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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Using Filter Methods to Guide Convergence for ADMM, with Applications to Nonnegative Matrix Factorization Problems

Nonconvex, nonlinear optimization problems arise naturally in parameter fitting and machine learning. While augmented Lagrangian methods have demonstrated robust convergence for classes of these problems, their convergence for block updates has been relatively unexplored outside of the context of the alternating direction method of multipliers (ADMM). ADMM has seen extensive use in these applications, but may exhibit uncertain convergence behavior in many practical nonconvex settings, and struggles with general nonlinear constraints. In contrast, filter methods have proved effective in enforcing convergence for sequential quadratic programming methods and interior point methods with feasibility criteria. We develop an ADMM-filter method for highly nonlinear and nonconvex problems. Here, we show convergence under mild assumptions for several types of coordinate descent schemes, and demonstrate our algorithm on nonnegative matrix factorization and completion problems in imaging and chemical spectrum analysis.

Nonconvex optimization↗
DOE OSTI2022

Approximate recoverability and relative entropy II: 2-positive channels of general von Neumann algebras

Abstract We generalize our results in paper I in this series to quantum channels between general von Neumann algebras, proving the approximate recoverability of states which undergo a small change in relative entropy through the channel. To this end, we derive a strengthened form of the quantum data processing inequality for the change in relative entropy of two states under a channel between two von Neumann algebras. Compared to the usual inequality, there is an explicit lower bound involving the fidelity between the original state and a recovery channel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Consequences and benefits of utilizing continuous vibro-impact representations in constrained pipeline conveying fluid systems

Here, the effectiveness of continuous vibro-impact forcing representations for the cantilevered pipe that conveys fluid is explored and analyzed. The previously accepted forcing model utilizing a smoothened trilinear spring is estimated using three continuous forcing representations, namely, polynomial, rational polynomial, and hyperbolic tangent. The accuracy of the estimated forcing functions is investigated and analyzed by calculating the root mean square error, and bifurcation diagrams are generated and compared to the nominal system. Additionally, the dynamic response of the system is further characterized using Poincare maps, power spectra, and basins of attraction. Once all continuous forcing representations are analyzed and compared to the nominal system, the computational cost of each method is examined, and further limitations of the hyperbolic tangent method are discovered. It is proved that the hyperbolic tangent forcing representation most accurately captures the dynamic response of the pipeline, and the least accurate representation is the rational polynomial representation. Additionally, considerable computational cost is saved when employing the hyperbolic tangent representation compared to the discontinuous representation.

42 ENGINEERING↗
DOE OSTI2020

Asynchronous Richardson iterations: theory and practice

We consider asynchronous versions of the first- and second-order Richardson methods for solving linear systems of equations. These methods depend on parameters whose values are chosen a priori. We explore the parameter values that can be proven to give convergence of the asynchronous methods. This is the first such analysis for asynchronous second-order methods. We find that for the first-order method, the optimal parameter value for the synchronous case also gives an asynchronously convergent method. For the second-order method, the parameter ranges for which we can prove asynchronous convergence do not contain the optimal parameter values for the synchronous iteration. In practice, however, the asynchronous second-order iterations may still converge using the optimal parameter values, or parameter values close to the optimal ones, despite this result. We explore this behavior with a multithreaded parallel implementation of the asynchronous methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics

In this article, we consider an optimization problem where the objective function is evaluated at the fixed-point of a contraction mapping parameterized by a control variable, and optimization takes place over this control variable. Since the derivative of the fixed-point with respect to the parameter can usually not be evaluated exactly, an adjoint dynamical system can be used to estimate gradients. Using this estimation procedure, the optimization algorithm alternates between derivative estimation and an approximate gradient descent step. We analyze a variant of this approach involving dynamic time-scaling, where after each parameter update the adjoint system is iterated until a convergence threshold is passed. Here, we prove that, under certain conditions, the algorithm can find approximate stationary points of the objective function. We demonstrate the approach in the settings of an inverse problem in chemical kinetics, and learning in attractor networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Real-time dispatch optimization for concentrating solar power with thermal energy storage

Concentrating solar power (CSP) plants present a promising path towards utility-scale renewable energy. The power tower, or central receiver, configuration can achieve higher operating temperatures than other forms of CSP, and, like all forms of CSP, naturally pairs with comparatively inexpensive thermal energy storage, which allows CSP plants to dispatch electricity according to market price incentives and outside the hours of solar resource availability. Currently, CSP plants commonly include a steam Rankine power cycle and several heat exchange components to generate high-pressure steam using stored thermal energy. The efficiency of the steam Rankine cycle depends on the temperature of the plant's operating fluid, and so is a main concern of plant operators. However, the variable nature of the solar resource and the conservatism with which the receiver is operated prevent perfect control over the receiver outlet temperature. Therefore, during periods of solar variability, collection occurs at lower-than-design temperature. To support operator decisions in a real-time setting, we develop a revenue-maximizing non-convex mixed-integer, quadradically-constrained program which determines a dispatch schedule with sub-hourly time fidelity and considers temperature-dependent power cycle efficiency. The exact nonlinear formulation proves intractable for real-time decision support. Here we present exact and inexact techniques to improve problem tractability that include a hybrid nonlinear and linear formulation. Our approach admits solutions within approximately 3% of optimality, on average, within a five-minute time limit, demonstrating its usability for decision support in a real-time setting.

14 SOLAR ENERGY↗
DOE OSTI2026

Memory-efficient nonsmooth dynamic optimization using adaptive randomized compression

Dynamic optimization problems arise in many applications including flow control, full waveform inversion, and medical imaging. These problems are plagued by significant computational challenges. One such challenge — and the focus of this work — is the memory limitation induced by the size of the underlying dynamical system. In particular, the entire dynamic trajectory is required for derivative computation and therefore must be stored or recomputed using, e.g., checkpointing. Although recent work demonstrated the use of adaptive randomized sketching to overcome the memory challenge, that work only applies to smooth unconstrained problems, prohibiting its use for nonsmooth regularized and constrained problems. The inclusion of nonsmooth regularizers and constraints is critical as they often arise in an attempt to preserve certain physical properties or to promote sparsity. To solve these problems, we introduce a trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function that leverages randomized sketching to compress the dynamical system trajectories and adaptively adjust the sketch rank to satisfy a gradient inexactness condition. We prove convergence of this algorithm and demonstrate that it achieves substantial memory reduction on three discretized PDE-constrained optimization applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Empirical performance bounds for quantum approximate optimization

The quantum approximate optimization algorithm (QAOA) has been put forth as a method for near-term quantum computers to solve optimization problems. However, assessments of QAOA performance have mostly focused on small structured problem instances while performance on more general instances is less clear. Here, we numerically simulate QAOA pure state dynamics for every instance of MaxCut on non-isomorphic unweighted graphs with nine or fewer vertices with depth parameters p ≤ 3. We find the approximation ratios and optimized circuit parameters concentrate across graphs of a given size and empirically show increases in concentration as graph size increases. The parameter concentration leads to two median-angle heuristics that overcome difficulties in QAOA parameter optimization and obtain mean approximation ratios within 3% and 0.2% of the optimal. We also analyze the probability to measure an optimal solution and find increasing variations between graphs as depth increases, in stark contrast to the approximation ratios which concentrate as depth increases. Furthermore, the resulting benchmark data set gives empirical bounds for on-going experimental realizations and lays groundwork for theoretical extensions to greater problem sizes and depths where QAOA may prove important for practically relevant problems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Quantum multi-programming for Grover’s search

Quantum multi-programming is a method utilizing contemporary noisy intermediate-scale quantum computers by executing multiple quantum circuits concurrently. Despite early research on it, the research remains on quantum gates or small-size quantum algorithms without correlation. In this paper, we propose a quantum multi-programming (QMP algorithm for Grover's search. Our algorithm decomposes Grover's algorithm by the partial diffusion operator and executes the decomposed circuits in parallel by QMP. We proved that this new algorithm increases the rotation angle of the Grover operator which, as a result, increases the success probability. The new algorithm is implemented on IBM quantum computers and compared with the canonical Grover's algorithm and other variations of Grover's algorithms. So, the empirical tests validate that our new algorithm outperforms other variations of Grover's algorithms as well as the canonical Grover's algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Formal verification and validation of run-to-completion style state charts using Event-B

State chart notations with ‘run to completion’ semantics are popular with engineers for designing controllers that react to environment events with a sequence of state transitions but lack formal refinement and rigorous verification methods. State chart models are typically used to design complex control systems that respond to environmental triggers with a sequential process. The model is usually constructed at a concrete level and verified and validated using animation techniques relying on human judgement. Event-B, on the other hand, is based on refinement from an initial abstraction and is designed to make formal verification by automatic theorem provers feasible. Abstraction and formal verification provide greater assurance that critical (e.g. safety or security) properties are not violated by the control system. In this paper, we introduce a notion of refinement into a ‘run to completion’ state chart modelling notation and leverage Event-B’s tool support for theorem proving. We describe the difficulties in translating ‘run to completion’ semantics into Event-B refinements and suggest a solution. We illustrate our approach and show how models can be validated at different refinement levels using our scenario checker animation tools. We show how critical invariant properties can be verified by proof despite the reactive nature of the system and how behavioural aspects of the system can be verified by testing the expected reactions using a temporal logic, model checking approach. To verify liveness, we outline a proof that the run to completion is deadlock-free and converges to complete the run.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Derivation and assessment of regional electricity generation emission factors in the USA

Electricity production is one of the largest sources of environmental emissions - especially greenhouse gases (GHGs) - in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs). To address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs. We explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated. Here this work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers - considering the detailed sources of the information and the breadth of emissions covered by it.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Local convergence analysis of an inexact trust-region method for nonsmooth optimization

In Baraldi, we introduced an inexact trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function in Hilbert space—a class of problems that is ubiquitous in data science, learning, optimal control, and inverse problems. Furthermore, this algorithm has demonstrated excellent performance and scalability with problem size. In this paper, we enrich the convergence analysis for this algorithm, proving strong convergence of the iterates with guaranteed rates. In particular, we demonstrate that the trust-region algorithm recovers superlinear, even quadratic, convergence rates when using a second-order Taylor approximation of the smooth objective function term.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Cladding and Butt-Joining Dissimilar Aluminum Alloys Simultaneously via Shear Assisted Processing and Extrusion

Shear assisted processing and extrusion has been used to fabricate multi-metallic tubing that incorporates regions with clad and butt-joined structures. In this work, aluminum alloys 6061, 7075, and 1100 are combined to create a monolithic extruded tubing with weldable ends (6061), a high strength center (7075), and a corrosion-resistant cladding (1100). Optical microscopy shows a gradual, defect-free, transition between alloys at the butt-joint interfaces and a sharp transition between alloys at the clad interface. Tensile testing proved the mechanical coherency of the joint, with failure occurring outside the tri-metallic structure. In conclusion, smoothed particle hydrodynamic simulations identified a spiral pattern of the cladding alloy that leads to inconsistent cladding thickness, and identified a simplified 2-alloy combination of aluminum alloys 6082 and 2014 that accomplished the design objectives of the tri-metallic structure.

Coatings↗
DOE OSTI2025

Experimental Evaluation of Interfacial Bonding Strength Between 304 Stainless Steel Substrate and Electrodeposited Nickel Coating Using Mesoscale Mechanical Testing Methods

Electrodeposition is a commonly used method for depositing a metal layer on a metallic substrate, to provide a decorative finish or for functional purposes including wear and corrosion resistance. The interfacial bonding strength is a critical factor in determining the quality of electrodeposition, as it ensures the adhesion of the deposited layer to the substrate. Despite its importance, there has not previously been a suitable mechanical testing method to quantitatively characterize the bonding strength between the electrodeposited layers and the substrate, due to the high strength of the materials and to restrictions imposed by the geometry (due to the small thickness of the plating layer) to manufacture tensile bars. In this study, we introduce a novel mesoscale mechanical testing method to overcome these limitations. This technique was applied to assess the bonding strength between a 304 stainless steel (SS) substrate and electrodeposited nickel with three different plating formulae (nickel sulfamate, Watts bath, and hard nickel). The thickness of the pure nickel coatings achieved for each of the three electrolyte solutions was approximately 1 mm on each side of the substrate. Mesoscale tensile bars of 1 mm length were manufactured by a femtosecond laser, with the material interface at the center of the gauge section, and then tensile-tested with digital image correlation. Further, the results proved that the electrodeposition technique is able to produce a very high bonding strength that is close to the yield strength of both the substrate material and the electrodeposited nickel layer. Additionally, in the case of the Watts bath formula, the interfacial bonding strength between the SS 304 and electrodeposited nickel can exceed that of the nickel layer. This method is expected to be useful for quantifying the interfacial bonding strength in numerous applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards an Integrated Process for CO 2 Capture and Utilization: Cultivation of Scenedesmus acutus Using Gaseous CO 2 and NH 3

Integrating CO 2 scrubbing from flue gas with its utilization in algae cultivation represents a potential means of lowering the cost of CO 2 capture. Towards this goal, this study sought to assess the feasibility of using gaseous a CO 2 /NH 3 stream, derived from CO 2 capture using aqueous ammonia, as a C- and N-source for algae cultivation. Scenedesmus acutus was cultured in 800 mL photobioreactors using gaseous CO 2 /NH 3 in mole ratios varying from 7 to 18. Excellent growth of Scenedesmus acutus was observed, the average growth rate for CO 2 /NH 3 = 10 of 0.171 ± 0.015 g/L·day exceeding that obtained using 1% CO 2 /N 2 and urea as the N-source (0.099 ± 0.28 g/L·day). Under optimal growth conditions (CO 2 /NH 3 mole ratio of 10), CO 2 utilization ranged from 57% to 72%, while the NH 3 utilization was >90%. The CO 2 /NH 3 feed rate was also found to exert a significant effect on algae productivity, excessive feed rates leading to accumulation of NH 3 in the culture at concentrations that were toxic to the algae. Consequently, to avoid the toxic effects of high NH 3 concentrations (>2.0 mM), it proved necessary to balance the NH 3 supply with the algae growth rate so that excessive NH 3 accumulation was prevented. This indicates that for practical applications, a CO 2 /NH 3 feed control strategy would be required that takes into account the ammonium ion concentration in solution and the pH so as to avoid significant concentrations of free NH 3 . Furthermore, analysis of the harvested biomass revealed a high protein (≥ 47 wt%) and a low ash content (< 3.6 wt%), suggesting it would be well suited for use as animal feed or as a feedstock for the production of bioplastics.

03 NATURAL GAS↗
DOE OSTI2022

Scalable branching on dual decomposition of stochastic mixed-integer programming problems

In this work, we present a scalable branching method for the dual decomposition of stochastic mixed-integer programming. Our new branching method is based on the branching method proposed by Caroe and Schultz that creates branching disjunctions on first-stage variables only. We propose improvements to the process for creating branching disjunctions, including (1) branching on the optimal solutions of the Dantzig-Wolfe reformulation of the restricted master problem and (2) using a more comprehensive (yet simple) measure for the dispersions associated with subproblem solution infeasibility. We prove that the proposed branching process leads to an algorithm that terminates finitely, and we provide conditions under which globally optimal solutions can be identified after termination. We have implemented our new branching method, as well as the Caroe-Schultz method and a branch-and-price method, in the open-source software package DSP. Using SIPLIB test instances, we present extensive numerical results to demonstrate that the proposed branching method significantly reduces the number of node subproblems and solution times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Adaptive Sampling-Based Bi-Fidelity Stochastic Trust Region Method for Stochastic Derivative-Free Optimization

Bi-fidelity stochastic optimization has gained increasing attention as an efficient approach to reduce computational costs by leveraging a low-fidelity (LF) model to optimize an expensive high-fidelity (HF) objective. In this paper, we propose ASTRO-BFDF, an adaptive sampling trust-region method specifically designed for unconstrained bi-fidelity stochastic derivative-free optimization problems. In ASTRO-BFDF, the LF function serves two purposes: (i) to identify better iterates for the HF function when the optimization process indicates a high correlation between them and (ii) to reduce the variance of the HF function estimates using bi-fidelity Monte Carlo (BFMC). The algorithm dynamically determines sample sizes while adaptively choosing between crude Monte Carlo and BFMC to balance the trade-off between optimization and sampling errors. We prove that the iterates generated by ASTRO-BFDF converge to a first-order stationary point almost surely. Additionally, we demonstrate the effectiveness of the proposed algorithm through numerical experiments on synthetic benchmarks and simulation optimization problems involving discrete event systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A New Method for Polishing Laminated Dissimilar Metals

Here, this paper presents a novel method for preparing metallographic samples of laminated dissimilar metals, specifically a layered structure of zirconium (Zr), vanadium (V), and tungsten (W), which pose a challenge due to their differing crystal structures and hardness levels. The work was motivated by the need to develop a cladding for tungsten targets in spallation neutron sources, where a zirconium face is applied over an interlayer of either vanadium or titanium to prevent the formation of brittle intermetallic compounds such as ZrW 2 . Traditional polishing techniques prove inadequate because each constituent metal requires a different polishing approach based on its unique material properties. The methodology outlined includes a sequential process involving conductive epoxy mounting, successive grinding and polishing with specific abrasives, and a final chemical-mechanical polish using a colloidal silica-based solution. Optical microscopy confirmed that this multi-step process successfully produced scratch-free surfaces and minimized surface relief across the different metal interfaces. Although developed for refractory metals, this approach shows promise for other layered structures comprised of transition metals, offering a meticulous yet effective technique for high-quality metallographic sample preparation.

Newberry, Daniel [Oak Ridge National Laboratory (O↗