Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Optimization”

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

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

At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An energy storage dispatch optimization for demand-side management in industrial facilities

An energy storage (ES) dispatch optimization was implemented to test lithium-ion battery ES, supercapacitor ES, and compressed air ES on two different industrial facilities – one intermittent process facility and one continuous process facility. The model first shows the capability of optimizing the size of a single technology on a single industrial facility to maximize the return on investment. Then, for the same profile, the model identifies significant differences in the optimal size for different technologies based on their performance parameters. Third, the model identifies differences in the optimal size for each technology based on the facilities profile. Here the results show compressed air ES yields the highest return on investment for both facilities and provides insight for future development of ES systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Co-optimization of fuel properties, combustion system geometry, and injection strategy for conventional diesel fuel

Here, studies have shown that fuel properties can impact an engine’s operation in several ways, including ignition delay, sooting tendency, mixture formation, and combustion temperature. In mixing-controlled compression ignition (MCCI) engines, the fuel system design and piston bowl geometry significantly affect combustion performance and emissions. Based on current information, it is difficult to draw conclusions about fuel property effects and sensitivities. The central fuel hypothesis approach used in the US Department of Energy Co-Optima program has worked well for spark ignition fuels: identifying critical fuel property ranges is sufficient to screen fuel blends that are expected to maximize efficiency and reduce pollutant emissions. However, for MCCI-relevant fuels, the information gained from past studies is not sufficient to build such a merit function or to allow for performing a similar screening of fuel blends. It is hypothesized that a co-optimization of a fuel’s physical and chemical properties, combustion system geometry, and injection strategy could leverage synergies between the effects of the fuel properties and geometries, resulting in improved performance over state-of-the-art. A machine learning–assisted unconstrained global optimization algorithm was used to explore a design space comprising 23 independent variables. The results show that physical property effects were minimal even for large variations in fuel properties, and the only interaction effect that was observed was the effect of varied fuel density parameters on fuel/air mixture formation. Nevertheless, these interactions were not sufficient in magnitude to significantly affect optimization results. Therefore, analysis of the results suggests that fuel physical properties cannot be leveraged in a co-optimization context to increase engine efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

A CPS Based Optimal Operational Control System for Fused Magnesium Furnace

Fused magnesia smelting for fused magnesium furnace (FMF) is an energy intensive process with high temperature and comprehensive complexities. Its operational index namely energy consumption per ton (ECPT) is defined as the consumed electrical energy per ton of acceptable quality and is difficult to measure online. Moreover, the dynamics of ECPT cannot be precisely modelled mathematically. The model parameters of the three-phase currents of the electrodes such as the molten pool level, its variation rate and resistance are uncertain and nonlinear functions of the changes in both the smelting process and the raw materials composition. In this paper, an integrated optimal operational control algorithm proposed is composed of a current set-point control, a current switching control and a self-optimized tuning mechanism. The tight conjoining of and coordination between the computational resources including the integrated optimal operational control, embedded software, industrial cloud, wireless communication and the physical resources of FMF constitutes a cyber-physical system (CPS) based embedded optimal operational control system. Successful application of this system has been made for a production line with ten fused magnesium furnaces in a factory in China, leading to a significant reduced ECPT.

Cyber-Physical Systems↗
DOE OSTI2018

Fuel-Optimal Periodic Control of Passenger Cars in Cruise Based on Pontryagin's Minimum Principle

This paper aims to exploit the fuel saving potential of a passenger car in cruising scenarios by deriving an optimal periodic control strategy based on Pontyagin’s Minimum Principle. Gear shifting and coupling between the engine and the driveline are explicitly included in the optimal control problem, and this leads to a hybrid system. By investigating the analytically derived optimal trajectory in the state space, we confirm the optimal periodic control. Considerable advantages in fuel economy up to 4.8% compared to conventional steady control are demonstrated.

automotive control↗
DOE OSTI2023

Resilient Design of Continuous-time Distributed Optimization Algorithm in the Presence of Cyber-attacks

This paper presents a continuous-time resilient distributed optimization algorithm based on competitive interaction design method on connected graphs in the presence of adversaries. Here, the competitive interaction method allows us to design a network that protects the multi-agent systems from adversaries without requiring high network connectivity. In addition, the proposed algorithm does not require the global information about the number of adversaries. First, we show that the proposed distributed algorithm solves the resilient distributed optimization problem with no attack on the communication links. Second, we show that the proposed continuous-time distributed optimization algorithm on connected graphs converges to the small neighborhood of the optimal solution in the presence of cyber-attacks onto the communication channel. Simulations are presented to illustrate our theoretical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Here the results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Higher-order triangular spectral element method with optimized cubature points for seismic wavefield modeling

The mass-lumped method avoids the cost of inverting the mass matrix and simultaneously maintains spatial accuracy by adopting additional interior integration points, known as cubature points. To date, such points are only known analytically in tensor domains, such as quadrilateral or hexahedral elements. Thus, the diagonal-mass-matrix spectral element method (SEM) in non-tensor domains always relies on numerically computed interpolation points or quadrature points. However, only the cubature points for degrees 1 to 6 are known, which is the reason that we have developed a p-norm-based optimization algorithm to obtain higher-order cubature points. In this way, we obtain and tabulate new cubature points with all positive integration weights for degrees 7 to 9. The dispersion analysis illustrates that the dispersion relation determined from the new optimized cubature points is comparable to that of the mass and stiffness matrices obtained by exact integration. Simultaneously, the Lebesgue constant for the new optimized cubature points indicates its surprisingly good interpolation properties. As a result, such points provide both good interpolation properties and integration accuracy. The Courant–Friedrichs–Lewy (CFL) numbers are tabulated for the conventional Fekete-based triangular spectral element (TSEM), the TSEM with exact integration, and the optimized cubature-based TSEM (OTSEM). A complementary study demonstrates the spectral convergence of the OTSEM. A numerical example conducted on a half-space model demonstrates that the OTSEM improves the accuracy by approximately one order of magnitude compared to the conventional Fekete-based TSEM. In particular, the accuracy of the 7th-order OTSEM is even higher than that of the 14th-order Fekete-based TSEM. Furthermore, the OTSEM produces a result that can compete in accuracy with the quadrilateral SEM (QSEM). The high accuracy of the OTSEM is also tested with a non-flat topography model. In terms of computational efficiency, the OTSEM is more efficient than the Fekete-based TSEM, although it is slightly costlier than the QSEM when a comparable numerical accuracy is required. - Highlights: • Higher-order cubature points for degrees 7 to 9 are developed. • The effects of quadrature rule on the mass and stiffness matrices has been conducted. • The cubature points have always positive integration weights. • Freeing from the inversion of a wide bandwidth mass matrix. • The accuracy of the TSEM has been improved in about one order of magnitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Hippocampal dose from stereotactic radiosurgery for 4 to 10 brain metastases: Risk factors, feasibility of dose reduction via re-optimization, and patient outcomes

This study aimed to report hippocampal dose from single-fraction stereotactic radiosurgery (SRS) for 4 to 10 brain metastases and determine feasibility of hippocampal-sparing SRS. Patients with 4 to 10 brain metastases receiving single-isocenter, multi-target single-fraction SRS were identified. Hippocampi were contoured using the Radiation Therapy Oncology Group (RTOG) 0933 atlas. RTOG 0933 dose constraints were converted to a biologically effective dose using an alpha/beta of 2 (D{sub 100} 421 cGy, D{sub max} 665 cGy). Number of metastases, total target volume, prescribed dose, and distance of nearest metastasis (dmin) were analyzed as risk factors for exceeding hippocampal constraints. If hippocampi exceeded constraints, the SRS plan was re-optimized. Key dosimetric parameters were compared between original and re-optimized plans. To determine if a single target can exceed constraints, all targets but the closest metastasis were removed from the plan, and dosimetry was compared. Forty plans were identified. Fifteen hippocampi (19%) exceeded constraints in 12 SRS plans. Hippocampal sparing was achieved in 10 of 12 replanned cases (83%). Risk factors associated with exceeding hippocampal constraints were decreasing dmin (24.0 vs 8.0 mm, p = 0.002; odds ratio [OR] 1.14, 95% confidence interval [CI] 1.04 to 1.26) and total target volume (5.46 cm{sup 3}vs 1.98 cm{sup 3}, p = 0.03; OR 1.14, 95% CI 1.00 to 1.32). There was no difference in exceeding constraints for 4 to 5 vs 6 to 10 metastases (27% vs 21%, p = 0.409) or prescribed dose (18 Gy, p = 0.58). For re-optimized plans, there were no significant differences in planning target volume (PTV) coverage (99.6% vs 99.0%, p = 0.17) or conformality index (1.47 vs 1.4, p = 0.78). Six (50%) plans exceeded constraints with a single target. A substantial minority of hippocampi receive high radiation dose from SRS for 4 to 10 brain metastases. Decreasing distance of the closest metastasis and total target volume are associated with exceeding hippocampal constraints. Re-optimizing these plans yielded hippocampal-sparing SRS plans with acceptable dosimetry. Prospective evaluation of the impact of hippocampal dose from SRS on neurocognition merits consideration.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Cost optimization of high recovery single stage gap membrane distillation

Estimating the cost of membrane distillation (MD) processes at scale is confounded by the vast array of possible process configurations, module configurations, and operating conditions and the extreme sensitivity of MD costs to these parameters. This work develops a comprehensive MD cost optimization framework for minimizing the levelized cost of water (LCOW) as a function of feed salinity and temperature. Our single stage gap MD model fully captures solution properties, heat transfer, and mass transfer relationships in the modules to determine the cost optimal design as a function of multiple decision variables, including process configuration (feed location and connectivity between the hot and cold-side), the gap type (air, permeate, conductive), equipment sizes (membrane, heater, chiller, and heat exchanger units), and operating conditions (flow rates, hot and cold side temperatures, and heating and cooling duties). We estimate that the minimum LCOW of conductive gap MD ranges between $\$$10 and $\$$16 per m 3 for feed salinities of 25 g/L to 200 g/L and water recoveries of 30%–75%. Further, we update several heuristic assumptions for cost optimal system operation, including: increased thermal conductivity of the gap decreases system costs; cost optimal MD operates at the highest allowable temperature; and the LCOW is a strong function of brine salinity. Finally, we find that improving membrane properties and lowering heating and cooling costs will provide only modest reductions in costs (<25%).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Numerical optimization of a low emittance lattice cell

In the lattice designs for the next generation storage ring light sources, longitudinal gradient bending magnets and anti-bending magnets have been adopted. A logical question raised by the trend of varying the longitudinal distribution of dipole strength is: what are the optimal distributions of the dipole and quadrupole fields in a lattice cell for the purpose of minimizing the natural emittance? We studied this problem by numerically optimizing the dipole and quadrupole distributions of the normalized cell optics using the particle swarm optimization algorithm. Furthermore the results reveal the features of the longitudinal field variation of the optimized cell and show that when the quadrupole gradient is increased enough, the cell tends to split into two identical cells with similar features.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Coupled beam-target-moderator optimization for the Second Target Station

To support the design of the Second Target Station, that aims to provide the world’s highest peak brightness of cold neutrons, studies that optimize the dimensions of the target and moderators are invaluable. In this work, we investigate the influence of the target dimensions and beam profile on the performance and optimal size of the moderators. We perform optimization runs with detailed MCNP6.2 simulations using high-fidelity unstructured mesh geometries generated from parametrized CAD models. We demonstrate that small changes in target height do not influence the moderator performance if the beam dimensions are chosen adequately. We quantify the effect of beam footprint and target width on the moderator performance and show that the optimal moderator dimensions are insensitive to limited changes in target and beam profile.

Dakota↗
DOE OSTI2021

Optimization with the OpenACC-to-FPGA framework on the Arria 10 and Stratix 10 FPGAs

The reconfigurable computing paradigm with field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs’ unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. We perform this exploration on Intel Arria 10 and Stratix 10 FPGAs. We also explored the relationships between performance, resource usages, and compilation times, and investigated implications for performance portability. Finally, we present an initial evaluation of a real-world proxy application, LULESH.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors

This work exploits a connection between optimal transport theory and the physics of image propagation to yield a locally low-dimensional model of turbulence-corrupted imagery. Optimal transport produces an invertible, pixel-wise linear trajectories to approximate the globally nonlinear turbulence between a clean and turbulence corrupted image pair. We use the low-dimensional model to fit subsets of the optimal transport vector fields and stitch the local models into a surrogate for the global map to be used for image cleaning. Experiments are performed on laboratory generated data of beam propagation using different values of the Fried parameter (a scale measuring turbulence coherence) as well as a toy data set. The results suggest this is a fruitful direction, and first step, towards using multiple realizations of turbulence corrupted images to learn a blind surrogate for the optimal transport vector field for image cleaning.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states

Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.

42 ENGINEERING↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2017

A modeling framework for optimizing current density in four-terminal tandem solar cells: A case study on GaAs/Si tandem

Here we present a modeling framework to quantify current density losses and determine optimization steps for four-terminal tandem solar cells, which consist of a high-bandgap thin-film top cell and an industrial-type Si wafer bottom cell. We demonstrate the framework on the example of a 21.3% efficient stacked four-terminal GaAs/Si tandem solar cell. In a first step, we develop an optical model for stacked tandem configuration and carry out the current loss analysis for this tandem device. Photon absorption in the tandem is broken down into different channels. All parasitic absorptions that do not contribute to current generation are considered part of a loss channel. In a second step, we quantify the potential current gains from single-parameter optimizations, the redistribution of photon losses into other channels, and the synergistic gains that can be achieved by co-optimization. Finally, using the simulation model, we are able to determine the best sequence of optimizing the short-circuit current density in the GaAs/Si tandem solar cell to possibly achieve 37.8 mA/cm 2 with experimentally feasible parameters.

14 SOLAR ENERGY↗
DOE OSTI2019

Derivative-free optimization methods

In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in these methods, with an emphasis on highlighting recent developments and on unifying treatment of such problems in the non-linear optimization and machine learning literature. We categorize methods based on assumed properties of the black-box functions, as well as features of the methods. We first overview the primary setting of deterministic methods applied to unconstrained, non-convex optimization problems where the objective function is defined by a deterministic black-box oracle. We then discuss developments in randomized methods, methods that assume some additional structure about the objective (including convexity, separability and general non-smooth compositions), methods for problems where the output of the black-box oracle is stochastic, and methods for handling different types of constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Operation and Optimization of Microwave-Heated Continuous-Flow Microfluidics

Microwave (MW) technology can be powerful for electrification and process intensification but limited fundamental understanding of scalability and design principles hinders its effective use. In this work, we build a continuous-flow microreactor inside a commercial single-mode MW applicator and the corresponding computational fluid dynamics model to simulate the temperature profile. The model is in good agreement with experiments for various microreactor dimensions and operating conditions. The model indicates that MW heating is greatly influenced by reactor geometry as well as the operating parameters. We observe a strong correlation between parameters and develop a gradient boost regression tree model to predict the outlet temperature accurately. This model is then applied to optimize the dimensions and operating conditions to maximize the outlet temperature and energy efficiency, resulting in a Pareto optimal. We demonstrate computationally and experimentally that it is possible to surpass the Pareto optimal and achieve an energy efficiency of ~90% or greater at temperatures relevant for liquid-phase chemistry via salting of the solvent. The present methodology can be applied to other complex MW reactors. Lastly, the combined numerical and experimental approach provides insights into and a framework for scale-up and optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗