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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Technoeconomic Design Optimization for Fast Reactors. Part I: Workflow Development and Case Study for Small LFR District Energy Application

The nuclear industry is developing small reactor designs that can target a variety of deployment locations and energy products. Smaller nuclear designs have traditionally struggled to handle the steep trade-offs between size and cost that have historically incentivized large reactors. This motivates computational optimization of small reactors to minimize costs and quantify the trade-off between size and cost. In this paper, the cost/size trade-off for a small fast reactor is derived using a multi-objective genetic algorithm optimization, with steady-state, transient, and cost analysis of the fast reactor being performed. Specifically, the method is demonstrated on a small 10- to 120-MW(thermal) U-Pu-Zr–fueled lead-cooled fast reactor with a 10-year core life for district energy applications, which can have a thermal load compatible with this range. The results reinforced that fast reactor cores at the lower end of this power range suffer cost penalties due to critical mass considerations. It was found that high power density cores with strong reactivity swings and many control rods were favored over designing to minimize reactivity swing. Furthermore, this contrasts with some traditional configurations designed using engineering judgment and demonstrates that optimizers can find nontraditional but realistic solutions, along with demonstrating the value of incorporating cost functions into whole-reactor design optimization.

Fast reactor↗
DOE OSTI2023

Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes

Understanding the impact of extractant functionalization on metal-binding energetics in liquid-liquid extraction is essential to guide the development of better separation processes. Traditionally, computational extractant design uses electronic structure calculations on metal-ligand clusters to determine the metal-binding energy of the lowest energy state. Although highly accurate, this approach does not account for all of the relevant physics encountered under experimental conditions. Such methodologies often neglect entropic contributions such as temperature effects and ligand flexibility, in addition to approximating solvent-extractant interactions with implicit solvent models. In this study, we use classical molecular dynamics simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. Here we generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the headgroup and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal-binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the headgroup has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal-binding energetics based on experimentally reported distribution ratios. We also consider a different bidentate extractant molecule, carbamoylmethylphosphine oxide, and show how the choice of solvent can further reshape the conformational energetic landscape. This study demonstrates the feasibility of using molecular dynamics simulations with advanced sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
DOE OSTI2026

RegularizedOptimization.jl: A Julia framework for regularized and nonsmooth optimization

RegularizedOptimization.jl is a Julia package that implements families of quadratic regularization and trust-region methods for solving the nonsmooth optimization problem $^{\textrm{minimize}}_{𝑥∈ℝ^𝑛}$ 𝑓(𝑥) + ℎ(𝑥) subject to 𝑐(𝑥) = 0, (1) where 𝑓 ∶ ℝ 𝑛 → ℝ and 𝑐 ∶ ℝ 𝑛 → ℝ 𝑚 are continuously differentiable, and ℎ ∶ ℝ 𝑛 → ℝ∪{+∞} is lower semi-continuous. The nonsmooth objective ℎ can be a regularizer, such as a sparsity inducing penalty, model simple constraints, such as 𝑥 belonging to a simple convex set, or can be a combination of both. All 𝑓, ℎ, and 𝑐 can be nonconvex. RegularizedOptimization.jl provides a modular and extensible framework for solving (1), and developing novel solvers. Currently, the following solvers are implemented: • Trust-region solvers TR and TRDH (Aravkin et al., 2022; Leconte & Orban, 2025) • Quadratic regularization solvers R2, R2DH and R2N (Aravkin et al., 2022; Diouane, Habiboullah, et al., 2024) • Levenberg-Marquardt solvers LM and LMTR (Aravkin et al., 2024) used when 𝑓 is a least-squares residual. • Augmented Lagrangian solver AL (De Marchi et al., 2023). All solvers rely on first derivatives of 𝑓 and 𝑐, and optionally on their second derivatives in the form of Hessian-vector products. If second derivatives are not available, quasi-Newton approximations can be used. In addition, the proximal mapping of the nonsmooth part ℎ, or adequate models thereof, must be evaluated. At each iteration, a step is computed by solving a subproblem of the form (1) inexactly, in which 𝑓, ℎ, and 𝑐 are replaced with appropriate models around the current iterate. The solvers R2, R2DH, and TRDH are particularly well suited to solve the subproblems, though they are general enough to solve (1). All solvers are allocation-free, so re-solves incur no additional allocations. To illustrate our claim of extensibility, a first version of the AL solver was implemented by an external contributor. Furthermore, a nonsmooth penalty approach, described in Diouane, Gollier, et al. (2024), is currently being developed, that relies on the library to efficiently solve the subproblems.

Gollier, Maxence [Polytechnique Montréal, QC (Cana↗
DOE OSTI2021

Fuel Property Effects of a Broad Range of Potential Biofuels on Mixing Control Compression Ignition Engine Performance and Emissions

Conventional diesel engines will continue to hold a vital role in the heavy- and medium-duty markets for the transportation of goods along with many other uses. The ability to offset traditional diesel fuels with low-net-carbon biofuels could have a significant impact on reducing the carbon footprint of these vehicles. A prior study screened several hundred candidate biofuel blendstocks based on required diesel blendstock properties and identified 12 as the most promising. Eight representative biofuel blendstocks were blended at a 30% volumetric concentration with EPA certification ultra-low-sulfur diesel (ULSD) and were investigated for emissions and fuel efficiency performance. This study used a single cylinder engine (based on the Ford 6.7L engine) using Conventional Diesel Combustion (CDC), also known as Mixing Control Compression Ignition (MCCI). The density, cetane number, distillation curve and sooting tendency (using the yield sooting index method) of the fuels were measured. Start of injection (SOI) timing and exhaust gas recirculation (EGR) sweeps were conducted at three separate speed-load operating points to examine fuel effects on the NOX/soot and NOX/efficiency trade-offs as well as to evaluate EGR tolerance. The results show that the biofuel blends all reduced NOX and soot emissions without penalty to engine efficiency, even improving efficiency for some of the blends at certain points, with one particular blend of polyoxymethylene ethers (POMEs) improving efficiency at all points while drastically reducing soot by 79%.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Global Explainability of A Deep Abstaining Classifier for Cancer Pathology Reports

We present a global explainability method to characterize sources of errors in a real-world multitask deep abstaining classifier (DAC), in the context of cancer histology prediction. Our multitask classifier, currently deployed for automated annotation of cancer pathology reports from NCI-SEER registries, was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and confusing classes to achieve the target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for hundreds of thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations (ALE), offers a tractable path to true global explainability. It enabled identification of sources of errors in histology classification, globally, as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies for iterative improvement of our DAC, including well-designed exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Interface Problem Formulation Improvements with Application to Nuclear Fuel Performance Analysis

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation Program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This report documents new developments and robustness improvements in mechanical and thermal (gap heat transfer) contact formulations. The improvements range from the migration of industrial level (“assessment”) nuclear fuel model setups to the usage of mortar formulations, the addition of frictional contact to one-dimensional layered representations of fuel and cladding components, and the addition of the Petrov-Galerkin approach to dual mortar, which improves performance on curved, relatively coarse meshes. In addition, the Lagrange-multiplier enforcement of mechanical mortar contact constraints has been extended to two additional types of enforcement: penalty and augmented Lagrange-Uzawa. We show that the latter approach yields the same interface results as dual mortar in the Multiphysics Object-Oriented Simulation Environment, with the advantage of not worsening the condition number of the system matrix—thereby enabling the use of some general implementations of iterative preconditioners, at the expense of additional system evaluations (i.e., augmentations).

42 ENGINEERING↗
DOE OSTI2024

Improvement of Coal Power Plant Dry Cooling Technology through Application of Cold Thermal Energy Storage

The U.S. power infrastructure is currently heavily reliant on water cooling. The power plants in the U.S. account for approximately 40% of freshwater withdrawals, with 90% of it used in condenser cooling. The most used cooling technology in coal-fired power plants is a once-through condenser; however, this cooling method requires high water withdrawal rates and results in thermal pollution of the water source. Wet WCTs offer an alternative to once-through condensers due to much lower water withdrawals. However, these systems suffer from water consumption through evaporation making them undesirable options in areas subject to droughts and in arid areas. The direct dry cooled condensers (ACCs) and dry cooling towers (DCT) account for 1.8%, hybrid cooling (ACC + WCT) accounts for 0.5%, while other cooling technologies represent the rest (0.7%). The ACC/DCTs represent an attractive alternative for power plants; however, this technology has not been widely adopted in the US (less than 2% of power plants) due to its negative impact on plant performance. As a rule of thumb, dry cooling results in performance penalty equivalent to approximately 2%-point efficiency loss compared to wet cooling, although the actual magnitude varies with ambient dry bulb temperature, DBT which may vary considerably during the day. As DBT increases, the plant power output decreases, reaching a minimum at the hottest period of the day, which usually coincides with the highest electricity demand for air condition load. Therefore, performance of power plants using DCT/ACC cooling technology is the lowest during the summer mid-day when ambient temperature is the highest. For example, the decrease of the inlet air temperature to the DCT/ACC by 2 Deg C could generate up to 5% additional power at peak demand. It is, therefore, important to improve dry cooling technology to maintain the viability of coal-fired power plants in a carbon constrained future. The method for reducing the cooling air temperature and keeping it constant would mitigate this problem significantly. Objectives of this project were to develop, design, evaluate, and demonstrate a cost-effective system for improving performance of a DCT or ACC for thermal (coal-fired) power plant applications using a low-cost heat storage materials, such as pervious concrete (PC) and phase change material (PCM). Thus, the study focused on development of the system(s) that could be used to alleviate the difficulties in operating DCT/ACC during the summer months by storing cold energy during the nighttime in inexpensive materials PC and PCM and using it during the hottest period(s) of the day. Since very large quantities of cold energy need to be stored to make an impact on performance of a large power plant, it is essential that the storage materials and associated cold energy storage design(s) are inexpensive and the system is simple to build, maintain and operate. To achieve the project objectives, a comprehensive approach, including material development and characterization, component and system modeling, and laboratory- and prototype-scale experiments, was employed including modeling of the system components and of the entire system, development (engineering) of the materials for the heat storage modules of the Cold Thermal Energy Storage System (CTESS) and determination of their properties, design, manufacturing and setup of the laboratory- and prototype-scale test facility and testing, design, manufacturing and setup of the prototype-scale test facility. A modular design of CTESS was employed, where representative modules were designed as the integrated direct contact heat exchanger and thermal energy storage (TES) system. CTESS modules were manufactured and tested. Two prototype-scale designs of the CTESS modules were developed and tested. The use of CTESS increases plant generation increases since it lowers air temperature entering ACC/DCT and keeps it constant during the hottest time of the day. For a PCM-based CTESS, the ambient air temperature is lowered close to the PCM phase change temperature. The duration of the cooling effect depends on the latent heat and mass of PCM in the CTESS. For this project, commercial grade CaCl2 hexahydrate (CaCl2·6H2O or CC6) PCM with phase change temperature of 25 Deg. C was used due to its low cost. For practical reasons, the CTESS was designed to maintain the cooling effect for four hours. The low phase change temperature associated with the commercial grade PCM used in CTESS results in considerably higher improvement in net generation compared to the laboratory (pure) grade. The resistance to heat transfer results in lower net generation compared to the ideal case where resistance to heat transfer is zero. The results demonstrate that CTESS is effective in improving the performance of a dry cooling system. However, its effectiveness depends on the relationship between the ambient air conditions and PCM phase change temperature. As is the case with the heat rejection system, for the best performance, the PCM used in CTESS would need to be matched to the ambient air conditions. The results obtained in this report for selected geographical locations are valid for CC6 and demonstrate that the level of performance to be achieved by the technology will be location-dependent, as is the case with the air cooled condensers. The methodology for engineering of PC-PCM-based heat storage medium is applicable to other PCMs that may need to be used for other ambient air conditions and geographical locations.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Multi-Functional Smart Structures for Smart Vehicles

This report summarizes the development of a new class of recyclable multi-functional composite materials for production of lightweight smart structures and surfaces. Functional high stiffness conductive composites were processed using molding methods that integrated continuous fiber and additively manufactured features. Methods for integration of sensing functionality and controls were also developed to reduce system cost while providing a new capability for structural health monitoring. This new class of composites is applicable to a broad range of vehicle interior, exterior and battery enclosure systems. By way of demonstration, a vehicle instrument panel cross car beam was developed that provided a 38% mass savings compared to steel while maintaining a cost penalty competitive to alternate lightweight material solutions. These technologies were validated for implementation by a uniquely qualified project team comprising a US automotive OEM, Tier 1 and Tier 2 supplier, with key contributions from Oak Ridge National Lab, Purdue University and Michigan State University.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Development and verification of the pyIsoDep depletion package for nuclear thermal propulsion reactors

Nuclear thermal propulsion technology is currently being developed by the National Air and Space Administration for future manned Mars missions. There are multiple on-going activities to develop and enhance computational capabilities, single- and multi-physics, to advance the readiness level of this technology. The prediction of short-lived fission products is crucial to understand the reactivity penalty and the decay heat production during the different burnout stages of the vehicle. Generally, the solution entails coupled neutronic-depletion analysis. However, such simulations are somewhat expensive, especially if the duration of each engine burn is to be optimized. Therefore, there is a need to complement such high-fidelity techniques with time-efficient alternatives that do not require the sequential neutronic and depletion analysis. This paper details the methods and capabilities of pyIsoDep, an open-source code used for depletion analysis of nuclear thermal propulsion reactors. The package is general, in principle, and thus can be applied to any fission system, and can potentially be extended for fusion systems. The package was developed to provide a flexible and efficient alternative to pre-existing higher order depletion codes that could integrate well into mission and transient analysis of nuclear thermal propulsion reactors. Finally, this paper presents a validation and verification of pyIsoDep against analytical and Serpent-generated solutions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Enhancing CO 2 Mineralization Rate and Extent of Iron and Steel Slag via Grinding

Roughly 10% of the CO 2 emissions from iron and steel making are attributable to the direct release of CO 2 from the thermal decomposition of carbonates to produce flux, mainly CaO, used for impurity removal. Notably, these direct emissions remain even if carbon-based steelmaking is replaced by hydrogen-based steelmaking. After removing impurities from the molten metal, this flux becomes the solid waste product called ‘slag’, a primarily Ca-silicate material. The transformation of slag back into carbonates is thermodynamically spontaneous with negative ΔG in the ambient environment, meaning that ~10% of the CO 2 emissions from iron and steel making could be negated if equipment and methods were developed to support CO 2 mineralization. However, the rate of CO 2 mineralization using slag is slowed by several environmental, geometric, and processing factors. We leverage an experimentally verified model of CO 2 mineralization to determine how to efficiently accelerate the process. Increasing the crystallinity of slag, increasing the relative humidity, and reducing the grain size of slag particles provide the greatest increase in CO 2 mineralization rate at the lowest energy penalty. Increasing the concentration of CO 2 and the temperature provide only modest increases in the CO 2 mineralization rate while incurring a substantial energy penalty. For steelmaking slags, CO 2 mineralization represents low-hanging fruit as the current reuse pathways are low value. For ironmaking slag, replacing the production of amorphous slag for the cement industry with the production of crystalline slag for CO 2 mineralization becomes financially preferable when a carbon price/tax exceeds 67.40 USD/t-CO 2 .

42 ENGINEERING↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
DOE OSTI2025

Isoconversional Kinetic Analysis of Oxygen Desorption for Sr 0.75 Ca 0.25 FeO 3-δ Perovskite in Dry and Steam-Based Environments

The desorption kinetics of Sr 0.75 Ca 0.25 FeO 3-δ perovskite material are examined in both dry and steam-based environments using redox gaseous products of a laboratory-scale fixed bed. First, desorption kinetics associated with the dry environment are proposed based on Friedman’s isoconversional method. It is found from the reconstructed reaction model that the desorption kinetics of this perovskite are controlled by a three-step mechanism, where hypothetically, the phase-boundary reaction rapidly prevails first, followed by diffusion and/or nucleation, and finally, unimolecular decay or random nucleation-controlled reaction. Subsequent analyses of the Arrhenius parameters indicated that both the early and later desorption stages, respectively, controlled by the phase boundary and unimolecular/random nucleation reactions, are associated with low energetic demand. In contrast, a high energetic penalty is required for the diffusion/nucleation reaction. Further, a satisfactory a priori verification of the proposed kinetics suggested that the three-step reaction mechanism reasonably describes the desorption of this perovskite in a dry environment. With the presence of steam in the desorption environment, oxygen production is substantially inhibited, but the hypothetical three-step mechanism is still found to control the desorption. Meanwhile, the primary effects of the steam on these mechanisms include the widening of the conversion extent associated with the diffusion/nucleation reaction, along with a higher energetic demand compared to the dry environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Knowledge-Based Hazardous Waste Determinations for Solar Photovoltaic (PV) Modules: A Foundational Study

This report explores using "generator knowledge" to determine whether a solar photovoltaic (PV) module must be managed as hazardous waste prior to recycling or landfilling. Generator knowledge is a legal term and existing regulatory pathway for making a hazardous waste determination that has been used by other industries but is a relatively unknown option to the PV industry. In the United States, a hazardous waste determination often acts as a pre-requisite to recycle or landfill a PV module. The results of the hazardous waste determination dictate whether the PV module must be managed as hazardous waste or nonhazardous solid waste. Managing a PV module as hazardous waste requires compliance with stringent U.S. federal and state hazardous waste law. In addition to increased management costs, which can be ten times higher, legal liability for PV modules regulated as hazardous waste is also heightened with both civil and criminal penalties for noncompliance which includes making an inaccurate or faulty hazardous waste determination. The most common reason a PV module would be regulated as hazardous is if it contains a regulated metal in an amount that equals or exceeds the toxicity characteristic limits. To determine whether a PV module exhibits a hazardous characteristic, the regulated person/entity must "apply knowledge...in light of the materials and processes used." In the absence of adequate knowledge to determine whether the PV module is hazardous, it must be tested using Test Method 1311 Toxicity Characteristic Leaching Procedure (TCLP) or an equivalent EPA-approved method. Although TCLP is the predominant method used to today to make a hazardous waste determination for PV modules, evidence from this study concludes it is not practical to TCLP test every PV module even in a single utility-scale installation, and a scalable solution is needed. This study finds that knowledge-based hazardous waste determinations may allow a regulated person/entity to make a hazardous waste determination for more than one PV module at one time - making this regulatory pathway a potential scalable solution. Through legal analysis and interviews with 44 experts, the authors explore what it means to make an accurate knowledge-based hazardous determination for PV modules considering sources and forms of information as well as potential limitations. The work aims to provide a foundation for building consensus on whether knowledge-based hazardous determinations are a viable, scalable industry approach for solar.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Smoothed Augmented Lagrangian Framework for Convex Optimization with Nonsmooth Constraints

Augmented Lagrangian (AL) methods have proven remarkably useful in solving optimization problems with complicated constraints. The last decade has seen the development of overall complexity guarantees for inexact AL variants. Yet, a crucial gap persists in addressing nonsmooth convex constraints. To this end, we present a smoothed augmented Lagrangian (AL) framework where nonsmooth terms are progressively smoothed with a smoothing parameter $\eta _k$ . The resulting AL subproblems are $\eta _k$ -smooth, allowing for leveraging accelerated schemes. By a careful selection of the inexactness level $\epsilon _k$ (for inexact subproblem resolution), the penalty parameter $\rho _k$ , and smoothing parameter $\eta _k$ at epoch k, we derive rate and complexity guarantees of $\tilde{\mathcal {O}}(1/{\varepsilon }^{3/2})$ and $\tilde{\mathcal {O}}(1/{\varepsilon })$ in convex and strongly convex regimes for computing an ${\varepsilon }$ -optimal solution, when $\rho _k$ increases at a geometric rate, a significant improvement over the best available guarantees for AL schemes for convex programs with nonsmooth constraints. Analogous guarantees are developed for settings with $\rho _k = \rho$ as well as $\eta _k = \eta$ . Preliminary numerics on a fused Lasso problem display promise.

augmented Lagrangian↗
DOE OSTI2023

A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming

We propose a fast temporal decomposition procedure for solving long-horizon nonlinear dynamic programs. The core of the procedure is sequential quadratic programming (SQP) that utilizes a differentiable exact augmented Lagrangian as the merit function. Within each SQP iteration, we approximately solve the Newton system using an overlapping temporal decomposition strategy. We show that the approximate search direction is still a descent direction of the augmented Lagrangian provided the overlap size and penalty parameters are suitably chosen, which allows us to establish the global convergence. Moreover, we show that a unit step size is accepted locally for the approximate search direction and further establish a uniform, local linear convergence over stages. This local convergence rate matches the rate of the recent Schwarz scheme (Na et al. 2022). However, the Schwarz scheme has to solve nonlinear subproblems to optimality in each iteration, whereas we only perform a single Newton step instead. Numerical experiments validate our theories and demonstrate the superiority of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection

Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Life cycle assessment of a novel gas switching reforming for sustainable hydrogen production with CO2 capture

Gas switching reforming for hydrogen production (GSR-H2) presents an efficient, low-carbon hydrogen production method that incorporates integrated carbon capture, offering efficiency gains over traditional methods such as proton exchange membrane (PEM) electrolysis, steam methane reforming (SMR) and the newer method of chemical looping reforming (CLR). GSR-H2 has been demonstrated in lab scale which operates as an exothermic process that eliminates the need for additional natural gas combustion, using its own waste heat to generate process steam and partially offset energy usage through electricity production. Beyond its thermal self-sufficiency, GSR-H2 advances upon CLR by integrating all reaction stages within a single reactor cluster, eliminating the complexities of solid circulation, reducing capital costs, and enhancing overall process efficiency. This streamlined design simplifies scale-up and enables inherent CO2 separation with minimal energy penalty, making GSR-H2 a highly competitive pathway for low-carbon hydrogen production. This study presents the first life cycle assessment (LCA) of GSR-H2, offering a novel evaluation of this new process’s environmental impacts across diverse energy scenarios. Key findings reveal that in the renewables-powered scenario, GSR-H2 achieves a GWP of 2.77 kg CO2 eq per kg H2, a substantial improvement over SMR’s 10.4 kg CO2 eq and close to the low emissions of CLR (1.84 kg CO2 eq) and PEM electrolysis (1.85 kg CO2 eq). These results demonstrate GSR-H2’s competitive advantage as a lower-emission alternative, combining design simplicity and efficiency gains, especially in renewable-integrated systems. These results establish GSR-H2 as a competitive, scalable option for hydrogen production, particularly in decarbonization efforts.

03 NATURAL GAS↗