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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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We show that Bayesian model selection is equivalent to optimization of a suitably defined free energy. The entropy term in the free energy is defined over the space of possible model selections and model parameters for a given model. Bayesian model selection thus follows a minimum description length principle.
QSPIN is a high level Java language API for experimentation in QC models used in the calculation of Ising spin glass ground states and related quadratic unconstrained binary optimization (QUBO) problems. The Java API is intended to facilitate research in advanced QC algorithms such as hybrid quantum-classical solvers, automatic selection of constraint and optimization parameters, and techniques for the correction and mitigation of model and solution errors. QSPIN includes high level solver objects tailored to the D-Wave quantum annealing architecture that implement hybrid quantum-classical algorithms [Booth et al.] for solving large problems on small quantum devices, elimination of variables via roof duality, and classical computing optimization methods such as GPU accelerated simulated annealing and tabu search for comparison. A test suite of documented NP-complete applications ranging from graph coloring, covering, and partitioning to integer programming and scheduling are provided to demonstrate current capabilities.
Medium-chain (mc-) oleochemicals are an important class of renewable chemicals with broad industrial applications; however, their sustainable microbial production remains challenging. In this study, we developed a versatile metabolic engineering and fed-batch strategy to produce C 10 -oleochemicals in Escherichia coli. Central to this approach is an engineered mc-acyl-ACP thioesterase Cl FatB3-tr-D10S with C 10 species accounting for around 70% of the total fatty acids produced. To expand product diversity, we established a decanoyl-CoA pool through co-expression of fadD, enabling downstream conversion into multiple product classes. Through pathway tuning, enzyme bioprospecting, strain engineering and fermentation optimization strategies, we demonstrated selective production of 1-decanol, 2-nonanone and poly(3-hydroxydecanoate) (C 10 -PHA). Production of decanoic acid and 1-decanol were achieved by optimizing expression of Cl fatB3-tr-D10S and, Mt fadD6 and Ma acr, respectively. Leveraging β-oxidation enabled the production of β-ketoacyl-CoA intermediates, which were converted to 2-nonanone via heterologous Mlu fadE, Vf fadB and Ps fadM expression. Additionally, expression of phaJ2 and phaC2 facilitated the conversion of decanoyl-CoA pool into C 10 -PHA homopolymer. Altogether, this work demonstrates a versatile and tunable platform for medium-chain oleochemical production.
A general analytical treatment is presented of a single-stage vehicle with multiple propulsion phases. A closed-form solution for the cost and for the performance and a derivation of the optimal phasing of the propulsion are included. Linearized variations in the inert weight elements are included, and the function to be minimized can be selected. The derivation of optimal phasing results in a set of nonlinear algebraic equations for optimal fuel volumes, for which a solution method is outlined. Three specific example cases are analyzed: minimum gross lift-off weight, minimum inert weight, and a minimized general function for a two-phase vehicle. The results for the two-phase vehicle are applied to the dual-fuel rocket. Comparisons with single-fuel vehicles indicate that dual-fuel vehicles can have lower inert weight either by development of a dual-fuel engine or by parallel burning of separate engines from lift-off.
Highlights: • Cancer cells interact with immune checkpoints to shut down T cell response. • An optimization system for the selection of modified immune checkpoint proteins. • Using the described system improved PD1 variants were identified. • The improved variants are active in a cellular assay that mimics T cell:cancer interaction. Immune-checkpoint receptors are a set of signal transduction proteins that can stimulate or inhibit specific anti-tumor responses. It is well established that cancer cells interact with different immune checkpoints to shut down T-cell response, thereby enabling cancer proliferation. Given the importance of immune checkpoint receptors, a structure-function analysis of these systems is imperative. However, recombinant expression and purification of these membrane originated proteins is still a challenge. Therefore, many attempts are being made to improve their expression and solubility while preserving their biological relevance. For this purpose, we designed an E. coli-based optimization system that enables the acquisition of mutations that increases protein solubility and affinity towards its native ligand, while maintaining biological activity. Here we focused on the well-characterized extracellular domain of the 'programmed cell death protein 1' (PD1), an immune checkpoint receptor known to inhibit T-cell proliferation by interacting with its ligands PD-L1 and PD-L2. The simple ELISA-based screening system shown here enabled the identification of high-affinity, highly soluble, functional variants derived from the extracellular domain of human PD1. The system was based on the expression of a GST-tagged variants library in E. coli, which enabled the selection of improved PD1 variants after a single optimization round. Within only two screening rounds, the most active variant showed a 5-fold higher affinity and 2.4-fold enhanced cellular activity as compared to the wild type protein. This scheme can be translated toward other types of challenging receptors toward development of research tools or alternative therapeutics.
The Experiment for Unresolved Region Of Plutonium Actinides (EUROPA) is an integral critical experiment currently being designed to target the intermediate energy region of plutonium. Intermediate energies, those between 0.625 eV - 100 keV, contain the end of resolved and beginning of the unresolved resonance region, making experiments in this energy range prudent for validating the representation of cross sections in this transition region. Below is the preliminary design of the experiment to be performed at the National Criticality Experiments Research Center (NCERC). Experiment optimization required an exhaustive down selection of several moderating, absorbing, and reflecting materials using Particle Swarm Optimization (PSO) in order to achieve maximum sensitivity to the intermediate energy region.
We present a new method for online selection of the penalty parameter for the alternating direction method of multipliers (ADMM) algorithm. ADMM is a widely used method for solving a range of optimization problems, including those that arise in signal and image processing. In its standard form, ADMM includes a scalar hyperparameter, known as the penalty parameter, which usually has to be tuned to achieve satisfactory empirical convergence. In this work, we develop a framework for analyzing the ADMM algorithm applied to a quadratic problem as an affine fixed point iteration. Using this framework, we develop a new method for automatically tuning the penalty parameter by detecting when it has become too large or small. We analyze this and several other methods with respect to their theoretical properties, i.e., robustness to problem transformations, and empirical performance on several optimization problems. Our proposed algorithm is based on a theoretical framework with clear, explicit assumptions and approximations, is theoretically covariant/invariant to problem transformations, is simple to implement, and exhibits competitive empirical performance.
Data sets selected for mini-batches and the software modifications required for processing these sets are described. Initial analysis was performed on minibatch field model recovery. Studies are being performed to examine the convergence of the solutions and the maximum expansion order the data will support in the constant and secular terms.
The selection of the structure of optimal control type models for the human gunner in an anti aircraft artillery system is considered. Several structures within the LQG framework may be formulated. Two basic types are considered: (1) kth derivative controllers; and (2) proportional integral derivative (P-I-D) controllers. It is shown that a suitable criterion for model structure determination can be based on the ensemble statistics of the tracking error. In the case when the ensemble tracking steady state error is zero, it is suggested that a P-I-D controller formulation be used in preference to the kth derivative controller.
A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.
The viability of using fluidized bed heat exchangers (FBHX) for thermal energy storage (TES) in applications with potential for waste heat recovery was investigated. Of the candidate applications screened, cement plant rotary kilns and steel plant electric arc furnaces were identified, via the chosen selection criteria, as having the best potential for successful use of FBHX/TES system. A computer model of the FBHX/TES systems was developed and the technical feasibility of the two selected applications was verified. Economic and tradeoff evaluations in progress for final optimization of the systems and selection of the most promising system for further concept validation are described.
Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.
We report how the unique temporal and spectral features of pulsed entangled photons from a parametric downconversion source can be utilized for manipulating electronic excitations through the optimization of their spectral phase. A new comprehensive optimization protocol based on Bayesian optimization has been developed in this work to selectively excite electronic states accessible by two-photon absorption. Using our optimization method, the entangled two-photon absorption probability for a thiophene dendrimer can be enhanced by up to a factor of 20, while classical light turns out to be nonoptimizable. Moreover, the optimization involving photon entanglement enables selective excitation that would not be possible otherwise. In addition to optimization, we have explored entangled two-photon absorption in the small entanglement time limit showing that entangled light can excite molecular electronic states that are vanishingly small for classical light. As a result, we demonstrate these opportunities with an application to a thiophene dendrimer.
This study proposes a strategic trajectory planning framework to support development of Urban Air Mobility (UAM) traffic networks and optimization of UAM aircraft trajectories that are robust to uncertain wind fields. The development of UAM traffic networks considers static aviation constraints and dynamic weather constraints in urban airspace and the connections to ground transportation networks for preliminary selection of feasible time-optimal routes. The trajectory optimization of UAM aircraft utilizes an unscented optimal guidance approach to generate cost-optimal trajectories constrained on the selected sigma values of probability distribution of uncertain wind fields while ensuring that the end-point constraints are met for reducing wind-induced trajectory uncertainty. Unscented guidance commands are assessed based on trajectory perturbations at subsequent end-points in various wind fields by conducting Monte Carlo simulations.An example of wind-optimal UAM corridor and the associated trajectory-based operation volume is created utilizing the perturbations of the unscented trajectories for preliminary assessment of required aircraft separation minima without knowledge of aircraft navigation performance.
The Personnel Launch System (PLS) being studied by NASA is a system to complement the Space Shuttle and provide alternative access to space. The PLS consists of a manned spacecraft launched by an expendable launch vehicle (ELV). A candidate for the manned spacecraft is the HL-20 lifting body. In the event of an ELV malfunction during the initial portion of the ascent trajectory, the HL-20 will separate from the rocket and perform an unpowered return-to-launchsite (RTLS) abort. This paper describes an investigation of the RTLS abort scenario using optimal control theory. The objective of the abort trajectory is to maximize final altitude at a point near the runway. The assumption is then made that there exists a control history to steer the vehicle to any final altitude lower than the final optimal altitude. With this selection of cost function, and with this assumption, the feasibility of an RTLS abort at different times along the ascent trajectory can be determined. The method of differential inclusions, which allows the determination of optimal states and eliminates the need for determining the optimal controls, is used to determine the optimal trajectories.
The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.
A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function F.sub.ij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score L.sub.i,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores L.sub.j to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.