SEARCH · Engineering Papers
Results for “optimization analysis”
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.
Core design optimization and analysis of the Purdue Novel Modular Reactor (NMR-50)
Not Available
Comparison of box-wing and conventional aircraft mission performance using multidisciplinary analysis and optimization
Box-wing aircraft designs have the potential to achieve significant reductions in fuel consumption. Closed non-planar wing designs have been shown to reduce induced drag and the statically indeterminate wing structure can lead to reduced wing weight. In addition, the streamwise separation of the two main wings can provide the moments necessary for static stability and control, eliminating the weight and aerodynamic drag of a horizontal tail. Proper assessment of the disciplinary interactions in box-wing designs is essential to determine any realistic performance benefits arising from the use of such a configuration. This study analyzes both box-wing and conventional aircraft designed for representative regional-jet missions. A preliminary parametric investigation shows a lift-to-drag ratio advantage for box-wing designs, while a more detailed multidisciplinary study indicates that the requirement to carry the mission fuel in the wings leads to an increase of between 5% and 1% in total fuel burn compared to conventional designs. Furthermore, the multidisciplinary study identified operating conditions where the box-wing can have superior performance to conventional aircraft despite the fuel volume constraint.
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.
Design and analysis of optimal pre-cooling in residential buildings
Existing pre-cooling strategies provide a means of shifting or reducing the peak demand and/or energy cost in residential buildings. However, majority of them are rule-based and therefore may not be optimal in terms of cost saving, leaving room for improvement. In this paper, an integer linear programming problem that accounts for the thermal properties of a specific home, HVAC system capacity, utility rate structure, and weather conditions and makes use of a home thermal model is formulated. This problem determines the HVAC on/off control signal that minimizes the 24-h energy cost while maintaining thermal comfort and calculates the corresponding optimal indoor air temperature. The model is constructed using home thermal properties identified via data training in real-time. Through simulation, the energy performance of the proposed optimal pre-cooling strategy is investigated and compared with three rule-based operation strategies from the literature. It is found that the optimal strategy requires the least energy consumption without sacrificing thermal comfort. The superb energy performance of the optimal strategy is attributed to a longer runtime of the HVAC system in cool outdoor air conditions and to the elimination of deadband in HVAC operation, which is required by the rule-based strategies, to allow the indoor air temperature to stay near the thermal comfort upper bound as much as possible. In terms of energy cost, the rule-based operation strategies require 3.52, 1.90, and 2.79, respectively, while the optimal strategy only requires 1.52. These figures represent a saving of 56.82%, 20.00%, and 45.52%, respectively. The results suggest that the optimal strategy is indeed significantly more effective than the existing rule-based operation strategies.
Techno-economic analysis and optimization of water-gas shift membrane reactors for low-carbon hydrogen production
Not Available
Characteristic analysis and optimal survey area definition for semi-airborne transient electromagnetics
Not Available
Structural Analysis and Optimization of the Covalent Association between SpyCatcher and a Peptide Tag
Abstract not provided
Structural Analysis and Optimization of Context-Independent Anti-Hypusine Antibodies
Context-independent anti-hypusine antibodies that bind to the post-translational modification (PTM), hypusine, with minimal dependence on flanking amino acid sequences, were identified. The antibodies bind to both hypusine and deoxyhypusine or selectively to hypusine but not to deoxyhypusine. Phage display was used to further enhance the affinity of the antibodies. Affinity maturation of these anti-hypusine antibodies improved their performance in affinity capture of the only currently known hypusinated protein, eukaryotic translation initiation factor 5A. These anti-hypusine antibodies may have utility in the identification of novel hypusinated proteins. Crystal structures of the corresponding Fab fragments were determined in complex with hypusine- or deoxyhypusine-containing peptides. The hypusine or deoxyhypusine moiety was found to reside in a deep pocket formed between V H and V L domains of the Fab fragments. Interaction between the antibodies and hypusine includes an extensive hydrogen bond network. These are, to our knowledge, the first reported structures of context-independent anti-PTM antibodies in complex with the corresponding PTM.
Parametric analysis on optimized design of hybrid solar power plants
There is increasing interest in utility-scale solar power plants with storage which can flexibly dispatch renewable energy to the grid. However, plant design possesses many degrees of freedom and non-obvious trade-offs in performance. Software tools can estimate or optimize the performance of a specific plant configuration under market and weather conditions of interest; the associated cost parameters and operating assumptions strongly influence estimates of plant performance and decisions regarding optimal sizing. We employ the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform, which incorporates optimal dispatch when evaluating plant performance, and investigate the sensitivity to weather and market conditions, operating limitations, and the presence of a capacity-based incentive. We demonstrate changes in plant performance and optimal sizing with respect to these inputs and discuss implications. Here results show that PV-with-battery designs are more profitable under our assumptions, but that designs including a concentrated solar power (CSP) system produce significantly greater annual energy; and that CSP-with-thermal energy storage designs maximizing the benefit-to-cost ratio have an input-dependent linear relationship between the CSP field solar multiple and the hours of storage as the project budget varies.
Linewidth narrowing in self-injection-locked on-chip lasers
Abstract Stable laser emission with narrow linewidth is of critical importance in many applications, including coherent communications, LIDAR, and remote sensing. In this work, the physics underlying spectral narrowing of self-injection-locked on-chip lasers to Hz-level lasing linewidth is investigated using a composite-cavity structure. Heterogeneously integrated III–V/SiN lasers operating with quantum-dot and quantum-well active regions are analyzed with a focus on the effects of carrier quantum confinement. The intrinsic differences are associated with gain saturation and carrier-induced refractive index, which are directly connected with 0- and 2-dimensional carrier densities of states. Results from parametric studies are presented for tradeoffs involved with tailoring the linewidth, output power, and injection current for different device configurations. Though both quantum-well and quantum-dot devices show similar linewidth-narrowing capabilities, the former emits at a higher optical power in the self-injection-locked state, while the latter is more energy-efficient. Lastly, a multi-objective optimization analysis is provided to optimize the operation and design parameters. For the quantum-well laser, minimizing the number of quantum-well layers is found to decrease the threshold current without significantly reducing the output power. For the quantum-dot laser, increasing the quantum-dot layers or density in each layer increases the output power without significantly increasing the threshold current. These findings serve to guide more detailed parametric studies to produce timely results for engineering design.
Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device
The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.
Polarisation analysis of optimal conditions for stationary second-harmonic generation in a solid-state laser
The Jones matrix method is used to study the optimal conditions for steady-state generation through intracavity frequency conversion in a solid-state laser under type-II phase matching based on a weakly anisotropic model of an active medium (amplitude and phase anisotropy) and a nonlinear element. The optimal rotation angles of the nonlinear element are found. (nonlinear optical phenomena)
Design analysis for optimal calibration of diffusivity in reactive multilayers
Not provided.
Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design
SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.
Simulation analysis and optimization of fresh-slice multistage free-electron lasers
Not Available