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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Hydrolytic Softening of Ocean Water for Carbon Dioxide Removal

This project provided the proof of concept testing for hydrolytic softening, a process for ocean carbon dioxide removal (CDR) based on precipitation softening, where the hydrated lime used to initiate carbonate precipitation is regenerated using a low-energy thermochemical cycle. These proof of concept tests demonstrated each key step of the hydrolytic CDR process including: 1) precipitation of dissolved carbon dioxide from seawater using hydrated lime, 2) decomposing the precipitated calcium carbonate to separate a high-purity stream of carbon dioxide, and 3) regenerating hydrated lime from the decomposition products to complete the cycle. The experimental results were used as the basis for a chemical process model of hydrolytic CDR, which was then used to estimate the techno-economic performance of a conceptual full-scale system. Results included a total energy consumption estimate of 248 kJ per mole of separated and compressed CO 2 . The predicted levelized cost of ocean CDR using hydrolytic softening could reach the Department of Energy’s Carbon Negative Shot target of $\$$100/tCO 2 assuming optimally-sited wind power resources with a power cost of $\$$26/MWh. If developed further, hydrolytic CDR could be a near-term, scalable, and cost-effective method to draw down legacy carbon dioxide emissions that have been absorbed in the oceans. Offshore operations would create highly skilled jobs directly at the CDR facilities in addition to supporting a wider network of coastal service providers and their communities.

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

Simple and Scalable Process for Nanocellulose Production from Residues and Waste (CRADA Final Report)

This project successfully developed an innovative, cost-effective, and scalable method for converting low-cost agricultural waste feedstocks into nanocellulose—a sustainable material with wide-ranging industrial applications. It addressed the critical challenges of high production costs and limited global supply, which have hindered the widespread adoption of nanocellulose in industry. Through this work, significant advancements were made in optimizing resource use, improving process efficiency, and reducing costs. Notable achievements included a 50% reduction in water usage, 30% lower chemical consumption, and 20% energy savings, all while maintaining high-quality product standards. The technical feasibility of the process was further validated at a 10L scale in collaboration with the Advanced Biofuels and Bioproducts Development Unit (ABPDU), demonstrating scalability and replicability. Key challenges in reducing nanocellulose production costs were addressed by utilizing low-cost biomass feedstocks and implementing low-temperature conversion reactions, leading to lower capital and operating expenses. The project also mitigated financial risk by generating critical data on the feasibility, adaptability, and scalability of the conversion technology, paving the way for its commercial implementation. Public benefits include advancing the circular bioeconomy, reducing environmental impact, fostering job creation, and enabling a shift toward biobased materials as sustainable alternatives to fossil-based products. These outcomes align closely with national goals to reduce greenhouse gas emissions and promote sustainable technological innovation.

09 BIOMASS FUELS↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Autonomous online optimization of a closed-circuit reverse osmosis system

As freshwater becomes increasingly scarce, many industrial and municipal water utilities look at premise-scale water treatment and reuse to meet water demand. Closed-circuit reverse osmosis (CCRO) has been proposed as a promising process design to do so. This sequencing batch process enables operation at higher brine salinity levels by means of a recycle flow. Optimal operation requires that the maximum salinity level at the membrane surface represents an optimal trade-off between brine disposal costs and energy efficiency. This maximum salinity level may change over time as the feed water composition changes and electricity markets fluctuate. In this article, we present the results of the experimental evaluation of an automatic technique for continuous online optimization, known as extremum seeking control. This technique has a long history in the process control community but has received little traction so far in the water industry. We modify this technique to enable its use for online optimization of CCRO, specifically to account for its sequential batch operation. We challenge the optimization schemes through several experimental tests and illustrate the advantages and drawbacks of extremum-seeking control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Cost of Deconstruction Depots for Diversified, Waste-Based Lignocellulosic Sugars Using Distillable Solvents

Transitioning to a bioeconomy that makes use of low-emission and waste feedstocks requires greater flexibility to accommodate seasonal variations and mitigate long-term storage challenges, such as material loss and fire risk. To achieve this goal, biomass deconstruction technologies must efficiently handle diverse feedstocks. Here, we assess the cost of using butylaminea distillable solventto deconstruct 22 different biomass feedstocks: 7 herbaceous, 9 woody, 4 food processing residues, and 2 blends. Lignocellulosic sugar production costs, based on current empirical data, range from $1.3 to 6.1/kg, suggesting that substantial improvements are required to compete with conventional sugars. The high solvent loading (850 g/kg of whole slurry) is a process bottleneck. Lowering the solvent loading to 59 g/kg of whole slurry, demonstrated in an L-scale reactor using poplar biomass, reduces the minimum sugar selling price by 33%. Solvent loading and recovery, solid loading, sugar yield, enzyme use, and delivered biomass cost all play key roles in reaching sugar production costs of $0.45-0.79/kg. Strategic feedstock blending to maximize carbohydrate content, process optimization to improve conversion efficiency, and the selection of low-cost feedstocks are important to advancing feedstock-flexible biorefineries.

TEA↗
DOE OSTI2022

An Exact Algorithm for the Linear Tape Scheduling Problem

Magnetic tapes are often considered as an outdated storage technology, yet they are still used to store huge amounts of data. Their main interests are a large capacity and a low price per gigabyte, which come at the cost of a much larger file access time than on disks. With tapes, finding the right ordering of multiple file accesses is thus key to performance. Moving the reading head back and forth along a kilometer long tape has a non-negligible cost and unnecessary movements thus have to be avoided. However, the optimization of tape request ordering has rarely been studied in the scheduling literature, much less than I/O scheduling on disks. For instance, minimizing the average service time for several read requests on a linear tape remains an open question. Therefore, in this paper, we aim at improving the quality of service experienced by users of tape storage systems, and not only the peak performance of such systems. To this end, we propose a reasonable polynomial-time exact algorithm while this problem and simpler variants have been conjectured NP-hard. We also refine the proposed model by considering U-turn penalty costs accounting for inherent mechanical accelerations. Then, we propose a low-cost variant of our optimal algorithm by restricting the solution space, yet still yielding an accurate suboptimal solution. Finally, we compare our algorithms to existing solutions from the literature on logs of the mass storage management system of a major datacenter. This allows us to assess the quality of previous solutions and the improvement achieved by our low-cost algorithm. Aiming for reproducibility, we make available the complete implementation of the algorithms used in our evaluation, alongside the dataset of tape requests that is, to the best of our knowledge, the first of its kind to be publicly released.

Honoré, Valentin↗
DOE OSTI2026

Technoeconomic analysis and life cycle assessment of purification processes for captured CO 2 streams

Captured carbon dioxide (CO 2 ) streams contain impurities that must be removed to meet specifications for safe transport, storage, and utilization. Among these impurities, oxygen poses challenges due to its high reactivity and potential to cause corrosion, motivating stringent purity limits below 10 ppmv. Building on recent experimental demonstrations of catalytic oxygen removal using hydrogen (H 2 ), carbon monoxide (CO), methanol (CH 3 OH), and methane (CH 4 ) as reducing agents, this study presents a technoeconomic (TEA) and life cycle assessment (LCA) of these four catalytic purification pathways. Process flowsheets were developed and simulated in Aspen Plus for CO 2 streams representative of both low-temperature and high-temperature capture processes, with integrated heat recovery and energy optimization. Results showed that total purification costs were dominated by feedstock procurement and electricity consumption. Among the studied reducing agents, the CH 4 -assisted route achieved the lowest purification cost and highest CO 2 recovery. Sensitivity analyses showed that the H 2 route became competitive at H 2 prices below $\$$0.56/kg to $\$$0.84/kg, depending on the CO 2 feed temperature conditions. In conclusion, environmental impacts were primarily driven by indirect CO 2 emissions from raw material production and utility consumption.

CO2 pipeline specifications↗
DOE OSTI2025

A comparison of surrogate constitutive models for viscoplastic creep simulation of HT-9 steel

Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models become increasingly more complex – often involving coupled differential equations describing the effect of specific deformation modes – their associated computational costs can become prohibitive, particularly in optimization or uncertainty quantification tasks that require numerous model evaluations. To address this challenge, surrogate constitutive models that balance accuracy and computational efficiency are highly desirable. Data-driven surrogate models, that learn the constitutive relation directly from data, have emerged as a promising solution. In this work, we develop two local surrogate models for the viscoplastic response of a steel: a piecewise response surface method and a mixture of experts model. These surrogates are designed to adapt to complex material behavior, which may vary with material parameters or operating conditions. The surrogate constitutive models are applied to creep simulations of HT-9 steel, an alloy of considerable interest to the nuclear energy sector due to its high tolerance to radiation damage, using training data generated from viscoplastic self-consistent (VPSC) simulations. In conclusion, we define a set of test metrics to numerically assess the accuracy of our surrogate models for predicting viscoplastic material behavior, and show that the mixture of experts model outperforms the piecewise response surface method in terms of accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Performance improvement of proton exchange membrane water electrolysis by surface modification of porous transport layers

Proton exchange membrane water electrolysis (PEMWE) is a promising option for hydrogen production from a variety of energy sources. Cost-effective production of hydrogen with PEMWE requires reduction of costly precious metals as well as optimization of the components and interfaces in the cell. The interface between the platinum-coated titanium porous transport layer (PTL) and catalyst layer (CL) significantly impacts the performance of the electrolyzer cell. Here, we report on two PTL modification methods, mechanical abrasion and chemical etching, which we find improve PEMWE performance by lowering the resistance of the PTL/CL interface. The PTL surface modifications enabled performance improvements up to 77 mV at 4 A cm −2 and reduction of the high frequency resistance, demonstrating that the resistance of the PTL/CL interface is a significant factor in the cell performance. The PTL surface roughness was varied using abrasive materials ranging from 0.1 to 140 μm in grain sizes and chemical etching was found to also increase the surface roughness. The surface roughness was quantified using confocal laser microscopy and the surface oxidation was measured using X-ray photoemission spectroscopy. These results demonstrate that both increasing surface roughness and oxide removal contribute to lowering the PTL/CL interfacial resistance and increasing cell performance.

08 HYDROGEN↗
DOE OSTI2024

An economic perspective of the circular bioeconomy in the food and agricultural sector

Transforming the agri-food system from a “take-make-waste”, or linear production system, to a circular bioeconomy that reduces, recycles, recovers, reuses, and regenerates wastes and transitions from fossil to biobased fuels and products is being hailed as critical for meeting a growing population’s food and fuel needs in environmentally sustainable ways. While a transformation towards a circular bioeconomy is an appealing strategy to achieve multiple environmental goals, we argue that this strategy needs to go beyond a techno-centric focus and adopt an economic value-based lens to balance the desire for circularity with its costs, benefits, and distributional effects on society. This perspective analyzes the mechanisms that sustain the existing linear economy and proposes a novel social cost-benefit framework to determine the optimal level and path to circularity. We present five critical pathways to achieve a sustainable circular bioeconomy in a market economy consisting of decentralized decision-makers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Tensor decompositions for count data that leverage stochastic and deterministic optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the maximum likelihood estimator (MLE) of the Poisson CPD model. Here, this work presents two new algorithms that extend state-of-the-art local methods for Poisson CPD. Hybrid GCP-CPAPR combines Generalized Canonical Decomposition (GCP) with stochastic optimization and CP Alternating Poisson Regression (CPAPR), a deterministic algorithm, to increase the probability of converging to the MLE over either method used alone. Restarted CPAPR with SVDrop uses a heuristic based on the singular values of the CPD model unfoldings to identify convergence toward optimizers that are not the MLE and restarts within the feasible domain of the optimization problem, thus reducing overall computational cost when using a multi-start strategy. We provide empirical evidence that indicates our approaches outperform existing methods with respect to converging to the Poisson CPD MLE.

CPAPR↗
DOE OSTI2025

Error field measurements and correction on MUSE permanent magnet stellarator

The magnetic field topology and error fields are measured in the MUSE permanent magnet stellarator with an electron beam and fluorescent rod. The effective magnetization of the magnets is determined by measuring rational flux surface locations. Electron magnetic drift effects must be considered self-consistently due to the small magnetic shear on MUSE. Error fields are characterized by flux surface shape deviation and magnetic island chain width and phase. Error fields are then corrected by adjusting the permanent magnet (PM) holders as rigid bodies. Resonant error fields are on the order of or smaller than 3 x 10 -6 of the toroidal field after correction and surface shape matches well with the design. Resonant components of the quasi-symmetry deviation are estimated, using a novel magnetic drift measurement, to be in the 10 −4 range. The PM approach is validated as a low-cost and precise way to build optimized stellarators.

electron beam mapping↗
DOE OSTI2020

MCP-PMT development at Argonne for particle identification

The proposed high-energy high-luminosity polarized Electron-Ion Collider (EIC) demands excellent particle identification over a wide range of particles momenta to achieve the best separation between signal and background particles. Various Cherenkov imaging detector sub-systems are being considered in EIC detector concepts for hadron identification for particles momenta up to 50 GeV/c. One important challenge in this case is to provide a reliable highly pixelated photosensor working in the high radiation and high magnetic field environment. The recently commercialized Large Area Picosecond Photo-Detector (LAPPD) provides a promising low-cost photosensor solution for the various EIC Cherenkov imaging sub-systems. Optimization of the phtosensor design for high magnetic field tolerance, precision timing resolution, and pixelated readout was performed at Argonne National Laboratory with 6 x 6 cm(2) microchannel plate photomultipliers. The techniques and optimized design parameters would be transferrable to industrial partners for low-cost LAPPD production to achieve the demonstrated performance.

atomic layer deposition↗
DOE OSTI2026

Beam-dump ceiling and its experimental implications: The case of a portable experiment

We generalize the nature of the so-called beam-dump “ceiling” beyond which the improvement on the sensitivity reach in the search for fast-decaying mediators dramatically slows down, and we point out its experimental implications that motivate tabletop-sized beam-dump experiments for the search. Light (bosonic) mediators are well-motivated new-physics particles, as they can appear in dark-sector portal scenarios and models to explain various laboratory-based anomalies. Due to their low mass and feebly interacting nature, beam-dump-type experiments, utilizing high-intensity particle beams, can play a crucial role in probing the parameter space of such visibly decaying mediators—in particular, the “prompt decay” region, where the mediators feature relatively large coupling and mass. We present a general and semianalytic proof that the ceiling effectively arises in the prompt-decay region of an experiment and show its insensitivity to data statistics, background estimates, and systematic uncertainties, considering a concrete example, the search for axion-like particles interacting with ordinary photons at three benchmark beam facilities: PIP-II at FNAL, and SPS and LHC-dump at CERN. We then identify optimal criteria to perform a cost-effective and short-term experiment to reach the ceiling, demonstrating that very short-baseline compact experiments enable access to the parameter space unreachable thus far.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Inference offers a metric to constrain dynamical models of neutrino flavor transformation

The multimessenger astrophysics of compact objects presents a vast range of environments where neutrino flavor transformation may occur and may be important for nucleosynthesis, dynamics, and a detected neutrino signal. Development of efficient techniques for surveying flavor evolution solution spaces in these diverse environments, which augment and complement existing sophisticated computational tools, could leverage progress in this field. To this end we continue our exploration of statistical data assimilation (SDA) to identify solutions to a small-scale model of neutrino flavor transformation. SDA is a machine learning formula wherein a dynamical model is assumed to generate any measured quantities. Specifically, we use an optimization formulation of SDA wherein a cost function is extremized via the variational method. Regions of state space in which the extremization identifies the global minimum of the cost function will correspond to parameter regimes in which a model solution can exist. Our example study seeks to infer the flavor transformation histories of two monoenergetic neutrino beams coherently interacting with each other and with a matter background. We require that the solution be consistent with measured neutrino flavor fluxes at the point of detection, and with constraints placed upon the flavor content at various locations along their trajectories, such as the point of emission, and the locations of the Mikheyev-Smirnov-Wolfenstein resonances. We show how the procedure efficiently identifies solution regimes and rules out regimes where solutions are infeasible. Overall, results in this work intimate the promise of this “variational annealing” methodology to efficiently probe an array of fundamental questions that traditional numerical simulation codes render difficult to access.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Reduced Switching Frequency Finite Control Set Model Predictive Control (FCS-MPC) for DFIG

An enhanced Finite Control Set Model Predictive Control (FCS-MPC) strategy with a two-step prediction horizon for a Doubly-Fed Induction Generator (DFIG) is the focus of this paper. The DFIG and RL filter's discrete-time model is used in the proposed control scheme for a two-step prediction horizon of rotor and filter currents for the converter's eight possible switching states. Afterward, the control algorithm selects the ideal switching state, which minimizes currents' objective function. The proposed control scheme does not require a modulation stage for internal controllers. We use Lagrange extrapolations to predict the dynamic changes in currents. The switching frequency reduction is achieved by integrating an optimization constraint in the algorithm's cost function. Furthermore, the Total Harmonic Distortion level of currents is kept below 5%, according to IEEE Std 519-14. Obtained results reveal that the switching frequency of the DFIG converters is considerably reduced without losing control; it is reduced by 13.89KHz for the Grid Side Converter (GSC) and by 1.78KHz for the Rotor Side Converter (RSC).

doubly-fed induction generator↗
DOE OSTI2023

Toward Consistent High-Fidelity Quantum Learning on Unstable Devices via Efficient In-Situ Calibration

In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing. What's worse, recent works reveal that the noise on quantum devices is not stable, that is, the noise is dynamically changing over time. This leads to an imminent challenging problem: At run-time, is there a way to efficiently achieve a consistent high-fidelity quantum system on unstable devices? To study this problem, we take quantum learning (a.k.a., variational quantum algorithm) as a vehicle, which has a wide range of applications, such as combinatorial optimization and machine learning. A straightforward approach is to optimize a variational quantum circuit (VQC) with a parameter-shift approach on the target quantum device before using it; however, the optimization has an extremely high time cost, which is not practical at run-time. To address the pressing issue, in this paper, we proposed a novel quantum pulse-based noise adaptation framework, namely QuPAD. In the proposed framework, first, we identify that the CNOT gate is the fidelity bottleneck of the conventional VQC, and we employ a more robust parameterized multi-qubit gate (i.e., Rzx gate) to replace CNOT gate. Second, by benchmarking Rzx gate with different parameters, we build a fitting function for each coupling qubit pair, such that the deviation between the theoretic output of Rzx gate and its on-device output under a given pulse amplitude and duration can be efficiently predicted. On top of this, an evolutionary algorithm is devised to identify the pulse amplitude and duration of each Rzx gate (i.e., calibration) and find the quantum circuits with high fidelity. Experiments show that the runtime on quantum devices of QuPAD with 8–10 qubits is less than 15 minutes, which is up to 270 x faster than the parameter-shift approach. In addition, compared to the vanilla VQC as a baseline, QuPAD can achieve 59.33% accuracy gain on a classification task, and average 66.34% closer to ground state energy for molecular simulation.

Hu, Zhirui↗
DOE OSTI2020

RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

Today’s high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic priority functions to prioritize and schedule jobs. But, once configured and deployed by the experts, such priority functions can hardly adapt to the changes of job loads, optimization goals, or system settings, potentially leading to degraded system efficiency when changes occur. To address this fundamental issue, we present RLScheduler, an automated HPC batch job scheduler built on reinforcement learning. RLScheduler relies on minimal manual interventions or expert knowledge, but can learn high-quality scheduling policies via its own continuous ‘trial and error’. We introduce a new kernel-based neural network structure and trajectory filtering mechanism in RLScheduler to improve and stabilize the learning process. Through extensive evaluations, we confirm that RLScheduler can learn high-quality scheduling policies towards various workloads and various optimization goals with relatively low computation cost. Moreover, we show that the learned models perform stably even when applied to unseen workloads, making them practical for production use.

Zhang, Di↗