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Results for “emergent constraint”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Supernova Shock Breakout/Emergence Detection Predictions for a Wide-field X-Ray Survey

There are currently many large-field surveys that are operational and are being planned including the powerful Vera C. Rubin Observatory Legacy Survey of Space and Time. These surveys will increase the number and diversity of transients dramatically. However, for some transients, like supernovae (SNe), we can gain more understanding by directed observations (e.g., shock breakout and γ-ray detections) than by simply increasing the sample size. For example, the initial emission from these transients can be a powerful probe of these explosions. Upcoming ground-based detectors are not ideally suited to observing the initial emission (shock emergence) of these transients. These observations require a large field-of-view X-ray mission with a UV follow-up within the first hour of shock breakout. The emission in the first 1 hr to even 1 day provides strong constraints on the stellar radius and asymmetries in the outer layers of stars, the properties of the circumstellar medium (e.g., inhomogeneities in the wind for core-collapse SNe and accreting companions in thermonuclear SNe), and the transition region between these two areas. This paper describes a simulation for the number of SNe that could be seen by a large field-of-view lobster-eye X-ray and UV observatory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A safe reinforcement learning algorithm for supervisory control of power plants

Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly. Model-free Reinforcement learning (RL) has emerged as a promising solution for control tasks due to its ability to learn from trial-and-error interactions with the environment. It eliminates the need for explicitly modeling the environment’s dynamics, which is potentially inaccurate. However, the direct imposition of state constraints in power plant control raises challenges for standard RL methods. To address this, we propose a chance-constrained RL algorithm based on Proximal Policy Optimization for supervisory control. Our method employs Lagrangian relaxation to convert the constrained optimization problem into an unconstrained objective, where trainable Lagrange multipliers enforce the state constraints. In conclusion, our approach achieves the smallest distance of violation and violation rate in a load-follow maneuver for an advanced Nuclear Power Plant design.

constrained optimization↗
DOE OSTI2024

Emergent flat band and topological Kondo semimetal driven by orbital-selective correlations

Flat electronic bands are expected to show proportionally enhanced electron correlations, which may generate a plethora of novel quantum phases and unusual low-energy excitations. They are increasingly being pursued in d-electron-based systems with crystalline lattices that feature destructive electronic interference, where they are often topological. Such flat bands, though, are generically located far away from the Fermi energy, which limits their capacity to partake in the low-energy physics. Here we show that electron correlations produce emergent flat bands that are pinned to the Fermi energy. We demonstrate this effect within a Hubbard model, in the regime described by Wannier orbitals where an effective Kondo description arises through orbital-selective Mott correlations. Moreover, the correlation effect cooperates with symmetry constraints to produce a topological Kondo semimetal. Our results motivate a novel design principle for Weyl Kondo semimetals in a new setting, viz.d-electron-based materials on suitable crystal lattices, and uncover interconnections among seemingly disparate systems that may inspire fresh understandings and realizations of correlated topological effects in quantum materials and beyond.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Near-cryogenic direct air capture using adsorbents

Direct air capture (DAC) of CO 2 is a key component in the portfolio of negative emissions technologies for mitigating global warming. However, even with the most potent amine sorbents, large-scale DAC deployment remains limited by high energy and capital costs. Recently, adsorbents relying on weak interactions with CO 2 have emerged as a potential alternative, thanks to their rapid adsorption kinetics and superior long-term stability, particularly under sub-ambient conditions (∼253 K). Despite these advantages, their use is hindered by the need for a water-removal process, location-specific constraints, and insufficient working capacity even in cold climates. In this study, we hypothesized that further reducing the adsorption temperature to a near-cryogenic range (160–220 K) could enable cost-effective DAC by utilizing the full potential of physisorbents. We primarily consider integrating DAC with a relatively untapped source of cold energy—liquified natural gas (LNG) regasification—to perform near-cryogenic DAC. From large-scale molecular simulations, Zeolite 13X and CALF-20 were identified as promising candidates. These materials were subsequently examined through experiments, including breakthrough analyses at 195 K. Their high CO 2 sorption capacity (4.5–5.5 mmol g −1 ), combined with a low desorption enthalpy and robust long-term stability, led to a threefold reduction in the levelized cost of capture (down to 68.2 USD per tonne CO 2 ). Estimates of the global LNG regasification resource suggest that LNG–DAC coupling could potentially enable the capture of 103–142 megatonnes of CO 2 annually as of 2050.

Kim, Seo-Yul [Georgia Institute of Technology, Atl↗
DOE OSTI2020

Microscopic study of the coupled-wire construction and plausible realization in spin-dependent optical lattices

Coupled-wire constructions offer particularly simple and powerful models to capture the essence of strongly correlated topological phases of matter. They often rely on effective theories valid in the low-energy and strong-coupling limits, which impose severe constraints on the physical systems where they could be realized. In this work, we investigate the microscopic relevance of a class of coupled-wire models and their possible experimental realization in cold-atom experiments. We connect with earlier results and prove the emergence of fractional quantum Hall states in the limit of strong interwire tunneling. Contrary to previous studies relying on renormalization group arguments, our microscopic approach exposes the connection between coupled-wire constructions and model wave functions in continuum Landau levels. Then, we use exact-diagonalization methods to investigate the appearance of these fractional quantum Hall states in more realistic settings. We examine the parameter regimes where these strongly correlated phases arise, and provide a way to detect their appearance in cold-atom experiments through standard time-of-flight measurements. Motivated by this experimental probe, we finally propose a realization of our model with cold atoms in spin-dependent optical lattices. Our estimates show that the previous fractional quantum Hall phases lie within experimentally accessible parameter regimes, giving a viable route toward their experimental study.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Transformational Challenge Reactor – On the Application of Design for Additive Manufacturing (DfAM) Techniques to the Conception of Nuclear Core

Additive manufacturing (AM) technologies are radically changing the way objects are designed and manufactured. They allow building by deposition and solidification of material layer by layer, enabling the possibility to create simple and complex features alike, almost seamlessly. Generally, the design optimization process requires to define objectives, design variables and constraints. Additive manufacturing does not challenge this process per se but does allow designers to completely redefine the constraints space as the ones originating from fabrication can be considerably relaxed compared to more “traditional” manufacturing. Thus, design optimization becomes naturally far more responsive to the actual physics being solved and considerably less influenced by fabrication limitations, leading to dramatically different designs. To take advantage of these new opportunities, so-called Designing for Additive Manufacturing (DfAM) techniques are emerging. Development of design techniques specifically tailored for additive manufacturing is warranted because, considering AM, the design space is typically considerably larger than with traditional manufacturing. The ability to explore the design space efficiently is of paramount importance for designers. This study proposes to investigate and apply some of these DfAM techniques to the conception of nuclear core. The goal being to assess if these new methods can be applied to core design and if core design could benefits from additive manufacturing technologies. After a brief investigation on the pertinence of some DfAM techniques for core design, algorithms are proposed and a workflow is established to carry neutronics and steady-state thermal-hydraulics analyses. To diminish the work load, the workflow has been automated using python modules. These modules allow the rapid creation of input files, post-treatment of output files and visualization. To test the pertinence of the proposed workflow, three test cases have been investigated: a research and test reactor, a micro-reactor and a space propulsion reactor. These test cases offered a variety of objectives, constraints and operating conditions. It is observed that the proposed workflow is capable of converging quickly and efficiently to valid design solutions. It is then concluded that DfAM techniques can be applied to core design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Lepto-axiogenesis with light right-handed neutrinos

We study lepto-axiogenesis in theories where the right-handed neutrino is light enough that its dynamics affect the determination of the baryon asymmetry. When compared with theories of high-scale lepto-axiogenesis where the Majorana neutrino mass may be treated as an effective dimension-five operator, we find that the predicted saxion mass is lower. Two distinct scenarios emerge. In the first, processes that generate the baryon asymmetry are in equilibrium down to the mass of the right-handed neutrino. In the second, the relevant processes never reach equilibrium; the baryon number freezes in. We comment on implications for supersymmetric spectra and discuss constraints on late decays of supersymmetric relics and from dark radiation. In contrast to high-scale lepto-axiogenesis, which predicts superpartners with masses of 10–100 TeV or more, we find this scenario is consistent with a wider range of superpartner masses, all the way down to current direct search bounds.

Axions and ALPs↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Role of Blockchains in Multi-Stakeholder Transactive Energy Systems

Power grids are undergoing major changes due to rapid growth in renewable energy and improvements in battery technology. Prompted by the increasing complexity of power systems, decentralized solutions are emerging that arrange local communities into transactive microgrids. This paper addresses the problem of implementing transactive energy mechanisms in a distributed setting, providing both privacy and safety. Specifically, we design and implement an automated auction and matching system that ensures safety (e.g., satisfaction of line capacity constraints), preserves privacy, and promotes local trade and market efficiency for transactive energy systems. This design problem is challenging because safety, market efficiency, and privacy are competing objectives. We implement our solution as a decentralized trading platform built on blockchain technology and smart contracts. To demonstrate the viability of our platform, we analyze the results of experiments with dozens of embedded devices and energy production and consumption profiles using an actual dataset from the transactive energy domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Robust Interfacial Effect in Multi-interface Environment through Hybrid Reconstruction Chemistry for Enhanced Energy Storage

Electrochemical-oxidation-driven reconstruction has emerged as an efficient approach for developing advanced materials, but the reconstructed microstructure still faces challenges including inferior conductivity, unsatisfying intrinsic activity, and active-species dissolution. In this report we present hybrid reconstruction chemistry that synergistically couples electrochemical oxidation with electrochemical polymerization (EOEP) to overcome these constraints. During the EOEP process, the metal hydroxides undergo rapid reconstruction and dynamically couple with polypyrrole (PPy), resulting in an interface-enriched microenvironment. We observe that the interaction between PPy and the reconstructed metal center (i.e., Mn > Ni, Co) is strongly correlated. Theoretical calculation results demonstrate that the strong interaction between Mn sites and PPy breaks the intrinsic limitation of MnO 2 , rendering MnO 2 with a metallic property for fast charge transfer and enhancing the ion-adsorption dynamics. Operando Raman measurement confirms the promise of EOEP-treated Mn(OH) 2 (E-MO/PPy) to stably work under a 1.2 V potential window. The tailored E-MO/PPy exhibits a high capacitance of 296 F g –1 at a large current density of 100 A g –1 . Here our strategy presents breakthroughs in upgrading the electrochemical reconstruction technique, which enables both activity and kinetics engineering of electrode materials for better performance in energy-related fields.

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

Energy Budget Constraints on the Time History of Aerosol Forcing and Climate Sensitivity

Abstract An observationally constrained time series of historical aerosol effective radiative forcing (ERF) from 1750 to 2019 is developed in this study. We find that the time history of aerosol ERFs diagnosed in CMIP6 models exhibits considerable variation and explore how the time history of aerosol forcing influences the probability distributions of present‐day aerosol forcing and emergent metrics such as climate sensitivity. Using a simple energy balance model, trained on CMIP6 climate models and constrained by observed near‐surface warming and ocean heat uptake, we derive estimates for the historical aerosol forcing. We find 2005–2014 mean aerosol ERF to be −1.1 (−1.8 to −0.5) W m −2 relative to 1750. Assuming recently published historical emissions from fossil fuel and industrial sectors and biomass burning emissions from SSP2‐4.5, aerosol ERF in 2019 is −0.9 (−1.5 to −0.4) W m −2 . There is a modest recovery in aerosol forcing (+0.025 W m −2 decade −1 ) between 1980 and 2014. This analysis also gives a 5%–95% range of equilibrium climate sensitivity of 1.8°C –5.1°C (best estimate 3.1°C) with a transient climate response of 1.2°C –2.6°C (best estimate 1.8°C).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Use of Multi-Site Radiation Therapy for Systemic Disease Control

Metastatic cancer is a heterogeneous entity, some of which could benefit from local consolidative radiation therapy (RT). Although randomized evidence is growing in support of using RT for oligometastatic disease, a highly active area of investigation relates to whether RT could benefit patients with polymetastatic disease. This article highlights the preclinical and clinical rationale for using RT for polymetastatic disease, proposes an exploratory framework for selecting patients best suited for these types of treatments, and briefly reviews potential challenges. The goal of this hypothesis-generating review is to address personalized multimodality systemic treatment for patients with metastatic cancer. The rationale for using high-dose RT is primarily for local control and immune activation in either oligometastatic or polymetastatic disease. However, the primary application of low-dose RT is to activate distinct antitumor immune pathways and modulate the tumor stroma in efforts to better facilitate T cell infiltration. We explore clinical cases involving high- and low-dose RT to demonstrate the potential efficacy of such treatment. We then group patients by extent of disease burden to implement high- and/or low-dose RT. Patients with low-volume disease may receive high-dose RT to all sites as part of an oligometastatic paradigm. Subjects with high-volume disease (for whom standard of care remains palliative RT only) could be treated with a combination of high-dose RT to a few sites for immune activation, while receiving low-dose RT to several remaining lesions to enhance systemic responses from high-dose RT and immunotherapy. We further discuss how emerging but speculative concepts such as immune function may be integrated into this approach and examine therapies currently under investigation that may help address immune deficiencies. The review concludes by addressing challenges in using RT for polymetastatic disease, such as concerns about treatment planning workflows, treatment times, dose constraints for multiple-isocenter treatments, and economic considerations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Rare and Experimentally Challenging Supersymmetry Signatures

Supersymmetry has long played a central role in the search for physics beyond the Standard Model at colliders, providing a comprehensive and internally consistent framework for generating well-motivated experimental signatures. For more than 15 years of Large Hadron Collider (LHC) operation, the CMS and ATLAS Collaborations have achieved remarkable sensitivity to a wide range of supersymmetric signatures. Despite this unprecedented reach, no conclusive evidence for supersymmetry has emerged. If supersymmetry is nature's solution to outstanding questions in particle physics, it is necessarily challenging to find. In this article, we review supersymmetric signatures that are particularly rare or otherwise challenging, with a focus on searches at the LHC. We highlight experimental challenges related to detector constraints and analysis difficulties, in addition to model challenges in the interpretation and optimization of searches. Here, we also identify regions of signature space that remain comparatively unconstrained and therefore represent promising targets for future exploration.

Large Hadron Collider↗
DOE OSTI2021

Solar PV Curtailment in Changing Grid and Technological Contexts: Preprint

Solar photovoltaic (PV) systems generate electricity with no marginal costs or emissions. As a result, PV output is almost always prioritized over other fuel sources and delivered to the electric grid. At increasing levels of PV penetration situations arise where PV is curtailed, either because of local supply/demand imbalances or to maintain system flexibility. In 2018, we estimate that about 6.5 million MWh of PV output was curtailed in four key countries: Chile, China, Germany, and the United States. We find that PV curtailment peaks in the spring and fall, when PV output is relatively high but electricity demand is relatively low. Similar to the case of wind, some PV curtailment is attributable to limited transmission capacity connecting sparsely populated solar-heavy regions to load centers.Grid policies generally seek to minimize curtailment because it is viewed as an economic and environmental loss. However, we argue that changing grid and technological contexts warrant new thinking on PV curtailment. In the grid context, as grids integrate more PV and other renewable energy generation, seeking an optimal level of accepted curtailment becomes more efficient than preventing it. In the technological context, emerging technologies such as advanced inverters and low-cost battery storage are making PV systems more flexible. With flexible PV, grid operators can use withheld PV output to provide various non-generation grid services. This withheld PV output is a form of curtailment under prevailing definitions of the term. Hence, policies that aim to minimize curtailment may undercut the ability of grid operators to fully use the emerging capabilities of flexible PV systems. We argue that the changing grid and technological contexts require a re-examination of the curtailment paradigm. We argue that PV output that is withheld to provide grid services is fundamentally different from output that goes unused in response to system constraints. As a result, we propose a more exclusive definition of curtailment as unused PV output rather than the more expansive conventional definition as any reduction in system output from its technical potential. The terminological distinction is more than a question of semantics. Facilitating grid services by withholding PV output may increase the potential value of flexible PV systems to the grid. This shift in thinking may allow grid operators and policymakers to think in terms of PV curtailment management rather than minimization. Effective curtailment management may include policies that increase PV system dispatchability, alternative PV compensation schemes that decouple generator revenue from system output, and policies to increase grid flexibility.

Chile↗
DOE OSTI2020

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms’ metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. Furthermore, this review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Emerging Two-Dimensional Organic Semiconductor-Incorporated Perovskites–A Fascinating Family of Hybrid Electronic Materials

Halide perovskites have attracted a great amount of attention owing to their unique materials chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional (2D) halide perovskites, originating from three-dimensional (3D) perovskite structures, are structurally more diverse and therefore create functional possibilities beyond 3D perovskites. The much less restrictive size constraints on the organic component of these hybrid materials particularly provide an exciting platform for designing unprecedented materials and functionalities at the molecular level. In this Perspective, we discuss the concept and recent development of a sub-class of 2D perovskites, namely, organic semiconductor-incorporated perovskites (OSiPs). OSiPs combine the electronic functionality of organic semiconductors with the soft and dynamic halide perovskite lattice, offering opportunities for tailoring the energy landscape, lattice and carrier dynamics, and electron/ion transport properties for various fundamental studies, as well as device applications. Specifically, we summarize recent advances in the design, synthesis, and structural analysis of OSiPs with various organic conjugated moieties as well as the application of OSiPs in photovoltaics, light-emitting devices, and transistors. As a result, challenges and further opportunities for OSiPs in molecular design, integration of novel functionality, film quality, and stability issues are addressed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗