An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds

Complete computation of turbulent combustion flow involves two separate steps: mapping reaction kinetics to low-dimensional manifolds and looking-up this approximate manifold during CFD run-time to estimate the thermo-chemical state variables. In our previous work, we showed that using a deep architecture to learn the two steps jointly, instead of separately, is 73% more accurate at estimating the source energy, a key state variable, compared to benchmarks and can be integrated within a DNS turbulent combustion framework. In their natural form, such deep architectures do not allow for uncertainty quantification of the quantities of interest: the source energy and key species source terms. In this paper, we expand on such architectures, specifically ChemTab, by introducing deep ensembles to approximate the posterior distribution of the quantities of interest. We investigate two strategies of creating these ensemble models: one that keeps the flamelet origin information (Flamelets strategy) and one that ignores the origin and considers all the data independently (Points strategy). To train these models we used flamelet data generated by the GRI--Mech 3.0 methane mechanism, which consists of 53 chemical species and 325 reactions. Our results demonstrate that the Flamelets strategy is superior in terms of the absolute prediction error for the quantities of interest, but is reliant on the types of flamelets used to train the ensemble. The Points strategy is best at capturing the variability of the quantities of interest, independent of the flamelet types. We conclude that, overall, ChemTab Deep Ensembles allows for a more accurate representation of the source energy and key species source terms, compared to the model without these modifications

Tensor BM-Decomposition for Compression and Analysis of Spatio-Temporal Third-order Data

Given tensors $\boldsymbol{\mathscr{A}}, \boldsymbol{\mathscr{B}}, \boldsymbol{\mathscr{C}}$ of size $m \times 1 \times n$, $m \times p \times 1$, and $1\times p \times n$, respectively, their Bhattacharya-Mesner (BM) product will result in a third order tensor of dimension $m \times p \times n$ and BM-rank of 1 (Mesner and Bhattacharya, 1990). Thus, if a third-order tensor can be written as a sum of a small number of such BM-rank 1 terms, this BM-decomposition (BMD) offers an implicitly compressed representation of the tensor. Therefore, in this paper, we give a generative model which illustrates that spatio-temporal video data can be expected to have low BM-rank. Then, we discuss non-uniqueness properties of the BMD and give an improved bound on the BM-rank of a third-order tensor. We present and study properties of an iterative algorithm for computing an approximate BMD, including convergence behavior and appropriate choices for starting guesses that allow for the decomposition of our spatial-temporal data into stationary and non-stationary components. Several numerical experiments show the impressive ability of our BMD algorithm to extract important temporal information from video data while simultaneously compressing the data. In particular, we compare our approach with dynamic mode decomposition (DMD): first, we show how the matrix-based DMD can be reinterpreted in tensor BMP form, then we explain why the low BM-rank decomposition can produce results with superior compression properties while simultaneously providing better separation of stationary and non-stationary features in the data. We conclude with a comparison of our low BM-rank decomposition to two other tensor decompositions, CP and the t-SVDM

Tensor Completion with BMD Factor Nuclear Norm Minimization

This paper is concerned with the problem of recovering third-order tensor data from limited samples. A recently proposed tensor decomposition (BMD) method has been shown to efficiently compress third-order spatiotemporal data. Using the BMD, we formulate a slicewise nuclear norm penalized algorithm to recover a third-order tensor from limited observed samples. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the resulting minimization problem. Experimental results on real data show our method to give reconstruction comparable to those of HaLRTC (Liu et al., IEEE Trans Ptrn Anal Mchn Int, 2012), a well-known tensor completion method, in about the same number of iterations. However, our method has the advantage of smaller subproblems and higher parallelizability per iteration.Comment: 10 page

Linking microbial contamination to food spoilage and food waste: the role of smart packaging, spoilage risk assessments, and date labeling

Ensuring a safe and adequate food supply is a cornerstone of human health and food security. However, a significant portion of the food produced for human consumption is wasted annually on a global scale. Reducing harvest and postharvest food waste, waste during food processing, as well as food waste at the consumer level, have been key objectives of improving and maintaining sustainability. These issues can range from damage during processing, handling, and transport, to the use of inappropriate or outdated systems, and storage and packaging-related issues. Microbial growth and (cross)contamination during harvest, processing, and packaging, which causes spoilage and safety issues in both fresh and packaged foods, is an overarching issue contributing to food waste. Microbial causes of food spoilage are typically bacterial or fungal in nature and can impact fresh, processed, and packaged foods. Moreover, spoilage can be influenced by the intrinsic factors of the food (water activity, pH), initial load of the microorganism and its interaction with the surrounding microflora, and external factors such as temperature abuse and food acidity, among others. Considering this multifaceted nature of the food system and the factors driving microbial spoilage, there is an immediate need for the use of novel approaches to predict and potentially prevent the occurrence of such spoilage to minimize food waste at the harvest, post-harvest, processing, and consumer levels. Quantitative microbial spoilage risk assessment (QMSRA) is a predictive framework that analyzes information on microbial behavior under the various conditions encountered within the food ecosystem, while employing a probabilistic approach to account for uncertainty and variability. Widespread adoption of the QMSRA approach could help in predicting and preventing the occurrence of spoilage along the food chain. Alternatively, the use of advanced packaging technologies would serve as a direct prevention strategy, potentially minimizing (cross)contamination and assuring the safe handling of foods, in order to reduce food waste at the post-harvest and retail stages. Finally, increasing transparency and consumer knowledge regarding food date labels, which typically are indicators of food quality rather than food safety, could also contribute to reduced food waste at the consumer level. The objective of this review is to highlight the impact of microbial spoilage and (cross)contamination events on food loss and waste. The review also discusses some novel methods to mitigate food spoilage and food loss and waste, and ensure the quality and safety of our food supply