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At least 343 records · Page 19

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DOE OSTI2025

Fundamental Path Optimization Strategies for Extrusion-based Additive Manufacturing

Extrusion-based additive manufacturing processes begin with a software program, called a slicer, that generates layer geometry and fits toolpaths to each layer to define where material is to be extruded or deposited. Before the toolpaths are output as g-code for the additive manufacturing system to execute, the toolpaths should be optimized. Many complex optimization approaches using graph theory, Chinese postman problem, and other complex mathematical models exist, but these approaches are rarely used in daily printing operations and are not available through common slicing programs such as Cura and PrusaSlicer. Instead, path planning and optimization typically revolves around simpler, fully automated approaches such as inside out and next closest. This paper will explore the fundamental optimization strategies for toolpath planning and document a new implementation, available via open-source slicing software, that allows for greater control of the path planning process.

Roschli, Alex [ORNL] (ORCID:0000000213084632)↗

DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Tunable assembly of hybrid colloids induced by regioselective depletion

Assembling colloidal particles using site-selective directional interactions into predetermined colloidal superlattices with desired properties is broadly sought-after, but challenging to achieve. Here in this paper, we exploit regioselective depletion interactions to engineer the directional bonding and assembly of non-spherical colloidal hybrid microparticles. We report that the crystallization of a binary colloidal mixture can be regulated by tuning the depletion conditions. Subsequently, we fabricate triblock biphasic colloids with controlled aspect ratios to achieve regioselective bonding. Without any surface treatment, these biphasic colloids assemble into various colloidal superstructures and superlattices featuring optimized pole-to-pole or center-to-center interactions. Additionally, we observe polymorphic crystallization, quantify the abundancy of each form using algorithms we developed, and investigate the crystallization process in real time. We demonstrate selective control of attractive interactions between specific regions on an anisotropic colloid with no need of site-specific surface functionalization, leading to a general method for achieving colloidal structures with yet unforeseen arrangements and properties.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Chelation-Driven Chemistry Controls Dissolution Pathways for Facile Critical Mineral Recovery from Ultramafic Resources

The efficient recovery of critical minerals, such as nickel (Ni) and manganese (Mn) from the subsurface is of vital importance, given their role in modern technologies ranging from batteries to advanced alloys. This study explores an approach that employs chelation to enhance critical mineral recovery from ultramafic rocks using chelating ligands under ambient pressures and systematically evaluates the extraction process for the first time. Experimental results demonstrate the superior performance of EDTA and PrDTA in achieving high extraction efficiencies for both Ni and Mn. To further highlight the ability for optimization, the fluid exchange and variation of fluid-to-rock ratio experiments were conducted, revealing tunable controls for engineering recovery. We show that under optimized conditions, a Ni extraction efficiency of ?82% and a Mn extraction efficiency of ~60% is achieved at ambient pressures, which is equivalent to over 23 times the current global Ni production, and 2 million metric tonnes (MMT) greater than the current global Mn production. From both in-situ and ex-situ mining perspectives, the results highlight the potential of chelation to advance sustainable mineral processing. Overall, this work highlights the environmental benefits of innovative ligand-assisted recovery methods, addressing the critical need for the efficient utilization of olivine resources.

Krishnan, Keerthana↗

DOE OSTI2024

Reduced-dimension Bayesian optimization for model calibration of transient vapor compression cycles

Development and calibration of first-principles dynamic models of vapor compression cycles (VCCs) is of critical importance for applications that include control design and fault detection and diagnostics. Nevertheless, the inherent complexity of models that are represented by large systems of differential–algebraic equations leads to significant challenges for model calibration processes that utilize classical gradient-based methods. Bayesian optimization (BO) is a sample-efficient and gradient-free approach using a probabilistic surrogate model and optimal search over a feasible parameter space. Despite the benefits of BO in reducing computational costs, challenges remain in dealing with a high-dimensional calibration task resulting from a large set of parameters that have significant impacts on system behavior and need to be calibrated simultaneously. This paper presents a reduced-dimension BO framework for calibrating transient VCCs models where the calibration space is projected to a low-dimensional subspace for accelerating convergence of the solution algorithm and consequently reducing the number of transient simulations. The proposed approach was demonstrated via two case studies associated with different VCC applications where 10 parameters were calibrated in each case using laboratory measurements. The reduced-dimension BO framework only required 1 / 8 th of the iterations associated with a standard BO method that deals with high-dimensional calibration parameters for converged solutions and yielded comparable accuracy. Furthermore, both calibrated models revealed significant accuracy improvements compared to uncalibrated models.

Ma, Jiacheng↗

DOE OSTI2024

Predictive Control to Further Reduce DC-Link Capacitor Current Stress for Segmented Inverter

Two three-phase interleaved inverters have been used in traction drive applications to reduce the current stress in a DC link capacitor bank. In such applications, either carrier-based or space vector modulation is used to select the optimum switching sequences, and the results show 50% less capacitor current than that of a single three-phase inverter. The switching state selection process for this inverter is tedious, and there has been no research to find the optimal switching state. To overcome this challenge, this research employed a simple finite set model predictive control to select the optimum switching sequence for a dual three-phase interleaved topology, called a segmented inverter. The results show that the predictive control algorithm can provide a simple solution and can reduce the current stress by 27% compared with traditional modulation techniques for the segmented inverters.

Ribeiro, Pedro↗

DOE OSTI2022

Arbitrage and Capacity Firming in Coordination with Day-Ahead Bidding of a Hybrid PV Plant

A hybrid PV plant (HPP) combines a photovoltaic (PV) plant with a battery energy storage system (BESS), which is considered a promising step towards the future of renewable power plants by the U.S. Department of Energy. When the renewable penetration reaches a significant level, a hybrid PV plant can bid in as a controllable thermal plant in the future electricity market. In this study, a bidding and BESS scheduling model is proposed for the HPP. The robust optimization (RO) technique has been utilized to identify the worst-case scenario of uncertainties during the bidding process. To address the overly conservative issue of the single-stage RO, we have decoupled the BESS schedule for arbitrage and PV capacity firming by a two-stage RO formulation. By comparing the output of single-stage RO and two-stage RO, the two-stage RO bids and schedules in a more aggressive manner, which increases the income of HPP. Also, the penalty of under-generation is considered in our model so that the day-ahead bidding decision and arbitrage schedules can be adjusted based on the potential UNDER-GENERATION penalty. Because the proposed model is non-convex and contains multi-stages, the Column-and-Constraint Generation (C&CG) algorithm is applied to the model as the solution. The proposed model has shown better economic performance compared to a state-of-art single-stage bidding method in case studies.

BESS scheduling↗

DOE OSTI2023

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e

We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab). Our primary objective is to regulate the spill process to ensure a consistent intensity profile, with the ultimate goal of creating an automated controller capable of providing real-time feedback and calibration of the Spill Regulation System (SRS) parameters on a millisecond timescale. We treat the Mu2e accelerator system as a Markov Decision Process suitable for Reinforcement Learning (RL), utilizing PPO to reduce bias and enhance training stability. A key innovation in our approach is the integration of a neuralized Proportional-Integral-Derivative (PID) controller into the policy function, resulting in a significant improvement in the Spill Duty Factor (SDF) by 13.6%, surpassing the performance of the current PID controller baseline by an additional 1.6%. This paper presents the preliminary offline results based on a differentiable simulator of the Mu2e accelerator. It paves the groundwork for real-time implementations and applications, representing a crucial step towards automated proton beam intensity control for the Mu2e experiment.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

The role of gas flow distributions on CO 2 mineralization within monolithic cemented composites: coupled CFD-factorial design approach

The carbonation kinetics of monolithic cementing composites are strongly affected by gas transport which is, in turn, influenced by microstructural resistances and the presence of liquid water within pore networks. The non-uniform gas flow distribution within the CO 2 mineralization reactor can impart mass transfer resistance in the monolith microstructure, which affects the uptake of CO 2 (“carbonation”) of the cementing composites. This paper demonstrates how the gas spatial distribution (velocity and flow rate; quantified by CFD analysis) and processing conditions (temperature, relative humidity, and flow rate; quantified by factorial design) affect drying and carbonation, and in turn, the engineering properties of a representative ‘monolithic’ carbonate-cemented concrete component (i.e., herein concrete masonry unit: CMUs, also known as concrete block). It is shown that the gas flow distribution affects drying front penetration and results in moisture and carbonation gradients within the monolith. Particularly, variations in drying kinetics caused by non-uniformity of the contacting gas velocity impose gradients in moisture saturation, which results in increasing microstructural resistance to CO 2 transport. The resultant non-uniform carbonate-mineral formation (i.e., carbonate cementation), if not controlled, can produce gradients in mechanical properties and may alter failure patterns upon loading. Finally, these insights inform the optimal design of gas flow distribution systems and processing conditions within CO 2 mineralization reactors for the manufacturing of low-CO 2 concrete components using CO 2 -dilute industrial flue gas streams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Autonomous Nanoparticle Synthesis Guided by In Situ Multiscale Structural Characterization

Autonomous synthesis platforms promise rapid exploration of vast parameter spaces; yet, integrating in situ structural characterization in closed-loop synthesis optimization remains challenging. We demonstrate a realization of such a closed-loop platform coupled with a droplet-flow microreactor, in situ X-ray scattering methods (SAXS/WAXS), and Gaussian process optimization to synthesize citrate-reduced Au nanoparticles with targeted characteristics. The system efficiently explored ∼19,000 synthesis recipes through 365 experiments, achieving precise control over size (4–60 nm) and polydispersity (σ < 0.11) across large citrate/gold ratios, exceeding traditional synthesis boundaries (1–10). Beyond confirming classical Turkevich–Frens trends, partial-dependence analysis revealed strong nonlinear coupling among precursor, citrate, and pH effects. Combining quantitative SAXS/WAXS analysis with electron microscopy characterization, we uncovered that crystallite size (d c ) and particle size (d) follow d c = 0.18d + β, where synthesis chemistry controls the intercept β while maintaining a universal slope. This parallel-band structure enables independent tuning of crystallite domain size at fixed particle diameter through a combination of chloride, gold precursor, citrate, and pH contributions (cross-validated Spearman ρ = 0.7 ± 0.1). High-resolution electron microscopy shows multiple lattice-fringe orientations within single particles, directly confirming polycrystalline domains and the ability to tune d c at the fixed d. The platform’s validation includes indistinguishable static versus flowing measurements, stable droplet transport at 100 °C, and <5% run-to-run variation, establishing a robust framework for mapping and controlling multiscale nanoparticle structure across expansive chemical spaces. In conclusion, the developed closed-loop platform can be applied to a borad range of nanosyntheis processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗

DOE OSTI2020

Optogenetics and biosensors set the stage for metabolic cybergenetics

We report cybergenetic systems use computer interfaces to enable feed-back controls over biological processes in real time. The complex and dynamic nature of cellular metabolism makes cybergenetics attractive for controlling engineered metabolic pathways in microbial fermentations. Cybergenetics would not only create new avenues of research into cellular metabolism, it would also enable unprecedented strategies for pathway optimization and bioreactor operation and automation. Implementation of metabolic cybergenetics, however, will require new capabilities from actuators, biosensors, and control algorithms. The recent application of optogenetics in metabolic engineering, the expanding role of genetically encoded biosensors in strain development, and continued progress in control algorithms for biological processes suggest that this technology will become available in the not so distant future.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2023

DOE Bioenergy Technologies Office (BETO) 2023 Project Peer Review: WBS 2.4.1.100 Bench Scale Research & Development

Bench Scale Integration develops and optimizes fermentation processes to produce bio-based fuels and chemicals for commercial scale-up. The project uses fermentation science to achieve high titers and production rates by, for example, manipulating how the microorganisms are fed biomass sugars and nutrients, modifying fermentation conditions (pH, temperature, aeration) or developing online control strategies for better fermentation operations and high titer, rates, and yield (TRY). For this period of performance, we continued our development of a commercial-ready 2,3-butanediol (BDO) fermentation from biomass sugars utilizing NREL's proprietary Zymomonas mobilis microorganism. The engineered Z. mobilis can use all the main sugars in corn stover biomass, which are glucose, xylose, and arabinose. BDO is a versatile, low-carbon chemical which can be catalytically upgraded to a variety of hydrocarbon fuels and chemicals. The project had three goals during this review period; evaluate the technical feasibility of using whole slurry pretreated corn stover to achieve the techno-economic analysis (TEA) performance goals, continue optimizing a liquor-based fed-batch fermentation process for high titer, and develop strategies to enable scale-up. After evaluating different iterations of a whole slurry fermentation that did not meet the TEA goals, a Go/No-Go decision was made to pivot to liquor-only with new TEA performance targets, the main one being 140 g/L titer. We successfully met this goal, producing 141 g/L BDO at 1 g/L-hr productivity and 84% process yield. This titer and productivity attracted industrial interest to scale the fermentation resulting in a Technology Commercialization Fund project award in 2022. We used an NREL developed near-infra red (NIR) spectroscopy method for rapid analysis which allowed for changes to aeration levels and sugar feeding during the fermentation to maximize BDO production. The NIR analysis can be done using a hand-held spectrometer, essentially taking the analysis on to the plant floor during scale-up and preliminary work shows the feasibility of using an online probe for continuous monitoring and control. The other scale-up tool is mapping oxygen transfer coefficient (kLa) and oxygen transfer rate (OTR) in various vessels to find conditions that match the optimized 500 mL vessels. Showing a correlation to the mapping work, which is done with water and a dissolved oxygen probe, can reduce the risk of failed fermentations during scale-up. Our end-of-project goal is to meet the design target BDO titer (140-150 g/L) from DMR corn stover liquor at 1000L or larger scale to demonstrate BDO process design case and transfer the technology to industrial fermentation stakeholders for commercialization.

2 3-butanediol↗

DOE OSTI2021

Self-Stabilized LiNi 0.8 Mn 0.1 Co 0.1 O 2 in thiophosphate-based all-solid-state batteries through extra LiOH

Nickle-rich LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC 811) cathode material exhibits engaging properties in high energy density and low cost, making it great potential for the next generation high-energy all-solid-state lithium batteries (ASSLBs). However, NMC 811 suffers from severe surface electrochemical, chemical, and voltage incompatibility towards solid-state electrolytes (SSE), especially thiophosphate-based electrolytes like Li 6 PS 5 Cl. Although diverse coating methods have been made to overcome this issue, they are typically cumbersome and expensive. A coating strategy that satisfied all the requirements of cost-efficiency, stability, uniformity, scalability, and easy-achieving is still challenging. Here, we developed a LiOH-based surface stabilization strategy that provides a ~10 nm stable permeable layer on NMC 811. After one-step sintering of NMC 811 precursor mixed with LiOH, which is commonly used for NMC 811 lithiation process, excessive LiOH simultaneously distributes on NMC 811 particles. Unlike other reported methods, this coating method can be easily controlled and fabricated without additional complicated processes. By simply controlling the thickness of LiOH layer, which protects the Li 6 PS 5 Cl solid electrolyte materials from being oxidized, optimized cycling stability can be obtained for 600 cycles with capacity of 130 mAh g – 1 on average at a wide electrochemical window of 2.50–4.20 V (vs. Li-In).

25 ENERGY STORAGE↗

DOE OSTI2025

ACHILLES-GENIE Interface for Neutrino Simulations, ADRIANO2 Tile Prototype for High-Granularity Dual-Readout Calorimetry

ICARUS (Imaging Cosmic And Rare Underground Signals) is a liquid argon time projection chamber (LArTPC) detector that pursues the sterile neutrino, which relies on accurate simulations of neutrino-argon interactions. REDTOP (Rare Eta Decays To Observe new Physics) is a proposed low-energy, high-intensity meson factory designed to explore rare $\eta$/$\eta'$ meson decays and probe physics beyond the Standard Model. As a next-generation experiment, this requires both accurate simulations and innovative detector technologies. This project contributes to both ICARUS, from a simulation perspective, and REDTOP, from both a simulation and detection perspective, through the event generation of lepton-nucleon interactions and the physical enhancement of the calorimeter technology within the REDTOP detector. We developed an interface between ACHILLES (A CHIcago Land Lepton Event Simulator), a theory-driven lepton-level event generator, and GENIE, a robust event generator framework used for neutrino physics. By incorporating the precise theoretical cross-section calculations of ACHILLES into the experimental realism of GENIE, the interface allows for improved accuracy of neutrino-nucleon simulations, which can be adapted for the proton beam specifications of the REDTOP meson factory as well as for the ICARUS experiment. In parallel, we developed an improved prototype for the ADRIANO2 (A Dual Readout Integrally Active Non-segmented Option) dual-readout calorimeter tiles for the REDTOP detector. To improve the efficiency of the lead-glass tiles trapping Cherenkov light for energy reconstruction and particle identification, we optimized the application of a highly reflective coating. Through viscosity and thickness control, masking, and a custom spray technique, we refined the coating process to reduce surface defects and improve light yield. Together, these efforts strengthen the ICARUS neutrino program and REDTOP's capability of detecting rare decay events.

Visser, Erin [Michigan State U.]↗

DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗

DOE OSTI2025

Enhancing ICARUS and REDTOP Software and Hardware: Event Generator Interface Development and Calorimeter Tile Prototype

Visser, Erin [Michigan State U.] (ORCID:0009000184↗