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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 847 records · Page 47

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NASA NTRS2011

On-Line Tool for the Assessment of Radiation in Space - Deep Space Mission Enhancements

The On-Line Tool for the Assessment of Radiation in Space (OLTARIS, https://oltaris.nasa.gov) is a web-based set of tools and models that allows engineers and scientists to assess the effects of space radiation on spacecraft, habitats, rovers, and spacesuits. The site is intended to be a design tool for those studying the effects of space radiation for current and future missions as well as a research tool for those developing advanced material and shielding concepts. The tools and models are built around the HZETRN radiation transport code and are primarily focused on human- and electronic-related responses. The focus of this paper is to highlight new capabilities that have been added to support deep space (outside Low Earth Orbit) missions. Specifically, the electron, proton, and heavy ion design environments for the Europa mission have been incorporated along with an efficient coupled electron-photon transport capability to enable the analysis of complicated geometries and slabs exposed to these environments. In addition, a neutron albedo lunar surface environment was also added, that will be of value for the analysis of surface habitats. These updates will be discussed in terms of their implementation and on how OLTARIS can be used by instrument vendors, mission designers, and researchers to analyze their specific requirements.12

Sandridge, Chris a.↗

NASA NTRS2015

OLTARIS - Overview and Recent Updates

The On-Line Tool for the Assessment of Radiation in Space (OLTARIS) is a web-based set of tools and models that allow engineers and scientists to assess the effects of space radiation in spacecraft, habitats, rovers, and spacesuits. The site is intended to be a design tool for those studying the effects of space radiation for current and future missions as well as a research tool for those developing advanced material and shielding concepts. The tools and models are built around the HZETRN radiation transport code and are currently focused on human-related responses. OLTARIS was deployed in 2008. Since that time, many improvements and additional capabilities have been added to the site. The purpose of this poster/presentation is to give an overview of the current capabilities of OLTARIS and focus on the updates to the site since the last workshop presentation in 2014. OLTARIS currently has 240 active accounts - 87 accounts are government (including NASA, ORNL, JPL, AFRL, and FAA), 76 are university professors/researchers/students, and 51 are industry (including Boeing, Space X, Lockheed-Martin, ATK, Northrup Grumman, and Bigelow Aerospace). There have been 14,000 jobs run through OLTARIS since counting began in November 2009. ITAR restrictions were recently reversed, so the site is now available to registered users worldwide.

Sandridge, C. A.↗

NASA NTRS1993

Noise levels from a model turbofan engine with simulated noise control measures applied

A study of estimated full-scale noise levels based on measured levels from the Advanced Ducted Propeller (ADP) sub-scale model is presented. Testing of this model was performed in the NASA Lewis Low Speed Anechoic Wind Tunnel at a simulated takeoff condition of Mach 0.2. Effective Perceived Noise Level (EPNL) estimates for the baseline configuration are documented, and also used as the control case in a study of the potential benefits of two categories of noise control. The effect of active noise control is evaluated by artificially removing various rotor-stator interaction tones. Passive noise control is simulated by applying a notch filter to the wind tunnel data. Cases with both techniques are included to evaluate hybrid active-passive noise control. The results for EPNL values are approximate because the original source data was limited in bandwidth and in sideline angular coverage. The main emphasis is on comparisons between the baseline and configurations with simulated noise control measures.

Hall, David G.↗

NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Tong, Michael T.↗

DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗

DOE OSTI2018

Numerical and Experimental Modeling of the Recirculating Melt Flow Inside an Induction Crucible Furnace

In the paper, a new water model of the turbulent recirculating flow in an induction furnace is introduced. The water model was based on the principle of the stirred vessel used in process engineering. The flow field in the water model was measured by means of particle image velocimetry in order to verify the model’s performance. Here, it is indicated that the flow consists of two toroidal vortices similar to the flow in the induction crucible furnace. Furthermore, the turbulent flow in the water model is investigated numerically by adopting eddy-resolving turbulence modeling. The two toroidal vortices occur in the simulations as well. The numerical approaches provide identical time-averaged flow patterns. Moreover, a good qualitative agreement is observed on comparing the experimental and numerical results. In addition, a numerical simulation of the melt flow in a real induction crucible furnace was performed. The turbulent kinetic energy spectrum of the flow in the water model was compared to that of the melt flow in the induction crucible furnace to show the similarity in the nature of turbulence.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Effects of Heat Transfer Coefficient Variation on Nuclear Thermal Propulsion Engine Performance

A physics-based Nuclear Thermal Propulsion (NTP) Testing Reference Design (TRD) power balance model was coded in Simulink to investigate engine performance for various design and parameter modifications. Since the primary mode of heat transfer in NTP engines is convective, the convective heat transfer coefficient (HTC) is a key parameter that requires accurate representation. The industry standard Westinghouse correlation has an uncertainty of ±20% which was investigated in this study. The results showed that a 20% decrease in the HTC led to a 4.14% increase in maximum fuel temperature while a 20% decrease in the HTC led to a 1.81% decrease in maximum fuel temperature suggesting that narrowing the uncertainty of this correlation through experimental work would be a critical step in the development of NTP engines. Furthermore, a maximum fuel temperature relationship with specific impulse was developed for the TRD engine which showed potential engine operation between specific impulse values of 715 and 900 seconds with minimal changes to the engine design. This graph could be useful for high level vehicle performance estimations for fuel types with different maximum operating temperatures.

Heat Transfer Coefficient↗

NASA NTRSDate not supplied

Effects of Varying the Heat Transfer Coefficient on Engine Performance

Heat Transfer Coefficient↗

NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Core Size↗

NASA NTRS2011

Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions

This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.

Strutzenberg, Louise L.↗

DOE OSTI2022

Inverse Calculation of Burden Distribution Matrix Using B-spline Model Based PDF control in Blast Furnace Burden Charging Process

The inverse calculation of burden distribution matrix (BDM) is one of the most important challenges in the blast furnace operation in iron-making processes. In general, blast furnace consumes 65% of the total energy for the whole steel-making. Focusing on this practical challenge, this article proposes a new burden distribution spatial model in calculating burden charging process, and develops a B-spline approximation-based probability density function (PDF) control algorithm to assign the expected thickness distribution of burden layer and, thus, develops a new method for the required inverse calculation of BDM. First, a novel method for the thickness distribution of burden layer is given using B-spline model to produce an expected distribution shape subjected to a desired tracking within a specific spatial constraint. Then, according to the coexistence of continuous and bounded discrete variables in BDM, a novel hybrid optimization control method by combining integer programming and PDF tracking is further established for the effective inverse calculation of BDM. Finally, the proposed PDF-based iterative inverse calculation of BDM using B-spline models are tested using various data from industrial examples. Furthermore, the simulation results show that the proposed method is well suited to solve the BDM inverse calculation problem in practice.

42 ENGINEERING↗

NASA NTRS2005

Model-Based, Multiscale Self-Tuning Controller Developed for Active Combustion Control

New challenges concerning system health-monitoring and life-extending robust controls for the Ultra-Efficient Engine Technology Project, as well as other advanced engine and power system concepts at NASA and elsewhere, have renewed the control community s interest in smart, model-based methods. In particular, these challenges have further motivated efforts at the NASA Glenn Research Center to exploit the versatility and superiority of the dynamic features extraction of multiscale analysis for controls--such as with "wavelets" and "wavelet filter-banks.' The accomplishments reported herein pertain to the active suppression of combustion instabilities in liquid-fuel combustors via fuel modulation. The fundamentals and initial success of this innovation were reported for a unique demonstration of active combustion control (a research collaboration of NASA Glenn with Pratt & Whitney and the United Technologies Research Center, UTRC). This demonstration, conducted in 2002 at UTRC on the NASA single nozzle rig (SNR) combustor, was the first known suppression of high-frequency instability with a liquid-fueled combustor. The SNR is based on a high-powered military engine combustor that exhibited well-known instabilities.

Le, Dzu K.↗

NASA NTRS1990

Real-time diagnostics of the reusable rocket engine using on-line system identification

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Guo, T.-H.↗

NASA NTRS2002

Rocket Engine Oscillation Diagnostics

Rocket engine oscillating data can reveal many physical phenomena ranging from unsteady flow and acoustics to rotordynamics and structural dynamics. Because of this, engine diagnostics based on oscillation data should employ both signal analysis and physical modeling. This paper describes an approach to rocket engine oscillation diagnostics, types of problems encountered, and example problems solved. Determination of design guidelines and environments (or loads) from oscillating phenomena is required during initial stages of rocket engine design, while the additional tasks of health monitoring, incipient failure detection, and anomaly diagnostics occur during engine development and operation. Oscillations in rocket engines are typically related to flow driven acoustics, flow excited structures, or rotational forces. Additional sources of oscillatory energy are combustion and cavitation. Included in the example problems is a sampling of signal analysis tools employed in diagnostics. The rocket engine hardware includes combustion devices, valves, turbopumps, and ducts. Simple models of an oscillating fluid system or structure can be constructed to estimate pertinent dynamic parameters governing the unsteady behavior of engine systems or components. In the example problems it is shown that simple physical modeling when combined with signal analysis can be successfully employed to diagnose complex rocket engine oscillatory phenomena.

Nesman, Tom↗

NASA NTRS2003

Advanced Information Technology in Simulation Based Life Cycle Design

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

Renaud, John E.↗

DOE OSTI2015

Advancement of Systems Designs and Key Engineering Technologies for Materials Based Hydrogen Storage

UTRC lead the development of the Simulink Framework model that enables a comparison of different hydrogen storage systems on a common basis. The Simulink Framework model was disseminated on the www.HSECoE.org website that is hosted by NREL. UTRC contributed to a better understanding of the safety aspects of the proposed hydrogen storage systems. UTRC also participated in the Failure Mode and Effect Analysis of both the chemical- and the adsorbent-based hydrogen storage system during Phase 2 of the Hydrogen Storage Engineering Center of Excellence. UTRC designed a hydrogen storage system with a reversible metal hydride material in a compacted form for light-duty vehicles with a 5.6 kg H2 storage capacity, giving it a 300 miles range. It contains a heat exchanger that enables efficient cooling of the metal hydride material during hydrogen absorption in order to meet the 3.3 minute refueling time target. It has been shown through computation that the kinetics of hydrogen absorption of Ti-catalyzed NaAlH4 was ultimately limiting the rate of hydrogen absorption to 85% of the material capacity in 3.3 minutes. An inverse analysis was performed in order to determine the material property requirements in order for a metal hydride based hydrogen storage system to meet the DOE targets. Work on metal hydride storage systems was halted after the Phase 1 to Phase 2 review due to the lack of metal hydride materials with the required material properties. UTRC contributed to the design of a chemical hydrogen storage system by developing an adsorbent for removing the impurity ammonia from the hydrogen gas, by developing a system to meter the transport of Ammonia Borane (AB) powder to a thermolysis reactor, and by developing a gas-liquid-separator (GLS) for the separation of hydrogen gas from AB slurry in silicone oil. Stripping impurities from hydrogen gas is essential for a long life of the fuel cell system on board of a vehicle. Work on solid transport of AB was halted after the Phase 1 to Phase 2 review in favor of studying the slurry-form of AB as it appeared to be difficult to transport a solid form of AB through the thermolysis reactor. UTRC demonstrated the operation of a compact GLS in the laboratory at a scale that would be required for the actual automotive application. The GLS met the targets for weight and volume. UTRC also reported about the unresolved issue associated with the high vapor pressure of fluids that could be used for making a slurry-form of AB. Work on the GLS was halted after the Phase 2 to Phase 3 review as the off-board regeneration efficiency of the spent AB was below the DOE target of 60%. UTRC contributed to the design of an adsorbent-based hydrogen storage system through measurements of the thermal conductivity of a compacted form of Metal Organic Framework (MOF) number 5 and through the development and sizing of a particulate filter. Thermal conductivity is important for the design of the modular adsorbent tank insert (MATI), as developed by Oregon State University (OSU), in order to enable a rapid refueling process. Stringent hydrogen quality requirements can only be met with an efficient particulate filtration system. UTRC developed a method to size the particulate filter by taking into account the effect of the pressure drop on the hydrogen adsorption process in the tank. UTRC raised awareness about the potential use of materials-based H2 storage systems in applications outside the traditional light-duty vehicle market segment by presenting at several conferences about niche application opportunities in Unmanned Aerial Vehicles (UAV), Autonomous Underwater Vehicles (AUV), portable power and others.

08 HYDROGEN↗

DOE OSTI2022

Techno-economic performances and life cycle greenhouse gas emissions of various ammonia production pathways including conventional, carbon-capturing, nuclear-powered, and renewable production

Ammonia (NH 3 ) is conventionally produced using fossil natural gas (NG) for hydrogen production through steam reformation and synthesis in the Haber–Bosch (HB) process. The global conventional ammonia production contributes more than 420 million tons of CO 2 emissions annually. Here, in this work, we investigated the techno-economics and well-to-plant-gate (WTG) greenhouse gas (GHG) emissions of conventional NG-based, carbon-capturing, nuclear-powered, and renewable ammonia production by developing an engineering process model for each. Carbon-capturing ammonia production refers to the NG-based ammonia production while capturing CO 2 and transporting it via pipelines for storage or utilization. Nuclear-powered and renewable ammonia production represents an alternative ammonia production through water electrolysis, air separation, and the HB process using carbon-free energy sources. Nuclear and renewable energy sources are assumed to be used for nuclear-powered and renewable ammonia production, respectively. Sensitivity analyses are performed for CO 2 pipeline transport distances, potential carbon capture tax credits, and clean H 2 production cost. Carbon-capturing ammonia production reduces WTG GHG emissions by 55–70% compared to conventional NG-based ammonia production methods. Nuclear-powered and renewable ammonia production almost eliminate GHG emissions since energy supply is either zero carbon or near-zero carbon. However, when $4.16–4.83 per kg is the cost assumed for clean H 2 production using state-of-the-art electrolysis technologies, the levelized costs of nuclear-powered and renewable ammonia are calculated to be $0.92–1.06 per kg NH 3 , which are approximately four times higher than the conventional NG-based ammonia production cost. The cost of CO 2 emission avoidance is estimated in the range of $266–318 per metric ton of CO 2 for nuclear-powered and renewable ammonia production. The clean H 2 production cost is the major contributor to the levelized costs of nuclear-powered and renewable ammonia production. A lower production cost, near $1 per kg H 2 , for clean H 2 is required for nuclear-powered and renewable ammonia production to be cost-competitive with conventional NG-based ammonia production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

What Questions Would a Systems Engineer Ask to Assess Systems Engineering Models as Credible

Digital Systems Engineering strategies typically call for digital Systems Engineering models to be retained in repositories and certified as an authoritative source of truth (enabling model reuse, qualification, and collaboration). In order for digital Systems Engineering models to be certified as authoritative (credible), they need to be assessed - verified and validated - and with the amount of uncertainty in the model quantified (consider reusing someone else's model without knowing the author). Due to this increasing model complexity, the authors assert that traditional human-based methods for validating, verifying, and uncertainty quantification - such as human-based peer-review sessions - cannot sufficiently establish that a digital Systems Engineering model of a complex system is credible. Digital Systems Engineering models of complex systems can contain millions of nodes and edges. The authors assert that this level of detail is beyond the ability of any group of humans - even working for weeks at a time - to discern and catch every minor model infraction. In contrast, computers are highly effective at discerning infractions with massive amounts of information. The authors suggest that a better approach might be to focus the humans at what model patterns should be assessed and enable the computer to assess the massive details in accordance with those patterns - by running through perhaps 100,000 test loops. In anticipation of future projects to implement and automate the assessment of models at Sandia National Laboratories, a study was initiated to elicit input from a group of 25 Systems Engineering experts. The authors positioning query began with - 'What questions would a Systems Engineer ask to assess Systems Engineering models for credibility?" This report documents the results of that survey.

42 ENGINEERING↗