Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cost optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Na + -gated nanochannel membrane for highly selective ammonia (NH 3 ) separation in the Haber-Bosch process

Currently, cryogenic condensation is the predominant process for recovering ammonia (NH 3 ) in the Haber-Bosch (HB) process, which is highly energy intensive. To be more compatible with the reaction conditions in the HB process and thus minimize the pressure and temperature swing during reactant recycling, energy-efficient technologies for NH 3 extraction at elevated temperature and pressure are greatly needed. In this work, the Na + -gated nanochannel membrane, shown exclusively for water conduction in our previous work, also exhibited highly NH 3 -selective performance, with NH 3 /H 2 selectivity as high as 4,280 and NH 3 /N 2 selectivity > 10,000 at temperature up to 250 °C and pressure up to 35 bar. Excellent stability of the Na + -gated nanochannel membrane was demonstrated during a 100-h run in ternary NH 3 /H 2 /N 2 gas mixture at 200 °C and 35 bar, consistent with structural characterization by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transfer infrared (FTIR) spectroscopy. A techno-economic analysis (TEA) was conducted for the HB process using the Na + -gated nanochannel membrane and the traditional HB process with condenser. Finally, under the optimized separation conditions, > 80% energy savings and approximately 20% reduction of the net NH 3 production cost can be achieved, demonstrating the great potential of the Na + -gated nanochannel membrane for NH 3 separation in the HB process.

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

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗
DOE OSTI2025

A Demand Bidding Model for Multi-Product Industrial Plants

The growing contribution of renewable energy sources has increased volatility and uncertainty in electricity markets, challenging traditional grid operation paradigms. Demand bidding (DB), a market participation model where (large) electricity users communicate their willingness to pay for electricity to the grid operator, was shown in previous work to enhance grid stability and lower generation cost. We present a DB model for multi-product industrial plants, based on an extended optimal power flow problem where the plant dynamics are represented using autoregressive with extra inputs (ARX) models. We compare DB to price-based demand-side management, showing that, under certain assumptions, the two approaches are equivalent, while DB provides more transparency and predictability to the grid operator. A case study based on an industrial air separation unit is discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings

Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. We report the simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2025

Design and full core fuel performance assessment of high burnup cores for 4-loop PWRs

Increasing the fuel discharge burnup of current light water reactors (LWRs) promises reductions in fuel cycle and/or operations costs. By assuming a constant core power density, the economic gain is enabled by better fuel utilization and/or an increased capacity factor. In this effort to investigate greater than 62 MWd/kgU maximum rod average burnup for 110+ kW/l core power density, two core designs have been developed for a standard 17x17, 193 fuel assemblies pressurized water reactor (PWR). The levelized unit cost methodology is employed to evaluate fuel cycle, operation and maintenance, and capital cost impacts and to examine the economic viability of both core design pathways. Core design and optimization are performed using the commercial STUDSVIK code package. Fuel performance analysis is realized in full core configuration via auditing FRAPCON4.1, FAST1.2, and the high-fidelity code BISON. To provide a realistic assessment, the core design process takes into consideration best practices in current PWR core design. It features acceptable performance in terms of various core design constraints on maximum allowable peaking and boron concentration. Gadolinia (Gd2O3) is used as a burnable poison with a maximum of 9 wt% concentration while feeding 89 or 77 fuel assemblies in a 3-batch refueling scheme. Full core fuel performance simulation, which allows for characterization of relevant fuel temperatures, plenum pressures, stresses, and strains, is performed with respect to two bounding burnup levels. Such performance is potentially licensable for the 18-month high burnup core (<68 MWd/kgU peak pin), while it is more challenging for the 24-month high burnup core design pathway (<75 MWd/kgU peak pin). Maximum rod plenum pressure is identified as the most limiting fuel performance parameter. Here, while the scope of the present study focuses on the steady-state plus overpower conditions, the acceptability of the new discharge burnup has to be further assessed by considering uncertainties and impacts under accident scenarios in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bio-inspired multiscale design for perovskite solar cells

Metal halide perovskite semiconductors have attractive light-harvesting and charge-carrier transport properties for photovoltaics. Perovskite solar cells (PSCs) and modules have demonstrated their commercial promise with high power conversion efficiencies, but still face stability challenges. In this Review, we explore how biomaterials offer design inspiration for the development of durable and efficient PSCs at three different scales. At the molecular level, bio-inspired molecular interactions are harnessed towards crystallization control and degradation prevention, which offers an enhancement in long-term maximum-power-point tracking stability. At the microstructural level, self-healing and strength-enhancing strategies, utilizing dynamic bonds and interfacial reinforcement, can help PSCs to recover from physical damage and maintain high performance. At the device level, macroscopic functionalities, such as moth-eye-inspired structures tailored to different layers, can collectively enable antireflection, radiative cooling and self-cleaning to optimize light management, heat dissipation and encapsulation in PSCs. Bio-inspired PSC research can combine improved efficiency and lifetime, with abundant, biocompatible alternatives to conventional stabilizers. Future efforts should focus on screening bioinspired molecules to optimize film crystallization and stability, developing self-healing mechanisms triggered by operational stress, designing cost-efficient biomicrostructures, and integrating multifunctional encapsulation to enhance the efficiency and lifespan of PSCs.

Duan, Tianwei↗
DOE OSTI2026

Solvent selection for a biomass-to-bioproduct pipeline through integrated reductive catalytic fractionation and microbial funneling

The growing significance of lignin-first biorefineries, which focus on upgrading the aromatics resulting from lignin depolymerization, presents opportunities for bioproduct synthesis using microbial strains capable of funneling a diverse array of phenolics into a single commodity chemical. In this study, we evaluated a biomass-to-bioproduct pipeline involving the reductive catalytic fractionation (RCF) of poplar biomass followed by biological funneling with a Novosphingobium aromaticivorans strain that produces 2-pyrone-4,6-dicarboxylic acid (PDC), a potential bioplastic precursor. Considering the impact of solvent on RCF reactor operating pressure, and the potential inhibitory effects of solvent on downstream microbial funneling, we performed an analysis of six pure solvents, namely methanol, ethanol, isopropanol, isobutanol, 1,4-dioxane and ethylene glycol, and different variations of their aqueous mixtures comprising 5 to 50 vol% water. For each pure solvent and solvent/water system, we measured phenolic monomer yields in the RCF process and PDC yields from the phenolic monomers. We then developed correlation models that relate phenolic monomer yields from RCF-derived samples to Hansen solubility parameters to determine solvent descriptors that contribute to high yields. Furthermore, we developed an integrated biorefinery system to estimate the minimum selling price (MSP) of PDC and the associated carbon footprint to identify solvent systems with better costs and sustainability metrics. These analyses resulted in the 50 vol% methanol/water system being identified as optimal because it reduces RCF reactor pressure and is compatible with microbial funneling with N. aromaticivorans. This solvent system produced 63 g PDC per kg biomass (264 g PDC per kg lignin) from 85 g phenolic monomers per kg biomass at a reduced reactor pressure of 48 bar (reduced by 26% compared to our previous poplar-to-PDC pipeline). The MSP for this system is $\$$13.98 per kg of purified PDC (carbon footprint of 1.47 kg CO 2 e per kg), which is about 24% lower than a previously described poplar-to-PDC pipeline and 46% lower than a lignin-to-PDC pipeline that used pure methanol as the solvent. The results from this study illustrate improvements that can be made in lignocellulosic biorefineries that are compatible with the hybrid chemical and biological processes needed to gain value from lignin.

Sripada, Sarada [Great Lakes Bioenergy Research Ce↗
DOE OSTI2026

Dynamics of argon metastables in Ar–CH 4 radio frequency capacitively-coupled plasma: real-time monitoring with neural network-augmented broadband optical emission spectroscopy

In moderate-pressure radio frequency (RF) capacitively coupled plasmas generated in argon–methane mixtures, the density of argon metastable atoms (Ar 1 s 5 ) exhibits a non-monotonic dependence on methane (CH 4 ) concentration. Laser-induced fluorescence (LIF) was used to measure and compare local Ar 1 s 5 densities in Ar and Ar–CH 4 plasmas at 2.6 Torr and RF powers of 17–117 W. The addition of 1% CH 4 increases the metastable density, and 2% CH 4 triggers a strong depletion by an order of magnitude, compared to 1% CH 4 case. This non-monotonic behavior demonstrates the sensitivity of metastable populations to small gas admixtures, which is critical for processes where metastables drive precursor dissociation. For real-time monitoring of metastable population, broadband optical emission spectroscopy (OES) is augmented with a feedforward neural network (NN) to predict Ar s1 5 densities from spectral features. When trained on LIF data, the NN replicates the absolute densities and the dynamic trends of Ar 1 s 5 density variation. The NN-augmented broadband OES approach can be used as a simple and cost-effective tool for tracking Ar metastables in Ar-rich plasmas, facilitating industrial-scale optimization.

Yatom, Shurik [Princeton Plasma Physics Laboratory↗
DOE OSTI2024

Quantifying Energy Justice Goals in the Power Sector: Developing and Using Metrics

New policy goals are explicitly guiding the future grid toward greater energy equity. At the same time, policy goals also guide the grid toward decarbonization and resilience, while maintaining cost, security, and reliability cornerstone requirements. In conclusion, these pressures create a dilemma: moving urgently to address climate change and respond to energy disruptions, but also slowly to engage communities on the climate frontlines in earnest.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Multi-Period Active Distribution Network Planning Using Multi-Stage Stochastic Programming and Nested Decomposition by SDDIP

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Monolithic Multigrid for a Reduced-Quadrature Discretization of Poroelasticity

Advanced finite-element discretizations and preconditioners for models of poroelasticity have attracted significant attention in recent years. The equations of poroelasticity offer significant challenges in both areas, due to the potentially strong coupling between unknowns in the system, saddle-point structure, and the need to account for wide ranges of parameter values, including limiting behavior such as incompressible elasticity. This paper was motivated by an attempt to develop monolithic multigrid preconditioners for the discretization developed in [C. Rodrigo et al., Comput. Methods App. Mech. Engrg, 341 (2018), pp. 467--484]; we show here why this is a difficult task and, as a result, we modify the discretization in [Rodrigo et al.] through the use of a reduced-quadrature approximation, yielding a more “solver-friendly” discretization. Local Fourier analysis is used to optimize parameters in the resulting monolithic multigrid method, allowing a fair comparison between the performance and costs of methods based on Vanka and Braess--Sarazin relaxation. Further, numerical results are presented to validate the local Fourier analysis predictions and demonstrate efficiency of the algorithms. Finally, a comparison to existing block-factorization preconditioners is also given.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multiphysics modeling of accelerators through code integration

This work aims to improve the ability of particle accelerator researchers to develop high-performance accelerator cavity designs by creating an overall multiphysics framework that integrates and couples existing application codes. This framework will allow accelerator researchers to build multiphysics models that will optimize cavity design, improve understanding of whole-device performance, and reduce the development and fabrication costs of accelerator research. We utilize the open-source VizSchema data standard as an intermediate data structure interface layer to standardize interfaces between individual application codes. VizScema is extensively documented online, and plugins for VizSchema are available for popular visualization packages, including VisIt and ParaView. Currently, the work focuses on coupling the EM field solver COMSOL and the electron gun code MICHELLE to allow COMSOL field-solve results to be seamlessly used by MICHELLE for particle-solve. Later work will extend this integration to include other fields, particles, and thermodynamics simulation codes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Challenges for and Pathways Toward Solid-State Batteries

Solid-state batteries utilizing lithium metal anodes have the potential to enable batteries with a specific energy of >500 Wh/kg and an energy density of >1,500 Wh/L for thousands of cycles. When optimized they will improve the energy efficiency, operating temperature range, sustainability, and safety at a lower cost compared to projections for advanced Li-ion batteries. This improved performance is critical for the widespread adoption of electric vehicles and may enable future applications such as electric aviation. Expectations for solid-state batteries are high, there are significant materials and processing challenges that need to be overcome. Some of these challenges are well known; others are more subtle and are just becoming known. The challenges and their solutions must be clearly identified to realize high-energy solid-state Li metal batteries. In the United States, the U.S. Department of Energy (DOE) funds the majority of energy storage research and development, including solid state battery research. Web of Science publication analysis shows that worldwide DOE is second largest funder. Work on solid state batteries is distributed across different arms of DOE and is coordinated at headquarters to ensure a broad national portfolio for advanced batteries, including lithium batteries and solid-state battery devices. We note that university and national laboratory researchers in the US with a long-standing interest in solid state batteries is a small, integrated community with recognized impact for publications, patents and startup companies. The opportunity for this group to engage for research with international colleagues is growing, fostered by periodic bilateral meetings supported by DOE and by the recent joint US German research program for lithium battery interface studies. These are valuable opportunities to speed solid-state battery development. The ORNL led virtual workshop, overviewed here, confirms the community’s shared vision of the exciting advances, opportunities, and challenges. Further, we are seeing that this workshop has spawned several new informal collaborations and draft proposals. This encourages us to propose future discussion. On May 11, 2020, Oak Ridge National Laboratory (ORNL) hosted a 6-hour, on-line national workshop to discuss recent advances and most the prominent obstacles to realizing solid-state Li metal batteries. The workshop included more than 30+ experts from national laboratories, universities, and companies, all of whom have worked on solid-state batteries for multiple years. The participants shared recent advances, many not yet in print, illustrating that the community has gained significant new insights for solid-state Li battery materials over the last 5 years. In this report, the major outcomes of the workshop are organized to identify the gaps in our scientific knowledge for four core materials science areas: (1) Li metal anodes, (2) the solid electrolyte in contact with Li metal, (3) active cathode materials and solid-state composite cathodes, and (4) solid electrolytes. Illustrative examples and discussions are reported with the more comprehensive issues in the report. Discussion of additional challenges related to processing of solid-state battery materials and to the designs and architectures for mechanically robust, long-lived batteries received less attention due to time constraints. These are good themes for deeper discussion at a follow-on workshop, where a complete list of critical research topics can be identified.

25 ENERGY STORAGE↗
DOE OSTI2021

PPPL Laboratory Directed Research and Development (Project Final Reports, FY2018 - FY2020)

The U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) is a collaborative national center for fusion energy science, basic sciences, and advanced technology. The Laboratory has three major missions: (1) to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world; (2) to advance the science of nanoscale fabrication for technologies of tomorrow; and (3) to further the development of the scientific understanding of the plasma universe from laboratory to astrophysical scales. PPPL’s Laboratory Directed Research and Development (LDRD) program supports and encourages creativity and innovation and contributes to its long-term viability. New scientific and technical research areas emerge and are nurtured through the program. Furthermore, new capabilities are developed to enable the Laboratory to meet its and DOE’s missions. The program is used to systematically diversify the Laboratory’s programs and mission. In the last few years, the program has started projects in nanomaterial synthesis, microelectronics, advanced x-ray spectroscopy, high-energy-density physics, superconducting magnet technology, machine learning and artificial intelligence, 3D magnetic fields to optimize fusion plasmas, integration of permanent magnets with simple high-field magnets to reduce the cost of producing complex 3D magnetic fields, advanced computational methods for predictive understanding and control of fusion plasma, development of quantum computing algorithms for plasma physics, liquid metal plasma-facing components for fusion reactors, virtual engineering, and plasma-based space propulsion. The program is also the vehicle to recruit and train talented scientists and engineers with the new skills needed to perform the Laboratory’s mission. Many of the new hires through the program go on to become world-class scientists and engineers in their fields. This report provides descriptions and accomplishments of those LDRD projects that were completed during fiscal years 2018 through 2020.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulating and Optimizing Quantum State Transport

The goal of this project was to produce an accurate simulator of the “pitch and catch” process of sending quantum states between SRF cavities, and to find a method for optimizing the control pumps. We developed a program in C++ to integrate the equations of motion with high speed and precision, and used this as a basis for finding an optimal control function to maximize the state transfer efficiency. To demonstrate this we have shown an example of an optimal drive function and the resulting system dynamics, as well as a heat map of the cost function for a range of drive amplitudes.

Weston, Jacob↗