SEARCH · Engineering Papers

Results for “time minimization”

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.

SourceResearch connectionFrom yearThrough year

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

At least 361 records · Page 20

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Signal Preconditioning to Minimize Impulse Response Contribution

A study was performed to identify a method to minimize the effect of a linear time-invariant (LTI) system impulse response on an input. Three methods were studied: Wiener filter, the N4SID algorithm and transfer function estimation, the latter two using functions from MATLAB’s System Identification Toolbox. Although all three methods were able estimate an unknown forward impulse response given an input/output time series pair, only the Wiener filter was able to estimate a system inverse which satisfactorily solved the problem using a cosine similarity measure.

42 ENGINEERING↗

DOE OSTI2022

Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO 2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5

This technical report presents work conducted by the sub surface analysis team of the Strategic Systems Analysis & Engineering group at NETL for Task 5 of SMART Phase 1. This study involved the development of deep learning models for CO 2 geologic storage that are capable of accurate prediction of spatio-temporal outputs of CO 2 saturation, pressure, and brine production in three dimensional space over a storage operation's injection and post-injection timeframes. The model framework involves ensembling multi-layer encoder networks that provide dimesionality reduction of geologic inputs with fully connected long short-term memory (LSTM) neural networks that generate time-series prediction This approach offers a means to maximize training time efficiency, reduce computational memory burden, and minimize prediction turnaround.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Robust Solar Receivers Using MAX Phase Materials

This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technologies Office Award Number 35928. The objective of the proposed effort was to develop and optimize additive manufacturing technologies for low-cost fabrication of high-temperature receivers using MAX phase-based materials (Ti 3 SiC 2 and Ti 3 AlC 2 ). MAX phase materials are a group of ternary metal carbides and nitrides where M stands for an early transition metal element, A is a group 13–16 element, and X is C and/or N. In Phase 1, the binder jetting additive manufacturing process was used to synthesize and characterize the Ti 3 SiC 2 MAX phase material. The typical process involved first producing a TiC preform using binder jetting followed by infiltration of the preform with silicon melt to form Ti 3 SiC 2 in situ. The reaction-infiltrated samples showed formation of MAX phase in the sample core; however, the surface showed cracking. Various process conditions—cooling rates, hold times, Si proportion, etc.—were varied to minimize the surface cracking. The fabricated MAX phase core was characterized by microstructure analysis and evaluations of mechanical properties such as hardness and thermal shock. In Phase 2, the focus included fabrication of Ti 3 SiC 2 MAX phase materials by spark plasma sintering (SPS) and synthesis of Ti 3 AlC 2 MAX phase materials by the Al melt infiltration process. It is expected that Al infiltration will not cause sample cracking, since Al does not expand during solidification. In addition, other processing approaches were investigated to fabricate the MAX phase materials, such as SPS with a graphite bedding approach for producing short-length Ti 3 AlC 2 MAX phase tubes for demonstration of prototypical Concentrating Solar Power receiver tubes. Fabricated samples underwent thermo-mechanical testing to validate the materials for the solar receiver application at temperatures >1000°C. In Phase 3, the effort focused on the development and optimization of the Ti-Al-C MAX phase composite material using the Al melt infiltration approach. We started with optimization of precursor powders and making preform structures by either pressing them in a die or using the binder jetting additive manufacturing process followed by Al melt infiltration. In addition, we investigated the formation of preform structures by cold isostatic pressing followed by Al melt infiltration for making Ti-Al-C MAX phase composite. Thermo-mechanical characterizations, such as creep, strength, and thermal shock, were conducted to establish the structures’ performance.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Zeteotech, LLC TRGR Project Final Report

Reliable detection of aerosolized pathogens is difficult due to need to distinguish between the benign bioaerosols such as dander and pollen and the thousands of pathogens capable of infecting people. Accurate identification of airborne pathogens of concern in the past has required the collection of aerosol samples in a filter, periodic collection of the samples, and processing and identification in the laboratory. This process is labor intensive and expensive. Additionally, this method necessarily has a time to detection window of hours to days depending on the collection frequency. Biological pathogens have an incubation period before the onset of symptoms and severe health effects and/or mortality, people will typically be exposed to them without realizing it. This has resulted in a detect to treat strategy for protection against bio releases. While prophylactic measures can still be effective over these time scales, reducing the time to detection will significantly improve the effectiveness of these measures and subsequently reduce the consequences of a release. Various attempts to reduce the time to detection and identification have been plagued by highly undesirable false-positives which degrade confidence in the system. Zeteotech, LLC has developed a mass spectrometer based bioaerosol sensing system which is capable of autonomously identifying airborne pathogens of concern and alert authorities within minutes instead of hours to days. They have deployed these instruments to protect high-risk facilities by alerting authorities of public health events and intentional bioterrorism events in near real time. This makes it possible to more accurately identify the time and location of the release and minimize the number of people that are exposed through prompt quarantining of affected areas.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A Simple, Revolutionary Approach to Waste Packaging Utilizing Proven Technology - 20201

Historically, removing hazardous waste from gloveboxes involves the use of bags as the primary containment method. This method can prove tedious, repetitive, and time consuming to ensure the process follows all safeguards required to contain the waste and mitigate any breach of containment. Layers of bags, yards of tape, and multiple filters are added to the waste stream in order to transfer the hazardous waste from the glovebox safely into the drum package. Then, finally, it can be shipped and disposed of. The process to cut, tie, and tape each bag for disposal involves manual effort by multiple operators. Following Administrative Safety Controls, good operators are able to safely perform those tasks; but, since the bag is the only confinement layer, incidents can occur. Operators, in some cases, are also at close proximity to this waste which may increase the risk of radiation exposure. Once the waste is transferred out through these bags, it is nearly impossible to remove packaged waste that may contain higher dosage levels, thus increasing drum disposal costs incurred by the facility. Exceeding the allowable radiation levels, and drums not fully filled, both contribute significantly to excess disposal costs. Central Research Laboratories (CRL), based in Red Wing Minnesota, has developed the Waste Drum Transfer System (WDTS) building upon the proven technology and containment of the Rapid Transfer Port (RTP) as an alternate, more efficient method of waste transfer. This solution consists of an RTP Alpha port mounted inside a glovebox and Beta assembly unit composed of a drum liner fitted inside a standard 55 gallon drum. The use of the Beta drum liner bypasses the typical use of bags, sleeves or tape to provide leak tight containment. The process to connect and disconnect a drum from the glovebox is achieved by the self-docking and rotating system controls of the RTP Alpha, all done with only one operator standing in an upright ergonomic position. This system also allows for greater flexibility in available Beta connection options, expanding the overall functionality of the system. This system can be disconnected and reconnected multiple times to maximize volume containment. This will minimize drum disposal costs and reduce the overall volume of waste output by more efficiently filling the drums. Radiation levels inside each drum can be monitored and adjusted more efficiently by operators utilizing the reconnect option. Minimal As Low As Reasonably Achievable (ALARA) levels are also achievable with this system. This paper will show how highly efficient packaging and disposal of hazardous waste can be accomplished in a cost-effective manner utilizing the proven RTP technology. Maximized operator productivity and safety, as well as minimized radiation dose exposure, are all attainable through the system's engineered controls. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Digitizing Today’s Buildings in the Real World: Lessons from Field Demonstrations

Digital twins, created by generating a virtual replica of a building, enable safe evaluation of operational scenarios and applications like fault detection and diagnosis and advanced controls. However, a prerequisite is the creation of a machine-readable digital representation of a building, currently hindered by fragmented information scattered across mechanical drawings, point lists, and natural language sequences. As a result, digital twin development remains labor-intensive, error-prone, and difficult to validate. To address these challenges, two efforts from ASHRAE aim to support the digitalization of buildings. ASHRAE s223 establishes a semantic model of buildings, representing system components, configuration, and data sources. ASHRAE s231 defines a vendor-neutral programming language for expressing their control logic. As the industry evaluates implementing them in their products, understanding the challenges that vendors and implementers may face is crucial. In this paper, we present findings and lessons learned from field demonstrations in five buildings that implemented control applications using ASHRAE s223 and s231. The demonstrations highlight how semantic modeling and formalized control descriptions can significantly reduce software development time, manual point mapping, and hard-coding. Beyond time efficiency, they enable reliable automation by minimizing human interpretation and providing a means for consistency across projects. We describe the processes and best practices for model creation and model usage, from translating heterogeneous building documentation into semantic representations to implementing control logic in real-world systems. Finally, we discuss the challenges that persist, including integration with legacy software environments, gaps in interoperability, and the level of expertise still required to effectively leverage semantic models.

Prakash, Anand Krishnan↗

DOE OSTI2021

Teeport: Break the Wall Between the Optimization Algorithms and Problems

Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The Mitigation Hierarchy

The mitigation hierarchy is a widely used framework to inform conservation decisions. The hierarchy offers a structured set of steps for how projects can lessen negative impacts or lead to an increase in biodiversity. In its simplest form, the mitigation hierarchy includes three stages: (1) avoid creating impacts from the outset, (2) minimize the impacts that cannot be avoided, and (3) compensate for or offset the impacts that cannot be minimized. Proper application of the hierarchy should decrease impacts of the project over time, such that most of the impact is alleviated through avoidance, leaving a modest amount remaining to minimize, and only a residual to compensate. In practice, avoidance should be prioritized and can be a cost-effective means of mitigation. Whereas compensation should be considered the lowest priority and should only be applied in situations in which the previous mitigation steps were unable to fully alleviate undesirable impacts.

biodiversity↗

DOE OSTI2023

La Jerarquia de Mitigacion (Spanish)

biodiversity↗

DOE OSTI2023

Current-Voltage Analysis Tool for Solar Fuel Production (CATS) v0.1

The program is expected to be a useful tool during operation of a range of solar-driven electrochemical devices, such as PV-electrolyzers and photoelectrochemical (PEC) devices. It makes use of current-voltage data collected during operation, operation parameters that are easily accessible for many devices of interest. The program captures the time-dependence of loss mechanisms at play during PEC device operation and opens the door for real-time optimizations of operational parameters like the feed humidity to minimize performance losses. Currently, the program is useful for applications where the current-voltage polarization response of the photoabsorber and electrolyzer are pre-recorded, and changes over time can be estimated based on the pre-recorded current-voltage scans. More complicated scenarios that involve permanent degradation of the PV component or complex calculations of the electrolyzer current-voltage characteristics may be included in future versions of this software. Aside from the possibility of deconvoluting loss mechanisms, this software can also reveal possible performance benefits by convective PV cooling when the photoabsorber is integrated into the electrolysis cell. The latter can highlight why certain device architectures may be beneficial over others. No similar technologies are known to the developer.

Kistler, Tobias↗

DOE OSTI2025

Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors.

LSTM↗

DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗

DOE OSTI2025

Bridging the gap: Deploying AI-based Models in Real-Time Fusion Plasma Control Systems

Achieving reliable real-time control in fusion plasma experiments requires strict timing guarantees across entire control algorithms. In earlier work by Abbate et al. (2023), we demonstrated the feasibility of neural-network-based control algorithms on the DIII-D tokamak using the internally developed open-source Keras2C library for model conversion into C (Conlin et al. (2021)). However, the initial implementations relied on data buffering and branching logic outside the neural network code, causing variability in execution times. Subsequent deployments on DIII-D and KSTAR—including the RTCAKENN algorithm for kinetic profile reconstruction—proved that minimizing branching and buffering throughout the pipeline yields consistent millisecond-level cycle times under real experimental conditions (Shousha et al. (2023)). However, keeping pace with rapidly evolving AI frameworks (e.g. PyTorch) is challenging. Finally, we, therefore, propose a community-driven open-source effort to expand the tool, enabling real-time deployment across diverse systems that require strictly bounded execution times.

AI-based models↗

DOE OSTI2020

Remediation of Temporary Storage Sites in Support of the Port Hope Area Initiative - 20295

The Port Hope Area Initiative is a community-based solution for the long-term management of historic low level radioactive waste (LLRW) resulting from 60 years of uranium and radium processing operations in the Town of Port Hope which is located in Ontario, Canada. The Eldorado refinery, on the north shore of Lake Ontario, began refining radium-226 from pitchblende ore, later transitioning to the refining of uranium. Through the history of the operation, LLRW was deposited throughout the town of Port Hope as a result of fugitive emissions from the plant and/or through the re-use of process residues as building material and backfill. Historical clean-up activities conducted in the late 1970's involved the remediation of approximately 400 properties and the relocation of 100,000 cubic metres of contaminated soil to a disposal facility in Chalk River operated by Atomic Energy of Canada Limited (AECL). Owing to space limitations at that disposal facility, any LLRW identified through construction monitoring since that time has been stored in the community at three temporary storage sites located throughout the town. These include: the Centre Pier mound that contained approximately of 19,800 m{sup 3} of LLRW-impacted soil that originated from the construction of a new water treatment plant; two mounds located at a licensed storage facility containing LLRW obtained from residential clean-up activities (11,000 m{sup 3}); and a small pad adjacent to the municipal sewage treatment plant containing 2200 m{sup 3} of LLRW-containing sludge. With the construction of a new long-term waste management facility (LTWMF) that has been designed to house all of the LLRW identified within Port Hope, the three sites were early candidates for remediation. The clean-up of the three temporary storage sites was a significant milestone for the Port Hope Area Initiative. After a decade of planning and consultation, this work represents the first sites in the municipality to be remediated with the waste being safety removed and transferred to the newly constructed LTWMF. This paper discusses the challenges associated with the clean-up activities for these three sites and the strategies employed to address those challenges. These included weather-related challenges, owing to the seasons over which the work was conducted as well as those associated with working within a closely-knit community. Canadian Nuclear Laboratories (CNL), working on behalf of the federal government, has worked diligently to develop a positive and trusting relationship with the community. Consequently, the successful execution of this project needed to be sensitive to, and respectful of the needs of the community. In addition to the usual Health, Safety and Environment training, project staff received community awareness training that spoke to the history of this community-based initiative and the expected behavior when working within the community. Transportation routes were defined based on safety and the need to minimize disruption to local traffic while haul-times where scheduled around school bus hours to enhance public safety. The successful completion of this first of many remediation projects to be completed under the Port Hope Area Initiative reflected years of careful planning. Nevertheless, there were a number of 'lessons learned' that have been applied on other ongoing projects be completed under the Port Hope Area Initiative. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Simulation of 24,000 Electron Dynamics: Real-Time Time-Dependent Density Functional Theory (TDDFT) with the Real-Space Multigrids (RMG)

Here, we present the theory, implementation, and benchmarking of a real-time time-dependent density functional theory (RT-TDDFT) module within the RMG code, designed to simulate the electronic response of molecular systems to external perturbations. Our method offers insights into nonequilibrium dynamics and excited states across a diverse range of systems, from small organic molecules to large metallic nanoparticles. Benchmarking results demonstrate excellent agreement with established TDDFT implementations and showcase the superior stability of our time integration algorithm, enabling long-term simulations with minimal energy drift. The scalability and efficiency of RMG on massively parallel architectures allow for simulations of complex systems, such as plasmonic nanoparticles with thousands of atoms. Future extensions, including nuclear and spin dynamics, will broaden the applicability of this RT-TDDFT implementation, providing a powerful toolset for studies of photoactive materials, nanoscale devices, and other systems where real-time electronic dynamics is essential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Molybdenum Carbide Electrocatalyst In Situ Embedded in Porous Nitrogen–Rich Carbon Nanotubes Promotes Rapid Kinetics in Sodium–Metal–Sulfur Batteries

This work is the first report of a molybdenum carbide-based electrocatalyst for sulfur-based sodium metal batteries (SMBs/NMBs). MoC/Mo 2 C is in-situ grown on nitrogen-doped carbon nanotubes in parallel with formation of extensive nanoporosity. Sulfur impregnation (50 wt% S) results in unique triphasic architecture termed MoC/Mo 2 C@PCNT-S. Quasi-solid-state phase transformation to Na 2 S is promoted in carbonate electrolyte, with in-situ time-resolved Raman, XPS and optical analysis demonstrating minimal soluble polysulfides. MoC/Mo 2 C@PCNT-S cathodes delivered among the most promising rate performance characteristics in literature, achieving 987 mAh g -1 at 1 Ag -1 , 818 mAh g -1 at 3 A g -1 , and 621 mAh g -1 at 5 A g -1 . The cells deliver superior cycling stability, retaining 650 mAh g -1 after 1000 cycles at 1.5 Ag -1 , corresponding to 0.028% capacity decay per cycle. High mass loading cathodes (64 wt% S, 12.7 mg cm -2 ) also show cycling stability, with anode degradation due to deep plating/stripping driving capacity decay. Density functional theory (DFT) demonstrates that formation energy of Na 2 S x (1 ≤ x ≤ 4) on surface of MoC/Mo 2 C is significantly lowered compared to analogous redox in liquid. Strong binding of Na 2 S x (1 ≤ x ≤ 4) on MoC/Mo 2 C surfaces results from charge transfer between the sulfur and Mo sites on carbides' surface.

25 ENERGY STORAGE↗

DOE OSTI2023

Precision cosmological constraints on atomic dark matter

Abstract Atomic dark matter is a simple but highly theoretically motivated possibility for an interacting dark sector that could constitute some or all of dark matter. We perform a comprehensive study of precision cosmological observables on minimal atomic dark matter, exploring for the first time the full parameter space of dark QED coupling and dark electron and proton masses (α D ,$$ {m}_{e_D} $$ m e D ,$$ {m}_{p_D} $$ m p D ) as well as the two cosmological parameters of aDM mass fractionf D and temperature ratioξat time of SM recombination. We also show how aDM can accommodate the (H 0 , S 8 ) tension from late-time measurements, leading to a better fit than ΛCDM or ΛCDM + dark radiation. Furthermore, including late-time measurements leads to closed contours of preferredξand dark hydrogen binding energy. The dark proton mass is seemingly unconstrained. Our results serve as an important new jumping-off point for future precision studies of atomic dark matter at non-linear and smaller scales.

Physics↗