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Results for “cycle-by-cycle data”

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.

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9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2023

Analysis of backwash settings to maximize net water production in an engineering-scale ultrafiltration system for water reuse

Ultrafiltration (UF) has been widely utilized as water pretreatment for different applications especially in water reuse. The UF system operation is characterized by a filtration phase, where particles accumulate on the membrane surface resulting in an increase in the transmembrane pressure (TMP) and a cleaning phase, where foulants are removed through cleaning cycles including physical backwash and chemical-enhanced backwash (CEB). In this study, data from an engineering-scale UF system treating reclaimed wastewater were used to assess the impact of backwashing on the filtration process. TMP backwash trigger, backwash duration, and CEB frequency were purposely varied for a cycle-by-cycle investigation on the net water production, water recovery, initial operating TMP, and filtration cycle duration. As the TMP backwash trigger was varied between 62 and 145kPa, the maximum net water production (63 m 3 /d) was achieved at 103kPa and water recovery remained relatively constant at approximately 92%. Backwash durations of 45, 65, and 85s were performed where both net water production and water recovery yielded similar results (~63 m 3 /d and ~ 91%) compared with 103kPa TMP backwash trigger. The CEB frequency was also lowered from one every three backwashes (1/3) to 1/6 and 1/12 and resulted in decreased net water production and water recovery while the initial TMP increased. Interestingly, the total number of CEBs remained approximately constant regardless of their frequency. In conclusion, results suggest that CEB is an important fouling control process to maximize water production.

42 ENGINEERING↗
DOE OSTI2023

Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data

Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.

Erdagi, Ismet Goksad↗
DOE OSTI2023

Battery data integrity and usability: Navigating datasets and equipment limitations for efficient and accurate research into battery aging

A tremendous commitment of resources is needed to acquire, understand and apply battery data in terms of performance and aging behavior. There are many state of performance (SOP) and state of health (SOH) metrics that are useful to guide alignment of batteries to end-use, yet how these metrics are measured or extracted can make the difference between usable, valuable datasets versus data that lacks the necessary integrity to meet baseline confidence levels for SOP/SOH quantification. This work will speak to 1) types of data that support SOP and SOH evaluations on mechanistic terms, 2) measurement conditions needed to assure high data integrity, 3) equipment limitations that can compromise data high fidelity, and 4) the impact of cell polarization on data quality. A common goal in battery research and field use is to work from a data platform that supports economical paths of data capture while minimizing down-time for battery diagnostics. An ideal situation would be to utilize data obtained during normal daily use (“pulses or cycles of convenience”) without stopping the daily duty cycles to perform dedicated SOP/SOH diagnostic routines. However, difficulties arise in trying to make use of daily duty cycle data (denoted as cycle-by-cycle, CBC) that underscores the need for standardization of conditions: temperature and duty cycles can vary over the course of a day and throughout a week, month and year; polarization can develop within an immediate cycle and throughout successive cycles as a hysteresis. If CBC data is envisioned as a data source to determine performance and aging trends, it should be recognized that polarization is a frequent consequence of CBC and thus makes it difficult to separate reversible and irreversible components to metrics such as capacity loss and resistance increase over aging. Since CBC conditions can have a major impact on data usability, we will devote part of this paper to CBC data conditioning and management. Differential analyses will also be discussed as a means to detect changing trends in data quality. Our target cell chemistries will be lithium-ion types NMC/graphite and LMO/LTO.

25 ENERGY STORAGE↗
NASA NTRS1976

Application of strainrange partitioning to the prediction of MPC creep-fatigue data for 2 1/4 Cr-1 Mo steel

Creep-fatigue tests were conducted on annealed 2.25Cr-1Mo steel in air at 538 C involving interspersion of rapid strain cycles (0 to 22 cycles) between constant tensile stress-hold periods (23 or 47 hours). Life predictions were made on the basis of the strain range-life relations obtained from short-time material characterization tests on tubular specimens taken from the same heat as the specimens for the creep-fatigue tests. The creep-fatigue tests lasted from about 500 to 5000 hours, while the short-time tests used as the prediction basis lasted from 0.1 to 100 hours. By partitioning the creep and plastic strains within every cycle and summing the damage on a cycle-by-cycle basis, the predicted lives agreed with the observed ones within factors of two.

Saltsman, J. F.↗
NASA NTRS1981

Methods and models for predicting fatigue crack growth under random loading

Papers are presented in the volume summarizing the baseline data, methodology, procedures, and results of a round-robin analysis which was conducted to predict the fatigue crack growth in 2219-T851 aluminum center-cracked specimens subjected to flight loading in random cycle-by-cycle format. The objective of the analysis was to assess whether data from constant-amplitude fatigue crack growth tests on center-cracked specimens can be used to predict fatigue crack growth lives under random loading. The following approaches are discussed in detail: a root-mean-square approach, a crack-closure model, a multi-parameter yield zone model, and a load-interaction model.

Chang, J. B.↗
NASA NTRS1989

Predictions Of Fatigue Damage From Strain Histories

Semiempirical mathematical model of fatigue damage in stressed objects uses experimental histories of strains in those objects to predict fatigue lives. Accounts for initiation and propagation of fatigue cracks on cycle-by-cycle basis. Measured strain history first digitized, then converted to history of turning-point strains for purposes of analysis. Data between turning points not used. When model calibrated against proper test data for each type of object characterized, its predictions of fatigue lives superior to statistical models as one based on root-mean-square strain.

Sire, Robert A.↗
NASA NTRS1994

Aircraft stress sequence development: A complex engineering process made simple

Development of stress sequences for critical aircraft structure requires flight measured usage data, known aircraft loads, and established relationships between aircraft flight loads and structural stresses. Resulting cycle-by-cycle stress sequences can be directly usable for crack growth analysis and coupon spectra tests. Often, an expert in loads and spectra development manipulates the usage data into a typical sequence of representative flight conditions for which loads and stresses are calculated. For a fighter/trainer type aircraft, this effort is repeated many times for each of the fatigue critical locations (FCL) resulting in expenditure of numerous engineering hours. The Aircraft Stress Sequence Computer Program (ACSTRSEQ), developed by Southwest Research Institute under contract to San Antonio Air Logistics Center, presents a unique approach for making complex technical computations in a simple, easy to use method. The program is written in Microsoft Visual Basic for the Microsoft Windows environment.

Schrader, K. H.↗
NASA NTRS2007

Forecast of the Amplitude of Solar Cycle 24 Based on the Disturbed Days Precursor

R. J. Thompson (1993, Solar Physics 148, 383) exhibited a significant linear relationship between the number of geomagnetically disturbed days (those that exceed some threshold value of the Ap or nu index) in a solar cycle and the sum of the peak sunspot number in that cycle and the next cycle. Thus, the number of disturbed days during a full cycle, together with the peak sunspot number in that cycle, is a predictor of the amplitude of the next cycle. The work reported here applies Thompson's method to the current cycle. Linear relationships as described above are derived both for the pure aa record (1868-2006) and for a composite of aa (1868-1931) and Ap (1932-2006). For the composite record, the relationship between aa and Ap is determined cycle-by-cycle during the period of overlap. The method is tested for sensitivity to the adopted Ap (or equivalent aa) threshold. The highest smoothed monthly sunspot number for Cycle 24 is forecasted to be R(sub z) = 115 plus or minus 30, where the uncertainty is conservatively based on the full spread of the data around the fitted line in the sunspot number direction. In terms of smoothed monthly 10.7-cm radio flux, the forecast is F10.7 = 164 plus or minus 28.

Rabin, Douglas M.↗