Results for “efficiency improvement” — Page 42

DOE OSTI2025

Linear Solver for Electromagnetic Simulation of General Distribution Feeders

High-fidelity electromagnetic transient (EMT) modeling is required for accurate simulation and analysis of power system dynamics in modern distribution feeders. However, the high-fidelity of EMT models often leads to significant computational challenges, particularly in terms of computational resources and simulation time. This paper investigates the development and application of a detailed EMT model for general distribution feeders, with a focus on improving computational efficiency. A direct linear solver is proposed for a bordered block diagonal (BBD) matrix structure commonly encountered in a EMT model of distribution feeders. The solver integrates the Schur complement method with the block tridiagonal matrix algorithm to enhance the computational performance. The proposed solver is validated using the primary feeder of the IEEE 342-node test system, demonstrating its accuracy and efficiency in EMT simulations. Furthermore, the solver’s performance is benchmarked against MATLAB’s built-in linear solvers, showing significant improvements in computation time while maintaining high fidelity and accuracy in simulation results.

Choi, Jongchan [ORNL] (ORCID:000000025952455X)↗

DOE OSTI2022

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Bioprocessing Separations Consortium Three-Year Overview: Technical Advances, Process Economics Influence, and State of the Science

The Bioprocessing Separations Consortium solves separations challenges that are unique to bioprocessing. To do this, the Consortium uses two methods: applying existing technologies to new challenges and developing new technologies that may address these unique challenges better than today’s commercial technologies. In its research and development, the Consortium leverages its three core capabilities: materials development and evaluation, process development, and analysis and computation. In its first three years, from 2016 to 2019, the Consortium addressed four critical challenges in biochemical and thermochemical processing. First, the Consortium targeted the fractionation of lignin to enable valorization of this portion of biomass that offers diverse, complex, and valuable building blocks and products. Second, Consortium researchers developed technologies to enable process intensification, which can reduce equipment needs, energy consumption, and waste generation, thereby cutting bioprocessing capital costs and rendering processes more efficient. Third, the Consortium sought to recover carbon from dilute aqueous streams that are common to bioprocessing; recovering dilute carbon can improve process efficiency and economics. Finally, Consortium researchers designed and developed new materials and catalysts to reduce targeted foulants and poisons in bioprocessing streams that can limit the lifetime of downstream catalysts or fermenting microorganisms. Table 1 lists the technologies that were examined and places them in the context of the Consortium’s capabilities, types of bioprocesses, and critical challenges in bioprocessing separations. Overall, across these projects, we have developed separations technologies for 10 bioprocesses, addressed 9 target compounds in bioprocessing, developed 10 materials, and evaluated 4 processes for cost and sustainability.

09 BIOMASS FUELS↗

DOE OSTI2020

Bioprocessing Separations Consortium Three-Year Overview: Technical Advances Process Economics Influence, and State of the Science

The Bioprocessing Separations Consortium solves separations challenges that are unique to bioprocessing. To do this, the Consortium uses two methods: applying existing technologies to new challenges and developing new technologies that may address these unique challenges better than today’s commercial technologies. In its research and development, the Consortium leverages its three core capabilities: materials development and evaluation, process development, and analysis and computation. In its first three years, from 2016 to 2019, the Consortium addressed four critical challenges in biochemical and thermochemical processing. First, the Consortium targeted the fractionation of lignin to enable valorization of this portion of biomass that offers diverse, complex, and valuable building blocks and products. Second, Consortium researchers developed technologies to enable process intensification, which can reduce equipment needs, energy consumption, and waste generation, thereby cutting bioprocessing capital costs and rendering processes more efficient. Third, the Consortium sought to recover carbon from dilute aqueous streams that are common to bioprocessing; recovering dilute carbon can improve process efficiency and economics. Finally, Consortium researchers designed and developed new materials and catalysts to reduce targeted foulants and poisons in bioprocessing streams that can limit the lifetime of downstream catalysts or fermenting microorganisms. Table 1 lists the technologies that were examined and places them in the context of the Consortium’s capabilities, types of bioprocesses, and critical challenges in bioprocessing separations. Overall, across these projects, we have developed separations technologies for 10 bioprocesses, addressed 9 target compounds in bioprocessing, developed 10 materials, and evaluated 4 processes for cost and sustainability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Bioprocessing Separations Consortium: Three-Year Overview - Technical Advances, Process Economics Influence, and State of the Science

The Bioprocessing Separations Consortium solves separations challenges that are unique to bioprocessing. To do this, the Consortium uses two methods: applying existing technologies to new challenges and developing new technologies that may address these unique challenges better than today’s commercial technologies. In its research and development, the Consortium leverages its three core capabilities: materials development and evaluation, process development, and analysis and computation. In its first three years, from 2016 to 2019, the Consortium addressed four critical challenges in biochemical and thermochemical processing. First, the Consortium targeted the fractionation of lignin to enable valorization of this portion of biomass that offers diverse, complex, and valuable building blocks and products. Second, Consortium researchers developed technologies to enable process intensification, which can reduce equipment needs, energy consumption, and waste generation, thereby cutting bioprocessing capital costs and rendering processes more efficient. Third, the Consortium sought to recover carbon from dilute aqueous streams that are common to bioprocessing; recovering dilute carbon can improve process efficiency and economics. Finally, Consortium researchers designed and developed new materials and catalysts to reduce targeted foulants and poisons in bioprocessing streams that can limit the lifetime of downstream catalysts or fermenting microorganisms. Table 1 lists the technologies that were examined and places them in the context of the Consortium’s capabilities, types of bioprocesses, and critical challenges in bioprocessing separations. Overall, across these projects, we have developed separations technologies for 10 bioprocesses, addressed 9 target compounds in bioprocessing, developed 10 materials, and evaluated 4 processes for cost and sustainability.

09 BIOMASS FUELS↗

DOE OSTI2026

Organic Rankine Cycle Integration and Optimization for High Efficiency CHP Genset Systems (Final Technical Report)

This project successfully advanced the integration of Organic Rankine Cycle (ORC) technology with reciprocating engine–based combined heat and power (CHP) systems to improve electrical efficiency, total CHP efficiency, and grid-responsive operation. Over three budget periods, the work progressed from high-temperature ORC component development and thermodynamic model validation to next-generation system design, working fluid transition, and techno-economic analysis. Key technical accomplishments include development and validation of a thermodynamic model capable of accurately predicting ORC performance across an expanded temperature and pressure envelope; successful identification and validation of low-global-warming-potential (GWP) working fluids—most notably R1233zd(E)—as viable replacements for R245fa; and demonstration of scalable ORC architectures suitable for integration with 1–20 MW class reciprocating engines. These advances enable flexible CHP configurations that can increase electrical output while maintaining high overall utilization of available thermal energy. The project also produced a clean-sheet design for a next-generation ORC system targeting substantially higher power output per unit, supported by detailed component selection, heat exchanger evaluation, and system-level modeling. Techno-economic analyses indicate that ORC-enabled flexible CHP systems can meet or exceed Department of Energy (DOE) efficiency targets while providing value to both facility operators and the electric grid.. Late-stage testing of the largest next-generation ORC prototype identified limitations related to pump net positive suction head (NPSH) requirements and condenser flooding under certain operating conditions. Although these issues constrained full validation of that configuration within the project timeframe, they provided clear and actionable design guidance for future system refinements. Importantly, validated modeling, smaller-scale testing, and working fluid evaluations confirmed the technical viability of the overall approach. In aggregate, this project met its core objectives by establishing validated design tools, de-risking key ORC technologies for CHP applications, and defining a credible pathway toward commercialization of flexible, high-efficiency CHP systems. The results form a strong foundation for continued development and deployment beyond the conclusion of the DOE-funded effort.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Constructing Regulatory Networks to Compare Axenic and Interspecies Microbial Gene Transcription

In this preliminary study, we constructed gene regulatory networks (GRNs) from transcriptional expression data of axenic and interspecies microbial cultures with the goal of predicting how cocultivation affected greenhouse gas respiration by these species. The specific strains of Methylotuvimicrobium alkaliphilum 20Z, a methylotroph, and Cyanobacterium stanieri HL-69, a phototroph, were chosen for their viability in industrial bioprocessing. We ranked directed interactions between gene pairs based on the ability of the input gene to predict the expression of a target gene relative to their transcriptomes. While we were able to identify topological differences between conditions, our initial findings require validation through experimental analysis and further modeling. We aimed to develop a systematic thresholding approach to optimize the accuracy of our networks. We filtered out trial networks separately from top gene interactions of the scored rankings. Parameters of unfiltered and filtered networks were used to test and develop thresholding approaches. Knee point detection of edge weight distributions was explored as an approach for separating significant interactions from insignificant interactions in unfiltered networks. While knee detection failed to produce analogous networks for broad cross-condition comparisons, the results informed us about the proportions of significant edges present in unfiltered networks. We also calculated the average mean degree for nodes in a selection of trial networks to find a thresholding value characteristic to all groups. While we did not reach a definitive conclusion, we gained insight into the coregulatory structures of our groups and made critical evaluations of systematic methods for filtering networks. We recommend an iterative process for the inference of GRNs, where the most significant results from preliminary explorations are used to improve the efficiency with which regulatory motifs are chosen for experimental characterization. Experimental results can then inform the framework of adjusted models to improve broad interpretations of GRNs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Importance of Window Installation in Residential Building Envelopes Having Continuous External Insulation in Order to Realize Energy Efficiency

Residential buildings are one of the prime candidates in the United States for reducing energy consumption. Continuous exterior insulation (CEI) is being used increasingly often in residential buildings to improve energy efficiency. Windows constitute 15–40% of a building envelope and are the weakest component in energy performance. The installation of windows in walls with CEI has not been well evaluated. We identified four cases of installing windows in walls with CEI of 25–76 mm (1–3 in.) thickness and analyzed the energy loss between the window and wall interface (flanking loss), structural issues, air leakage, and moisture penetration. Thermal analysis showed that the insulation value (RSI) of the 305 mm (12 in.) perimeter wall surrounding a window decreased by 7.6–34.5% in the four cases when compared with the RSI of the wall without the window. A window installation method is proposed to address the issues likely to occur with installation methods currently being used in the field. An out-of-the-box installation system was also designed to achieve a better thermal performance, cost effectiveness, and structural performance in high-performance residential buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Modeling and Experiments on a Dedicated Outdoor Air System Using Liquid Desiccant Heat and Mass Exchangers

Liquid desiccants can provide efficient dehumidification but have yet to see widespread adoption. Most systems studied previously use natural gas-combustion to heat and regenerate the desiccant, and a central chiller plant or cooling tower for removing the heat of absorption. Here we present modeling and experimental results on a novel packaged air conditioner integrating liquid-desiccant heat and mass exchangers with a vapor compression cycle. The packaged system does not need cold or hot water from a central plant or cooling tower and is suitable for rooftop unit air conditioners. The system uses the evaporator to cool the liquid desiccant that is absorbing moisture from the air, and the condenser to heat the liquid desiccant to regenerate it. Efficiency is improved by reducing the load on the evaporator for a given supply-air dewpoint. This paper presents the measured dehumidification efficiency for a 10- ton packaged air conditioner, focusing on dehumidifying ventilation air. The paper also presents a numerical model to predict the outlet conditions and dehumidification efficiency, which we compare with the measured data.

air conditioning↗

DOE OSTI2023

Correlation of Broad Absorption Band with Small Singlet‐Triplet Energy Gap in Organic Photovoltaics

Abstract Organic photovoltaics (OPV) are one of the most effective ways to harvest renewable solar energy, with the power conversion efficiency (PCE) of the devices soaring above 19 % when processed with halogenated solvents. The superior photocurrent of OPV over other emerging photovoltaics offers more opportunities to further improve the efficiency. Tailoring the absorption band of photoactive materials is an effective way to further enhance OPV photocurrent. However, the field has mostly been focusing on improving the near‐infrared region photo‐response, with the absorption shoulders in short‐wavelength region (SWR) usually being neglected. Herein, by developing a series of non‐fullerene acceptors (NFAs) with varied side‐group conjugations, we observe an enhanced SWR absorption band with increased side‐group conjugation length. The underpinning factors of how molecular structures and geometries improve SWR absorption are clearly elucidated through theoretical modelling and crystallography. Moreover, a clear relationship between the enhanced SWR absorption and reduced singlet‐triplet energy gap is established, both of which are favorable for the OPV performance and can be tailored by rational structure design of NFAs. Finally, the rationally designed NFA, BO‐TTBr, affords a decent PCE of 18.5 % when processed with a non‐halogenated green solvent.

Chemistry↗

DOE OSTI2023

Nanocomposite Materials for Accelerating Decarbonization

Here, decarbonization is demonstrated by catalytic conversion of CO 2 to fuel by means of exposure of cadmium selenide (CdSe) quantum dots-titania (TiO 2 ) nanophotocatalysts to sunlight illumination. The primary products resulted from this chemical reactions are methanol, carbon monoxide, and hydrogen after several hours of exposure to sun light. The overall CO 2 conversion efficiency of such quantum dot-titania nanostructures was compared with that of pure TiO 2 nanorod array photocatalyst. Data shows an improved conversion efficiency when composite quantum dot-titania nanostructures were used in comparison with titania nanophotocatalysts. It is postulated that this is due to the additional absorbance of visible light by the quantum dots and generation of additional charge separation at the CdSe-TiO 2 interfaces. The conversion efficiency of such an artificial photosynthesis process remains to be optimized for practical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection

Composite materials are widely used in aircraft structures because of their superior mechanical properties. However, their complex failure modes require sophisticated inspection methods to ensure structural integrity. Ultrasonic testing (UT) is a common non-destructive inspection (NDI) technique for aircraft composites that can detect internal and external defects with high resolution and accuracy. Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials. Here, we evaluated our approach on material with multiple thicknesses or domains comprising various defects. Our experimental results demonstrate higher accuracy and F1-Score, with only 10% training epochs compared to baseline MoE.

composite materials↗

DOE OSTI2025

ChemComp: Compiling and Computing with Chemical Reaction Networks

The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.

Bohm Agostini, Nicolas↗

DOE OSTI2021

BETO 2021 Peer Review - Cyanobacteria Photosynthetic Energy Platform (1.3.4.301)

Cyanobacteria Photosynthetic Energy Platform (WBS 1.3.4.301) The primary challenge in algal technology is low energy conversion efficiency, currently up to a few percentages from photon energy to biomass energy. Most incoming solar energy is lost in the processes of photosynthesis, through mechanisms that are not fully understood. There is large room for improvement. This project develops cyanobacteria genetic tools to improve photosynthetic efficiency and biomass productivity through manipulation of energy regulation mechanisms. Increasing algal biomass productivity by 20% translates to $2 reduction of MFSP. Genetic manipulation is an essential tool in understanding and improving algal productivity. While genetic manipulation is challenging for most algae, model cyanobacteria, with in-house mutant library and genetic engineering toolboxes, allow for rapid hypothesis testing and transfer of lessons to other cyanobacteria and eukaryotic algae. We developed Energome (energy-ome) concept to guide novel engineering strategies and unleash unused potential in photosynthesis. Energome consists of cellular energy management mechanisms. For example, we found that cyanobacteria use futile cycles around glycogen and sucrose to dissipate ATP. By genetic modification, the model cyanobacterium Synechocystis 6803 showed higher energy levels, increased photosynthetic capacity, and 20% improvement in biomass yield in simulated outdoor light conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Focusing Optics for Axion Detection: Simulating Sensing Enhancements of Photons in InfraBREAD

The Broadband Reflector Experiment for Axion Detection (BREAD) will search for axions, a promising particle candidate for dark matter, in the high mass range of [$10^{-3}$,1]eV. BREAD acts like a telescope for dark matter, using a parabolic reflector to direct axion-induced photons onto a precise focal spot. InfraBREAD is the experimental version designed to detect photons resulting from axions with infrared frequency using a 1mm × 1mm Superconducting Nanowire Singe Photon Detector (SNSPD). However, at this frequency scale two problems arise: 1) the focal spot becomes smeared across an area larger than the size of the SNSPD detector; 2) any millimeter-scale misalignment shifts in the SNSPD can greatly diminish photon signal. This study performed simulations of novel optical configurations of lenses and reflectors placed around the SNSPD to achieve a greater efficiency than the SNSPD alone through both of these effects. One optical setup was identified called the “Parabolo id to Winston Cone (PTW) Configuration,” which exploits the optics of parabolic reflectors to focus incoming photons from the larger area of the smeared focal spot onto the smaller 1mm 2 area of the detector. The PTW configuration demonstrated overall improvements in efficiency by around 10% through misalignment shifts, with a maximal improvement of 55% in one region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Renewable biohydrogen production from lignocellulosic biomass using fermentation and integration of systems with other energy generation technologies

Biohydrogen is a clean and renewable source of energy. It can be produced by using technologies such as thermochemical, electrolysis, photoelectrochemical and biological, etc. Among these technologies, the biological method (dark fermentation) is considered more sustainable and ecofriendly. Dark fermentation involves anaerobic microbes which degrade carbohydrate rich substrate and produce hydrogen. Lignocellulosic biomass is an abundantly available raw material and can be utilized as an economic and renewable substrate for biohydrogen production. Although there are many hurdles, continuous advancements in lignocellulosic biomass pretreatment technology, microbial fermentation (mixed substrate and co-culture fermentation), the involvement of molecular biology techniques, and understanding of various factors (pH, T, addition of nanomaterials) effect on biohydrogen productivity and yield render this technology efficient and capable to meet future energy demands. Further integration of biohydrogen production technology with other products such as bio-alcohol, volatile fatty acids (VFAs), and methane have the potential to improve the efficiency and economics of the overall process. In this study, various methods used for lignocellulosic biomass pretreatment, technologies in trends to produce and improve biohydrogen production, a coproduction of other energy resources, and techno-economic analysis of biohydrogen production from lignocellulosic biomass are reviewed.

09 BIOMASS FUELS↗

DOE OSTI2023

Deep learning-enhanced design for functionally graded auxetic lattices

Auxetic materials with counterintuitive negative Poisson’s ratio have been of significant interest due to potential applications across diverse engineering fields. Functionally grading such auxetics further enables customization of the structural response and harnesses the potential for multi-functional applications. However, a critical challenge in designing functionally graded lattices is to efficiently determine the spatial variation of the functional gradient and the corresponding geometric designs to achieve the desired response. In this paper, a highly efficient deep learning-based inverse design framework for functionally graded tetra-petal auxetics with spatially tailored properties is presented. This framework significantly improves the efficiency of tailoring functionally graded auxetics where many unit cells need to be tailor-designed. The graded tetra-petal auxetics obtained from the inverse design framework are additively manufactured and subjected to impact tests. The results show superior impact performance compared with uniform designs, demonstrating the effectiveness of the proposed inverse design framework, which can be inspirable to promote advanced structures/materials with enhanced impact resistance.

42 ENGINEERING↗

DOE OSTI2023

Ultra-thin on-chip ALD LiPON capacitors for high frequency application

Multi-layer ceramic capacitors have been used for high frequency decoupling application due to a lower overall impedance leading to fast current response. However, high parasitic inductance limits the application of these capacitors in ultra-high frequency domain. Thus, Multlayer Ceramic Capacitors (MLCCs) are placed close to the IC to improve circuit efficiency and reduce inductance. With next generation applications, the demand for frequency range has further increased which not only requires enhanced capacitor material but improved manufacturing techniques to limit the inductive path. Here, we demonstrate ALD of two different polymorphs of ultra-thin film lithium phosphorus oxynitride (LiPON) as an inorganic solid state electrolyte (SSE) for on chip capacitors for decoupling application. Both the LiPON capacitors shows an electric double layer behavior with a capacitance of 15 μF/cm 2 and a low leakage current (<20 nA/cm 2 ) at 2V. The LiPON shows EDLC behavior up to 10 kHz and beyond, both the polymorphs show an electrostatic behavior with a high dielectric constant (14). Furthermore, this dual frequency behavior along with low parasitic inductance and on chip integration allows for operation in extended frequency ranges.

25 ENERGY STORAGE↗