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DOE OSTI2026

Reference floating wind array designs for three representative regions

This work presents the systematic development of three open-source reference floating wind array designs. The designs are tailored to representative site conditions for three regions of the United States: Humboldt Bay off the coast of California, the Gulf of Maine, and the Gulf of America. We adopted existing reference designs for the individual 15 MW turbines, semisubmersible floating platforms, substations, mooring systems, and power cables – integrating and adapting them as needed for each location. We adapted existing dynamic cable designs to use larger conductor sizes to meet the arrays' power transmission requirements, and we set up redundant mooring systems for each substation. The layout of each array is a uniform-grid design optimized to approximately minimize the levelized cost of energy (LCOE) within a square lease area while satisfying spatial constraints. These constraints ensure adequate clearances between adjacent turbines and between underwater components during the layout optimization to prevent clashing and ensure that all components reside within the lease boundaries. Substations are included to allow accounting for intra-array cable costs. They are placed within the uniform grid to maintain the navigability of the arrays. For each feasible layout considered, annual energy production and cable routing costs are calculated and updated in the LCOE objective function. After the optimization, we refined the cable routing with a mix of algorithmic and manual methods to ensure that the cables avoid mooring system components and approach the substation with adequate clearances. We confirmed the suitability of each reference array's layout by comparing the wake losses at each wind heading angle to the wind rose, observing that the optimized layouts largely avoid wake losses in the predominant wind directions. These reference arrays provide open-source baseline designs to enable future research and innovation of floating wind technology at the array scale.

17 WIND ENERGY↗

DOE OSTI2020

Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions

We consider the problem of optimal feedback control of an unknown, noisy, time-varying, dynamic system that is initialized repeatedly. Examples include a robotic manipulator which must perform the same motion, such as assisting a human, repeatedly and accelerating cavities in particle accelerators which are turned on for a fraction of a second with given initial conditions and vary slowly due to temperature fluctuations. In this paper, we present an approach that applies to systems of practical interest. The method presented here is model independent; does not require knowledge of the objective function; is robust to measurement noise; is applicable for any set of initial conditions; is applicable to simultaneously controlling an arbitrary number of parameters; and may be implemented with a broad range of continuous or discontinuous functions such as sine or square waves. For systems with convex cost functions we prove that our algorithm will produce controllers that approach the minimal cost. For linear systems we reproduce the cost minimizing linear quadratic regulator optimal controller that could have been designed analytically had the system and cost function been known. We demonstrate the effectiveness of the algorithm with simulation studies of noisy and time-varying systems.

42 ENGINEERING↗

DOE OSTI2024

Optimizing Carbon Capture, Transport, and Storage: Overcoming Challenges with Machine Learning and Cost-Benefit Analysis

Carbon Capture, Utilization, and Storage (CCUS) is a critical strategy for reducing CO₂ emissions and mitigating climate change. However, its widespread deployment faces numerous challenges across the capture, transport, and storage phases. These challenges include the technical complexity of predicting subsurface behaviors during CO₂ injection, ensuring long-term storage integrity, optimizing transportation networks, and balancing the economic and environmental trade-offs. Addressing these issues requires an integrated approach combining advanced subsurface modeling with system-level analyses to assess costs, risks, and benefits. This presentation provides an overview of studies conducted by the National Energy Technology Laboratory (NETL) to tackle these challenges. NETL’s efforts encompass cutting-edge research in subsurface fluid behavior machine learning predictions, alongside the development of innovative tools for system optimization and economic evaluation. By bridging technical expertise and strategic analysis, NETL aims to advance the deployment of CCUS technologies to support global decarbonization efforts. Presented at the Carnegie Mellon University CEE IESS Student Seminar October 4, 2024.

Shih, Chung Yan↗

DOE OSTI2022

Self Configuring Digital Twin for Optimizing E-Waste Recycling

Electronic waste recycling industry needs a decision support tool for optimizing their processes to become cost competitive. We developed a software called CMAT, Comprehensive Manufacturing Assessment Tool. The aim of CMAT is to provide the e-waste recycling companies with a fully customizable decision support framework that analyzes the optimal supply chain configurations. The software optimizes the logistics operations, helps to identify the best recycling process configuration, and generates valuable insights regarding the economic performance of different categories of e-waste. The ultimate purpose of the tool is to assist the users developing a digital twin of their processes and to provide insights on questions pertinent to the e-waste recycling industry including how to increase efficiency and reduce costs, energy consumption, and greenhouse gas emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Optimal Design and Operation of a Solvent-Sorbent Hybrid Capture Process for Minimizing the Cost of High Capture

High capture can cause a significant increase in energy penalty if the appropriate technology/technologies are not utilized. The optimal technology for bulk capture is not necessarily the optimal technology for polishing capture (i.e., high extent of capture from the flue gas with very low partial pressure of CO2). This work investigates the use of MEA for bulk capture with the polishing capture being accomplished by a functionalized metal organic framework (MOF).

Kasturi, Pooja↗

DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

14 SOLAR ENERGY↗

DOE OSTI2025

Open-source microreactor design models for technoeconomic assessments

Technoeconomic analyses for advanced nuclear technologies are essential for identifying cost drivers which allow for optimizing the technology to achieve better economic performance. Microreactors are emerging as a promising and reliable-energy solution, offering inherent safety, low capital investment, and rapid deployment capabilities. Past studies have conducted technoeconomic analyses for microreactors, but there remains a need for open-source technoeconomic models that can enhance collaboration, transparency, and consistency when performing this type of analysis. The present article introduces an open-source technoeconomic model for microreactors based on bottom-up cost estimation. Since no real cost data for microreactors exists, this model leverages cost data and insights gleaned from the Microreactor Applications, Research, Validation and Evaluation (MARVEL) project. It estimates the first-of-a-kind cost of two microreactor technologies and can also calculate the N th of a kind (NOAK) cost via accounting for learning and mass factory production. The two technologies considered in this paper are the liquid–metal thermal reactor (LTMR) and the gas-cooled microreactor (GCMR). In conclusion, the goal of this study is to demonstrate bottom-up cost estimation of these microreactor technologies and provide the cost estimation models that other users can leverage for various applications such as design optimization and financial planning.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Integrated Heat Pump Thermal Storage and Power Cycle for CSP (Final Technical Report)

Pumped thermal energy storage (PTES) is a storage system that stores electricity in thermal reservoirs. In this project, methods of integrating PTES with concentrating solar power (CSP) systems were investigated and their feasibility evaluated. Hybrid "solar-PTES" devices can provide both flexible renewable power generation as well as a variety of electricity storage services. Techno-economic models of PTES and solar-PTES were developed and used to assess their performance, cost, and commercial viability. Systems were optimized using multi-objective optimization techniques. The value that these storage devices provide to the grid was estimated using production cost modelling. Key advances made in this project include the development of detailed techno-economic models that improve state-of-the-art assessments of these technologies. Off-design models were developed and used in grid-analysis models. This work describes for the first time how PTES and solar-PTES behave over a range of operating points and assesses the value that these systems can provide to the electrical grid. Feedback was obtained from experts in the field, which clarified key technological and economic barriers that must be addressed to improve the feasibility of these devices.

14 SOLAR ENERGY↗

DOE OSTI2026

Performance assessment of 3D printed multi-material energy absorber for automotive bumper: pedestrian lower extremity protection

Designing an energy absorber for automotive bumpers involves balancing low-speed and high-speed impacts to ensure safety, reduce repair costs, and meet regulatory standards. Here, this study explores a novel design using multi-material 3D printing and structural optimization to fabricate a lightweight and cost-efficient energy absorber. The design effectively dissipates energy in low-speed collisions and minimizes force transmission in high-speed pedestrain impacts, helping to meet both safety and performance requirements. The energy absorber design combines 20% carbon fiber-reinforced acrylonitrile butadiene styrene (CF-ABS) and thermoplastic polyurethane (TPU) for optimal stiffness and flexibility. It uses 3D-printed lattice structures optimized through finite element simulations to help meet both low-speed and high-speed impact requirements. Full-scale energy absorbers were 3D-printed using optimized CF-ABS/TPU blends and tested under high-speed impact using the Flexible Pedestrian Legform Impactor (Flex-PLI). For fair comparison, a baseline bumper with a traditional triangular lattice structure, also 3D-printed from the same CF-ABS/TPU materials, was similarly tested. Interestingly, both the optimized and baseline 3D-printed energy absorbers showed nearly identical performance, successfully meeting injury limits. Their performances were also benchmarked against an injection-molded energy absorber. While both 3D-printed and injection-molded designs met injury limits, the 3D-printed absorber exhibited a higher tibia bending moment, indicating an opportunity for further optimization. A Techno-Economic Analysis compared the costs of producing energy absorbers using traditional manufacturing and 3D printing. The analysis highlighted that 3D printing offers cost benefits for low to medium production volumes, with the total cost per energy absorber at ∼ $\$$74, compared to traditional methods that become economical beyond 2000 units.

Additive manufacturing↗

DOE OSTI2021

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Low-Cost Heliostat for High-Flux Small-Area Receivers (Final Technical Report)

This project analyzed a two-stage heliostat concept consisting of a tracking stage and a concentrating stage. The tracking stage uses mirrors mounted on a common drive that move to track the sun. The concentrating stage consists of stationary mirrors that each have a unique angle to direct rays towards a small-area, high-flux, point-focused receiver. By splitting the collection and concentrating process into two stages, multiple small, inexpensive mirrors can share a structure and be controlled by a single drive in the tracking stage. The project effort developed modeling techniques that were specifically relevant to this two-stage heliostat concept. Both field-level and unit-level models were developed. The field-level model does not explicitly consider unit-level losses which are predicted by the unit-level model and then integrated into the field-level model through a correlation referred to as an efficiency modifier. This approach is referred to as the two-model approach; the development and demonstration of this two-model approach for a multi-stage heliostat technology is a key outcome of this work. The field-level model is used to design a field that hits a specific design day power given a set of heliostat design parameters. An oversized field is simulated and then heliostat units are removed based on their annual energy production in order to generate the highest performing field. The field reduction procedure fits a smooth curve fit to annual energy production as a function of position in the field which has the effect of reducing the noise that is otherwise caused by the Monte Carlo ray tracing technique. This approach is referred to as the annual energy fit method and substantially reduces computational run time for a given field level modeling accuracy. The annual energy fit approach enables the selection of a properly sized, high-performing field using orders of magnitude fewer rays than would otherwise be possible and the development of this approach is a second key outcome of this work. These models are used within a genetic optimization algorithm in order to optimize the geometric parameters associated with a heliostat in order to achieve the lowest cost per unit of collected design day power. The cost modeling that underlies the optimization is a simple, scaling type analysis backed up by a much more detailed Design for Manufacture and Assembly (DFMA) analysis. Although the figure of merit used for optimization was not cost per mirror area, this metric is reasonable to use as a means of comparison. The optimally designed 500 kW design has a tracking mirror specific cost of $181.85/m 2 , which is significantly larger than the target value and also larger than the current state of the art. The cost of the torque-tube type linkages contributed substantially to the overall cost. Based on this observation, potentially attractive alternative design configuration utilizing a capstan type actuation system should be investigated. Finally, NREL compared the performance of the two-stage heliostat to the performance of a focused and different sized flat conventional heliostats and showed that, as expected, additional losses versus the convention heliostat caused by a worse cosine efficiency, two stages of reflection, and interstage interactions. The two-stage heliostat requires around 75% more reflective area than a flat 1x1 meter conventional heliostat (similar to a focused heliostat) and 40% more than a flat 2x2 meter conventional heliostat.

14 SOLAR ENERGY↗

DOE OSTI2021

A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. Finally, we review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimal operation of solid-oxide electrolysis cells considering long-term chemical degradation

Optimizing the performance of solid oxide electrolysis cells (SOECs) for long-term hydrogen (H 2 ) production at high temperatures is crucial, as prolonged operation leads to efficiency losses and shorter cell lifespans due to chemical degradation. Here, in this work, we adopt a quasi-steady state approach for dynamic optimization over extended operational periods to address the disparity in timescales between cell operation and degradation. Integrating a 2-D non-isothermal SOEC model with balance-of-plant (BOP) equipment, we explore three optimization objectives: minimizing terminal degradation, maximizing integral efficiency, and minimizing the levelized cost of H 2 (LCOH). Our dynamic optimization algorithm reduces LCOH by 9.5% and 16% compared to strategies focusing solely on terminal degradation and integral efficiency, respectively. For electricity prices of 0.03 $\$$/mWh and 0.3 $\$$ mWh optimal replacement schedules range from 5 to 2 years, depending on the operational mode. Furthermore, a flexible operational mode yields additional improvements in LCOH over traditional galvanostatic and potentiostatic modes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Installation Quality Framework: Investment Return Approach for Energy Savings on Building Product Installation

A case study was conducted on 44 residential homes using both traditional house wrap and ZIP System systems to measure the overall airtightness and compare estimated energy usages. The labor, material, and overhead and profit (O&P) costs were analyzed and used to determine the optimal choice for long-term benefits in terms of cost and performance. The impact of insulation installation is considered a key factor in improving the strategy of reducing energy consumption. Improved installation practices can affect the airtightness of common wall assemblies to reduce the building energy performance gaps and provide insight on how to allocate resources better. A framework was developed to analyze operational costs and building energy performance to address how installation quality is a factor in the return of investment in building construction for heating and cooling systems within the thermal envelope. With this methodology, aggressive energy performance goals will be met while balancing the tradeoff between installation techniques and building systems efficiency based on the introduced probabilistic investment return (PIR) metric.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

One-pot ethanol production under optimized pretreatment conditions using agave bagasse at high solids loading with low-cost biocompatible protic ionic liquid

Agave bagasse (AG) is a potential bioenergy feedstock due to its high biomass productivity, even in semiarid lands. In particular, ionic liquid (IL) pretreatment using aprotic ILs (AILs) has greatly reduced AG recalcitrance towards downstream processing by lowering lignin content and achieving high sugar yields. However, AIL's low biocompatibility towards enzymes and bacteria combined with the high initial cost has limited further development of this technology. In a wash-free one-pot (OP) ethanol conversion process, the evaluation of AG pretreatment with a biocompatible low-cost protic IL (PIL), 2-hydroxyethylammonium acetate ([2-HEA][OAc]) was achieved, where PIL pretreatment was followed by enzymatic saccharification, then ethanol fermentation in a single vessel. The pretreatment conditions were optimized using a central composite design to enable high sugar conversion at low PIL content. Under optimized pretreatment conditions (160 °C, 60% IL loading and 1.5 h), a yield of 132 kg of ethanol per Ton of untreated biomass was estimated using high solids loading (30% solids loading) under a PIL-OP scheme. High lignin removal (>50%), a decreased cellulose crystallinity, and high glucan conversion (>85%) were achieved with PIL-pretreated AG comparable to yields obtained in an AIL-AG pretreated sample using 1-ethyl-3-methyl-imidazolium acetate ([C 2 C 1 Im][OAc]). Here, these results using [2-HEA][OAc] demonstrate the potential of AG in an OP scheme with improved total ethanol yields paving the way towards a more feasible IL-based biorefinery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Drive Cycles, Battery Pack Scaling, and Usage Considerations for Long-Haul and Regional-Haul Electric Trucks

Electrifying Class-8 heavy-duty trucks presents a promising opportunity to enhance energy efficiency and reduce freight transport costs. Battery electric trucks (BETs), once considered niche, are gaining traction due to advancements in battery technology and cost reductions. However, accurately predicting battery lifespan under realistic usage conditions remains a key challenge. Understanding battery failure mechanisms and their links to design, operation, and management is essential for developers and fleet operators. This study introduces a method to develop simplified, lab-testable dynamic stress test (DST) cycles for regional and long-haul Class-8 BETs, derived from real-world diesel truck usage. These DSTs enable benchmarking of battery technologies, identification of aging stressors, and optimization of battery design, life, and cost. The approach supports evaluation of key metrics such as levelized cost of driving and total cost of ownership, aiding fair comparisons and adoption decisions. We also propose feasible battery pack sizes that meet current driving demands with strategic charging, and a method to scale pack-level DSTs to cell-level cycles for lab-based testing. These tools facilitate tradeoff analysis across battery chemistries, pack sizing, and charging strategies, while offering means to get insights into battery aging under realistic conditions-ultimately supporting informed BET deployment decisions.

25 ENERGY STORAGE↗