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Results for “simultaneous 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.

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Contextual modeling and Bayesian Optimization for Improved Injection at the Fermilab Booster

The Fermilab accelerator complex delivers high-intensity proton beams to serve the lab’s neutrino, muon, and fixed-target programs. A normal-conducting Linac accelerates H− beam to 400 MeV and injects into the Booster rapid cycling synchrotron via charge exchange, which accelerates protons to 8 GeV. Injection from the Linac into the Booster is a critical area for high-power performance of the Fermilab proton complex. The Booster is a high-intensity proton ring with extreme space-charge forces which necessitates precise control over the beam losses through the acceleration cycle. The main challenge for the reliability of Booster performance is compensating for drifting conditions in the beam from the Linac, which can drift daily in energy by up to O(1) MeV w.r.t. design. Drifts in Linac orbit and energy must be corrected to match the Booster, while simultaneously accommodating interdependent drifts in transverse and longitudinal beam quality. Operationally, compensation for these changes is addressed by manual tuning of the Linac output energy and/or Booster acceptance, which can be inefficient and time-consuming. This works describes contextual Bayesian Optimization for injection tuning that takes into account the state of Linac beam via information from instrumentation in the injection line (Beam position monitors (BPMs), beam loss monitors (BLMs), wire scanners for transverse profiles (WSs)), as well as RF cavity setting parameters from the Linac.

Sharankova, R. [Fermilab] (ORCID:000000027014593X)↗
DOE OSTI2022

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. Here, we show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Double Perovskite Interlayer Stabilized Highly Efficient Perovskite Solar Cells

Metal halide perovskite solar cell (PSC) technology has an impressive power conversion efficiency (PCE) exceeding 26.1% and demonstrates cost-effective manufacturing. However, the stability of these PSCs poses a significant challenge, hindering their widespread manufacturing and commercialization. To tackle the degradation issue inherent in PSCs, surface passivation techniques, particularly employing a thin layer of two-dimensional (2D) perovskites, create a 2D/3D heterostructure. Beyond this, the exploration of metal halide double perovskites adds a new dimension to the chemical and band gap phase space of materials for optoelectronic applications. In this study, we leverage a wide band gap double perovskite interlayer to enhance the stability of 3D metal halide perovskite. Specifically, the double perovskite nanoparticle Cs 2 AgBiBr 6 , with its substantial band gap of 2.2 eV and exceptional air stability, is utilized. Through optimization, a Cs 2 AgBiBr 6 -treated PSC achieves an open-circuit voltage of 1.12 V and an impressive PCE of 19.52%. Additionally, the Cs 2 AgBiBr 6 passivation layer proves to be effective in bolstering the stability of PSCs. This work demonstrates an additional strategy and design motif to simultaneously increase the PCE of PSCs along with achieving improved stability.

14 SOLAR ENERGY↗
DOE OSTI2025

Iterative Reconstruction for Multimodal Neutron Tomography

Here, we describe a unified framework for model-based iterative 3-D reconstruction of multimodal neutron transmission, hydrogen-scatter, and induced-fission images from low resolution data recorded using 14.1-MeV neutrons and the associated-particle imaging (API) technique. The framework, which was developed to facilitate use in challenging field-deployment scenarios, is centered around physics-based system models and a total variation (TV) constrained implementation of the simultaneous iterative reconstruction technique (SIRT). Modified to solve a statistically weighted least squares (WLS) problem, the SIRT algorithm is accelerated using ordered subsets and Nesterov’s momentum for which we derive a near-optimal value of the governing Lipschitz constant. The approach enables the reconstruction of images that are high resolution compared to the acquired data and is robust to both limited statistics and a limited number of projection angles. Moreover, the framework is fast enough to be practical. Example images are provided that demonstrate both the ability to perform fast-neutron imaging of high-atomic-number materials with low radiation dose and the benefit of multimodal neutron imaging to identify key materials.

Hydrogen scatter↗
DOE OSTI2021

Deterministic symbolic regression with derivative information: General methodology and application to equations of state

Symbolic regression methods simultaneously determine the model functional form and the regression parameter values by generating expression trees. Symbolic regression can capture the complexity of real–world phenomena but the use of deterministic optimization for symbolic regression has been limited due to the complexity of the search space of existing formulations. Herein we present a novel deterministic mixed–integer nonlinear programming formulation for symbolic regression that incorporates derivative constraints through auxiliary expression trees. By applying the chain rule to mathematical operations, binary expression trees are capable of representing the calculation of first and second derivatives. We apply this formulation to illustrative examples using derivative information to show increased model discrimination capability. In addition, we perform a case study of a thermodynamic equation of state to gain insight on valid functional forms with thermodynamics–based constraints on the first and second derivatives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data

SUMMARY Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson’s ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth’s structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.

Geochemistry & Geophysics↗
DOE OSTI2025

Performance and Achievable Rates of the Gottesman-Kitaev-Preskill Code for Pure-Loss and Amplification Channels

Quantum error-correction codes protect information from realistic noisy channels and lie at the heart of quantum computation and communication tasks. Understanding the optimal performance and other information-theoretic properties, such as the achievable rates, of a given code is crucial, as these factors determine the fundamental limits imposed by the encoding in conjunction with the noise channel. Here, we use the transpose channel to analytically obtain the near-optimal performance of any Gottesman-Kitaev-Preskill (GKP) code under pure loss and pure amplification. We present rigorous connections between GKP code’s near-optimal performance and its dual lattice geometry and average input energy. With no energy constraint, we show that when |𝜏/(1−𝜏)| is an integer, specific families of GKP codes simultaneously achieve the loss and amplification capacity. 𝜏 is the transmissivity (gain) for loss (amplification). Our results establish GKP code as the first structured bosonic code family that achieves the capacity of loss and amplification.

Zheng, Guo [Univ. of Chicago, IL (United States)] ↗
DOE OSTI2024

Enabling Lignin Valorization Through Integrated Advances in Plant Biology and Biorefining

Despite lignin having long been viewed as an impediment to the processing of biomass for the production of paper, biofuels, and high-value chemicals, the valorization of lignin to fuels, chemicals, and materials is now clearly recognized as a critical element for the lignocellulosic bioeconomy. However, the intended application for lignin will likely require a preferred lignin composition and form. To that end, effective lignin valorization will require the integration of plant biology, providing optimal feedstocks, with chemical process engineering, providing efficient lignin transformations. Recent advances in our understanding of lignin biosynthesis have shown that lignin structure is extremely diverse and potentially tunable, while simultaneous developments in lignin refining have resulted in the development of several processes that are more agnostic to lignin composition. Here, we review the interface between in planta lignin design and lignin processing and discuss the advances necessary for lignin valorization to become a feature of advanced biorefining.

09 BIOMASS FUELS↗
DOE OSTI2022

Flux balance analysis of the ammonia-oxidizing bacterium Nitrosomonas europaea ATCC19718 unravels specific metabolic activities while degrading toxic compounds

The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants (e.g., aromatic hydrocarbons such as benzene and toluene) enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N . europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N . europaea , i GC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Simultaneous inference of plate boundary stresses and mantle rheology using adjoints: large-scale 2-D models

SUMMARY Plate motions are a primary surface constraint on plate and mantle dynamics and rheology, plate boundary stresses and the occurrence of great earthquakes. Within an optimization method, we use plate motion data to better constrain uncertain mantle parameters. For the optimization problem characterizing the maximum a posteriori rheological parameters we derive gradients using adjoints and expressions to approximate the posterior distributions for stresses within plate boundaries. We apply these methods to a 2-D cross section from the western to eastern Pacific, with temperature distributions and fault zone geometries developed primarily from seismic and plate motion data. We find that the best-fitting stress exponent, n, is about 2.8 and the yield stress about 100 MPa or less. The normal stress on the interplate fault zones is about 100 MPa and the shear stresses about 10 MPa or less.

Rudi, Johann (ORCID:0000000265639265)↗
DOE OSTI2022

Optimization-based modeling and analysis of brine reflux osmotically assisted reverse osmosis for application toward zero liquid discharge systems

Significant amounts of high-salinity wastewater generated by water-intensive industrial activities such as shale oil and gas production have raised serious environmental concerns in recent years. Existing and emerging desalination technologies offer promise to manage these high salinity wastewater streams while simultaneously producing fresh water that could be diverted for beneficial uses. Osmotically assisted reverse osmosis (OARO) is one such emerging desalination technology capable of handling hypersaline brines and achieving high recoveries. However, rigorous modeling and analysis is needed to evaluate the process performance, energy consumption, and treatment cost of various OARO configurations. Here, this work presents detailed modeling and analysis of brine-reflux OARO (BR-OARO) system and compares it with other commonly discussed configurations, including cascading osmotically mediated reverse osmosis (COMRO), consecutive loop OARO, and split feed counterflow RO, through a cost optimization-based framework. We analyze and compare the treatment costs, membrane area, specific energy consumption, and design parameters of the aforementioned configurations with the ultimate goal of achieving zero liquid discharge (ZLD). The results indicate that the BR-OARO system with treatment cost of 5.1 US $/m 3 of produced water with 10% salinity outperforms other configurations in terms of number of stages needed, treatment cost, membrane area, and energy consumption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Scalable computations for nonstationary Gaussian processes

Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial datasets. However, the large number of observations in modern datasets makes fitting such models computationally intractable with conventional dense linear algebra. In addition, derivative-free or even first-order optimization methods can be very slow to converge when estimating many spatially varying parameters. In this paper, we present a computational framework which couples an algebraic block diagonal plus low-rank covariance matrix approximation with stochastic trace estimation to facilitate the efficient use of second-order solvers for maximum likelihood estimation of Gaussian process models with many parameters. We demonstrate the effectiveness of these methods by simultaneously fitting 192 parameters in the popular nonstationary model of Paciorek and Schervish using 107,600 sea surface temperature anomaly measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN

We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) computations in both training scale and data diversity. It abstracts over message passing algorithms, allowing both reproduction of and comparison across algorithmic innovations that define nearest-neighbor convolution in GNNs. This work discusses a series of optimizations that have allowed scaling up the GFMs training to tens of thousands of GPUs on datasets consisting of hundreds of millions of graphs. Our GFMs use multitask learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on two state-of-the-art US Department of Energy (US-DOE) supercomputers, namely the Perlmutter petascale system at the National Energy Research Scientific Computing Center and the Frontier exascale system at Oak Ridge Leadership Computing Facility. The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2000 GPUs on Perlmutter and 16,000 GPUs on Frontier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonlinear control of the minimum safety factor in tokamaks by optimal allocation of spatially moving electron cyclotron current drive

The minimum value of the safety factor profile is related to the magnetohydrodynamic (MHD) stability of the plasma confined in a tokamak. Therefore, active control of the minimum safety factor may mitigate MHD instabilities that can degrade or even terminate plasma confinement. Typically, in most tokamak scenarios, the minimum safety factor evolves spatially with time, i.e., the location at which the safety factor achieves the minimum value changes with time. In addition to the inherent nonlinearities in the minimum safety factor evolution, its spatial variation makes the control design challenging. In particular, complexity in control design may arise from the need for time-dependent nonlinear models that account for spatial variation of the minimum safety factor. Furthermore, the minimum safety factor may drift to locations where the actuator authority is low. The problem of minimum safety factor control with target location tracking and moving electron cyclotron current drive (ECCD) is addressed in this work. A nonlinear time-dependent model that incorporates the spatial variation of the minimum safety factor is presented. A nonlinear controller based on optimal feedback linearization is developed to track a target minimum safety factor. The proposed controller treats the ECCD position as a controllable variable. In other words, the controller prescribes the ECCD position (in addition to the non-inductive powers) in real time based on an optimal criterion that is defined a priori. This work also presents the steps necessary to integrate the minimum safety factor controller with a total energy controller to achieve multiple control objectives simultaneously. In conclusion, the proposed integrated control algorithm is tested using nonlinear simulations in the Control Oriented Transport SIMulator (COTSIM) for a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Harnessing nanoscale densification for controlling gas selectivity in flexible zeolitic imidazolate frameworks

Flexible metal-organic frameworks (MOFs) are promising for gas separation due to their molecular recognition capabilities. However, gate-opening behavior in certain flexible MOFs can reduce gas selectivity, enabling simultaneous adsorption of multiple gases. We introduce a binder-free, pressure-free densification process to enhance gas selectivity in flexible zeolitic imidazolate frameworks (ZIF-7 and ZIF-9) by optimizing nanoparticle packing. Unlike powders, the densified ZIF-7 and ZIF-9 retain rhombohedral crystal structures after solvent removal, confirmed via synchrotron powder X-ray diffraction. This process modifies adsorption behavior by introducing a diffusion barrier, enabling diffusivity-driven kinetic separation and enhanced CO2/hydrocarbon selectivity. At 0.5 bar, ZIF-7 mono shows 2×, 4×, and 9.5× higher CO 2 selectivity over C 2 H 2 , C 2 H 4 , and C 2 H 6 , respectively, versus ZIF-7 pwd . ZIF-9 mono similarly shows 6.5×, 3×, and 39× improvements over ZIF-9 pwd . Both monoliths outperform powders in ternary gas mixtures. This study provides a novel strategy to control gate opening and enhance gas selectivity in flexible MOFs for practical separation applications.

CO2/hydrocarbon separation↗
DOE OSTI2022

Enhanced CO 2 Methanation Activity of Sm 0.25 Ce 0.75 O 2-δ –Ni by Modulating the Chelating Agents-to-Metal Cation Ratio and Tuning Metal–Support Interactions

Highly active and selective CO 2 methanation catalysts are critical to CO 2 upgrading, synthetic natural gas production, and CO 2 emission reduction. Wet impregnation is widely used to synthesize oxide-supported metallic nanoparticles as the catalyst for CO 2 methanation. However, as the reagents cannot be homogeneously mixed at an atomic level, it is challenging to modulate the microstructure, crystal structure, chemical composition, and electronic structure of catalysts via wet impregnation. In this work, a scalable and straightforward catalyst fabrication approach has been designed and validated to produce Sm 0.25 Ce 0.75 O 2-δ -supported Ni (SDC–Ni) as the CO 2 methanation catalyst. By varying the chelating agents-to-total metal cations ratio (C/I ratio) during the catalyst synthesis, we can readily and simultaneously modulate the microstructure, metallic surface area, crystal structure, chemical composition, and electronic structure of SDC–Ni, consequently fine-tuning the oxide–support interactions and CO 2 methanation activity. The optimal C/I ratio (0.1) leads to an SDC–Ni catalyst that facilitates C–O bond cleavage and significantly improves CO 2 conversion at 250 °C. A CO 2 -to-CH 4 yield of >73% has been achieved at 250 °C. Furthermore, a stable operation of >1500 hours has been demonstrated, and no degradation is observed. Extensive characterizations were performed to fundamentally understand how to tune and enhance CO 2 methanation activity of SDC–Ni by modulating the C/I ratio. The correlation of physical, chemical, and catalytic properties of SDC–Ni with the C/I ratio is established and thoroughly elaborated in this work. This study could be applied to tune the oxide–support interactions of various catalysts for enhancing the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗