Discrete mechanism damping effects in the solar array flight experiment

Accelerometer data were collected during on-orbit structural dynamic testing of the Solar Array Flight Experiment aboard the Space Shuttle, and were analyzed at Lockheed Missile and Space Co. to determine the amount of damping present in the structure. The results of this analysis indicated that the damping present in the fundamental in-plane mode of the structure substantially exceeded that of the fundamental out-of-plane mode. In an effort to determine the source of the higher in-plane damping, a test was performed involving a small device known as a constant-force spring motor or constant-torque mechanism. Results from this test indicate that this discrete device is at least partially responsible for the increased in-plane modal damping of the Solar Array Flight Experiment structure

Characterization of Environmental Conditioning of Lithium Hydride Using Spectroscopy and Machine Learning

Lithium compounds such as lithium hydride (LiH) and anhydrous lithium hydroxide (LiOH) have various applications in industry but are highly reactive when exposed to moisture and CO2. These reactions create new molecular forms, including compounds such as lithium oxide (Li2O), lithium hydroxide monohydrate (LiOH ·H2O), and lithium carbonate (Li2CO3). These new compounds degrade the effectiveness in applications using these compounds. The negative effects induced by new lithium compounds creates a need for the ability to characterize the in-growth of such compounds. To study these in-growths, this work will present environmental conditions such as heat, moisture, and the atmospheric conditions, as examples of storage conditions. A pulsed laser and an echelle spectrograph are used in a novel single setup to conduct both Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS) in tandem. By employing spectroscopic techniques such as LIBS and Raman spectroscopy, in conjunction with multivariate modeling techniques (PCA,PCR,PLSR,Random Forest), these various conditions will be explored. These measurements and analysis techniques will enable collection of critical information required for validation of modeling on environmental characterizations of the lithium based compounds and their reactions that have significant implications on industrial technologies, such as batteries, and nuclear security applications

Combinatorial optimization in production and logistics systems

avec FIInternational audienc

Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

Network Synthesis

Contains research objectives and reports on two research projects

Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product oriented framework. Verification results over a 3 month period show that the innovative method d-ECC outperforms or performs as well as ECC in all investigated aspects