Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Process 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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bridging the Gap Between Pure and Mixed-Gas Performance of Thin-Film Composite Membranes

This study examines the influence of cell design on mixed-gas testing with PolyActive™ TFC membranes, featuring pure-gas CO₂ permeance of 1700–3100 GPU, representative of state-of-the-art CO₂/N₂ separation membranes. By designing and 3D-printing a counter-current permeation cell with optimized flow efficiency, we achieved a 33–41% increase in mixed-gas CO₂ permeance compared to traditional cells. Additionally, a comparison of sweep-gas and vacuum permeation methods revealed that vacuum operation mitigates downstream polarization, enhancing mixed-gas CO₂ permeance by 41%. These results underscore the critical role of permeation cell design and testing methods in accurately evaluating membrane performance, with significant implications for scaling up TFC membranes and optimizing industrial processes.

3D printing↗
DOE OSTI2024

Transmission Interconnection Roadmap: Transforming Bulk Transmission Interconnection by 2035

The U.S. electricity system is amid a rapidly occurring and widespread energy transition. Regional, Tribal, state, and customer demand for new energy resources, combined with favorable policies, is driving a rapid rise of interconnection requests. Interconnection processes will need to evolve to handle this larger number of requests today and into the future, as policy and economic drivers continue to motivate significant resource development. This roadmap identifies and organizes nearer- and longer-term solutions to enable transmission interconnection processes to meet this expected demand, and it is intended for a diverse audience of stakeholders participating within transmission interconnection processes. The roadmap is a result of the Interconnection Innovation e-Xchange (i2X) program launched by the U.S. Department of Energy (DOE) in June 2022 to convene stakeholders and address interconnection challenges. The roadmap is organized into four primary goal areas, each important to the overall i2X mission to enable a simpler, faster, and fairer interconnection of clean energy resources while enhancing the reliability, resiliency, and security of our electric grid. The first goal aims to improve interconnection data transparency, to aid interconnection customers’ ability to screen and site potential projects, better enable third-party modeling, facilitate more process automation, enhance competition while ensuring equitable outcomes, and enable benchmarking, tracking, and auditing of interconnection processes and reforms. The second goal covers solutions to improve queue management practices, affected system studies, fair processes, and workforce development. The third goal incorporates solutions that aim to improve cost allocation, reduce costs to electricity consumers, enhance the coordination between transmission planning and the interconnection process, and optimize the rightsizing of transmission investment through improvements in interconnection studies. The fourth and final goal aims to reduce the performance issues not identified during interconnection studies by updating technical requirements within interconnection studies, models, and tools while also improving industry interconnection standards.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Additive Manufacturing of Fe-based Alloys for Nuclear Reactor Environments

This report is a Milestone 2 deliverable in FY2025, under work package MT-25AN090211 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program. It provides an update on the work done on the laser based additive manufacturing of steels for nuclear applications, conducted collaboratively by Argonne National Laboratory and Pacific Northwestern National Laboratory. Building upon FY23 and FY24 efforts, the focus of FY25 includes continued optimization of process parameters for the alloys: A709 & G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. The major accomplishments of this work package are summarized below, applicable to both Laser Powder Bed Fusion (LPBF) processes conducted at Argonne National Laboratory and Laser Powder Directed Energy Deposition (LP-DED) processes conducted at Pacific Northwest National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

In-Tank Processing Using Low Temperature Aluminum Dissolution: Year 1 Progress Report

The Hanford Site stores approximately 56 million gallons of radioactive legacy defense waste in underground tanks. This study investigates the feasibility of utilizing in-tank low-temperature aluminum dissolution to favorably alter the transport properties of aluminum-rich southeast quadrant waste sludges, thereby de-risking sludge delivery for Direct Feed of High-Level Waste. Specifically, this study aims to assess the rate and extent of gibbsite dissolution under various conditions, including different particle sizes (~10 to 90 µm), NaOH concentrations (1 to 6 M), and the presence of specific analytes such as NO 2 - , NO 3 - , and other aluminum-rich waste background analytes, using simulated waste solids and liquids. Experimental tests were designed to highlight the impact of aluminum leaching on simulated waste transport properties, quantified by the just-suspended mixing speed (NJS), and were conducted under well-mixed, turbulent conditions, ensuring full suspension of gibbsite particles using an overhead mixer. The results suggest that the presence of NO 2 - and NO 3 - , in conjunction with leaching at relatively low free hydroxide contents, improved transport requirements (i.e., lowered NJS) for pure gibbsite slurries. These findings will inform the design of potential in-tank processing systems, optimizing aluminum dissolution for improved waste management in the Hanford Tank Farms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

High-Throughput Characterization Tools/Algorithms To Outline Porosity Variability in AM Samples as a Function of Processing Conditions

This report documents the development and deployment of advanced algorithms and tools that enable high-throughput characterization for metal additive manufacturing (AM), with a particular focus on process parameter optimization and material/part qualification for nuclear applications. While the method ologies presented support diverse characterization techniques, the majority of the work is centered on AI-driven algorithms for X-ray computed tomography (XCT) to accelerate defect detection and materials analysis at scale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems (Final Report for AEOLUS)

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for: (1) learning predictive models from data; and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addresses the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models. This report summarizes the key highlights of our research during the period of performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Producing Jet Fuel from Biogas Using Cool GTL SM

GTI Energy advanced the development of Cool GTL SM through a multi-phase project that studied and confirmed the promising value of this technology. It optimized the process by creating an integrated pilot plant capable of producing 50 gallons of total liquid hydrocarbon fuel. It also performed testing campaigns and developed a conceptual commercial design to support technoeconomic and life-cycle analyses, resulting in data that demonstrated a greenhouse gas emissions benefit.

09 BIOMASS FUELS↗
DOE OSTI2023

Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches

The air conditioning (AC) system is the primary building end-use contributor to the peak demand for energy. The energy consumed by this system has grown as fast as it has in the last few decades, not only in the residential section but also in the industry and transport sectors. Therefore, to combat energy crises, urgent actions on energy efficiency should be taken to support energy security. Consequently, the faults in AC system components increase energy consumption due to the degradation of the system’s performance and the losses in the energy conversion procedure. In this work, AC system fault detection and diagnosis (FDD) methods are investigated to propose analytic tools to identify faults and provide solutions to those problems. The analysis of existing work shows that data-driven approaches are more accurate for both soft and hard fault detection and diagnosis in AC systems. Therefore, the proposed methods are not accurate for simultaneous fault detection, while in some works, authors tested the method with several faults separately without investigating scenarios that combine more than one fault. Moreover, this study shows that integrating data-driven approaches requires deploying an optimal sensing and measurement architecture that can detect a maximum number of faults with minimally deployed sensors. The new sensing, information, and communication technologies are discussed for their integration in AC system monitoring in order to optimize system operation and detect faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Characterization of Highly Thermally Conductive Organic Substrates for a Double-Sided Cooled Power Module

Abstract Silicon-Carbide (SiC) power devices have become a promising option for traditional Silicon (Si) due to the superior material properties. To fully take advantage of the SiC devices, a high-performance power device packaging solution is necessary. This study proposes a cost-effective double-sided cooled (DSC) 1.2 kV SiC half-bridge power module using organic epoxy-resin composite dielectric (ERCD) substrates. The high mechanical and thermal performance of the power module is achieved by the low-modulus, moderate thermal conductivity, and relatively thin (120 μm) layer of ERCD material compared with traditional metal-clad ceramic approaches. This novel organic dielectric can withstand high voltage (5 kV @ 120 μm) and operate up to 250°C continuously, which is indispensable for high power applications. The thermal modeling results show that the equivalent thermal resistance junction-to-case (Rjc_eq) of the DSC power module using dual direct bonded copper (DBC) is 17% higher than the dual ERCD configuration. Furthermore, a non-insulated DSC power module concept is proposed for maximizing thermal performance by considering thermal vias in the ERCD substrate and direct-soldered heat sink. A thought process for optimization of thermal via design is demonstrated and it shows up to 24% of improvement on thermal performance compared with the insulated DSC power module.

Double-sided cooling, epoxy-resin composite dielec↗
DOE OSTI2024

Jacobian-based Model Diagnostics and Application to Equation Oriented Modeling of a Carbon Capture System

Equation-oriented (EO) modeling has the potential to enable the effective design and optimization of the operation of advanced energy systems. However, advanced modeling of energy systems results in a large number of variables and non-linear equations, and it can be difficult to search through these to identify the culprit(s) responsible for convergence issues. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms so they can be rescaled. A further singular value decomposition can be per-formed to identify degenerate sets of equations and remaining scaling issues. This work presents an EO model of a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. The IDAES diagnostics tools were successfully applied to this flowsheet to identify problems to improve model robustness and enable the optimization of process design and operating conditions of a carbon capture system.

Allan, Douglas↗
DOE OSTI2021

BETO 2021 Peer Review - Bench Scale Integration WBS 2.4.1.100

Bench Scale Integration develops and optimizes fermentation processes to produce bio-based fuels and chemicals from biomass sugars. 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 that increase production rates. For this period of performance, we conducted small scale fermentation research using strategies mentioned to produce 2,3-butanediol (BDO) from biomass utilizing NREL's proprietary Zymomonas mobilis microorganism. BDO is a versatile chemical which can be catalytically upgraded to a variety of hydrocarbon fuels and chemicals. The 3-yr project goal was to increase BDO titers from 50 g/L (FY17 level) to 125 g/L on biomass sugars by 2020. This is a 150% increase in titer showing the commercial potential of Z. mobilis while reducing the cost to downstream processing. We successfully achieved this target. The project also showed yearly progress improvements to the process design case by using a whole biomass slurry. Future work will focus on procedures for scaling to produce large quantities of BDO broth for separations and upgrading R&D and improve the titer and rates from biomass whole slurry. Our goal is to produce 100 g/L BDO titer from whole slurry at >1000L by 2023 which will meet the technical targets set by our techno-economic analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Feedstock Supply Chain Analysis

Addressing the challenge of biomass feedstock variability that affects the optimization of processing and conversion processes, the project's goal is to maximize biorefinery economics by improving the process quality control of feedstock, which will lead to greater plant availability and predictable yields of high value biofuels and co-products.

09 BIOMASS FUELS↗
DOE OSTI2022

Plasma Processing for In-Situ Field Emission Mitigation of Superconducting Radiofrequency (SRF) Cryomodules

Field emission (FE) is one of the main limiting factors of superconducting radio-frequency (SRF) cavities operating in accelerators and it occurs whenever contaminants, like dust, metal flakes or even absorbates, are present on the surface of the cavity high electric field region. Field emission reduces the maximum achievable accelerating field and generates free electrons that may interact with the beam, damage or activate the beamline. One practical method that can be used to mitigate this problem is in-situ plasma cleaning, or plasma processing. The development of a processing that can be applied in-situ is extremely advantageous, since it enables the recovery of the cryomodule performance without the need of disassembling the whole cryomodule, which is an extremely expensive and time-consuming process. On the other hand, plasma processing only requires the cryomodule warm-up to room-temperature and the subsequent processing of the contaminated cavities. The entire process is reasonably quick and involves a limited number of personnel. For these reasons we would like to advocate for continuing to invest in the R&D of plasma processing to optimize its applicability in cryomodules and for extending the technique to other frequency ranges and cavities geometries.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Data Analytics Methods to Measure Plant Outage Resilience

Every 18 or 24 months nuclear power plants (depending on plant configuration, pressurized or boiling water reactor respectively) undergo a period of outage where the plant is taken offline and a large number of maintenance and surveillance activities (that cannot be performed while plant is running) are performed in typically 2–3 weeks. Planning of a plant outage is very challenging since all the activities are required to be performed in the shortest amount of time given available resources (typically contractor crews hired for the duration of the outage). Consequently, plant outages can be costly due the actual loss of power generation and crew costs and, because of it, there is a need to maximize resource usage in the outage planning phase and reduce the risk of outage delays. This paper is addressing these needs by providing a set of analytical methods designed to analyze plant outage schedule and identify critical elements based on available resources (time and crews). These methods are based on natural language processing and optimization algorithms. In this respect, two classes of methods have been developed: one that focuses on the time resource and how variability in the time to complete outage tasks may impact outage delays, and one that minimizes the risk of outage delays by integrating available resources to assess when daily activities should be performed.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Performance Analysis of Data Processing in Distributed File Systems with Near Data Processing

In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark and Hadoop manage the increasing amount and velocity of data by improving data placement and processing speeds. However, they face inherent limitations due to the essential data movement required for processing. In this paper, we explore the Skyhook framework, a novel extension of the Ceph distributed system, which significantly reduces the need for data movement. We present an extensive case study using the Skyhook framework, applying it with the TPC-H and K-means clustering algorithms. More specifically, we leverage the TPC-H benchmark to distinguish between CPU-intensive and I/O-intensive tasks. We explore the integration of K-means clustering into SQL, coupled with a near-data processing system to offload the computational burden of the K-means clustering algorithm to storage nodes. We conduct a comprehensive performance evaluation of distributed data processing applications across three processing approaches: traditional layout (baseline), optimized layout, and near-data processing. Additionally, we introduce the use of the FIO tool to simulate real-world system workloads, enabling the measurement of performance metrics such as average latency and CPU utilization. Our research is a significant advance in understanding how to optimize data processing systems to meet the demands of the modern data landscape.

Hou, Shiyue↗