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

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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Eddy Current Probe Parameters Identification Using a Genetic Algorithm and Simultaneous Perturbation Stochastic Approximation

This study tries to identify the coil parameters using numerical methods. The eddy current testing (ECT) is used for evaluation of a crack with the aid of numerical simulations by utilizing the identification of these parameters. In this study, a comparison of the performance of the GA and SPSA algorithms to identify the parameter values of the coil sensors are presented. So, the optimization probe geometry is introduced in the simulation with Three-dimensional finite element simulations (FLUX finite element code) were conducted to obtain eddy current signals resulting from a crack in a plate made of aluminium. The simulation results are compared with experimental measurements for the defect present in a plate.

42 ENGINEERING↗
DOE OSTI2018

Removal of Congo Red from Aqueous Solution by Hydroxyapatite Nanoparticles Loaded on Zein as an Efficient and Green Adsorbent: Response Surface Methodology and Artificial Neural Network-Genetic Algorithm

This study is based on the application of hydroxyapatite nanoparticles loaded on Zein (Zein/nHAp) as an efficient adsorbent for the removal of Congo red from aqueous solutions. The properties of the adsorbent were characterized using various techniques including FT-IR, XRD, FE-SEM, and BET. The influence of five parameters such as pH, temperature, contact time, initial dye concentration, and adsorbent dosage on the removal percentage of the dye was investigated. The optimum conditions of 5.83, 34.32 °C, 5.20 min, 392.10 ppm, and 0.007 g were achieved for pH, temperature, contact time, initial dye concentration, and adsorbent dosage, respectively. The maximum removal percentage of 99.48% was obtained under the optimum condition that exhibited high adsorption potential of the used adsorbent. Central composite design (CCD) under response surface methodology and artificial neural network-genetic algorithm (ANN-GA) were utilized for optimization of parameters. Comparison of the results of the two models in terms of coefficient of determination (R{sup 2}) and mean absolute percentage error confirmed the prediction potential of CCD and ANN-GA. Higher ability and accuracy of ANN-GA in prediction was found based on given results. The experimental equilibrium data were studied by Langmuir, Freundlich, Temkin and Dubinin-Radushkevic isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 416.66 mg/g. The adsorption kinetic was well-fitted by the pseudo-second-order model. The thermodynamics of the adsorption displayed spontaneous and endothermic nature of the process. Regeneration investigation showed that Zein/nHAp can impressively be reused, indicating that the adsorbent was a promising one for the removal of Congo red from aqueous solution. Graphical Abstract: .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Ensemble-Based Support Vector Regression with Gravitational Search Algorithm Optimization for Estimating Magnetic Relative Cooling Power of Manganite Refrigerant in Magnetic Refrigeration Application

Magnetic refrigeration technology (MRT) is considered an energy-efficient and environmental-friendly system of refrigeration that has a considerable potential of replacing the classical gas-compression expansion method of refrigeration. Inclusion of manganite-based material (MBM) in MRT as a magnetic refrigerant has attracted significant attention recently due to cost effectiveness of the refrigerant as well as better resistance to oxidation and corrosion as compared to the commonly used metal gadolinium refrigerant. Relative cooling power (RCP) is one of the most important parameters to be considered while assessing the usefulness of MBM. Its value can be altered through doping with external materials and accurate estimation of the dopant influence is required to achieve the right amount of RCP. This present research work proposes support vector regression (SVR) ensemble models with gravitational search algorithm (GSA) hyper-parameters optimization, for estimating RCP of MBM and to determine the influence of dopants on RCP using ionic radii and dopant concentrations as descriptors. GSA-SVR ensemble model (GSE) is developed by employing the outputs of five different SVR models as descriptors while GSA-SVR ensemble model with averaging (GSEA) uses the average of the five different SVR models as its descriptor. The novel ensemble models outperform other SVR models, specifically; GSE performs better than GSA-SVR model and the conventional SVR model with performance improvement of 269.14% and 283.61%, respectively on the basis of root mean square error (RMSE). Furthermore, GSEA outperforms GSE, GSA-SVR model and conventional SVR with performance improvement of 27.51%, 370.70%, and 389.14%, respectively on the basis of RMSE. The developed GSE and GSEA also perform better than the existing RCP model in the literature with performance improvement of 11.53% and 42.21%, respectively. The results of this research work will not only serve to circumvent the experimental challenges of RCP measurement without loss of experimental precision but also further promotes environment-friendly system of refrigeration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Lower bounds on circuit depth of the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which classes of problems are best suited for it. One factor in demonstrating quantum advantage is the relationship between a problem instance and the circuit depth required to implement the QAOA method. As errors in noisy intermediate-scale quantum (NISQ) devices increase exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage could be feasible. In this work, we identify how the structure of problem instances can be used to identify lower bounds for circuit depth for each iteration of QAOA and examine the relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut and MaxIndSet. Specifically, we show how to derive a graph, G, that describes a general combinatorial optimization problem and show that the depth of circuit is at least the chromatic index of G. By looking at the scaling of circuit depth, we argue that MaxCut, MaxIndSet, and some instances of vertex covering and Boolean satisfiability problems are suitable for QAOA approaches while knapsack and traveling salesperson problems are not.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Globally convergent algorithms for finding zeros of duplomonotone mappings

We introduce a new class of mappings, called duplomonotone, which is strictly broader than the class of monotone mappings.We study some of themain properties of duplomonotone functions and provide various examples, including nonlinear duplomonotone functions arising from the study of systems of biochemical reactions. Finally,we present three variations of a derivative-free line search algorithm for finding zeros of systems of duplomonotone equations, and we prove their linear convergence to a zero of the function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Furthermore we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2018

A new accelerated algorithm for ill-conditioned ridge regression problems

In this work, we study the primal and dual formulations of the regularized least squares problem, in special norm L{sub 2}, named ridge regression. We state the Gradient method for the corresponding primal-dual problem through a fixed point approach. For this formulation, we present an original convergence analysis involving the spectral radius of a suitable iteration matrix. The main contribution of this work is to accelerate the studied method by means of a memory-like strategy. Some connections with the spectral properties of the associated iteration matrices were established to prove the convergence of the accelerated method. Preliminary experiments showed the good performance of the proposed algorithm when applied to solve ill-conditioned problems, providing better results than the conjugate gradient method for this class of problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm

Classical and advanced hybridization methods are employed in the control literature to calibrate the tuning parameters of internal model control (IMC) and proportional-integral-derivative (PID) controllers. This paper presents an alternative tuning design for the generalized predictive controller (GPC) that is based on both the positional process model and cost function. The proposed method involves the selection of an integral polynomial weighting filter for reference and output signals to deal with reference tracking, disturbance rejection, model-plant mismatch (MPM), minimum control energy and closed-loop robustness. The filter positional GPC (FP-GPC) obtained is transformed into a two-degree-of-freedom polynomial filtered RST structure and then into filtered internal model control (F-IMC) and PID controllers. Also, a multi-objective optimization based on genetic algorithm is applied to tune the filter parameters. Numerical and experimental essays show the effectiveness of the proposed control methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the numerical algorithms of parametrization method for solving a two-point boundary-value problem for impulsive systems of loaded differential equations

A linear two-point boundary value problem for a system of loaded differential equations with impulse effect is investigated. Values in the previous impulse points are taken into consideration in the conditions of impulse effect. The considered problem is reduced to an equivalent multi-point boundary value problem for the system of ordinary differential equations with parameters. A numerical implementation of parametrization method is offered using the Runge–Kutta method of 4th-order accuracy for solving the Cauchy problems for ordinary differential equations. The constructed numerical algorithms are illustrated by examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗
DOE OSTI2023

A semi-agnostic ansatz with variable structure for variational quantum algorithms

Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Statistical Methods Applied to Gamma-ray Spectroscopy Algorithms in Nuclear Security Missions

In a wide range of nuclear security missions, gamma-ray spectroscopy is a critical research and development priority. One particularly relevant challenge is the interdiction of special nuclear material for which gamma-ray spectroscopy supports the goals of detecting and identifying gamma-ray sources. This manuscript examines the existing set of spectroscopy methods, attempts to categorize them by the statistical methods on which they rely, and identifies methods that have yet to be considered. Our examination shows that current methods effectively estimate the effect of counting uncertainty but in many cases do not address larger sources of decision uncertainty—ones that are significantly more complex. We thus explore the premise that significantly improving algorithm performance requires greater coupling between the problem physics that drives data acquisition and statistical methods that analyze such data. Untapped statistical methods, such as Bayes Modeling Averaging and hierarchical and empirical Bayes methods have the potential to reduce decision uncertainty by more rigorously and comprehensively incorporating all sources of uncertainty. We expect that application of such methods will demonstrate progress in meeting the needs of nuclear security missions by improving on the existing numerical infrastructure for which these analyses have not been conducted.

detection algorithm.↗
DOE OSTI2018

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

We present that occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants’ spatial distribution, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. Finally, the results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.

42 ENGINEERING↗
DOE OSTI2019

Unitary qubit lattice algorithm for three-dimensional vortex solitons in hyperbolic self-defocusing media

To obtain stable vortex structures in three-dimensional (3D) nonlinear optics, Efremidis et al. (2007) have introduced a generalized Nonlinear Schrodinger equation (NLS) in which the transverse quantum vortex components are stabilized by a longitudinal bright soliton: i.e., the elliptic operator ∇ 2 is replaced by its hyperbolic counterpart ∇ ⊥ 2 - ∂ 2 / ∂ z 2 . A new 3D mesoscopic qubit unitary lattice algorithm is developed for this generalized NLS. One introduces 2 qubits for each lattice site and entangles them with a local unitary collision operator. This entanglement is then spread throughout the lattice by nearest neighbor streaming. These interwined operators lead to an extremely well parallelized code on classical supcomputers while their unitary structure will permit encoding onto a quantum computer. Somewhat unexpectedly, the hyperbolic operator can be realized from variations in the collision operator, without introducing variations in the streaming operator. The initial line vortices are generated by Pade asymptotics. The energy constraint is conserved to 10 digit accuracy.

3D solitons↗
DOE OSTI2020

Grain boundary structure search by using an evolutionary algorithm with effective mutation methods

Grain boundaries (GBs) accommodate the misorientation between adjacent grains in a polycrystalline material. GBs are geometrically described by the macroscopic and microscopic degrees of freedom. Besides, at the atomistic level, GBs exhibit complicated behaviors under varying thermodynamic conditions. The complexity of atomistic GB structures demands stochastic searching for possible states. The effectiveness of stochastic search methods relies on techniques to recreate and select atomistic structures. In this work, we developed a new mutation operator that can induce direct and collective atomistic structure changes to boost the search efficiency of exploring GB structures with evolutionary algorithms (EA). We implemented the mutation methods along with innovative selection, crossover, boundary condition preprocessing methods to form an EA-based package to explore GB structures in grand canonical ensembles with atomistic simulations. We used this package to study the [001] symmetric tilt grain boundaries (STGBs) in FCC copper (Cu), the [110] STGBs in BCC tungsten (W), and the $[1\bar{2}10]$ STGBs in HCP magnesium (Mg). The results show that our design and implementation based on new mutation procedures, selection, and boundary conditions provide a high-quality search of atomistic GB structures in the grand canonical ensemble for different crystal lattices.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Temporal parallelization of edge plasma simulations using the parareal algorithm and the SOLPS code

We show that numerical modelling of edge plasma physics may be successfully parallelized in time. The parareal algorithm has been employed for this purpose and the SOLPS code package coupling the B2.5 finite-volume fluid plasma solver with the kinetic Monte-Carlo neutral code Eirene has been used as a test bed. The complex dynamics of the plasma and neutrals in the scrape-off layer (SOL) region makes this a unique application. It is demonstrated that a significant computational gain (more than an order of magnitude) may be obtained with this technique. The use of the IPS framework for event-based parareal implementation optimizes resource utilization and has been shown to significantly contribute to the computational gain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A biophysical approach to the performance diagnosis of human–building energy interaction: Information (bits) modeling, algorithm, and indicators of energy flow complexity

Highlights: • Energy-related human-building interaction is analyzed at the biophysical system scale. • Energy flow-networking model of environmental building performance is suggested. • Information is suggested as a holistic building performance metric. - Abstract: This article addresses the utmost upstream impacts of human–building energy interaction by proposing a network-based model, algorithms, and indicators. Hypothesizing that human behavior is a key factor for the symbiotic development of building and the geobiosphere system, the author seeks emergy (spelled with an “m”)-information integrated measures to indicate dynamic system-level performance of the interaction with building energy flow topology. To validate the hypothesis and methods, four representative building cases were tested on the building form (envelope) of (i) a building with no occupant intervention (baseline), (ii) a building controlled by responsive human behavior (bioclimatic adaptation), (iii) a building with reinforced insulation and behavior-dominated control (passive design), and (iv) a net-zero energy building (NZEB). The results demonstrate that adaptive human behavior in building operation increases the information content and complexity of energy-flow networking, improving performance and sustainability. Findings also reveal that increasing information, complexity, and power (energy availability over time) parallel the general energetic features of developing biophysical systems (greater feedback, internalization, and recycling of materials and energies). It becames clear that active behavioral response is a dominant agent of sustainable environments even on a far broader system scale.

54 ENVIRONMENTAL SCIENCES↗