability of fitting within a mass or cost constraint early in a concurrent design session. When only the heat shield cost is known, the variance of the final estimate is higher, whereas when the full probe gets defined, naturally, the variance of the estimate decreases. A methodology, model, verification and demo implementation for cost limit breach are presented.

Hihn, Jairus M.↗
DOE OSTI2020

Design Advancement of an Indoor Soil Deposition Chamber: A Road to Standardization

An indoor soil deposition chamber has been designed and constructed at the Arizona State University Photovoltaic Reliability Lab (ASU-PRL) to simulate photovoltaic (PV) module soiling in the field. This paper discusses the next set of revisions to the design, which are organized into three categories: design improvements, cost reduction, and new features. The design process was carried out with the intention of working toward the development of a national or international soiling standard. The improvements made to the chamber are described in detail to provide a guide for other laboratories or research facilities who wish to study photovoltaic (PV) soiling.

14 SOLAR ENERGY↗
NASA NTRS1997

An Efficient Multiblock Method for Aerodynamic Analysis and Design on Distributed Memory Systems

The work presented in this paper describes the application of a multiblock gridding strategy to the solution of aerodynamic design optimization problems involving complex configurations. The design process is parallelized using the MPI (Message Passing Interface) Standard such that it can be efficiently run on a variety of distributed memory systems ranging from traditional parallel computers to networks of workstations. Substantial improvements to the parallel performance of the baseline method are presented, with particular attention to their impact on the scalability of the program as a function of the mesh size. Drag minimization calculations at a fixed coefficient of lift are presented for a business jet configuration that includes the wing, body, pylon, aft-mounted nacelle, and vertical and horizontal tails. An aerodynamic design optimization is performed with both the Euler and Reynolds Averaged Navier-Stokes (RANS) equations governing the flow solution and the results are compared. These sample calculations establish the feasibility of efficient aerodynamic optimization of complete aircraft configurations using the RANS equations as the flow model. There still exists, however, the need for detailed studies of the importance of a true viscous adjoint method which holds the promise of tackling the minimization of not only the wave and induced components of drag, but also the viscous drag.

Reuther, James↗