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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.

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At least 1,441 records · Page 80

Additive Manufacturing of Low Cost Upper Stage Propulsion Components

NASA is currently developing Additive Manufacturing (AM) technologies and design tools aimed at reducing the costs and manufacturing time of regeneratively cooled rocket engine components. These Low Cost Upper Stage Propulsion (LCUSP) tasks are funded through NASA's Game Changing Development Program in the Space Technology Mission Directorate. The LCUSP project will develop a copper alloy additive manufacturing design process and develop and optimize the Electron Beam Freeform Fabrication (EBF3) manufacturing process to direct deposit a nickel alloy structural jacket and manifolds onto an SLM manufactured GRCop chamber and Ni-alloy nozzle. In order to develop these processes, the project will characterize both the microstructural and mechanical properties of the SLMproduced GRCop-84, and will explore and document novel design techniques specific to AM combustion devices components. These manufacturing technologies will be used to build a 25K-class regenerative chamber and nozzle (to be used with tested DMLS injectors) that will be tested individually and as a system in hot fire tests to demonstrate the applicability of the technologies. These tasks are expected to bring costs and manufacturing time down as spacecraft propulsion systems typically comprise more than 70% of the total vehicle cost and account for a significant portion of the development schedule. Additionally, high pressure/high temperature combustion chambers and nozzles must be regeneratively cooled to survive their operating environment, causing their design to be time consuming and costly to build. LCUSP presents an opportunity to develop and demonstrate a process that can infuse these technologies into industry, build competition, and drive down costs of future engines.

Protz, Christopher↗

Utilization of waste heat from a commercial GT for freshwater production, cooling and additional power: Exergoeconomic analysis and optimization

In this work, a multigeneration system is proposed for power, cooling and freshwater production. The waste heat from a SIEMENS SGT6-2000E GT is utilized to drive a supercritical CO 2 recompression Brayton cycle (SCRBC) and multi-effect desalination (MED) with thermal vapor compressor (TVC), while the waste heat from the pre-cooler of the SCRBC is utilized to drive an absorption cooling cycle. Detailed thermodynamic, exergy and exergoeconomic analyses are performed for the proposed multigeneration system using Engineering Equation Solver (EES). A sensitivity study is also performed to study the effects of the main operational parameters on the proposed system performance. Finally, multi-objective optimization using a genetic algorithm approach in EES is carried out to reach the optimal design. The proposed system can consume 34.2% less energy input and reduce CO 2 emission by 39.3% compared to the stand-alone systems for power, cooling and freshwater production. The results show that the energy utilization factor and exergy efficiency of the proposed system are improved by 33.6% and 14.3%, respectively, while the total unit product cost is reduced by 11.3% compared to the stand-alone GT cycle. Further, from exergoeconomic optimization, the proposed system can produce 132.4 MW of power, 7.09 MW of cooling and 650 kg/s of freshwater at energy utilization factor of 57.5%, exergy efficiency of 39.7% and total unit product cost of 16.3 $/GJ.

42 ENGINEERING↗

Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems

Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Shuttle cryogenic supply system optimization study. Volume 5A-2: Users manual for Space shuttle orbit injection system analysis (SOPSA)

SOPSA (Shuttle Orbit Injection Propulsion System Analysis) is a computer program to aid in the preliminary design of propellant feed systems for the space shuttle orbiter main engines. The primary purpose of the program is to evaluate propellant tank ullage pressure requirements imposed by the need to accelerate propellants rapidly during the engine start sequence. These requirements can influence the system design for a given engine as well as the suitability of a given system for use with different engines. During the vehicle design phase, the SOPSA program is used to establish feed system weight variations as a function of nominal line diameter and component and line configurations. The weight data are used in conjunction with pertinent cost data to establish optimum feed system designs.

Source record↗

"Genetically Engineered" Nanoelectronics

The quantum mechanical functionality of nanoelectronic devices such as resonant tunneling diodes (RTDs), quantum well infrared-photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs) is enabled by material variations on an atomic scale. The design and optimization of such devices requires a fundamental understanding of electron transport in such dimensions. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool based on a fundamental non-equilibrium electron transport theory. NEW was combined with a parallelized genetic algorithm package (PGAPACK) to evolve structural and material parameters to match a desired set of experimental data. A numerical experiment that evolves structural variations such as layer widths and doping concentrations is performed to analyze an experimental current voltage characteristic. The genetic algorithm is found to drive the NEMO simulation parameters close to the experimentally prescribed layer thicknesses and doping profiles. With such a quantitative agreement between theory and experiment design synthesis can be performed.

Klimeck, Gerhard↗

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

Simpson, Timothy W.↗

FIDVR Capability of Hybrid Grid-Forming PV Power Plants During Feeder Restoration

Grid-forming inverters must optimally transfer power from dc-coupled photovoltaic arrays and batteries into an ac grid. Further, they must be able to restore single-phase induction motors (SPIMs) and withstand fault-induced delayed voltage-recovery (FIDVR) events. These resilience and reliability challenges are addressed here by: (i) engineering a controller to optimally operate dc-coupled hybrid resources; (ii) modeling residential air-conditioning compressors for restoration/FIDVR studies; and (iii) analyzing SPIM thermal-relay performance under limited inverter currents and designing an electronic protection for stalled SPIMs. Furthermore, these contributions are demonstrated via electromagnetic-transient simulations and can be helpful to understand recommendations by the North American Electric Reliability Corporation.

14 SOLAR ENERGY↗

Heating Ventilation Air-Conditioning (HVAC) System Analysis and Optimization for Revit with the OpenStudio Analytical Framework (CRADA Final Report)

This project proposes to integrate the NREL-developed OpenStudio (OS) and its accompanying Analytical Framework (OSAF) into Autodesk’s (ADSK’s) market leading suite of building information modeling (BIM) tools. As a result, BIM users will gain access to advanced (but user-friendly) building energy modeling (BEM), calibration, and design optimization capabilities. Following integration, the BIM software will automatically generate a consistent OS-based BEM model, allowing users to develop projects in an integrated manner, moving back and forth between architectural and engineering perspectives without redundancy or data loss, saving time and effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Research pressure instrumentation for NASA Space Shuttle main engine, modification no. 5

The objective of the research project described is to define and demonstrate methods to advance the state of the art of pressure sensors for the space shuttle main engine (SSME). Silicon piezoresistive technology was utilized in completing tasks: generation and testing of three transducer design concepts for solid state applications; silicon resistor characterization at cryogenic temperatures; experimental chip mounting characterization; frequency response optimization and prototype design and fabrication. Excellent silicon sensor performance was demonstrated at liquid nitrogen temperature. A silicon resistor ion implant dose was customized for SSME temperature requirements. A basic acoustic modeling software program was developed as a design tool to evaluate frequency response characteristics.

Anderson, P. J.↗

A brief overview of computational structures technology related activities at NASA Lewis Research Center

The presentation gives a partial overview of research and development underway in the Structures Division of LeRC, which collectively is referred to as the Computational Structures Technology Program. The activities in the program are diverse and encompass four major categories: (1) composite materials and structures; (2) probabilistic analysis and reliability; (3) design optimization and expert systems; and (4) computational methods and simulation. The approach of the program is comprehensive and entails exploration of fundamental theories of structural mechanics to accurately represent the complex physics governing engine structural performance, formulation, and implementation of computational techniques and integrated simulation strategies to provide accurate and efficient solutions of the governing theoretical models by exploiting the emerging advances in computer technology, and validation and verification through numerical and experimental tests to establish confidence and define the qualities and limitations of the resulting theoretical models and computational solutions. The program comprises both in-house and sponsored research activities. The remainder of the presentation provides a sample of activities to illustrate the breadth and depth of the program and to demonstrate the accomplishments and benefits that have resulted.

Hopkins, Dale A.↗

Macroscopic balance model for wave rotors

A mathematical model for multi-port wave rotors is described. The wave processes that effect energy exchange within the rotor passage are modeled using one-dimensional gas dynamics. Macroscopic mass and energy balances relate volume-averaged thermodynamic properties in the rotor passage control volume to the mass, momentum, and energy fluxes at the ports. Loss models account for entropy production in boundary layers and in separating flows caused by blade-blockage, incidence, and gradual opening and closing of rotor passages. The mathematical model provides a basis for predicting design-point wave rotor performance, port timing, and machine size. Model predictions are evaluated through comparisons with CFD calculations and three-port wave rotor experimental data. A four-port wave rotor design example is provided to demonstrate model applicability. The modeling approach is amenable to wave rotor optimization studies and rapid assessment of the trade-offs associated with integrating wave rotors into gas turbine engine systems.

Welch, Gerard E.↗

Hard Constraints in Optimization Under Uncertainty

This paper proposes a methodology for the analysis and design of systems subject to parametric uncertainty where design requirements are specified via hard inequality constraints. Hard constraints are those that must be satisfied for all parameter realizations within a given uncertainty model. Uncertainty models given by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles, are the focus of this paper. These models, which are also quite practical, allow for a rigorous mathematical treatment within the proposed framework. Hard constraint feasibility is determined by sizing the largest uncertainty set for which the design requirements are satisfied. Analytically verifiable assessments of robustness are attained by comparing this set with the actual uncertainty model. Strategies that enable the comparison of the robustness characteristics of competing design alternatives, the description and approximation of the robust design space, and the systematic search for designs with improved robustness are also proposed. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, this methodology is applicable to a broad range of engineering problems.

Crespo, Luis G.↗

CFD-Based Design of a Filming Injector for N+3 Combustors

An effort was undertaken to perform CFD analysis of fluid flow in Lean-Direct Injection (LDI) combustors with axial swirl-venturi elements coupled with a new fuel-filming injector design for next-generation N+3 combustors. The National Combustion Code (NCC) was used to perform non-reacting and two-phase reacting flow computations on a N+3 injector configuration, in a single-element and a five-element injector array. All computations were performed with a consistent approach towards mesh-generation, spray-, ignition- and kinetics-modeling with the NCC. Computational predictions of the aerodynamics of the injector were used to arrive at an optimal injector design that met effective area, aerodynamics, and fuel-air mixing criteria. LDI-3 emissions (EINOx, EICO and UHC) were compared with the previous generation LDI-2 combustor experimental data at representative engine cycle conditions.

combustion chambers↗

Mars Sample Return (MSR) Sample Receiving Facility (SRF) Assessment Study (MSAS)

NASA, in partnership with the European Space Agency (ESA), is seeking to return Martian geological and atmospheric samples to Earth for scientific study in the early 2030s. Due to the possibility that the samples could contain extraterrestrial life, Mars Sample Return (MSR) is classified as a Category V: Restricted Earth Return mission by the NASA Planetary Protection Office. As a result of this classification, a MSR Sample Receiving Facility (SRF) must not only provide a pristine environment to ensure samples are protected from terrestrial contamination for scientific investigations, it must also provide high-containment (biosafety level 4 [BSL-4]-equivalence) to isolate the samples from Earth’s biosphere until the samples are deemed safe for release and/or sterilized. The nominal utilization period for a SRF is anticipated to be 2-5 years and is intended to enable curation activities, biohazard assessment, select early science activities, and the rapid release of samples to the scientific community. However, to account for possible delays in schedule or the identification of extant life, this anticipated period of time must be flexible to accommodate schedule extensions and contingency plans. Due to requirements for high-level biological containment and cleanliness, a traditional receiving/curation facility cannot be utilized for MSR. Therefore, beginning in 2022, NASA Johnson Space Center is performing a MSR SRF Assessment Study (MSAS) to investigate the most optimal facility modality for a MSR SRF, as well as start to define programmatic early estimate of costs and schedules before the initial design phase begins. NASA is partnering with industry contractors (architectural and engineering firms with BSL-4 and cleanroom technology experience, as well as other contracted infrastructure and construction specialists) along with selected experts from NASA, ESA, existing U.S. BSL-4 facilities, and other U.S. government agencies, to carry out the assessment study. The MSAS should also aid in the future refinement of the science requirements (e.g., contamination control, equipment accommodations) before site-specific design would commence. As part of the MSAS, NASA is planning to assess an array of possibilities for a MSR SRF. One of the main considerations is the facility modality and whether an existing BSL-4 facility can be utilized (for some or all functions); or, if new constructure would be required, would a traditional fixed facility or a modular facility the best choice. MSAS will also investigate the ability of the modalities to accommodate two different facility capability endmembers: 1) a minimal facility focusing on biohazard assessment and curation tasks with a small footprint, and 2) an enhanced facility with additional capabilities to enable expedited processing and the completion of time-sensitive and (some) sterilization-sensitive science. The assessment is intended to generate information that will inform the selection of facility modalities for high-level conceptual design development. While the assessment study will focus on SRF requirements for accommodating curation, science, and sample safety assessment infrastructure, it will also consider an array of other factors, such as ease of access for international users, decommissioning, repurposing, future sale or lease following MSR’s use of the facility, and uncontained preparatory laboratory spaces. Upon completion of the study, the preferred modality and refined requirements would be utilized for site-specific design but will not be finalized until NASA’s completion of the National Environmental Policy Act (NEPA) process.

A.D. Harrington↗

Computational Fluid Dynamic Optimization of an Experimental Rotating Detonation Rocket Engine Nozzle

A parametric optimization study is performed on the nozzle of a laboratory rotating detonation rocket engine (RDRE) using a three-dimensional computational fluid dynamic simulation. The primary optimization objective is maximum nozzle thrust. The basic nozzle configuration is a shrouded, truncated plug. The fluid in the RDRE chamber leading to the nozzle is choked at its exit so that its cyclic behavior is unaffected by any changes to the nozzle design. Optimization is performed for a single operating point. Parameters varied are the overall nozzle area expansion ratio and the fraction of the expansion area that is provided by the shroud. These two parameters indirectly affect the angle of the plug nozzle cone, and the bluff body area associated with its truncation. Nozzle thrust is evaluated as the difference between the thrust of the RDRE chamber-plus-nozzle combination and that of the chamber alone. The nozzle produces approximately 20% of the total engine thrust. The baseline nozzle is found to perform well, yielding 58.1% of the thrust calculated for a notional ideal RDRE nozzle which can instantaneously change shape to allow isentropic expansion of every fluid element. Optimization improves the performance, bringing the nozzle thrust to 70.0% of the notional ideal, and total engine thrust (chamber-plus-nozzle) to 94% of the ideal.

detonation↗

Computational Fluid Dynamic Optimization of an Experimental Rotating Detonation Rocket Engine Nozzle

A parametric optimization study is performed on the nozzle of a laboratory rotating detonation rocket engine (RDRE) using a three-dimensional computational fluid dynamic simulation. The primary optimization objective is maximum nozzle thrust. The basic nozzle configuration is a shrouded, truncated plug. The fluid in the RDRE chamber leading to the nozzle is choked at its exit so that its cyclic behavior is unaffected by any changes to the nozzle design. Optimization is performed for a single operating point. Parameters varied are the overall nozzle area expansion ratio and the fraction of the expansion area that is provided by the shroud. These two parameters indirectly affect the angle of the plug nozzle cone, and the bluff body area associated with its truncation. Nozzle thrust is evaluated as the difference between the thrust of the RDRE chamber-plus-nozzle combination and that of the chamber alone. The nozzle produces approximately 20% of the total engine thrust. The baseline nozzle is found to perform well, yielding 58.1% of the thrust calculated for a notional ideal RDRE nozzle which can instantaneously change shape to allow isentropic expansion of every fluid element. Optimization improves the performance, bringing the nozzle thrust to 70.0% of the notional ideal, and total engine thrust (chamber-plus-nozzle) to 94% of the ideal.

detonation↗

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. Here, we summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗