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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Characterizing secondary neutrons at BLIP for isotope production applications

Fast secondary neutrons created at the Brookhaven Linac Isotope Producer (BLIP) facility following proton irradiation were characterized by the foil activation technique and compared with FLUKA Monte Carlo simulations. The FLUKA-simulated neutron flux was spectrally adjusted following the maximum entropy formalism using the International Reactor Dosimetry and Fusion File (IRDFF-II), with predictions agreeing with experimental measurements to within 9 % following the adjustment procedure. Here, a multitude of degrader configurations were simulated to assess the feasibility of improving the fast (E n > 20 MeV) secondary neutron yield at the proposed neutron target position (“N-slot”). A configuration where the N-slot is closest to the proton degrader produced the highest fast neutron yield, with tungsten degraders achieving the best performance. Assuming the optimized target-degrader configuration proposed in this work, we discuss potential isotope production opportunities with secondary neutrons. In most cases the yields are in the order of several mCi.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Optimal Control and Architecture Design Optimization for Hydraulic Drive Train of a Compact Track Loader

Hydrostatic transmission (HST) is a common hydraulic drive train transmission used in track loaders and other high power off-road vehicles. While reliable, traditional HST with fixed-displacement motors has relatively poor efficiencies. In this paper, alternative HST and series hybrid drive train architectures are proposed to replace the traditional HST to allow the implementation of optimal control and optimization methods for better vehicle fuel efficiency. Besides the optimal control methods applied to minimize fuel consumption, the proposed architectures are also optimized for the best configuration in terms of component sizing. Backward-facing (acausal) simulation models were developed to estimate the fuel efficiency of each architecture. Finally, each architecture is compared for optimized performance and fuel saving relative to the traditional HST as baseline.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1973

Optimization of structures on the basis of fracture mechanics and reliability criteria

Systematic summary of factors which are involved in optimization of given structural configuration is part of report resulting from study of analysis of objective function. Predicted reliability of performance of finished structure is sharply dependent upon results of coupon tests. Optimization analysis developed by study also involves expected cost of proof testing.

Heer, E.↗
NASA NTRS1977

A study of commuter airplane design optimization

Problems of commuter airplane configuration design were studied to affect a minimization of direct operating costs. Factors considered were the minimization of fuselage drag, methods of wing design, and the estimated drag of an airplane submerged in a propellor slipstream; all design criteria were studied under a set of fixed performance, mission, and stability constraints. Configuration design data were assembled for application by a computerized design methodology program similar to the NASA-Ames General Aviation Synthesis Program.

Roskam, J.↗
NASA NTRS1985

The space shuttle ascent vehicle aerodynamic challenges configuration design and data base development

The phase B Space Shuttle systems definition studies resulted in a generic configuration consisting of a delta wing orbiter, and two solid rocket boosters (SRB) attached to an external fuel tank (ET). The initial challenge facing the aerodynamic community was aerodynamically optimizing, within limits, this configuration. As the Shuttle program developed and the sensitivities of the vehicle to aerodynamics were better understood the requirements of the aerodynamic data base grew. Adequately characterizing the vehicle to support the various design studies exploded the size of the data base to proportions that created a data modeling/management challenge for the aerodynamicist. The ascent aerodynamic data base originated primarily from wind tunnel test results. The complexity of the configuration rendered conventional analytic methods of little use. Initial wind tunnel tests provided results which included undesirable effects from model support tructure, inadequate element proximity, and inadequate plume simulation. The challenge to improve the quality of test results by determining the extent of these undesirable effects and subsequently develop testing techniques to eliminate them was imposed on the aerodynamic community. The challenges to the ascent aerodynamics community documented are unique due to the aerodynamic complexity of the Shuttle launch. Never before was such a complex vehicle aerodynamically characterized. The challenges were met with innovative engineering analyses/methodology development and wind tunnel testing techniques.

Dill, C. C.↗
DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents the techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which are lacking in public literature. To identify the optimum plant configuration, the study considered multiple cases with varying levels of thermal integration with the plant air separation unit (ASU). Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE) for each case. The optimization design variables include the sCO2 cooler outlet temperatures, recuperator approach temperatures, and pressure drops. High fidelity models for recuperators, coolers, and turbines were developed and used to capture the impact of design variables on plant efficiency and capital costs. The optimization was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms available under NETL’s Framework for Optimization and Quantification of Uncertainty and Sensitivity platform. The optimized direct sCO2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on the F-class gas turbine with CCS. The LCOE of the optimized direct sCO2 plants is 13 to 17% higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to the high uncertainty in component capital costs for these new plants. Recuperators make up over 50% of the sCO2 power block costs. Consequently, any research and development efforts to reduce the recuperator capital costs will benefit the technology’s commercialization. The study also presents preliminary results showing the impact of co-firing landfill gas and natural gas on plant efficiency, LCOE, and CO2 emissions.

Pidaparti, Sandeep↗
DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents the techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which are lacking in public literature. To identify the optimum plant configuration, the study considered multiple cases with varying levels of thermal integration with the plant air separation unit (ASU). Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE) for each case. The optimization design variables include the sCO2 cooler outlet temperatures, recuperator approach temperatures, and pressure drops. High fidelity models for recuperators, coolers, and turbines were developed and used to capture the impact of design variables on plant efficiency and capital costs. The optimization was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms available under NETL’s Framework for Optimization and Quantification of Uncertainty and Sensitivity platform. The optimized direct sCO2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on the F-class gas turbine with CCS. The LCOE of the optimized direct sCO2 plants is 13 to 17% higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to the high uncertainty in component capital costs for these new plants. Recuperators make up over 50% of the sCO2 power block costs. Consequently, any research and development efforts to reduce the recuperator capital costs will benefit the technology’s commercialization. The study also presents preliminary results showing the impact of co-firing landfill gas and natural gas on plant efficiency, LCOE, and CO2 emissions.

Pidaparti, Sandeep↗
NASA NTRS1997

Choosing Sensor Configuration for a Flexible Structure Using Full Control Synthesis

Optimal locations and types for feedback sensors which meet design constraints and control requirements are difficult to determine. This paper introduces an approach to choosing a sensor configuration based on Full Control synthesis. A globally optimal Full Control compensator is computed for each member of a set of sensor configurations which are feasible for the plant. The sensor configuration associated with the Full Control system achieving the best closed-loop performance is chosen for feedback measurements to an output feedback controller. A flexible structure is used as an example to demonstrate this procedure. Experimental results show sensor configurations chosen to optimize the Full Control performance are effective for output feedback controllers.

Lind, Rick↗
NASA NTRS1991

Theoretical modeling, near-optimum design and predicted performance of n(+)pp(+) and p(+)nn(+) indium phosphide homojunction solar cells

Using a detailed simulation model of p(+)nn(+) and n(+)pp(+) indium phosphide (InP) homojunction solar cells, extensive parametric variation computer simulation runs are conducted to help arrive at near-optimum designs of these two solar cell configurations. Values of all the geometrical and material parameters corresponding to the near-optimal designs of both these configurations are presented. For each configuration, results are given for parametric variation runs showing how the performance parameters JSC, VOC, and eta vary with each of the cell parameters for the near-optimally designed cell.

Goradia, Chandra↗
DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗
NASA NTRS1987

Numerical optimization of conical flow waveriders including detailed viscous effects

A family of optimized hypersonic waveriders is generated and studied wherein detailed viscous effects are included within the optimization process itself. This is in contrast to previous optimized waverider work, wherein purely inviscid flow is used to obtain the waverider shapes. For the present waveriders, the undersurface is a streamsurface of an inviscid conical flowfield, the upper surface is a streamsurface of the inviscid flow over a tapered cylinder (calculated by the axisymmetric method of characteristics), and the viscous effects are treated by integral solutions of the boundary layer equations. Transition from laminar to turbulent flow is included within the viscous calculations. The optimization is carried out using a nonlinear simplex method. The resulting family of viscous hypersonic waveriders yields predicted high values of lift/drag, high enough to break the L/D barrier based on experience with other hypersonic configurations. Moreover, the numerical optimization process for the viscous waveriders results in distinctly different shapes compared to previous work with inviscid-designed waveriders. Also, the fine details of the viscous solution, such as how the shear stress is distributed over the surface, and the location of transition, are crucial to the details of the resulting waverider geometry. Finally, the moment coefficient variations and heat transfer distributions associated with the viscous optimized waveriders are studied.

Bowcutt, Kevin G.↗
DOE OSTI2019

Subdominant modes and optimization trends of DIII-D reverse magnetic shear configurations

Alfvén Eigenmodes (AE) and magneto-hydrodynamic (MHD) modes are destabilized in DIII-D reverse magnetic shear configurations and may limit the performance of the device. We use the reduced MHD equations in a full 3D system, coupled with equations of density and parallel velocity moments for the energetic particles as well as the geodesic acoustic wave dynamics, to study the properties of instabilities observed in DIII-D reverse magnetic shear discharges, with the purpose of discovering ways to avoid or minimize MHD and AE activity for different magnetic field configurations and neutral beam injector (NBI) operation regimes. The simulations show at the beginning of the discharge, before the reverse shear region is formed, a plasma AE unstable and marginally MHD stable. As soon as the reverse shear region appears, ideal MHD modes are destabilized with larger growth rate than the AE. Both MHD modes and AE coexist along the discharge, although the MHD modes are more unstable as the reverse shear region deepens. AE stability can be improved if the NBI injection is off axis, because on-axis injection leads to AE with larger growth rate and frequency. In addition, decreasing the beam energy or increasing the NBI injection lead to an AE with larger growth rate and frequency, so a NBI operation in the weakly resonant regime requires higher beam energies than in the experiment (V th,ƒ /V A0 > 0.3). The MHD linear stability can be also improved if the reverse shear region and the q profile near the magnetic axis are in between the rational surfaces q = 2 and q = 1, specifically if there is a region in the core with negative shear, avoiding a flat q profile near the magnetic axis. The simulations also shows a smooth transition between MHD modes and low frequency AE, no critical β ƒ , pointing out an overlap between MHD and AE activity for modes with frequency lower than 30 kHz. This is in the range of Beta Acosutic Alfvén Eigenmodes (BAAE) and Beta induced Alfvén Eigenmodes (BAE).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1991

Intelligent perturbation algorithms for space scheduling optimization

Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.

Kurtzman, Clifford R.↗
DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗
NASA NTRS1988

A preliminary component analysis of a Mach-7 hypersonic vehicle

As research continues in the development of an aircraft capable of travelling at hypersonic flight velocities, both the propulsion and thermal management systems stand out as areas requiring innovative technological breakthroughs. For propulsion, the difficulty involves efficiently compressing and combusting hydrogen in a supersonic stream, i.e., developing a viable scramjet with thermal management, the challenge lies in development of materials and active cooling systems capable of handling the enormous heat fluxes associated with hypersonic flight. This paper focuses on these problems and presents component designs for both an active cooling system and an all-external-compression scramjet. These systems are mated to a Mach-6 passenger cruise aircraft whose aerodynamic configuration was derived from an optimization of NASA windtunnel test results. The following outlines the development of the configuration and then focuses in on the design of the two component systems.

Source record↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
NASA NTRS1981

Supersonic cruise research aircraft structural studies: Methods and results

NASA Langley Research Center SCAR in-house structural studies are reviewed. In methods development, advances include a new system of integrated computer programs called ISSYS, progress in determining aerodynamic loads and aerodynamically induced structural loads (including those due to gusts), flutter optimization for composite and metal airframe configurations using refined and simplified mathematical models, and synthesis of active controls. Results given address several aspects of various SCR configurations. These results include flutter penalties on composite wing, flutter suppression using active controls, roll control effectiveness, wing tip ground clearance, tail size effect on flutter, engine weight and mass distribution influence on flutter, and strength and flutter optimization of new configurations. The ISSYS system of integrated programs performed well in all the applications illustrated by the results, the diversity of which attests to ISSYS' versatility.

Sobieszczanski-Sobieski, J.↗